From 307cd94edc9dbe17e87b384ed82d904b6f74f3b2 Mon Sep 17 00:00:00 2001 From: goergen95 Date: Mon, 21 Oct 2024 07:18:22 +0000 Subject: [PATCH] Built site for mapme.biodiversity@0.9.2.9000: b528b1a --- dev/news/index.html | 4 ++++ dev/pkgdown.yml | 2 +- dev/reference/mapme.html | 2 +- dev/search.json | 2 +- 4 files changed, 7 insertions(+), 3 deletions(-) diff --git a/dev/news/index.html b/dev/news/index.html index ff3297e3..98c03871 100644 --- a/dev/news/index.html +++ b/dev/news/index.html @@ -49,6 +49,10 @@

Bug fixesget_nasa_srtm() now uses GDAL’s VSI path option pc_url_signing=yes to sign URLs from Microsoft Planetary Computer (#383) +
+

Internal

+
  • test for .read_vector() now copies input GPKG to a directory with write permissions to avoid CRAN check failures when included in a read only directory
    • +

mapme.biodiversity 0.9.2

CRAN release: 2024-10-10

diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml index 25f6ee74..a6379ffb 100644 --- a/dev/pkgdown.yml +++ b/dev/pkgdown.yml @@ -9,7 +9,7 @@ articles: quickstart: quickstart.html terminology: terminology.html workflow: workflow.html -last_built: 2024-10-21T04:13Z +last_built: 2024-10-21T07:16Z urls: reference: https://mapme-initiative.github.io/mapme.biodiversity/reference article: https://mapme-initiative.github.io/mapme.biodiversity/articles diff --git a/dev/reference/mapme.html b/dev/reference/mapme.html index 2b5ff926..ce591fae 100644 --- a/dev/reference/mapme.html +++ b/dev/reference/mapme.html @@ -182,7 +182,7 @@

Examples
library(mapme.biodiversity)
 mapme_options()
 #> $outdir
-#> [1] "/tmp/RtmpQE90N9/mapme-data"
+#> [1] "/tmp/RtmpjQFEKr/mapme-data"
 #> 
 #> $chunk_size
 #> [1] 1e+08
diff --git a/dev/search.json b/dev/search.json
index bb305125..034bdc69 100644
--- a/dev/search.json
+++ b/dev/search.json
@@ -1 +1 @@
-[{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. Developers use GNU GPL protect rights two steps: (1) assert copyright software, (2) offer License giving legal permission copy, distribute /modify . developers’ authors’ protection, GPL clearly explains warranty free software. users’ authors’ sake, GPL requires modified versions marked changed, problems attributed erroneously authors previous versions. devices designed deny users access install run modified versions software inside , although manufacturer can . fundamentally incompatible aim protecting users’ freedom change software. systematic pattern abuse occurs area products individuals use, precisely unacceptable. Therefore, designed version GPL prohibit practice products. problems arise substantially domains, stand ready extend provision domains future versions GPL, needed protect freedom users. Finally, every program threatened constantly software patents. States allow patents restrict development use software general-purpose computers, , wish avoid special danger patents applied free program make effectively proprietary. prevent , GPL assures patents used render program non-free. precise terms conditions copying, distribution modification follow.","code":""},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_0-definitions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"0. Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Source Code","title":"GNU General Public License","text":"“source code” work means preferred form work making modifications . “Object code” means non-source form work. “Standard Interface” means interface either official standard defined recognized standards body, , case interfaces specified particular programming language, one widely used among developers working language. “System Libraries” executable work include anything, work whole, () included normal form packaging Major Component, part Major Component, (b) serves enable use work Major Component, implement Standard Interface implementation available public source code form. “Major Component”, context, means major essential component (kernel, window system, ) specific operating system () executable work runs, compiler used produce work, object code interpreter used run . “Corresponding Source” work object code form means source code needed generate, install, (executable work) run object code modify work, including scripts control activities. 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License explicitly affirms unlimited permission run unmodified Program. output running covered work covered License output, given content, constitutes covered work. License acknowledges rights fair use equivalent, provided copyright law. may make, run propagate covered works convey, without conditions long license otherwise remains force. may convey covered works others sole purpose make modifications exclusively , provide facilities running works, provided comply terms License conveying material control copyright. thus making running covered works must exclusively behalf, direction control, terms prohibit making copies copyrighted material outside relationship . Conveying circumstances permitted solely conditions stated . 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Protecting Users’ Legal Rights From Anti-Circumvention Law","title":"GNU General Public License","text":"covered work shall deemed part effective technological measure applicable law fulfilling obligations article 11 WIPO copyright treaty adopted 20 December 1996, similar laws prohibiting restricting circumvention measures. convey covered work, waive legal power forbid circumvention technological measures extent circumvention effected exercising rights License respect covered work, disclaim intention limit operation modification work means enforcing, work’s users, third parties’ legal rights forbid circumvention technological measures.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_4-conveying-verbatim-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"4. Conveying Verbatim Copies","title":"GNU General Public License","text":"may convey verbatim copies Program’s source code receive , medium, provided conspicuously appropriately publish copy appropriate copyright notice; keep intact notices stating License non-permissive terms added accord section 7 apply code; keep intact notices absence warranty; give recipients copy License along Program. may charge price price copy convey, may offer support warranty protection fee.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_5-conveying-modified-source-versions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"5. Conveying Modified Source Versions","title":"GNU General Public License","text":"may convey work based Program, modifications produce Program, form source code terms section 4, provided also meet conditions: ) work must carry prominent notices stating modified , giving relevant date. b) work must carry prominent notices stating released License conditions added section 7. requirement modifies requirement section 4 “keep intact notices”. c) must license entire work, whole, License anyone comes possession copy. License therefore apply, along applicable section 7 additional terms, whole work, parts, regardless packaged. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C)     This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see .   Copyright (C)    This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Contributing","text":"reading vignette probably contribute mapme.biodiversity package. great news happy receive Pull-Requests extending package’s functionality! find important -depth information add resources indicators make process seamless possible package’s maintainers. Please make sure read understand guide opening PR. doubt, especially feel framework support use case, always feel free raise issue happily discuss can support ideas. already done , make sure read Terminology vignette get familiar important concepts package. Note use tidyverse style guide package. specifically means function variable names follow snake case pattern. also use arrow assignment operator (<-). submitting PR consistently follow tidyverse style guide, maintainers package might change code adhere code style without notice accepting PR.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Contributing","text":"Ideally, clone GitHub repository via git command command line Linux MacOS systems via GitHub Desktop application Windows. Linux, command look like : accept pushes main, thus first step create specific branch extension. tutorial, pretend re-implement nasa_srtm resource associated elevation indicator, create branch reflecting . Don’t forget check newly created branch! , assume develop extension package R Studio. general guidelines follow also apply choose different tooling development process, however, covered vignette. assume R development dependencies installed. easiest way ensure using devtools:","code":"git clone https://github.com/mapme-initiative/mapme.biodiversity git branch add-elevation git checkout add-elevation devtools::install_dev_deps()"},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"checklist","dir":"Articles","previous_headings":"Adding a resource","what":"Checklist","title":"Contributing","text":"Create file necessary code download resource (R/get_.R) Include roxygen documentation resource following provided template Create outer-level function user facing arguments Check user-specified arguments () correctness Create inner-level function standard arguments Match spatio-temporal extent portfolio resource Create footprint object via make_footprints() resources matching portfolio Include opening (-oo) creation (-co) options resources needed Write testthat script testing newly added functionality write test/testthat/test-get_.R Add small example data set resource inst/res/ Add script producing sample resources data-raw Add useful information resource register via register_resource() Added new dependency? Make sure include supporting statement dependency PR!","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"overview-of-adding-a-resource","dir":"Articles","previous_headings":"Adding a resource","what":"Overview of adding a resource","title":"Contributing","text":"resource supported dataset can made available user’s perspective specifying one functions get_resources(). Currently, package supports raster vector resources. wish submit support new resource, please aware accept new resources associated least one indicator calculation. first step adding resource create new file holding required code. checked new branch project opened R Studio, adapt following command open new resource file:","code":"file.edit(\"R/get_.R\") # e.g. file.edit(\"R/get_soildgrids.R\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"documenting-the-new-resource","dir":"Articles","previous_headings":"Adding a resource","what":"Documenting the new resource","title":"Contributing","text":"first part resource function, make sure include detailed documentation. documentation explain resource represents, comes (including citation), user-facging arguments specified runtime. Importantly, documentation MUST receive roxygen tag @keywords resource, documentation identified resource. Also, add bare name resource @name tag (e.g. case example translates @name nasa_srtm). last two tags important add well. include statement mandatory register functionality () loaded resource function. export tag important resource actually exposed users package.","code":"#' Short title #' #' One or more description paragraphs might follow here. Please describe #' the spatio-temporal structure of your resource here briefly. #' #' @name  #' @param  #' @keywords resource  #' @references  #' @source  #' @returns A function that makes a resource available for a portfolio #' @include register.R #' @export"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---outer-level","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Outer level","title":"Contributing","text":"Resource functions constructed closures, .e. functions return function. outer level exposes arguments set users function fine-control flow function. Note, important check user input outer level correctness warning/error messages case miss specifications thrown immediately. nasa_srtm, outer level look really exciting becuase user-facing arguments checked (see check user-facing arguments constructing indicator ): Note, exported helper functions re-occurring argument checks free use (e.g. check_available_years() case query user temporal time frame). arguments defined outer level resource function ready used inner level, look next.","code":"get_nasa_srtm <- function() {   # .... inner function level }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---inner-level","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Inner level","title":"Contributing","text":"inner level resource function mandatory function signature checked run-time. function required exactly specify signature. nasa_srtm resource, looks like : x argument represents portfolio object handed user calling get_resources() sf-object can thus used derive spatial extent portfolio. Next, comes name type resource required backend correctly handle output log resource made available. arguments default respective output values mapme_options() represent character vector output directory, logical control verbosity. Note, output directory might NULL case user wishes access data directly remote source. look things come together now peak constructing actual body resource function.","code":"function(x,          name = \"nasa_srtm\",          type = \"raster\",          outdir = mapme_options()[[\"outdir\"]],          verbose = mapme_options()[[\"verbose\"]]) {   # ... function body }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---body","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Body","title":"Contributing","text":"expected output resource function sf-object geometries representing bounding box single resource elements (e.g. tiles case raster resource). provide functionality seamlessly produce footprint object via make_footprints(). Note, function either excepts character vector GDAL readable data sources sf object. case provide charachter vector, bounding box information retrieved automatically. comes performance penalty remote sources, file opened , always opt constructing sf object resource function, feasible. footprints sf object expected contain column called source points GDAL readable data source geometries correspond bounding box single resource element. might opt supply filename argument make_footprints(). defaults basename(srcs[[\"source]]), might supply better suited filename, .e. case source location ends API key value, similar. Next specifying resource wish turn footprint object represents raster vector resource, can specify opening creation options. Opening options relevant opening remote source requires location driver dependent options (e.g. specifying non-standard columns names longitude/latitude CSV driver). Creation options relevant user specified outdir argument refer arguments used gdal_translate. specifically specify data type compression algorithms raster resources, otherwise free optimize data layout efficient access. oo co can specified single character vector, case options applied elements resource, list order accommodate file specific options. Use verbose argument decide informative messages printed, e.g. inform users download progress. Errors warnings emitted either case. intersection x object resource, make sure return NULL early possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-sample-resource-for-package-internal-testing","dir":"Articles","previous_headings":"Adding a resource","what":"Adding sample resource for package internal testing","title":"Contributing","text":"ask provide small subset resource inst/res/resource_name indicators depend resource can tested without need actual download resource. restrictions final size package, ask put substantial effort reducing size files minimum. includes cropping resource samples spatial extent polygon provided inst/extdata/sierra_de_neibe_478140.gpkg polygon similar size supplied case spatial extent intersect resource. raster resources, original raster encoded float, consider changing data type integer introducing scale factor. Also, please use compression algorithm reduce file size. vector resources, consider reducing number vertices case geometries complex. Finally, put processing script resource data-raw ensure reproducibility. , required write unit-test resource function, execute much code possible without actually conducting download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"a-note-on-dependencies-for-resources","dir":"Articles","previous_headings":"Adding a resource","what":"A note on dependencies for resources","title":"Contributing","text":"Note, resource SHALL add additional dependencies package. add dependencies require add supporting statement PR explaining dependencies needed approaches fail. accepting PR, might request change code remove dependencies, feasible achieve functionality without.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-an-indicator","dir":"Articles","previous_headings":"","what":"Adding an indicator","title":"Contributing","text":"process adding indicator similar one resources. However, input-output requirements different. Note, case added new resource also expect new indicator taking advantage resource PR. see, two new important concepts mind adding indicator. processing mode computational engines. briefly explain concepts , however, can also head Terminology vignette interested comprehensive definition two terms.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"checklist-1","dir":"Articles","previous_headings":"Adding an indicator","what":"Checklist","title":"Contributing","text":"Create file necessary code compute indicator (R/calc_.R) Create outer-level function user facing arguments Check user-specified arguments () correctness Create inner-level function standard arguments applicable, implement , asset portfolio based processing modes Return tibble long format standardized column names Write testthat script testing newly added functionality write test/testthat/test-calc_.R Added new dependency? Make sure include supporting statement dependency PR!","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"overview-of-adding-a-new-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Overview of adding a new indicator","title":"Contributing","text":"indicator logical routine depending one resources extracts numeric outputs assets portfolio. user’s perspective, indicators processed via calc_indicators() function. developer construct indicator function closure, e.g. function returns another function. outer level exposes user-facing arguments checks correctly specified, inner level required follow specified signature returns tibble. checked new branch project opened R Studio, adapt following command open new indicator file:","code":"file.edit(\"R/calc_.R\") # e.g. file.edit(\"R/calc_precipitation\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"documenting-the-new-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Documenting the new indicator","title":"Contributing","text":"first part indicator function, make sure include detailed documentation. documentation explain resources required calculate indicator, user-facing arguments specified runtime structure output tibble. Importantly, documentation MUST receive roxygen tag @keywords indicator, documentation identified indicator. Also, add bare name indicator @name tag (e.g. @name elevation). last two tags important add well. include statement mandatory register functionality () loaded indicator function. export tag important resource actually exposed users package.","code":"#' Short title #' #' One or more description paragraphs might follow here. Please describe #' required resource and user arguments here. #' Please document which processing engines are available for your indicator #' and briefly describe how the indicator is derived from its inputs. #' #' @name  #' @param  #' @keywords indicator  #' @returns A function that calculates an indicator for a portfolio #' @include register.R #' @export"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---outer-level","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Outer level","title":"Contributing","text":"Indicator functions constructed closures, .e. functions return function. outer level exposes arguments set users function fine-control flow function. Note, important check user input outer level correctness warning/error messages case miss specifications thrown immediately. elevation, outer level look something like : exported helper functions re-occurring argument checks free use (e.g. check_engine()). Note, arguments defined way outer level indicator function ready used inner level look next.","code":"calc_elevation <- function(engine = \"extract\",                            stats = \"mean\") {   engine <- check_engine(engine)   stats <- check_stats(stats)    # ... inner function level }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---inner-level","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Inner level","title":"Contributing","text":"inner level indicator function mandatory function signature checked run-time. function required exactly specify signature. elevation indicator, looks like : x argument represents portfolio object handed user calling get_resources() sf-object 'POLYGON' features. Next, comes name(s) required resource(s) name indicator. follows computation mode, must one \"asset\" \"portfolio\". realized, large (potentially global) portfolios, depending spatial resolution resource, different processing modes substantially impact time needed computation. high medium resolution raster resources, processing asset level benefits computation time. However, spatially cropping coarse resolution datasets high number assets introduces significant overhead, thus processing resources portfolio level efficient. neither two processing modes lead satisfactory processing times indicator, please leave issue/comment discuss addition another processing mode maintainers package. argument aggregation governs chunked results large polygons combined single indicator. case uses supply polygons larger specified code path assets type MULTIPOLYGON, code path triggered splits assets sub-components. aggregation method specifies statistic used combine values share values remaining indicator columns (.e. datetime, variable, unit). stat keyword special keyword used indicators statistics specified user trigger select respective statistic aggregation statistic (e.g. take sum sums). available statistics : argument verbose defaults corresponding package-wide option control verbosity indicator function.","code":"function(x,          nasa_srtm = NULL,          name = \"elevation\",          mode = \"asset\",          aggregation = \"stat\",          verbose = mapme_options()[[\"verbose\"]]) {   # ... function body } mapme.biodiversity:::available_stats #> [1] \"mean\"   \"median\" \"sd\"     \"min\"    \"max\"    \"sum\"    \"var\""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---body","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Body","title":"Contributing","text":"expected output indicator function tibble. Depending mode specified processing, single tibble mode = \"asset\", list tibbles equal rows x case mode = \"portfolio\". may use helper functions provided package common interface e.g. vector-raster zonal statistics (e.g. using select_engine()). encouraged write helper function needed indicator processor. located file main processor, start dot exported. wish include roxygen documentation helpers, make sure add @keywords internal @noRd tags functions. feel one helper functions benefit just one indicator, please comment issue/pull-request discuss package maintainers helper function moved R/utils.R. Use verbose argument decide informative messages printed, e.g. inform users processing progress. Errors warnings emitted either case. intersection x object required resources, reason indicator might calculated given configuration, make sure return NA early possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-units-tests-for-an-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Adding units tests for an indicator","title":"Contributing","text":"required add unit tests indicator using package internal example data sets resources. Make sure properly test miss-specification user-facing arguments also check correctness numerical results indicator. might need construct portfolio scratch test indicator function. Instead, can directly call returned function appropriate polygon respective required resource. elevation indicator, looks like :","code":"x <- read_sf(system.file(   \"extdata\", \"sierra_de_neiba_478140.gpkg\",   package = \"mapme.biodiversity\" ))  nasa_srtm <- list.files(   system.file(     \"res\", \"nasa_srtm\",     package = \"mapme.biodiversity\"   ),   pattern = \".tif$\", full.names = TRUE )  nasa_srtm <- rast(nasa_srtm) ce <- calc_elevation(stats = c(\"mean\", \"median\", \"sd\")) result_multi_stat <- ce(shp, nasa_srtm)  expect_equal(   names(result_multi_stat),   c(\"elevation_mean\", \"elevation_median\", \"elevation_sd\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"objectives","dir":"Articles","previous_headings":"","what":"Objectives","title":"Introduction","text":"mapme.biodiversity facilitates statistical data analysis protected areas around globe. supports high number biodiversity related datasets associated indicators can utilized monitor evaluate effectiveness protection efforts. Several indicators available regular intervals almost two decades (2000 2020). allows users analyse spatial temporal dynamics biodiversity portfolios. package abstracts repetitive tasks, temporal spatial selection resources. allows seamless approach quantitative data analysis even large (potentially global) portfolios users enabled focus aims analysis. package tested Microsoft Azure’s cloud infrastructure well local machines. internal framework designed allow easy process provide extensions form custom resources indicators, unlocking potential future growth supported datasets. thus highly appreciate Pull-Requests contributing new resources/indicators. geographic data analysis, package uses sf operation vector data terra raster data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"mapme-biodiversity-package","dir":"Articles","previous_headings":"","what":"mapme.biodiversity package","title":"Introduction","text":"mapme.biodiversity provides standardized interface download analyse great variety biodiversity related spatial datasets allowing users focus aims analysis. sometimes cumbersome process handling different spatial data formats spatial temporal selection handled internally. Many organizations provide value-added datasets related biodiversity. organizations often use different technology stacks distribute data. mapme.biodiversity contains simple routines communicate different backends provide seamless access data. desired resources made available locally, users can decide indicators want calculate fine-control routines provided.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"functionalities","dir":"Articles","previous_headings":"","what":"Functionalities","title":"Introduction","text":"Currently, package offers several functionalities, ideally used consecutive order realize seamless analysis workflow: construct portfolio based sf object get resources spatio-temporal extent portfolio calculate indicators based available resources asset portfolio write results disk GeoPackage use Geo-Spatial software, conduct statistical analysis R","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"inputs-outputs","dir":"Articles","previous_headings":"","what":"Inputs, Outputs","title":"Introduction","text":"sf object containing geometries type 'POLYGON' arbitrary metadata raster vector resources matching spatio-temporal extent portfolio downloaded made available locally. necessary inputs subsequent calculation indicators, raw resource also can used, e.g. custom visualizations analysis. Importantly, resource directory can used different portfolios analysis runs, matching resources figured run time. Thus, need store multiple copies input resources. results indicator calculation added portfolio object nested list columns. approach makes feasible support variety indicators differently shaped outputs (e.g. time variant vs. invariant indicators). analysis done R, pose serious limitations, desired indicator can easily unnested via tidyr::unnest(). However, data shared use geospatial software (e.g.  QGIS), routine write portfolio object GeoPackage disk provided. indicator written independent table unique identifier allows joining attributes geometries later.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"limitations","dir":"Articles","previous_headings":"","what":"Limitations","title":"Introduction","text":"potential limiting factor now processing single large polygons. terra package provides memory-save framework process large raster extents, RAM overflows occur several large polygons processed parallel. advise process large polygons sequentially. took great effort evaluate efficient processing routines indicator. submit new indicator using efficient routine currently implemented package, please contact maintainers via e-mail, issue pull-request happily discuss options integrate routine wider framework planning add new features extend functionality mapme.biodiversity address limitations best possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"objectives","dir":"Articles","previous_headings":"","what":"Objectives","title":"How To: Transform indicator output to wide-format","text":"tutorial gives information transform output mapme-biodiversity package wide format exchange (geospatial-)software, QGIS. necessary package uses -called nested-list format default represent indicators. However, format specific R use data software thus requires additional steps taken. vignette shows can change data layout portfolio can easily serialize spatial format choice use software.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"what-are-long-vs--wide-tables","dir":"Articles","previous_headings":"","what":"What are long vs. wide tables?","title":"How To: Transform indicator output to wide-format","text":"Tabular data can structured two different ways, usually referred long wide format. people familiar wide format, format humans naturally structure data work spreadsheets, e.g. Excel. wide-format, identifier observation included exactly repeat (see Table ). long format, identifier well qualifying variables, might repeated several times uniquely identify observation single row (see Table B). long format often required interacting computers, e.g. make plots ggplot2. content two exactly either way, one might just friendly humans computers. familiar R tidyverse, might also heard term tidy data. terms tabular data can imagine tidy data referring data long table naturally fulfills following requirements: variable column observation row value cell Table , sense, tidy since year variable found column instead scattered two different columns. Table B long format variable found exactly one column. sense, individual row represents exactly one observation, meaning observation specific country specific year. structure data long format objects usually larger memory footprint compared wide format. smaller objects data types small memory consumption, might pose serious limitation workflow. However, geometry information, indicated WKT string, might quickly accumulate large proportion available memory, even portfolio consists high number complex geometries copied fit long-format requirement. reason, packages uses nested-list format hold tables indicators single columns within portfolio. remainder tutorial show detail can work R specific data format.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"the-simple-case---single-row-indicators","dir":"Articles","previous_headings":"","what":"The simple case - single-row indicators","title":"How To: Transform indicator output to wide-format","text":"start reading GeoPackage disk. sake argument, split original single polygon 9 distinct polygons simulate realistic portfolio consisting multiple assets. simple example, suppose interested extracting average traveltime cities 20,000 50,000 inhabitants portfolio. usual, make available Nelson et al. resource well requesting calculation respective indicator. can observe output, new column added sf object. called traveltime type list indicating represents nested-list column. means able maintain rectangular shape original data (e.g. one polygon per row), supporting arbitrarily shaped outputs indicators. Let’s observe traveltime indicator looks like instance: syntax , can see can access single object within nested list column (e.g. using list accessor [[). case, shape traveltime indicator single-row two-column tibble average minutes distance category value. can now use either two functions transform portfolio long wide formats: function , default, automatically detect nested-list columns change data layout. case, result still 9 rows just like original data frame indicator traveltime consisted just single row per asset. serialize object disk either format calling write_portfolio() respective format argument:","code":"aoi <- read_sf(   system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",     package = \"mapme.biodiversity\"   ) ) aoi <- st_as_sf(st_make_grid(aoi, n = 3)) print(aoi) #> Simple feature collection with 9 features and 0 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #>                                x #> 1 POLYGON ((-71.80933 18.5766... #> 2 POLYGON ((-71.65022 18.5766... #> 3 POLYGON ((-71.49111 18.5766... #> 4 POLYGON ((-71.80933 18.6175... #> 5 POLYGON ((-71.65022 18.6175... #> 6 POLYGON ((-71.49111 18.6175... #> 7 POLYGON ((-71.80933 18.6584... #> 8 POLYGON ((-71.65022 18.6584... #> 9 POLYGON ((-71.49111 18.6584... outdir <- file.path(tempdir(), \"mapme-resources\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- get_resources(aoi, get_nelson_et_al(ranges = \"100k_200k\")) aoi <- calc_indicators(aoi, calc_traveltime(stats = \"mean\")) print(aoi) #> Simple feature collection with 9 features and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 3 #>   assetid traveltime                                                           x #>                                                          #> 1       1  ((-71.80933 18.57668, -71.65022 18.57668, -71.65022 … #> 2       2  ((-71.65022 18.57668, -71.49111 18.57668, -71.49111 … #> 3       3  ((-71.49111 18.57668, -71.33201 18.57668, -71.33201 … #> 4       4  ((-71.80933 18.61756, -71.65022 18.61756, -71.65022 … #> 5       5  ((-71.65022 18.61756, -71.49111 18.61756, -71.49111 … #> 6       6  ((-71.49111 18.61756, -71.33201 18.61756, -71.33201 … #> 7       7  ((-71.80933 18.65844, -71.65022 18.65844, -71.65022 … #> 8       8  ((-71.65022 18.65844, -71.49111 18.65844, -71.49111 … #> 9       9  ((-71.49111 18.65844, -71.33201 18.65844, -71.33201 … print(aoi$traveltime[[1]]) #> # A tibble: 1 × 4 #>   datetime            variable                  unit    value #>                                          #> 1 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  206. portfolio_long(aoi) #> Simple feature collection with 9 features and 6 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 7 #>   assetid indicator  datetime            variable                  unit    value #>                                                   #> 1       1 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  206. #> 2       2 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  273. #> 3       3 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  283. #> 4       4 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  237. #> 5       5 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  308. #> 6       6 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  265. #> 7       7 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  287. #> 8       8 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  299. #> 9       9 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  235. #> # ℹ 1 more variable: x  portfolio_wide(aoi) #> Simple feature collection with 9 features and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 3 #>   assetid traveltime_2015-01-01_100k_200k_traveltime…¹                         x #>                                                           #> 1       1                                         206. ((-71.80933 18.57668, -7… #> 2       2                                         273. ((-71.65022 18.57668, -7… #> 3       3                                         283. ((-71.49111 18.57668, -7… #> 4       4                                         237. ((-71.80933 18.61756, -7… #> 5       5                                         308. ((-71.65022 18.61756, -7… #> 6       6                                         265. ((-71.49111 18.61756, -7… #> 7       7                                         287. ((-71.80933 18.65844, -7… #> 8       8                                         299. ((-71.65022 18.65844, -7… #> 9       9                                         235. ((-71.49111 18.65844, -7… #> # ℹ abbreviated name: #> #   ¹​`traveltime_2015-01-01_100k_200k_traveltime_mean_minutes` dsn_long <- tempfile(fileext = \".gpkg\") dsn_wide <- tempfile(fileext = \".gpkg\") write_portfolio(aoi, dsn_long, format = \"long\", quiet = TRUE) write_portfolio(aoi, dsn_wide, format = \"wide\", quiet = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"the-harder-case---indicators-with-multi-row-output","dir":"Articles","previous_headings":"","what":"The harder case - indicators with multi-row output","title":"How To: Transform indicator output to wide-format","text":"Let’s continue query indicator multi-row output, .e. precipitation statistics WorldClim. see addition traveltime indicator, now obtained additional nested-list column called precipitation_wc. Note, however, differences shape indicator tibble take closer look specific asset: single asset, obtain tibble 12 rows (month queried year 2018). Now, let’s look happens transform table long format, time specifically requesting extract precipitation_wc indicators: Instead 9 rows, get tibble 108 rows (9 assets * 12), metadata asset geometry column identifying values repeated 12 times . large portfolios, data layout might memory intensive. cases might favorable transform portfolio wide layout. example output see case, obtain resulting object 9 rows . indicator data now found respective columns named according schema: ___ values found rows unique combination pattern. Note, traveltime still represented nested-list column. serializing disk, present indicators going extracted order able serialize spatial data formats. desired include certain indicators subset portfolio indicated following code block:","code":"aoi <- get_resources(aoi, get_worldclim_precipitation(years = 2018)) aoi <- calc_indicators(aoi, calc_precipitation_wc(stats = \"mean\")) print(aoi) #> Simple feature collection with 9 features and 3 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 4 #>   assetid traveltime       precipitation_wc                                    x #>                                                    #> 1       1   ((-71.80933 18.57668, -71.65022 18… #> 2       2   ((-71.65022 18.57668, -71.49111 18… #> 3       3   ((-71.49111 18.57668, -71.33201 18… #> 4       4   ((-71.80933 18.61756, -71.65022 18… #> 5       5   ((-71.65022 18.61756, -71.49111 18… #> 6       6   ((-71.49111 18.61756, -71.33201 18… #> 7       7   ((-71.80933 18.65844, -71.65022 18… #> 8       8   ((-71.65022 18.65844, -71.49111 18… #> 9       9   ((-71.49111 18.65844, -71.33201 18… print(aoi$precipitation_wc[[1]]) #> # A tibble: 12 × 4 #>    datetime            variable            unit  value #>                                   #>  1 2018-01-01 00:00:00 worldclim_prec_mean mm      NaN #>  2 2018-02-01 00:00:00 worldclim_prec_mean mm      NaN #>  3 2018-03-01 00:00:00 worldclim_prec_mean mm      NaN #>  4 2018-04-01 00:00:00 worldclim_prec_mean mm      NaN #>  5 2018-05-01 00:00:00 worldclim_prec_mean mm      NaN #>  6 2018-06-01 00:00:00 worldclim_prec_mean mm      NaN #>  7 2018-07-01 00:00:00 worldclim_prec_mean mm      NaN #>  8 2018-08-01 00:00:00 worldclim_prec_mean mm      NaN #>  9 2018-09-01 00:00:00 worldclim_prec_mean mm      NaN #> 10 2018-10-01 00:00:00 worldclim_prec_mean mm      NaN #> 11 2018-11-01 00:00:00 worldclim_prec_mean mm      NaN #> 12 2018-12-01 00:00:00 worldclim_prec_mean mm      NaN portfolio_long(aoi, indicators = \"precipitation_wc\") #> Simple feature collection with 108 features and 7 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 108 × 8 #>    assetid traveltime       indicator   datetime            variable unit  value #>                                             #>  1       1  precipitat… 2018-01-01 00:00:00 worldcl… mm      NaN #>  2       1  precipitat… 2018-02-01 00:00:00 worldcl… mm      NaN #>  3       1  precipitat… 2018-03-01 00:00:00 worldcl… mm      NaN #>  4       1  precipitat… 2018-04-01 00:00:00 worldcl… mm      NaN #>  5       1  precipitat… 2018-05-01 00:00:00 worldcl… mm      NaN #>  6       1  precipitat… 2018-06-01 00:00:00 worldcl… mm      NaN #>  7       1  precipitat… 2018-07-01 00:00:00 worldcl… mm      NaN #>  8       1  precipitat… 2018-08-01 00:00:00 worldcl… mm      NaN #>  9       1  precipitat… 2018-09-01 00:00:00 worldcl… mm      NaN #> 10       1  precipitat… 2018-10-01 00:00:00 worldcl… mm      NaN #> # ℹ 98 more rows #> # ℹ 1 more variable: x  portfolio_wide(aoi, indicators = \"precipitation_wc\") #> Simple feature collection with 9 features and 14 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 15 #>   assetid traveltime       precipitation_wc_2018-01-01_…¹ precipitation_wc_201…² #>                                                             #> 1       1                           NaN                    NaN   #> 2       2                            29                     27.2 #> 3       3                            24.4                   24.0 #> 4       4                            31.9                   28.0 #> 5       5                            28.5                   28.2 #> 6       6                            18.9                   21.1 #> 7       7                            24.4                   24.2 #> 8       8                            19.3                   21.0 #> 9       9                           NaN                    NaN   #> # ℹ abbreviated names: ¹​`precipitation_wc_2018-01-01_worldclim_prec_mean_mm`, #> #   ²​`precipitation_wc_2018-02-01_worldclim_prec_mean_mm` #> # ℹ 11 more variables: #> #   `precipitation_wc_2018-03-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-04-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-05-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-06-01_worldclim_prec_mean_mm` , … dsn <- tempfile(fileext = \".gpkg\") write_portfolio(select(aoi, traveltime), dsn, quiet = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Quickstart","text":"following demonstrate idealized workflow based subset Global Forest Watch (GFW) data set delivered together package. can follow along code snippets reproduce results. Please note reduce time takes process vignette, download resources internet. real use case, thus processing time might substantially increase resources downloaded real portfolios might larger one created example. vignette assumes already followed steps Installation familiarized terminology used package. unfamiliar terminology used , please head Terminology article learn important concepts. idealized workflow using mapme.biodiversity consists following steps: prepare sf-object containing geometries type 'POLYGON' 'MULTIPOLYGON' decide indicator(s) wish calculate make required resource(s) available conduct indicator calculation, adds nested list column portfolio object continue analysis R decide export results spatial data format use geospatial software","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Quickstart","text":"First, load mapme.biodiversity sf package handling spatial vector data. tabular data handling, also load dplyr tidyr packages. , read internal GeoPackage includes part geometry protected area Dominican Republic WDPA database.","code":"library(mapme.biodiversity) library(sf) library(dplyr) library(tidyr)  aoi_path <- system.file(\"extdata\", \"gfw_sample.gpkg\", package = \"mapme.biodiversity\") aoi <- st_read(aoi_path, quiet = TRUE) aoi #> Simple feature collection with 1 feature and 0 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #>                             geom #> 1 POLYGON ((-71.73417 18.6435..."},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"setting-standard-option","dir":"Articles","previous_headings":"","what":"Setting standard option","title":"Quickstart","text":"use mapme_options() function set arguments, output directory, important govern subsequent processing. , create temporary directory. Internally, save time downloading building vignette, copied already existing files output location (code shown ). outdir argument points towards directory local file system machine. downloaded resources written respective directories nested within outdir. request specific resource portfolio, files downloaded missing match spatio-temporal extent. behavior beneficial, e.g. case share outdir different projects ensure resources matching current portfolio returned. verbose logical controls whether package print informative messages calculations. Note, even set FALSE, package inform users potential errors warnings.","code":"outdir <- file.path(tempdir(), \"mapme-resources\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = TRUE )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"getting-the-right-resources","dir":"Articles","previous_headings":"","what":"Getting the right resources","title":"Quickstart","text":"can check indicators available via available_indicators() function: Say, interested treecover_area indicator. can learn indicator required resources using either commands , viewing online version, head treecover_area documentation. inspecting help page learned indicator requires gfw_treecover gfw_lossyear resources requires specify three extra arguments: years calculate treecover, minimum size patches considered forest minimum canopy coverage single pixel considered forested. information hand, can start retrieve required resource. can learn available resources using available_resources() function: purpose vignette, going download , gfw_treecover gfw_lossyear resources. can get detailed information given resource, using either commands open help page. viewing online version documentation, can simply head gfw_treecover resource documentation. can now make required resources available portfolio. use common interface used resources, called get_resources(). specify portfolio object supply one resource functions respective arguments. download matching resources output directory specified earlier.","code":"available_indicators() #> # A tibble: 40 × 3 #>    name                          description                           resources #>                                                                  #>  1 biodiversity_intactness_index Averaged biodiversity intactness ind…   #>  2 biome                         Areal statistics of biomes from TEOW    #>  3 burned_area                   Monthly burned area detected by MODI…   #>  4 deforestation_drivers         Areal statistics of deforestation dr…   #>  5 drought_indicator             Relative wetness statistics based on…   #>  6 ecoregion                     Areal statstics of ecoregions based …   #>  7 elevation                     Statistics of elevation based on NAS…   #>  8 exposed_population_acled      Number of people exposed to conflict…   #>  9 exposed_population_ucdp       Number of people exposed to conflict…   #> 10 fatalities_acled              Number of fatalities by event type b…   #> # ℹ 30 more rows available_indicators(\"treecover_area\") #> # A tibble: 1 × 3 #>   name           description                  resources        #>                                                #> 1 treecover_area Area of forest cover by year  ?treecover_area help(treecover_area) available_resources() #> # A tibble: 35 × 5 #>    name                          description                licence source type  #>                                                         #>  1 accessibility_2000            Accessibility data for th… See JR… https… rast… #>  2 acled                         Armed Conflict Location &… Visit … Visit… vect… #>  3 biodiversity_intactness_index Biodiversity Intactness I… CC-BY-… https… rast… #>  4 chelsa                        Climatologies at High res… Unknow… https… rast… #>  5 chirps                        Climate Hazards Group Inf… CC - u… https… rast… #>  6 esalandcover                  Copernicus Land Monitorin… CC-BY … https… rast… #>  7 fritz_et_al                   Drivers of deforestation … CC-BY … https… rast… #>  8 gfw_emissions                 Global Forest Watch - CO2… CC-BY … https… rast… #>  9 gfw_lossyear                  Global Forest Watch - Yea… CC-BY … https… rast… #> 10 gfw_treecover                 Global Forest Watch - Per… CC-BY … https… rast… #> # ℹ 25 more rows available_resources(\"gfw_treecover\") #> # A tibble: 1 × 5 #>   name          description                                 licence source type  #>                                                         #> 1 gfw_treecover Global Forest Watch - Percentage of canopy… CC-BY … https… rast… ?gfw_treecover help(gfw_treecover) ?gfw_lossyear help(gfw_lossyear) aoi <- get_resources(   x = aoi,   get_gfw_treecover(version = \"GFC-2023-v1.11\"),   get_gfw_lossyear(version = \"GFC-2023-v1.11\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"calculate-specific-indicators","dir":"Articles","previous_headings":"","what":"Calculate specific indicators","title":"Quickstart","text":"next step consists calculating specific indicators. Note indicator requires one resources made available via get_resources() function explained . re-run function every new R session, note data already available re-downloaded. , going calculate treecover_area indicator based resources GFW. Since resources made available previous step, can continue requesting calculation desired indicator. Note command issue error case required resource made available via get_resources() beforehand. Now let’s take look results. addition metadata already familiar , see additional column called treecover_area contains tibble. indicator represented nested-list column sf-object named alike requested indicator. single asset, column contains tibble 6 rows four columns. Let’s closer look object tibble follows standard output format, indicators. indicator represented tibble four columns datetime, variable, unit, value. case treecover_area indicator, variable called treecover expressed ha. Let’s quickly visualize results:  wish change layout portfolio, can use portfolio_long() portfolio_wide() (see respective online tutorial). Especially large portfolios, usually good idea keep geometry information separated variable keep size data object relatively small.","code":"aoi <- calc_indicators(   aoi,   calc_treecover_area(years = 2000:2023, min_size = 1, min_cover = 30) ) aoi #> Simple feature collection with 1 feature and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 3 #>   assetid treecover_area                                                    geom #>                                                          #> 1       1  ((-71.73735 18.64734, -71.71386 18.63179, -71.69 18… aoi$treecover_area #> [[1]] #> # A tibble: 24 × 4 #>    datetime            variable  unit  value #>                         #>  1 2000-01-01 00:00:00 treecover ha    1975. #>  2 2001-01-01 00:00:00 treecover ha    1975. #>  3 2002-01-01 00:00:00 treecover ha    1973. #>  4 2003-01-01 00:00:00 treecover ha    1940. #>  5 2004-01-01 00:00:00 treecover ha    1930. #>  6 2005-01-01 00:00:00 treecover ha    1926. #>  7 2006-01-01 00:00:00 treecover ha    1919. #>  8 2007-01-01 00:00:00 treecover ha    1908. #>  9 2008-01-01 00:00:00 treecover ha    1905. #> 10 2009-01-01 00:00:00 treecover ha    1903. #> # ℹ 14 more rows geoms <- st_geometry(aoi) portfolio_long(aoi, drop_geoms = TRUE) #> # A tibble: 24 × 6 #>    assetid indicator      datetime            variable  unit  value #>                                      #>  1       1 treecover_area 2000-01-01 00:00:00 treecover ha    1975. #>  2       1 treecover_area 2001-01-01 00:00:00 treecover ha    1975. #>  3       1 treecover_area 2002-01-01 00:00:00 treecover ha    1973. #>  4       1 treecover_area 2003-01-01 00:00:00 treecover ha    1940. #>  5       1 treecover_area 2004-01-01 00:00:00 treecover ha    1930. #>  6       1 treecover_area 2005-01-01 00:00:00 treecover ha    1926. #>  7       1 treecover_area 2006-01-01 00:00:00 treecover ha    1919. #>  8       1 treecover_area 2007-01-01 00:00:00 treecover ha    1908. #>  9       1 treecover_area 2008-01-01 00:00:00 treecover ha    1905. #> 10       1 treecover_area 2009-01-01 00:00:00 treecover ha    1903. #> # ℹ 14 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"a-note-on-parallel-computing","dir":"Articles","previous_headings":"Calculate specific indicators","what":"A note on parallel computing","title":"Quickstart","text":"mapme.biodiversity follows parallel computing paradigm {future} package. means user control like set parallel processing. Since {mapme.biodiversity} v0.9, apply pre-chunking assets portfolio. means assets split components roughly size chunk_size. components can iterated parallel speed processing. Indicator values aggregated automatically. another example, code one apply parallel processing 2 assets, 4 workers available process chunks, thus requiring total 8 available cores host machine. sure request workers available machine.","code":"library(future) plan(cluster, workers = 6) library(progressr)  plan(cluster, workers = 2)  with_progress({   aoi <- calc_indicators(     aoi,     calc_treecover_area_and_emissions(       min_size = 1,       min_cover = 30     )   ) })  plan(sequential) # close child processes"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"exporting-an-portfolio-object","dir":"Articles","previous_headings":"","what":"Exporting an portfolio object","title":"Quickstart","text":"can use write_portfolio() function save processed portfolio object disk GeoPackage. allows sharing data contributors might using R, geospatial software. Simply point towards non-existing file local disk write portfolio. can use read_portfolio() read back GeoPackage written way R:","code":"dsn <- tempfile(fileext = \".gpkg\") write_portfolio(x = aoi, dsn = dsn, quiet = TRUE) from_disk <- read_portfolio(dsn, quiet = TRUE) from_disk #> Simple feature collection with 1 feature and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 3 #>   assetid treecover_area                                                    geom #>                                                          #> 1       1  ((-71.73735 18.64734, -71.71386 18.63179, -71.69 18… #> [1] TRUE"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Darius . Görgen. Author, maintainer. Om Prakash Bhandari. Author. Andreas Petutschnig. Contributor.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Görgen D, Bhandari O (2024). mapme.biodiversity: Efficient Monitoring Global Biodiversity Portfolios. R package version 0.9.2.9000, https://github.com/mapme-initiative/mapme.biodiversity/, https://mapme-initiative.github.io/mapme.biodiversity/index.html.","code":"@Manual{,   title = {mapme.biodiversity: Efficient Monitoring of Global Biodiversity Portfolios},   author = {Darius A. Görgen and Om Prakash Bhandari},   year = {2024},   note = {R package version 0.9.2.9000,     https://github.com/mapme-initiative/mapme.biodiversity/},   url = {https://mapme-initiative.github.io/mapme.biodiversity/index.html}, }"},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"about","dir":"","previous_headings":"","what":"About","title":"An R package for processing global biodiversity data","text":"Biodiversity areas, especially primary forests, provide multiple ecosystem services local population planet whole. rapid expansion human land use natural ecosystems impacts global climate crisis put natural ecosystems global biodiversity threat. mapme.biodiversity package helps analyse number biodiversity related indicators biodiversity threats based freely available geodata-sources Global Forest Watch. supports computational efficient routines heavy parallel computing cloud-infrastructures AWS Microsoft Azure using statistical programming language R. package allows analysis global biodiversity portfolios thousand millions AOIs normally possible dedicated platforms Google Earth Engine. provides possibility e.g. analyse World Database Protected Areas (WDPA) number relevant indicators. primary use case package support scientific analysis data science individuals organizations seek preserve planet biodiversity. development funded German Development Bank KfW.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"An R package for processing global biodiversity data","text":"package dependencies can installed CRAN via: install development version, use following command:","code":"install.packages(\"mapme.biodiversity\", dependencies = TRUE) remotes::install_github(\"https://github.com/mapme-initiative/mapme.biodiversity\", dependencies = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"available-resources-and-indicators","dir":"","previous_headings":"","what":"Available resources and indicators","title":"An R package for processing global biodiversity data","text":"list resources currently supported mapme.biodiversity. Next, list supported indicators.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"usage-example","dir":"","previous_headings":"","what":"Usage example","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity works constructing portfolio sf object. Specific raster vector resource matching spatio-temporal extent portfolio made available locally. required resources available, indicators can calculated individually asset portfolio. decided indicator interested , can start making required resource available portfolio. Using mapme_options() can set output directory, control maximum size polygons chunked smaller parts, control verbosity package. portfolio represented sf-object. required object contain geometries type POLYGON MULTIPOLYGON assets. can request download resource spatial extent portfolio using get_resources() function. simply supply portfolio one resource functions. resources made available, can query calculation indicator using calc_indicators() function. function also expects portfolio input one indicator functions. indicator calculated assets portfolio, data returned nested list column original portfolio object. output indicator standardized common format, consisting tibble columns datetime, variable, unit, value. can transform data long format using portfolio_long().","code":"library(mapme.biodiversity) library(sf) ## Linking to GEOS 3.13.0, GDAL 3.9.2, PROJ 9.5.0; sf_use_s2() is TRUE mapme_options(   outdir = system.file(\"res\", package = \"mapme.biodiversity\"),   chunk_size = 1e6, # in ha   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\", package = \"mapme.biodiversity\") %>%   sf::read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)) %>%   portfolio_long()  aoi ## Simple feature collection with 4 features and 8 fields ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 ## Geodetic CRS:  WGS 84 ## # A tibble: 4 × 9 ##   WDPAID ISO3  assetid indicator        datetime            variable unit  value ##                                         ## 1 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 emissio… Mg    4296. ## 2 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 treecov… ha    2370. ## 3 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 emissio… Mg    4970. ## 4 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 treecov… ha    2358. ## # ℹ 1 more variable: geom "},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"using-cloud-storages","dir":"","previous_headings":"","what":"Using cloud storages","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity leverages GDAL’s capabilities data /O. users package, means integrating cloud storage easy setting configuration file changing outdir argument mapme_options(). also decide use environment variables, recommend set GDAL config file. can find GDAL’s documentation topic . Suppose want use AWS S3 bucket control write resource data . Let’s assume bucket already set wish refer R code mapme-data. GDAL configuration file look something like : connection handled based GDAL’s virtual file system. can find documentation specific options cloud provider . Ideally, also set following .Renviron file user’s home directory ensure GDAL aware configuration R session started: , scripts set outdir option value specified path variable configuration file:","code":"[credentials]  [.mapme-data] path=/vsis3/mapme-data AWS_SECRET_ACCESS_KEY= AWS_ACCESS_KEY_ID= GDAL_CONFIG_FILE = \"\" mapme_options(outdir = \"/vsis3/mapme-data\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"a-note-on-parallel-computing","dir":"","previous_headings":"","what":"A note on parallel computing","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity follows parallel computing paradigm {future} package. means user control like set parallel processing. Since {mapme.biodiversity} v0.9, apply pre-chunking assets portfolio. means assets split components roughly size chunk_size. components can iterated parallel speed processing. Indicator values aggregated automatically. another example, code one apply parallel processing 2 assets, 4 workers available process chunks, thus requiring total 8 available cores host machine. sure request workers available machine. Head online documentation find detailed information package.","code":"library(future) plan(cluster, workers = 6) library(progressr)  plan(cluster, workers = 2)  with_progress({   aoi <- calc_indicators(     aoi,     calc_treecover_area_and_emissions(       min_size = 1,       min_cover = 30     )   ) })  plan(sequential) # close child processes"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessibility to Cities in 2000 — accessibility_2000","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"resource provides global maps travel time cities 50,000 people year 2000. Accessibility refers ease larger cities can reached certain location. dataset represents travel time major cities globally year 2000, encoded minutes. data essential historical analyses, understanding impact accessibility land use socio-economic outcomes period.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"","code":"get_accessibility_2000()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"https://forobs.jrc.ec.europa.eu/gam","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"European Commission, Joint Research Centre (JRC), Global Accessibility Maps (GAM), 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Armed Conflict Location & Event Data (ACLED) — acled","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"ACLED's homepage: Armed Conflict Location & Event Data Project (ACLED) disaggregated data collection, analysis, crisis mapping project. ACLED collects information dates, actors, locations, fatalities, types reported political violence protest events around world. ACLED team conducts analysis describe, explore, test conflict scenarios, makes data analysis open free use public.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"","code":"get_acled(   years = 2000,   key = Sys.getenv(\"ACLED_ACCESS_KEY\"),   email = Sys.getenv(\"ACLED_ACCESS_EMAIL\"),   accept_terms = FALSE )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"Armed Conflict Location & Event Data Project (ACLED).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"years numeric vector specifying years make ACLED data available (1997 today). Defaults 2000. key ACLED API key obtained registering ACLED (see Details). email Email addressed used register ACLED (see Details). accept_terms logical indicating agree abid ACLED's terms use. Defaults FALSE, thus must manually set TRUE.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"order access data ACLED API, first must register account. Note, ACLED API used provides living database single events altered removed altogether time.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"Raleigh, C., Kishi, R. & Linke, . Political instability patterns obscured conflict dataset scope conditions, sources, coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"function calculates mean biodiversity intactness index region.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"","code":"calc_biodiversity_intactness_index()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"function returns indicator tibble variable biodiversity_intactness_index corresponding values (unitless) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"required resources indicator : biodiversity_intactness_index_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"","code":"# \\dontrun{ library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  lbii <- system.file(\"res\", \"biodiversity_intactness_index\", \"lbii.asc\",                     package = \"mapme.biodiversity\")  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) aoi <- get_resources(aoi, get_biodiversity_intactness_index(lbii)) aoi <- calc_indicators(aoi, calc_biodiversity_intactness_index()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 biodiver… 2005-01-01 00:00:00 biodive… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"variable modeled average abundance originally-present species, relative abundance intact ecosystem. Please refer Newbold et al. (2016) details, please cite using data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"","code":"get_biodiversity_intactness_index(path = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"path character vector biodiversity intactness index ASCII file.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"function returns sf footprints object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"Tim Newbold; Lawrence Hudson; Andy Arnell; Sara Contu et al. (2016). Global map Biodiversity Intactness Index, Newbold et al. (2016) Science [Data set]. Natural History Museum. doi:10.5519/0009936","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate biomes statistics (TEOW) based on WWF — biome","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"function allows efficiently retrieve name biomes compute corresponding area Terrestrial Ecoregions World (TEOW) - World Wildlife Fund (WWF) polygons. polygon, name area biomes (hectare) returned. required resources indicator : teow","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"","code":"calc_biome()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"function returns indicator tibble variable biome type corresponding area (ha) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_teow()) %>%   calc_indicators(calc_biome()) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 biome     2001-01-01 00:00:00 tropical_subt… ha    18349. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"Calculates Monthly Burned Area based Terra Aqua combined MCD64A1 Version 6.1. s monthly, global gridded 500 meter (m) product containing per-pixel burned-area information.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"","code":"calc_burned_area(engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"function returns indicator tibble variable burned area corresponding area (ha) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"required resources indicator : mcd64a1","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_mcd64a1(years = 2010)) %>%   calc_indicators(calc_burned_area(engine = \"extract\")) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator   datetime            variable    unit  value #>                                        #>  1 478140 DOM         1 burned_area 2010-12-01 00:00:00 burned_area ha      0   #>  2 478140 DOM         1 burned_area 2010-11-01 00:00:00 burned_area ha      0   #>  3 478140 DOM         1 burned_area 2010-10-01 00:00:00 burned_area ha      0   #>  4 478140 DOM         1 burned_area 2010-09-01 00:00:00 burned_area ha      0   #>  5 478140 DOM         1 burned_area 2010-08-01 00:00:00 burned_area ha      0   #>  6 478140 DOM         1 burned_area 2010-07-01 00:00:00 burned_area ha      0   #>  7 478140 DOM         1 burned_area 2010-06-01 00:00:00 burned_area ha      0   #>  8 478140 DOM         1 burned_area 2010-05-01 00:00:00 burned_area ha      0   #>  9 478140 DOM         1 burned_area 2010-04-01 00:00:00 burned_area ha      0   #> 10 478140 DOM         1 burned_area 2010-03-01 00:00:00 burned_area ha     42.8 #> 11 478140 DOM         1 burned_area 2010-02-01 00:00:00 burned_area ha      0   #> 12 478140 DOM         1 burned_area 2010-01-01 00:00:00 burned_area ha      0   #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"indicator calculates population exposed conflict events within specified buffer distance around events ACLED. Per default, first available WorldPop layer used estimate exposed populations years respective year, recent layer used years .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"","code":"calc_exposed_population_acled(   distance = 5000,   filter_category = c(\"event_type\", \"sub_event_type\", \"disorder_type\"),   filter_types = NULL,   years = c(1997:2024),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"distance numeric vector indicating buffer radius meters. length 1, buffer size around included conflict events drawn. Otherwise, must equal length included categories selected filter_types. filter_category character indicating categories used calculate exposed population . Defaults event_type meaning one estimation per event type returned. filter_types character vector event types respective category specified filter_category retain. Defaults NULL, meaning filter applied types retained. years numeric vector indicating years calculate exposed population. Restricted available years ACLED. years intersecting available WorldPop layers, first layer used earlier years last layer recent years. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"function returns indicator tibble conflict exposure variable precentage population value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"indicator inspired Conflict Exposure tool ACLED (see citation ), differs regard simply flatten buffered event layer instead applying voronoi tessellation. required resources indicator : acled worldpop Events ACLED classified according schema described extensively codebook. may filter certain types events. categories filter can applied either \"event_type\", \"event_sub_type\", \"disorder_type\". translated following categories: event_type: battles protests riots explosions/remote_violence violence_against_civilians strategic_developments event_sub_type: government_regains_territory non-state_actor_overtakes_territory armed_clash excessive_force_against_protesters protest_with_intervention peaceful_protest violent_demonstration mob_violence chemical_weapon air/drone_strike suicide_bomb shelling/artillery/missile_attack remote_explosive/landmine/ied grenade sexual_violence attack abduction/forced_disappearance agreement arrests change_to_group/activity disrupted_weapons_use headquarters_or_base_established looting/property_destruction non-violent_transfer_of_territory disorder_type: political_violence political_violence;_demonstrations demonstrations political_violence strategic_developments may supply buffer distances event categories. Custom buffers drawn per category. Supply single value wish differentiate categories. Otherwise, supply vector distances equal length included categories. may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 3 decreasing accuracy: value 1: source reporting indicates particular town, coordinates available town value 2: source material indicates activity took place small part region, mentions general area activity occurs near town city, event coded town geo-referenced coordinates represent area value 3: larger region mentioned, closest natural location noted reporting (like “border area,” “forest,” “sea,” among others) – provincial capital used information available temporal precision levels 1 3 decreasing precision: value 1: source material includes actual date event value 2: source material indicates event happened sometime week within similar period time value 3: source material indicates event took place sometime month (.e. past two three weeks, January), without reference particular date, month mid-point chosen","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"Raleigh, C; C Dowd; Tatem; Linke; N Tejedor-Garavito; M Bondarenko K Kishi. 2023. Assessing Mapping Global Local Conflict Exposure. Working Paper.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"","code":"# \\dontrun{ if (FALSE) {   library(sf)   library(mapme.biodiversity)    outdir <- file.path(tempdir(), \"mapme-data\")   dir.create(outdir, showWarnings = FALSE)    mapme_options(     outdir = outdir,     verbose = FALSE,     chunk_size = 1e8   )    aoi <- system.file(\"extdata\", \"burundi.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_acled(year = 2000),       get_worldpop(years = 2000)     ) %>%     calc_indicators(       conflict_exposure_acled(         distance = 5000,         years = 2000,         precision_location = 1,         precision_time = 1       )     ) %>%     portfolio_long()    aoi } # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate carbon statistics — carbon_indicators","title":"Calculate carbon statistics — carbon_indicators","text":"functions allow calculated statistics based harmonized carbon layers 2010 2018 Noon et al. (2022).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate carbon statistics — carbon_indicators","text":"","code":"calc_irr_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )  calc_man_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )  calc_vul_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate carbon statistics — carbon_indicators","text":"type One \"total\", \"soil\", \"biomass\", \"\". Determines data layer statistics calculated. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate carbon statistics — carbon_indicators","text":"function returns indicator tibble (type)_carbon_(stat) variable respective statistic (Mg) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate carbon statistics — carbon_indicators","text":"required resources indicators : carbon_resources Irrecoverable carbon amount carbon , lost today, recovered 2050. can calculated - -ground carbon, total amount carbon, layers. Manageable carbon amount carbon , principle, manageable human activities, e.g. release atmosphere can prevented. can calculated - -ground carbon, total amount carbon, layers. Vulnerable carbon amount carbon released typical land conversion activity. can calculated - -ground carbon, total amount carbon, layers.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate carbon statistics — carbon_indicators","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_man_carbon(),     get_vul_carbon(),     get_irr_carbon()   ) %>%   calc_indicators(     calc_man_carbon(stats = \"sum\"),     calc_vul_carbon(stats = \"sum\"),     calc_irr_carbon(stats = \"sum\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 6 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #>   WDPAID                       NAME                 DESIG_ENG ISO3 assetid #> 1  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 2  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 3  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 4  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 5  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 6  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #>    indicator   datetime             variable unit    value #> 1 man_carbon 2010-01-01 man_carbon_total_sum   Mg 819413.5 #> 2 man_carbon 2018-01-01 man_carbon_total_sum   Mg 819413.5 #> 3 vul_carbon 2010-01-01 vul_carbon_total_sum   Mg 696439.2 #> 4 vul_carbon 2018-01-01 vul_carbon_total_sum   Mg 696191.6 #> 5 irr_carbon 2010-01-01 irr_carbon_total_sum   Mg 406579.3 #> 6 irr_carbon 2018-01-01 irr_carbon_total_sum   Mg 407012.9 #>                             geom #> 1 POLYGON ((-59.84334 8.36199... #> 2 POLYGON ((-59.84334 8.36199... #> 3 POLYGON ((-59.84334 8.36199... #> 4 POLYGON ((-59.84334 8.36199... #> 5 POLYGON ((-59.84334 8.36199... #> 6 POLYGON ((-59.84334 8.36199... # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":null,"dir":"Reference","previous_headings":"","what":"Carbon Layers — carbon_resources","title":"Carbon Layers — carbon_resources","text":"resources publication Noon et al. (2022) \"Mapping irrecoverable carbon Earth’s ecosystems\". publication differentiates 3 different kinds carbon varying degrees manageability humans. three layers available ground carbon, well layer combining two.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carbon Layers — carbon_resources","text":"","code":"get_irr_carbon()  get_vul_carbon()  get_man_carbon()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Carbon Layers — carbon_resources","text":"https://zenodo.org/records/4091029","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carbon Layers — carbon_resources","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Carbon Layers — carbon_resources","text":"may required increase timeout option successfully download theses layers source location via e.g. options(timeout = 600). Irrecoverable carbon defined amount carbon, , lost today, recovered mid 21st century (within 30 years, considering publication date). Vulnerable carbon defined amount carbon lost hypothetical typical conversion event (without including information probability event actually occurring). Manageable carbon defined land areas, expect cyrosols, carbon loss driven direct land-use conversion halted climate change impacts affecting area can potentially directly mitigated adaptive management.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Carbon Layers — carbon_resources","text":"Noon, M.L., Goldstein, ., Ledezma, J.C. et al. Mapping irrecoverable carbon Earth’s ecosystems. Nat Sustain 5, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to check yearly availability — check_available_years","title":"Helper to check yearly availability — check_available_years","text":"Use function check specifed vector years intersects yearly availablity resource.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to check yearly availability — check_available_years","text":"","code":"check_available_years(target_years, available_years, indicator)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to check yearly availability — check_available_years","text":"target_years Numeric indicating target year. available_years Numeric indicating available years. indicator character vector target resource/indicator name.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if namespace is available — check_namespace","title":"Checks if namespace is available — check_namespace","text":"Use function resource/indicator function requires namespace certain package available. informative error/warning message printed case.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if namespace is available — check_namespace","text":"","code":"check_namespace(pkg, error = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if namespace is available — check_namespace","text":"pkg character vector length one indicating package name namespace tested error logical indicating whether promote missing namespace error. FALSE, warning emitted.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if namespace is available — check_namespace","text":"TRUE, invisible, namespace available. error message error = TRUE, FALSE warning otherwise.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"CHELSA data (Karger et al. 2017) consists downscaled model output temperature precipitation estimates horizontal resolution 30 arc sec. precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, boundary layer height, subsequent bias correction. spatial resolution 1-arc second (~1km equator). resource makes V2 available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"","code":"get_chelsa(years = 1979:2018)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"https://envicloud.wsl.ch/#/?prefix=chelsa/chelsa_V2/GLOBAL/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"years numeric vector years make CHELSA monthly precipitation layers available . Must greater 1979, defaults c(1979:2018).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2021) Climatologies high resolution earth’s land surface areas. EnviDat. doi:10.16904/envidat.228.v2.1 Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies high resolution Earth land surface areas. Scientific Data. 4 170122. doi:10.1038/sdata.2017.122","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":null,"dir":"Reference","previous_headings":"","what":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"resource published Funk et al. (2015) represents quasi-global (50°S-50°S) rainfall estimation monthly resolution starting year 1981 near-present. spatial resolution 0.05°. data can used retrieve information amount rainfall. Due availability +30 years, anomaly detection long-term average analysis also possible. routine download complete archive order support long-term average anomaly calculations respect 1981 - 2010 climate normal period. Thus additional arguments need specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"","code":"get_chirps(years = 1981:2020)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/cogs/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"years numeric vector years download CHIRPS precipitation layers. Must greater 1981, defaults c(1981:2020).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"Funk, C., Peterson, P., Landsfeld, M. et al. climate hazards infrared precipitation stations—new environmental record monitoring extremes. Sci Data 2, 150066 (2015). doi:10.1038/sdata.2015.66","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate deforestation drivers — deforestation_drivers","title":"Calculate deforestation drivers — deforestation_drivers","text":"function extracts areal statistics drivers deforestation based data source produced Fritz et al (2022).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate deforestation drivers — deforestation_drivers","text":"","code":"calc_deforestation_drivers()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate deforestation drivers — deforestation_drivers","text":"function returns indicator tibble deforestation drivers variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate deforestation drivers — deforestation_drivers","text":"required resource indicator : fritz_et_al","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate deforestation drivers — deforestation_drivers","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_fritz_et_al(resolution = 100)) %>%   calc_indicators(calc_deforestation_drivers()) %>%   portfolio_long()  aoi #> Simple feature collection with 10 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 10 × 9 #>    WDPAID ISO3  assetid indicator      datetime            variable unit   value #>                                         #>  1 478140 DOM         1 deforestation… 2008-01-01 00:00:00 commerc… ha        0  #>  2 478140 DOM         1 deforestation… 2008-01-01 00:00:00 commerc… ha        0  #>  3 478140 DOM         1 deforestation… 2008-01-01 00:00:00 managed… ha        0  #>  4 478140 DOM         1 deforestation… 2008-01-01 00:00:00 mining   ha        0  #>  5 478140 DOM         1 deforestation… 2008-01-01 00:00:00 natural… ha        0  #>  6 478140 DOM         1 deforestation… 2008-01-01 00:00:00 pasture  ha        0  #>  7 478140 DOM         1 deforestation… 2008-01-01 00:00:00 roads    ha        0  #>  8 478140 DOM         1 deforestation… 2008-01-01 00:00:00 wildfire ha        0  #>  9 478140 DOM         1 deforestation… 2008-01-01 00:00:00 other_s… ha    16809. #> 10 478140 DOM         1 deforestation… 2008-01-01 00:00:00 shiftin… ha        0  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate drought indicator statistics — drought_indicator","title":"Calculate drought indicator statistics — drought_indicator","text":"function allows efficiently calculate relative wetness shallow groundwater section regard 1948-2012 reference period. values represent wetness percentile given area achieves given point time regard reference period. polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate drought indicator statistics — drought_indicator","text":"","code":"calc_drought_indicator(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate drought indicator statistics — drought_indicator","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"mean\", \"median\" \"sd\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate drought indicator statistics — drought_indicator","text":"function returns indicator tibble specified drought indicator statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate drought indicator statistics — drought_indicator","text":"required resources indicator : nasa_grace","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate drought indicator statistics — drought_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_grace(years = 2022)) %>%   calc_indicators(     calc_drought_indicator(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 40 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 40 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 drought_indica… 2022-01-03 00:00:00 wetness… perc…  57.5 #>  2 478140 DOM         1 drought_indica… 2022-01-03 00:00:00 wetness… perc…  57.5 #>  3 478140 DOM         1 drought_indica… 2022-01-10 00:00:00 wetness… perc…  55.5 #>  4 478140 DOM         1 drought_indica… 2022-01-10 00:00:00 wetness… perc…  55.5 #>  5 478140 DOM         1 drought_indica… 2022-01-17 00:00:00 wetness… perc…  54   #>  6 478140 DOM         1 drought_indica… 2022-01-17 00:00:00 wetness… perc…  54   #>  7 478140 DOM         1 drought_indica… 2022-01-24 00:00:00 wetness… perc…  53   #>  8 478140 DOM         1 drought_indica… 2022-01-24 00:00:00 wetness… perc…  53   #>  9 478140 DOM         1 drought_indica… 2022-01-31 00:00:00 wetness… perc…  43.5 #> 10 478140 DOM         1 drought_indica… 2022-01-31 00:00:00 wetness… perc…  43.5 #> # ℹ 30 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"function allows efficiently retrieve name ecoregions compute corresponding area Terrestrial Ecoregions World (TEOW) - World Wildlife Fund (WWF) polygons. polygon, name area ecoregions (hectare) returned. required resources indicator : teow","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"","code":"calc_ecoregion()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"function returns indicator tibble ecoregion type variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_teow()) %>%   calc_indicators(calc_ecoregion()) %>%   portfolio_long() #> Resource 'teow' is already available.  aoi #> Simple feature collection with 1 feature and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 ecoregion 2001-01-01 00:00:00 hispaniolan_p… ha    18349. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate elevation statistics — elevation","title":"Calculate elevation statistics — elevation","text":"function allows calculate elevation statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate elevation statistics — elevation","text":"","code":"calc_elevation(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate elevation statistics — elevation","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"mean\", \"median\" \"sd\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate elevation statistics — elevation","text":"function returns indicator tibble specified elevation statistics variable corresponding values (meters) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate elevation statistics — elevation","text":"required resources indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate elevation statistics — elevation","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_elevation(engine = \"extract\", stats = c(\"mean\", \"median\", \"sd\", \"var\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_mean m      1704. #> 2 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_med… m      1702  #> 3 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_sd   m       219. #> 4 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_var  m     48085. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to select processing engines — engine","title":"Function to select processing engines — engine","text":"check_engine() checks extraction engine zonal vector-raster operations supported backend. check_stats checks one multiple statistics supported zonal vector-raster extraction backend. select_engine extracts zonal vector-raster statistics supported engine one statistics. Columns named according argument name plus respective stat. portfolio asset modes supported.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to select processing engines — engine","text":"","code":"check_engine(queried_engine)  check_stats(queried_stats)  select_engine(x, raster, stats, engine, name = NULL, mode = \"asset\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to select processing engines — engine","text":"queried_engine character vector length one indicating engine check . queried_stats character vector statistic names checked supported backend x sf object representing portfolio. raster terra SpatRaster values extracted. stats character vector statistics aggregate raster values . engine character vector length one specifying engine used extraction. name character vector indicating name append columns names. mode character vector indicating mode conduct extraction (e.g. asset-wise whole portfolio ).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to select processing engines — engine","text":"check_engine() returns character queried engine, supported. Throws error otherwise. check_stats returns character vector supported statistics. Throws error queried statistics supported. select_engine returns tibble.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":null,"dir":"Reference","previous_headings":"","what":"ESA Copernicus Global Land Cover layer — esalandcover","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"100 meter spatial resolution land cover resource published Buchhorn et al. (2020) \"Copernicus Global Land Cover Layers—Collection 2\". resource represents actual surface cover ground available annually period 2015 2019. cell values range 0 200, representing total 23 discrete classifications ESA.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"","code":"get_esalandcover(years = 2015:2019)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"https://lcviewer.vito./download","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"© European Union, Copernicus Land Monitoring Service (year), European Environment Agency (EEA)\", f.ex. 2018: “© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"indicator calculates population exposed conflict events within specified buffer distance around violent events UCDP GED. Per default, first available WorldPop layer used estimate exposed populations years respective year, recent layer used years .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"","code":"calc_exposed_population_ucdp(   distance = 5000,   violence_types = 1:3,   years = c(1989:2023),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"distance numeric vector indicating buffer size around included conflict events calculate exposed population. Either length 1 apply types events, discrete values category included violence_types. violence_types numeric vector indicating types violence included (see Details). years numeric vector indicating years calculate exposed population. Restricted available years UCDP GED. years intersecting available WorldPop layers, first layer used earlier years last layer recent years. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"function returns indicator tibble conflict exposure variable precentage population value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"indicator inspired Conflict Exposure tool ACLED (see citation ), differs regard simply flatten buffered event layer instead applying voronoi tessellation. required resources indicator : ucdp_ged worldpop may filter certain types violence. coded types according UCDP codebook : value 1: state-based conflict value 2: non-state conflict value 3: one-sided conflict may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 7 decreasing accuracy: value 1: location information corresponds exactly geographical coordinates available value 2: location information refers limited area around specified location value 3: source refers can specified larger location level second order administrative divisions (ADM2), district municipality, GED uses centroid point coordinates ADM2. value 4: location information refers first order administrative division, province (ADM1), GED uses coordinates centroid point ADM1 value 5: used different cases source refers parts country larger ADM1, smaller entire country; two locations mentioned representiative point selected; location mentioned non-independend island; location specifically mentioned relation another location value 6: location mentioned refers entire country centroid used value 7: event takes place water international airspace, geographical coordinates dataset either represent centroid point certain water area estimated coordinates temporal precision levels 1 5 decreasing precision: value 1: exact date event known value 2: start enddates events unspecified character, spanning one calendar day though longer six days value 3: start end dates events specified certain week, specific dates provided value 4: start end dates events specified certain month value 5: start enddates events specified certain year, specific dates provided","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"Raleigh, C; C Dowd; Tatem; Linke; N Tejedor-Garavito; M Bondarenko K Kishi. 2023. Assessing Mapping Global Local Conflict Exposure. Working Paper.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"","code":"# \\dontrun{ if (FALSE) {   library(sf)   library(mapme.biodiversity)    outdir <- file.path(tempdir(), \"mapme-data\")   dir.create(outdir, showWarnings = FALSE)    mapme_options(     outdir = outdir,     verbose = FALSE,     chunk_size = 1e8   )    aoi <- system.file(\"extdata\", \"burundi.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_ucdp_ged(version = \"22.1\"),       get_worldpop(years = 2000)     ) %>%     calc_indicators(       conflict_exposure(         distance = 5000,         violence_types = 1:3,         years = 2000,         precision_location = 1,         precision_time = 1       )     ) %>%     portfolio_long()    aoi } # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"indicator aggregated number fatalities within given asset monthly cadence stratified either event type, sub-event type disorder type. learn different categorisation ACLED uses encode events please consult ACLED's codebook.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"","code":"calc_fatalities_acled(   years = 2000,   stratum = c(\"event_type\", \"sub_event_type\", \"disorder_type\"),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"years numeric vector indicating years summarize fatalities. stratum character vector indicating stratification applied. one \"event_type\", \"sub_event_type\", \"disorder_type\". Defaults \"event_type\". precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"function returns indicator tibble type violence variable counts civilian fatalities value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"required resources indicator : acled may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 3 decreasing accuracy: value 1: source reporting indicates particular town, coordinates available town value 2: source material indicates activity took place small part region, mentions general area activity occurs near town city, event coded town geo-referenced coordinates represent area value 3: larger region mentioned, closest natural location noted reporting (like “border area,” “forest,” “sea,” among others) – provincial capital used information available temporal precision levels 1 3 decreasing precision: value 1: source material includes actual date event value 2: source material indicates event happened sometime week within similar period time value 3: source material indicates event took place sometime month (.e. past two three weeks, January), without reference particular date, month mid-point chosen","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"Raleigh, C., Kishi, R. & Linke, . Political instability patterns obscured conflict dataset scope conditions, sources, coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE,   chunk_size = 1e8 )  aoi <- system.file(\"extdata\", \"burundi.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_acled(years = 2020)) %>%   calc_indicators(     calc_fatalities_acled(       years = 2020,       precision_location = 1,       precision_time = 1     )   ) %>%   portfolio_long() #> Error in get_acled(years = 2020): Please read and agree to ACLED's Terms of Use here: #> https://acleddata.com/terms-of-use/  aoi #> Error: object 'aoi' not found # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"indicator aggregated number fatalities within given asset monthly cadence stratified type conflict. different types conflicts encoded UCDP GED database : state-based conflict non-state conflict one-sided violence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"","code":"calc_fatalities_ucdp(   years = 1989:2023,   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"years numeric vector indicating years summarize fatalities. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"function returns indicator tibble type violence variable counts civilian fatalities value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"required resources indicator : ucdp_ged may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 7 decreasing accuracy: value 1: location information corresponds exactly geographical coordinates available value 2: location information refers limited area around specified location value 3: source refers can specified larger location level second order administrative divisions (ADM2), district municipality, GED uses centroid point coordinates ADM2. value 4: location information refers first order administrative division, province (ADM1), GED uses coordinates centroid point ADM1 value 5: used different cases source refers parts country larger ADM1, smaller entire country; two locations mentioned representiative point selected; location mentioned non-independend island; location specifically mentioned relation another location value 6: location mentioned refers entire country centroid used value 7: event takes place water international airspace, geographical coordinates dataset either represent centroid point certain water area estimated coordinates temporal precision levels 1 5 decreasing precision: value 1: exact date event known value 2: start enddates events unspecified character, spanning one calendar day though longer six days value 3: start end dates events specified certain week, specific dates provided value 4: start end dates events specified certain month value 5: start enddates events specified certain year, specific dates provided","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"Sundberg, Ralph, Erik Melander, 2013, “Introducing UCDP Georeferenced Event Dataset”, Journal Peace Research, vol.50, .4, 523-532","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE,   chunk_size = 1e8 )  aoi <- system.file(\"extdata\", \"burundi.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_ucdp_ged(version = \"22.1\")) %>%   calc_indicators(     calc_fatalities(       years = 1991:1992,       precision_location = 1,       precision_time = 1     )   ) %>%   portfolio_long() #> Error in calc_fatalities(years = 1991:1992, precision_location = 1, precision_time = 1): could not find function \"calc_fatalities\"  aoi #> Error: object 'aoi' not found # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":null,"dir":"Reference","previous_headings":"","what":"Drivers of deforestation for tropical forests — fritz_et_al","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"resource produced neirest-neighbour matching crowd-sourced campaign map dominant driver forest loss based visual interpretation VHR images matched Global Forest Loss data Hansen (2013) version 1.7 forest loss layer re sampled resolution 100 1.000 meters. Dominant drivers determined period 2008 2009.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"","code":"get_fritz_et_al(resolution = 100)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"https://zenodo.org/record/7997885","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"resolution integer indicating resolution download. Defaults 100.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"indicates 9 different classes: commercial agriculture commercial oil palm plantations managed forests mining natural disturbances pasture roads wildfire subsistence agriculture shifting cultivation","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"Steffen, F., Carlos, J.C.L., See. L., Schepaschenko D., Hofhansl F., Jung M., Dürauer M., Georgieva ., Danylo O., Lesiv M., McCallum . (2022) Continental Assessment Drivers Tropical Deforestation Focus Protected Areas. F.Cos.Sc.(3) doi:10.3389/fcosc.2022.830248","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest greenhouse gas emissions — gfw_emissions","title":"Forest greenhouse gas emissions — gfw_emissions","text":"resource part publication Harris et al. (2021) \"Global maps twenty-first century forest carbon fluxes.\". represents \"greenhouse gas emissions arising stand-replacing forest disturbances occurred modelled year (megagrams CO2 emissions/ha, 2001 2023). Emissions include relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) greenhouse gases (CO2, CH4, N2O).\" area unit downloaded corresponds \"megagrams CO2 emissions/pixel\" layer, order support calculation area-wise emissions.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest greenhouse gas emissions — gfw_emissions","text":"","code":"get_gfw_emissions()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Forest greenhouse gas emissions — gfw_emissions","text":"https://data.globalforestwatch.org/datasets/gfw::forest-greenhouse-gas-emissions/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest greenhouse gas emissions — gfw_emissions","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest greenhouse gas emissions — gfw_emissions","text":"arguments users need specify. However, users note spatial extent dataset totally cover extent treecover2000 lossyear resources Hansen et al. (2013). missing value (NA) inserted greenhouse gas emissions areas data available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest greenhouse gas emissions — gfw_emissions","text":"Harris, N.L., Gibbs, D.., Baccini, . et al. Global maps twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021). https://doi.org/10.1038/s41558-020-00976-6","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":null,"dir":"Reference","previous_headings":"","what":"Year of forest loss occurrence — gfw_lossyear","title":"Year of forest loss occurrence — gfw_lossyear","text":"resource part publication Hansen et al. (2013) \"High-Resolution Global Maps 21st-Century Forest Cover Change\". represents \"Forest loss period 2000–2021, defined stand-replacement disturbance, change forest non-forest state. Encoded either 0 (loss) else value range 1–20, representing loss detected primarily year 2001–2021, respectively.\" Due changes satellites products used compilation tree loss product, results year 2011 afterwards directly comparable reprocessing finished. Users aware limitation, especially timeframe analysis spans two periods delimited year 2011.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Year of forest loss occurrence — gfw_lossyear","text":"","code":"get_gfw_lossyear(version = \"GFC-2023-v1.11\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Year of forest loss occurrence — gfw_lossyear","text":"https://data.globalforestwatch.org/documents/tree-cover-loss/explore","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Year of forest loss occurrence — gfw_lossyear","text":"version version dataset download. Defaults \"GFC-2023-v1.11\". Check mapme.biodiversity:::.available_gfw_versions() get list available versions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Year of forest loss occurrence — gfw_lossyear","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Year of forest loss occurrence — gfw_lossyear","text":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. . Turubanova, . Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, . Kommareddy, . Egorov, L. Chini, C. O. Justice, J. R. G. Townshend. 2013. “High-Resolution Global Maps 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":null,"dir":"Reference","previous_headings":"","what":"Treecover for the year 2000 — gfw_treecover","title":"Treecover for the year 2000 — gfw_treecover","text":"resource part publication Hansen et al. (2013) represents \"tree cover year 2000, defined canopy closure vegetation taller 5m height. Encoded percentage per output grid cell, range 0–100.\" Due changes satellites products used compilation treecover product, results year 2011 afterwards directly comparable reprocessing finished. Users aware limitation, especially timeframe analysis spans two periods delimited year 2011.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Treecover for the year 2000 — gfw_treecover","text":"","code":"get_gfw_treecover(version = \"GFC-2023-v1.11\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Treecover for the year 2000 — gfw_treecover","text":"https://data.globalforestwatch.org/documents/tree-cover-2000/explore","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Treecover for the year 2000 — gfw_treecover","text":"version version dataset download. Defaults \"GFC-2023-v1.11\". Check mapme.biodiversity:::.available_gfw_versions() get list available versions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Treecover for the year 2000 — gfw_treecover","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Treecover for the year 2000 — gfw_treecover","text":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. . Turubanova, . Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, . Kommareddy, . Egorov, L. Chini, C. O. Justice, J. R. G. Townshend. 2013. “High-Resolution Global Maps 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Change — global_surface_water_change","title":"Global Surface Water Change — global_surface_water_change","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Change — global_surface_water_change","text":"","code":"get_global_surface_water_change(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Change — global_surface_water_change","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Change — global_surface_water_change","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Change — global_surface_water_change","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Change — global_surface_water_change","text":"change water occurrence intensity two periods derived homologous pairs months (.e. months containing valid observations periods). difference occurrence surface water calculated homologous pair months. average differences constitutes Surface Water Occurrence change intensity. raster files integer cell values [0, 200] 0 represents surface water loss 200 represents surface water gain.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Change — global_surface_water_change","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Occurrence — global_surface_water_occurrence","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"","code":"get_global_surface_water_occurrence(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"GSW occurrence raw data comes raster files integer cell values [0, 100]. value gives percentage time given pixel classified water entire observation period. 0 denotes pixel never classified water, 100 denotes pixel permanent water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Recurrence — global_surface_water_recurrence","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"","code":"get_global_surface_water_recurrence(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Water Recurrence measurement degree variability presence water year year. describes frequency water returned particular location one year another, expressed percentage. raster files integer cell values [0, 100], 100 represents water reoccurs predictably every year, whereas lower values indicate water occurs episodically.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Seasonality — global_surface_water_seasonality","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"","code":"get_global_surface_water_seasonality(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"GSW seasonality describes intra-annual distribution surface water pixel. raster files integer cell values [0, 12], indicating many months per year pixel classified water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Transitions — global_surface_water_transitions","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"","code":"get_global_surface_water_transitions(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"GSW transition data contains information type surface water change pixel. raster files integer cell values [0, 10] code different transition classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Mangrove Extent Polygon — gmw","title":"Global Mangrove Extent Polygon — gmw","text":"resource part publication Bunting et al. (2018) \"Global Mangrove Watch—New 2010 Global Baseline Mangrove Extent\". polygons represent mangrove, tropical coastal vegetation considered significant part marine ecosystem. resource available selected years period 1996- 2020 Global Mangrove Watch (GMW), providing geospatial information global mangrove extent.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Mangrove Extent Polygon — gmw","text":"","code":"get_gmw(years = c(1996, 2007:2010, 2015:2020))"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Mangrove Extent Polygon — gmw","text":"https://data.unep-wcmc.org/datasets/45","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Mangrove Extent Polygon — gmw","text":"years numeric vector years make GMW available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Mangrove Extent Polygon — gmw","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Mangrove Extent Polygon — gmw","text":"Bunting P., Rosenqvist ., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy ., Itoh T., Shimada M. Finlayson C.M. (2018). Global Mangrove Watch – New 2010 Global Baseline Mangrove Extent. Remote Sensing 10(10): 1669. doi:10.3390/rs10101669.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Change — gsw_change","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"change water occurrence intensity two periods derived homologous pairs months (.e. months containing valid observations periods). difference occurrence surface water calculated homologous pair months. average differences constitutes Surface Water Occurrence change intensity. raster files integer cell values [0, 200] 0 represents surface water loss 200 represents surface water gain.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"","code":"calc_gsw_change(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". stats Aggregation function data combined. Default: \"mean\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"function returns indicator tibble change intensity variable corresponding (unitless) values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"pixel values aggregated using method provided via stats parameter using specified engine. required resources indicator : global_surface_water_change","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_change()) %>%   calc_indicators(     calc_gsw_change(engine = \"extract\", stats = \"mean\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_chan… 2021-01-01 00:00:00 gsw_cha… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"GSW occurrence raw data comes raster files integer cell values [0, 100]. value gives percentage time given pixel classified water entire observation period. 0 denotes pixel never classified water, 100 denotes pixel permanent water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"","code":"calc_gsw_occurrence(engine = \"extract\", min_occurrence = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". min_occurrence Threshold define pixels count towards GSW occurrence area [0, 100].","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"function returns indicator tibble occurrence variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"raw data values aggregated based provided threshold parameter min_occurrence, function returns area covered values greater equal threshold. required resources indicator : global_surface_water_occurrence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_occurrence()) %>%   calc_indicators(     calc_gsw_occurrence(engine = \"extract\", min_occurrence = 10)   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_occu… 2021-01-01 00:00:00 gsw_occ… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"Water Recurrence measurement degree variability presence water year year. describes frequency water returned particular location one year another, expressed percentage. raster files integer cell values [0, 100], 100 represents water reoccurs predictably every year, whereas lower values indicate water occurs episodically.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"","code":"calc_gsw_recurrence(engine = \"extract\", min_recurrence = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". min_recurrence Threshold define pixels count towards GSW recurrence area [0, 100].","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"function returns indicator tibble recurrence variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"raw data values aggregated based provided threshold parameter min_recurrence, function returns area covered values greater equal threshold. required resources indicator : global_surface_water_recurrence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_recurrence()) %>%   calc_indicators(     calc_gsw_recurrence(engine = \"extract\", min_recurrence = 10)   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_recu… 2021-01-01 00:00:00 gsw_rec… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"GSW seasonality describes intra-annual distribution surface water pixel. raster files integer cell values [0, 12], indicating many months per year pixel classified water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"","code":"calc_gsw_seasonality()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"function returns indicator tibble seasonality categories variables corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"pixel values aggregated using method provided via stats parameter. required resources indicator : global_surface_water_seasonality","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_seasonality()) %>%   calc_indicators(calc_gsw_seasonality()) %>%   portfolio_long()  aoi #> Simple feature collection with 13 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 13 × 11 #>    WDPAID NAME    DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #>  1  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  2  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  3  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  4  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  5  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  6  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  7  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  8  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  9  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 10  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 11  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 12  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 13  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"function calculates total area global surface water time series data, separated following classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"","code":"calc_gsw_time_series()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"function returning tibble time series global surface water data classes.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"Observation: possible determine whether pixel water (may case frozen areas polar night extreme latitudes). Permanent Water: Water detected twelve months per year combination permanent observation. Seasonal Water: Water water detected. Water: Water detected. required resources indicator : gsw_time_series_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"","code":"# \\dontrun{ library(mapme.biodiversity) library(sf)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) aoi <- get_resources(aoi, get_gsw_time_series (years = 2000:2001)) aoi <- calc_indicators(aoi, calc_gsw_time_series()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 8 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 8 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 no_obse… #> 2  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 no_obse… #> 3  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 not_wat… #> 4  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 not_wat… #> 5  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 seasona… #> 6  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 seasona… #> 7  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 permane… #> 8  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 permane… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"function constructs  necessary data URLs given data set, version polygon downloads processing mapme.biodiversity package.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"","code":"get_gsw_time_series(years, version = \"LATEST\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"Raw Data: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GSWE/YearlyClassification/LATEST/tiles/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"years Numeric vector years process 1984 2021. Default: 1984:2021. version Version data set process. Available options (VER1-0, VER2-0, VER3-0, VER4-0, VER5-0 LATEST) Default: LATEST. Choosing LATEST result latest available version.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"function returns character vector file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"available surface water classes given pixel following: Observation: possible determine whether pixel water (may case frozen areas polar night extreme latitudes). Permanent Water: Water detected twelve months per year combination permanent observation. Seasonal Water: Water water detected. Water: Water detected.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"Global Surface Water Explorer: https://global-surface-water.appspot.com/ Data Users Guide: https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2021.pdf Research Article: https://www.nature.com/articles/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"GSW transition data contains information type surface water change pixel. raster files integer cell values [0, 10] code different transition classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"","code":"calc_gsw_transitions()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"function returns indicator tibble transition classes variable corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"aggregate, sum area transition class given region. required resources indicator : global_surface_water_transitions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_transitions()) %>%   calc_indicators(calc_gsw_transitions()) %>%   portfolio_long()  aoi #> Simple feature collection with 9 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_per… #> 2  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_new… #> 3  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_los… #> 4  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_sea… #> 5  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_new… #> 6  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_sea… #> 7  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_per… #> 8  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_eph… #> 9  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_eph… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate human footprint statistics — humanfootprint_indicator","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"Human footprint data measures pressure imposed natural environment different dimensions human actions. theoretical maximum value, representing highest level human pressure, 50. routine allows extract zonal statistics human footprint data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"","code":"calc_humanfootprint(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"function returns indicator tibble humanfootprint variable associated value (unitless) per year.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"required resources indicator : humanfootprint_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_humanfootprint(years = 2010)) %>%   calc_indicators(calc_humanfootprint(stats = \"median\")) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 humanfoo… 2010-01-01 00:00:00 humanfo… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial Human Foootprint — humanfootprint_resource","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"resource part publication Mu et al. (2022) \"global record annual terrestrial Human Footprint dataset 2000 2018\". calculated based 8 variables representing human pressures natural ecosystems collected yearly cadence 2000 2020 sampled 1km spatial resolution. variables used expansion built environments (expressed percentage built-areas within grid cell), population density (aggregated gridd cell), nighttime lights, crop pasture lands, roads railways (excluding trails minor roads), navigable waterways (compares waterways nighttime lights dataset). human footprint calculated based weighting scheme proposed Venter et al. (2016), assigning pixel value 0 50, 50 representing theoretical value highest human pressure.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"","code":"get_humanfootprint(years = 2000:2020)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"https://figshare.com/articles/figure/An_annual_global_terrestrial_Human_Footprint_dataset_from_2000_to_2018/16571064","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"years numeric vector indicating years download human footprint data, defaults 2000:2020.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"may required increase timeout option successfully download theses layers source location via e.g. options(timeout = 600). case 403 error occurs, can create account Figshare create personal access token. set FIGSHARE_PAT environment variable, used authenticate.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"Mu, H., Li, X., Wen, Y. et al. global record annual terrestrial Human Footprint dataset 2000 2018. Sci Data 9, 176 (2022). doi:10.1038/s41597-022-01284-8","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":null,"dir":"Reference","previous_headings":"","what":"Register or list indicators in mapme.biodiversity — indicators","title":"Register or list indicators in mapme.biodiversity — indicators","text":"register_indicator() used register new indicator function base information package's internal environment used inform users available indicators. Note, registering custom indicator effect current R session. available_indicators() returns tibble registered indicators basic information required resources.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Register or list indicators in mapme.biodiversity — indicators","text":"","code":"register_indicator(name = NULL, description = NULL, resources = NULL)  available_indicators(indicators = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Register or list indicators in mapme.biodiversity — indicators","text":"name character vector indicating name indicator. description character vector basic description resources character vector required resources need available calculate indicator. names must correspond already registered resources. indicators NULL returns list registered indicators (default). Otherwise ones specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Register or list indicators in mapme.biodiversity — indicators","text":"register_indicator() called side-effect registering indicator available_resources() returns tibble listing available indicators.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Register or list indicators in mapme.biodiversity — indicators","text":"","code":"# \\dontrun{ register_indicator(   name = \"treecover_area\",   description = \"Area of forest cover by year\",   resources = c(     \"gfw_treecover\",     \"gfw_lossyear\"   ) ) # } available_indicators() #> # A tibble: 41 × 3 #>    name                          description                           resources #>                                                                  #>  1 biodiversity_intactness_index Averaged biodiversity intactness ind…   #>  2 biome                         Areal statistics of biomes from TEOW    #>  3 burned_area                   Monthly burned area detected by MODI…   #>  4 deforestation_drivers         Areal statistics of deforestation dr…   #>  5 drought_indicator             Relative wetness statistics based on…   #>  6 ecoregion                     Areal statstics of ecoregions based …   #>  7 elevation                     Statistics of elevation based on NAS…   #>  8 exposed_population_acled      Number of people exposed to conflict…   #>  9 exposed_population_ucdp       Number of people exposed to conflict…   #> 10 fatalities_acled              Number of fatalities by event type b…   #> # ℹ 31 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"indicator calculates areal distribution different biome classes within asset based IBPES biomes dataset.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"","code":"calc_ipbes_biomes()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"function returns indicator tibble biome class variable respective area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"required resources indicator : ipbes_biomes","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_ipbes_biomes()) %>%   calc_indicators(calc_ipbes_biomes()) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 ipbes_bi… 2019-01-01 00:00:00 tropica… #> 2  41057 Shell B… Managed … GUY         1 ipbes_bi… 2019-01-01 00:00:00 shelf_e… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial and Aquatic Biomes — ipbes_biomes","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"resource part Global Assessment Report Biodiversity Ecosystem Services represents division Earth's surface several subcategories. classification differentiates biomes anthromes. Biomes differentiated terrestrial aquatic biomes.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"","code":"get_ipbes_biomes()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"https://zenodo.org/records/3975694","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"Terrestrial biomes include: Tropical subtropical dry humid forests Temperate boreal forests woodlands Mediterranean forests, woodlands scrub Tundra High Mountain habitats Tropical subtropical savannas grasslands Temperate Grasslands Deserts xeric shrublands Wetlands – peatlands, mires, bogs Aquatic biomes include: Cryosphere Aquaculture areas Inland surface waters water bodies/freshwater Shelf ecosystems (neritic intertidal/littoral zone) Open ocean pelagic systems (euphotic zone)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"IPBES (2019): Summary policymakers global assessment report biodiversity ecosystem services Intergovernmental Science-Policy Platform Biodiversity Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio, H. T. Ngo, M. Guèze, J. Agard, . Arneth, P. Balvanera, K. . Brauman, S. H. M. Butchart, K. M. . Chan, L. . Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, . Pfaff, S. Polasky, . Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, . J. Visseren-Hamakers, K. J. Willis, C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":null,"dir":"Reference","previous_headings":"","what":"IUCN Red List of Threatened Species — iucn","title":"IUCN Red List of Threatened Species — iucn","text":"resource part spatial data set Red List Threatened Species released IUCN. free use non-commercial licence. commercial uses, request sent Integrated Biodiversity Assessment Tool (IBAT).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IUCN Red List of Threatened Species — iucn","text":"","code":"get_iucn(paths = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IUCN Red List of Threatened Species — iucn","text":"https://www.iucnredlist.org/resources/-spatial-downloads","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IUCN Red List of Threatened Species — iucn","text":"paths character vector respective species range files GTiff format. Note, theses files downloaded manually.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IUCN Red List of Threatened Species — iucn","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IUCN Red List of Threatened Species — iucn","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"IUCN Red List of Threatened Species — iucn","text":"IUCN (2024). IUCN Red List Threatened Species. https://www.iucnredlist.org doi:10.1038/s41597-022-01284-8","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"function calculates total area key biodiversity areas given input polygon.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"","code":"calc_key_biodiversity_area()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"function returning indicator tibble key_biodiversity_area variable total overlap area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"required resources indicator : key_biodiversity_areas_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) kbas <- system.file(\"res\", \"key_biodiversity_areas\", \"kbas.gpkg\",                     package = \"mapme.biodiversity\") aoi <- get_resources(aoi, get_key_biodiversity_areas(kbas)) aoi <- calc_indicators(aoi, calc_key_biodiversity_area()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 key_biod… 2024-01-01 00:00:00 key_bio… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Key Biodiversity Areas — key_biodiversity_areas_resource","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"resource contains outlines key biodiversity areas, areas representing sites specific importance nature conservation.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"","code":"get_key_biodiversity_areas(path = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"https://www.keybiodiversityareas.org/kba-data","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"path character vector key biodiversity areas GPKG file. Note, file downloaded manually.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"function returns sf footprints object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"BirdLife International (2024). World Database Key Biodiversity Areas. Developed KBA Partnership: BirdLife International, International Union Conservation Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Re:wild, NatureServe, Rainforest Trust, Royal Society Protection Birds, Wildlife Conservation Society World Wildlife Fund. Available www.keybiodiversityareas.org.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate area of different landcover classes — landcover","title":"Calculate area of different landcover classes — landcover","text":"land cover data shows us much region covered forests, rivers, wetlands, barren land, urban infrastructure thus allowing observation land cover dynamics period time. function allows efficiently calculate area different landcover classes polygons. polygon, area classes hectare(ha) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate area of different landcover classes — landcover","text":"","code":"calc_landcover()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate area of different landcover classes — landcover","text":"function returns indicator tibble landcover classes variables corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate area of different landcover classes — landcover","text":"required resources indicator : esalandcover","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate area of different landcover classes — landcover","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_esalandcover(years = 2016:2017)) %>%   calc_indicators(calc_landcover()) %>%   portfolio_long()  aoi #> Simple feature collection with 22 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 22 × 9 #>    WDPAID ISO3  assetid indicator datetime            variable      unit   value #>                                         #>  1 478140 DOM         1 landcover 2016-01-01 00:00:00 shrubs        ha    5.06e2 #>  2 478140 DOM         1 landcover 2016-01-01 00:00:00 herbaceous_v… ha    1.84e3 #>  3 478140 DOM         1 landcover 2016-01-01 00:00:00 cropland      ha    1.15e0 #>  4 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    4.65e3 #>  5 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    1.03e1 #>  6 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    4.98e3 #>  7 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    1.46e2 #>  8 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    1.90e3 #>  9 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    8.85e1 #> 10 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    1.49e1 #> # ℹ 12 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create footprints for vector or raster data sets — make_footprints","title":"Create footprints for vector or raster data sets — make_footprints","text":"function can create footprints vector raster datasets. Specify character vector GDAL readable sources either vector raster type. Internally, GDAL used create sf object single column indicating source geometry indicating bounding box respective source. Note, performance remote sources dependent connection server. means create footprints resource function (e.g. using output {rstac::items_bbox()}) prefer means function remote files.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create footprints for vector or raster data sets — make_footprints","text":"","code":"make_footprints(   srcs = NULL,   filenames = if (inherits(srcs, \"sf\")) basename(srcs[[\"source\"]]) else basename(srcs),   what = c(\"vector\", \"raster\"),   oo = NULL,   co = NULL,   precision = 1e+05 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create footprints for vector or raster data sets — make_footprints","text":"srcs character vector GDAL readable paths either vector raster sources, internal footprint functions called, sf object appended filenames potential options. filenames character vector indicating filenames source data sets written destionation. Defaults basename(srcs) case character type basename(srcs[[\"source\"]]) case sf object. character vector indicating files vector raster files. oo Either list character vector opening options (-oo) respective GDAL driver. list must equal length input sources, vector recycled. co Either list character vector creation options (-co) respective GDAL driver. list must equal length input sources, vector recycled. precision numeric indicating precision coordinates binary round-trip done (see ?sf::st_as_binary()).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create footprints for vector or raster data sets — make_footprints","text":"sf object files sources geometry indicating spatial footprint.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create footprints for vector or raster data sets — make_footprints","text":"","code":"# a vector resource # requires GDAL >= 3.7.0 if (FALSE) {   vec <- system.file(\"shape/nc.shp\", package = \"sf\")   make_footprints(vec, what = \"vector\") }  # a raster resource ras <- system.file(\"ex/elev.tif\", package = \"terra\") make_footprints(ras, what = \"raster\") #> Simple feature collection with 1 feature and 6 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 5.7417 ymin: 49.4417 xmax: 6.5333 ymax: 50.1917 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 7 #>   filename location         type  oo     co     source                  geometry #>                                     #> 1 elev.tif /home/runner/wo… rast…   /home… ((5.7417 50.1917, 5.7417…"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to create a grid of regular resolution and CRS — make_global_grid","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"Use function create regular grid custom CRS. used e.g. create tile grid Global Forest Watch order retrieve intersecting tiles given portfolio.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"","code":"make_global_grid(   xmin = -180,   xmax = 170,   dx = 10,   ymin = -50,   ymax = 80,   dy = 10,   proj = NULL )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"xmin minimum longitude value (E/W) xmax maximum longitude value (E/W) dx difference longitude value per grid ymin minimum latitude value (S/N) ymax maximum latitude value (E/W) dy difference latitude value per grid proj projection system","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"sf object defined grid.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"function allows efficiently calculate area mangrove Global Mangrove Watch - World Conservation Monitoring Centre (WCMC) polygons. polygon, area mangrove (hectare) desired year returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"","code":"calc_mangroves_area()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"function returns indicator tibble mangroves variable corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"required resources indicator : gmw","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_gmw(years = c(1996, 2016))) %>%   calc_indicators(calc_mangroves_area()) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 mangrove… 1996-01-01 00:00:00 mangrov… #> 2  41057 Shell B… Managed … GUY         1 mangrove… 2016-01-01 00:00:00 mangrov… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":null,"dir":"Reference","previous_headings":"","what":"Portfolio methods for mapme.biodiversity — mapme","title":"Portfolio methods for mapme.biodiversity — mapme","text":"mapme_options() sets default options mapme.biodiversity control behavior downstream functions. Mainly, output path well chunk size (ha), can set. Additionally, verbosity can set path log directory can controlled. Might extended options future. get_resources() data sets required calculation indicators can made available. function supports specification several resource functions. determine output path, temporary directory verbosity, output mapme_options() used. calc_indicators() calculates specific biodiversity indicators. requirement resources mandatory inputs requested indicators available locally. Multiple indicators respective additional arguments can supplied. function reads crops available resources extent single asset. Specific resources can queried. supplied (default), available resources prepared.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Portfolio methods for mapme.biodiversity — mapme","text":"","code":"mapme_options(..., outdir, chunk_size, retries, verbose, log_dir)  get_resources(x, ...)  calc_indicators(x, ...)  prep_resources(   x,   avail_resources = NULL,   resources = NULL,   mode = c(\"portfolio\", \"asset\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Portfolio methods for mapme.biodiversity — mapme","text":"... One functions resources/indicators outdir length one character indicating output path. chunk_size numeric length one giving maximum chunk area ha. Defaults 100,000 ha. refers area asset's bounding box. lies value chunk_size, splitting chunking considered. asset processes -bounding box area specified value. retries numeric length one indicating number re-tries package attempt make resource available. Defaults 3. verbose logical, indicating informative messages printed. log_dir character path pointing toward GDAL-writable destination used log erroneous assets. Defaults NULL, meaning erroneous assets serialized disk. specified, GPKG named file.path(log_dir, paste0(Sys.Date(), \"_mapme-error-assets.gpkg\")) created appended case erroneous assets. x sf object features type \"POLYGON\" avail_resources list object available resources. NULL (default), available resources automatically determined. resources character vector resources prepared. NULL (default) available resources prepared. mode character indicating reading mode, e.g. either \"portfolio\" (default) \"asset\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Portfolio methods for mapme.biodiversity — mapme","text":"mapme_options() returns list options arguments specified. Otherwise sets matching arguments new values package's internal environment. get_resources() called side effect making resources available package environment. Returns x, invisibly. calc_indicators() returns x, invisibly, additional nested list column per requested indicator. prep_resources() returns list prepared vector raster resources sf SpatRaster-objects.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Portfolio methods for mapme.biodiversity — mapme","text":"","code":"library(mapme.biodiversity) mapme_options() #> $outdir #> [1] \"/tmp/RtmpQE90N9/mapme-data\" #>  #> $chunk_size #> [1] 1e+08 #>  #> $retries #> [1] 3 #>  #> $verbose #> [1] FALSE #>  #> $log_dir #> NULL #>"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":null,"dir":"Reference","previous_headings":"","what":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"Terra Aqua combined MCD64A1 Version 6.1 Burned Area data product monthly, global gridded 500 meter (m) product containing per-pixel burned-area quality information. MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled 1 kilometer (km) MODIS active fire observations.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"","code":"get_mcd64a1(years = 2000:2022)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"https://planetarycomputer.microsoft.com/dataset/modis-64A1-061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"years Numeric vector years make MCD64A1 product available . Must greater year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"algorithm uses burn sensitive Vegetation Index (VI) create dynamic thresholds applied composite data. VI derived MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 7 measure temporal texture. algorithm identifies date burn 500 m grid cells within individual MODIS tile. date encoded single data layer ordinal day calendar year burn occurred values assigned unburned land pixels additional special values reserved missing data water grid cells.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":null,"dir":"Reference","previous_headings":"","what":"NASA GRACE-based Drought Indicator layer — nasa_grace","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"resource published NASA GRACE Tellus. data set reflects potential drought conditions shallow groundwater section relative reference period spanning 1948 2012. available global raster weekly temporal resolution starting year 2003. value indicates wetness percentile given pixel regard reference period.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"","code":"get_nasa_grace(years = 2003:2022)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":null,"dir":"Reference","previous_headings":"","what":"NASADEM HGT v001 — nasa_srtm","title":"NASADEM HGT v001 — nasa_srtm","text":"resource processed Land Processes Distributed Active Archive Center (LP DAAC) made available Microsoft Planetery Computer. NASADEM distributed 1 degree latitude 1 degree longitude tiles consist land 60° N 56° S latitude. accounts 80% Earth’s total landmass.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NASADEM HGT v001 — nasa_srtm","text":"","code":"get_nasa_srtm()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NASADEM HGT v001 — nasa_srtm","text":"https://planetarycomputer.microsoft.com/dataset/nasadem","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NASADEM HGT v001 — nasa_srtm","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NASADEM HGT v001 — nasa_srtm","text":"NASA JPL (2020). NASADEM Merged DEM Global 1 arc second V001. NASA EOSDIS Land Processes DAAC. Accessed 2023-07-01 doi:10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessibility to Cities layer — nelson_et_al","title":"Accessibility to Cities layer — nelson_et_al","text":"resource published Weiss et al. (2018) \"global map travel time cities assess inequalities accessibility 2015\" journal nature. Accessibility ease larger cities can reached certain location. resource represents travel time major cities year 2015. Encoded minutes, representing time needed reach particular cell nearby city target population range. following ranges nearby cities available: \"5k_10k\" \"10k_20k\" \"20k_50k\" \"50k_100k\" \"100k_200k\" \"200k_500k\" \"500k_1mio\" \"1mio_5mio\" \"50k_50mio\" \"5k_110mio\" \"20k_110mio\" \"5mio_50mio\"","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessibility to Cities layer — nelson_et_al","text":"","code":"get_nelson_et_al(ranges = \"20k_50k\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Accessibility to Cities layer — nelson_et_al","text":"https://figshare.com/articles/dataset/Travel_time_to_cities_and_ports_in_the_year_2015/7638134/3","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessibility to Cities layer — nelson_et_al","text":"ranges character vector indicating one ranges download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessibility to Cities layer — nelson_et_al","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Accessibility to Cities layer — nelson_et_al","text":"Note, figshare server applies rather restrictive rate limit thus frequently resulting opaque error codes (see https://github.com/mapme-initiative/mapme.biodiversity/issues/308). Please set GDAL configuration options sensible values case running issue, e.g.: Sys.setenv(\"GDAL_HTTP_MAX_RETRY\" = \"5\", \"GDAL_HTTP_RETRY_DELAY\" = \"15\").","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Accessibility to Cities layer — nelson_et_al","text":"Weiss, D. J., Nelson, ., Gibson, H. S., Temperley, W., Peedell, S., Lieber, ., … & Gething, P. W. (2018). global map travel time cities assess inequalities accessibility 2015. Nature, 553(7688), 333-336.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population count statistics — population_count","title":"Calculate population count statistics — population_count","text":"WorldPop, initiated 2013, offers easy access spatial demographic datasets, claiming use peer-reviewed fully transparent methods create global mosaics years 2000 2020. function allows efficiently calculate population count statistics (e.g. total number population) polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population count statistics — population_count","text":"","code":"calc_population_count(engine = \"extract\", stats = \"sum\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population count statistics — population_count","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"min\", \"max\", \"sum\", \"mean\", \"median\" \"sd\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population count statistics — population_count","text":"function returns indicator tibble specified populations statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population count statistics — population_count","text":"required resources indicator : worldpop","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population count statistics — population_count","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldpop(years = 2010:2020)) %>%   calc_indicators(     calc_population_count(engine = \"extract\", stats = c(\"sum\", \"median\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 22 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 22 × 9 #>    WDPAID ISO3  assetid indicator      datetime            variable unit   value #>                                         #>  1 478140 DOM         1 population_co… 2010-01-01 00:00:00 populat… count 4016.  #>  2 478140 DOM         1 population_co… 2010-01-01 00:00:00 populat… count   15.5 #>  3 478140 DOM         1 population_co… 2011-01-01 00:00:00 populat… count 3991.  #>  4 478140 DOM         1 population_co… 2011-01-01 00:00:00 populat… count   13.8 #>  5 478140 DOM         1 population_co… 2012-01-01 00:00:00 populat… count 4068.  #>  6 478140 DOM         1 population_co… 2012-01-01 00:00:00 populat… count   15.8 #>  7 478140 DOM         1 population_co… 2013-01-01 00:00:00 populat… count 3958.  #>  8 478140 DOM         1 population_co… 2013-01-01 00:00:00 populat… count   15.2 #>  9 478140 DOM         1 population_co… 2014-01-01 00:00:00 populat… count 3981.  #> 10 478140 DOM         1 population_co… 2014-01-01 00:00:00 populat… count   15.3 #> # ℹ 12 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":null,"dir":"Reference","previous_headings":"","what":"Portfolio methods — portfolio","title":"Portfolio methods — portfolio","text":"write_portfolio() writes processed biodiversity portfolio disk. Portfolio data serialized disk GeoPackage including two tables: metadata indicators. metadata tables includes, among simple variables geometries primary key called assetid. 'indicators' tables includes foreign key assetid, column called indicator giving name original indicator well standard indicator columns datetime, variable, unit, value. convenience, use read_portfolio() read portfolio GeoPackage back R. portfolio_long() transforms portfolio long-format, potentially dropping geometries process. portfolio_wide() transforms portfolio wide-format, potentially dropping geometries process.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Portfolio methods — portfolio","text":"","code":"write_portfolio(x, dsn, ...)  read_portfolio(src, ...)  portfolio_long(x, indicators = NULL, drop_geoms = FALSE)  portfolio_wide(x, indicators = NULL, drop_geoms = FALSE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Portfolio methods — portfolio","text":"x portfolio object processed mapme.biodiversity. dsn file path output file (must end gpkg). ... Additional arguments supplied write_sf() read_sf() src character vector pointing GeoPackage previously written disk via write_portfolio() indicators NULL (default), indicator columns detected transformed automatically. character vector supplied, indicators transformed. drop_geoms logical, indicating geometries dropped.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Portfolio methods — portfolio","text":"write_portfolio() returns dsn, invisibly. read_portfolio() returns sf object object nested list columns every indicator found GeoPackage source file. portfolio_long() returns portfolio object long-format. portfolio_wide() returns portfolio object wide-format.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation average based on CHELSA — precipitation_chelsa","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"functions allows calculate averaged precipitation CHELSA downscaled precipitation layers. Based user-selected years, monthly averages precipitation calculated.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"","code":"calc_precipitation_chelsa(years = 1979:2018, engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"years numeric vector indicating years calculate precipitation statistics. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"function returns indicator tibble variable precipitation sum precipitation (mm/m^2) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"required resources indicator : chelsa","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_chelsa(years = 2010)) %>%   calc_indicators(     calc_precipitation_chelsa(       years = 2010,       engine = \"extract\"     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2010-01-01 00:00:00 precipi… mm/m…  52.5 #>  2 478140 DOM         1 precipitation_… 2010-02-01 00:00:00 precipi… mm/m…  10.4 #>  3 478140 DOM         1 precipitation_… 2010-03-01 00:00:00 precipi… mm/m…  32.7 #>  4 478140 DOM         1 precipitation_… 2010-04-01 00:00:00 precipi… mm/m… 104.  #>  5 478140 DOM         1 precipitation_… 2010-05-01 00:00:00 precipi… mm/m… 218.  #>  6 478140 DOM         1 precipitation_… 2010-06-01 00:00:00 precipi… mm/m… 142.  #>  7 478140 DOM         1 precipitation_… 2010-07-01 00:00:00 precipi… mm/m… 191.  #>  8 478140 DOM         1 precipitation_… 2010-08-01 00:00:00 precipi… mm/m… 153.  #>  9 478140 DOM         1 precipitation_… 2010-09-01 00:00:00 precipi… mm/m… 161.  #> 10 478140 DOM         1 precipitation_… 2010-10-01 00:00:00 precipi… mm/m… 116.  #> 11 478140 DOM         1 precipitation_… 2010-11-01 00:00:00 precipi… mm/m… 209.  #> 12 478140 DOM         1 precipitation_… 2010-12-01 00:00:00 precipi… mm/m…  39.5 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"functions allows calculate precipitation sums based CHIRPS rainfall estimates. Corresponding time-frame analysis portfolio, monthly precipitation sums calculated.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"","code":"calc_precipitation_chirps(years = 1981:2020, engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"years numeric vector indicating years calculate precipitation statistics. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"function returns indicator tibble variable precipitation sum precipitation (mm) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"required resources indicator : chirps","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_chirps(years = 2010)) %>%   calc_indicators(     calc_precipitation_chirps(       years = 2010,       engine = \"extract\"     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2010-01-01 00:00:00 precipi… mm      102 #>  2 478140 DOM         1 precipitation_… 2010-02-01 00:00:00 precipi… mm      129 #>  3 478140 DOM         1 precipitation_… 2010-03-01 00:00:00 precipi… mm      199 #>  4 478140 DOM         1 precipitation_… 2010-04-01 00:00:00 precipi… mm      827 #>  5 478140 DOM         1 precipitation_… 2010-05-01 00:00:00 precipi… mm     1067 #>  6 478140 DOM         1 precipitation_… 2010-06-01 00:00:00 precipi… mm     1220 #>  7 478140 DOM         1 precipitation_… 2010-07-01 00:00:00 precipi… mm      878 #>  8 478140 DOM         1 precipitation_… 2010-08-01 00:00:00 precipi… mm      588 #>  9 478140 DOM         1 precipitation_… 2010-09-01 00:00:00 precipi… mm      582 #> 10 478140 DOM         1 precipitation_… 2010-10-01 00:00:00 precipi… mm      560 #> 11 478140 DOM         1 precipitation_… 2010-11-01 00:00:00 precipi… mm      683 #> 12 478140 DOM         1 precipitation_… 2010-12-01 00:00:00 precipi… mm       59 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation statistics — precipitation_wc","title":"Calculate precipitation statistics — precipitation_wc","text":"function allows efficiently calculate precipitation statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation statistics — precipitation_wc","text":"","code":"calc_precipitation_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation statistics — precipitation_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation statistics — precipitation_wc","text":"function returns indicator tibble precipition statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation statistics — precipitation_wc","text":"required resources indicator : precipitation layer worldclim_precipitation","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation statistics — precipitation_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_precipitation(years = 2018)) %>%   calc_indicators(     calc_precipitation_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2018-01-01 00:00:00 worldcl… mm     26.7 #>  2 478140 DOM         1 precipitation_… 2018-01-01 00:00:00 worldcl… mm     26.7 #>  3 478140 DOM         1 precipitation_… 2018-02-01 00:00:00 worldcl… mm     26.1 #>  4 478140 DOM         1 precipitation_… 2018-02-01 00:00:00 worldcl… mm     26.5 #>  5 478140 DOM         1 precipitation_… 2018-03-01 00:00:00 worldcl… mm     66.7 #>  6 478140 DOM         1 precipitation_… 2018-03-01 00:00:00 worldcl… mm     68.6 #>  7 478140 DOM         1 precipitation_… 2018-04-01 00:00:00 worldcl… mm     82.0 #>  8 478140 DOM         1 precipitation_… 2018-04-01 00:00:00 worldcl… mm     82.1 #>  9 478140 DOM         1 precipitation_… 2018-05-01 00:00:00 worldcl… mm    330.  #> 10 478140 DOM         1 precipitation_… 2018-05-01 00:00:00 worldcl… mm    338.  #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":null,"dir":"Reference","previous_headings":"","what":"Register or list resources in mapme.biodiversity — resources","title":"Register or list resources in mapme.biodiversity — resources","text":"register_resource() used register new resource function base information package's internal environment used inform users available resources. Note, registering custom resource effect current R session. available_resources() returns tibble registered resources basic information source licence.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Register or list resources in mapme.biodiversity — resources","text":"","code":"register_resource(   name = NULL,   description = NULL,   licence = NULL,   source = NULL,   type = NULL )  available_resources(resources = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Register or list resources in mapme.biodiversity — resources","text":"name character vector indicating name resource. description character vector basic description licence character vector indicating licence resource. case custom licence, put link licence text. source Optional, preferably URL data found. type character vector indicating type resource. Either 'vector' 'raster'. resources NULL returns list resources (default). Otherwise ones specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Register or list resources in mapme.biodiversity — resources","text":"register_resource() called side-effect registering resource. available_resources() returns tibble listing available resources.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Register or list resources in mapme.biodiversity — resources","text":"","code":"# \\dontrun{ register_resource(   name = \"gfw_treecover\",   description = \"Global Forest Watch - Percentage of canopy closure in 2000\",   licence = \"CC-BY 4.0\",   source = \"https://data.globalforestwatch.org/documents/tree-cover-2000/explore\",   type = \"raster\" ) # } available_resources() #> # A tibble: 36 × 5 #>    name                          description                licence source type  #>                                                         #>  1 accessibility_2000            Accessibility data for th… See JR… https… rast… #>  2 acled                         Armed Conflict Location &… Visit … Visit… vect… #>  3 biodiversity_intactness_index Biodiversity Intactness I… CC-BY-… https… rast… #>  4 chelsa                        Climatologies at High res… Unknow… https… rast… #>  5 chirps                        Climate Hazards Group Inf… CC - u… https… rast… #>  6 esalandcover                  Copernicus Land Monitorin… CC-BY … https… rast… #>  7 fritz_et_al                   Drivers of deforestation … CC-BY … https… rast… #>  8 gfw_emissions                 Global Forest Watch - CO2… CC-BY … https… rast… #>  9 gfw_lossyear                  Global Forest Watch - Yea… CC-BY … https… rast… #> 10 gfw_treecover                 Global Forest Watch - Per… CC-BY … https… rast… #> # ℹ 26 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate slope statistics — slope","title":"Calculate slope statistics — slope","text":"function allows calculate slope statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate slope statistics — slope","text":"","code":"calc_slope(engine = \"exactextract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate slope statistics — slope","text":"engine preferred processing function either one \"zonal\", \"extract\" \"exactextract\" character string. stats Function applied compute statistics polygons. Accepts either single string vector strings, \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate slope statistics — slope","text":"function returns indicator tibble specified slope statistics variables corresponding values (degrees).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate slope statistics — slope","text":"required resource indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate slope statistics — slope","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_slope(stats = c(\"mean\", \"median\", \"sd\", \"var\"), engine = \"extract\")   ) %>%   portfolio_long() #> Resource 'nasa_srtm' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable     unit    value #>                                        #> 1 478140 DOM         1 slope     2000-02-01 00:00:00 slope_mean   degrees 17.8  #> 2 478140 DOM         1 slope     2000-02-01 00:00:00 slope_median degrees 17.0  #> 3 478140 DOM         1 slope     2000-02-01 00:00:00 slope_sd     degrees  9.93 #> 4 478140 DOM         1 slope     2000-02-01 00:00:00 slope_var    degrees 98.6  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":null,"dir":"Reference","previous_headings":"","what":"SoilGrids data layers — soilgrids","title":"SoilGrids data layers — soilgrids","text":"SoilGrids project combining global observation data machine learning map spatial distribution soil properties across globe. produced spatial resolution 250 meters parameters mapped different depths. order able assess prediction uncertainty, besides mean median prediction, 0.05 0.95 percentile predictions available. following parameters available: bdod Bulk density fine earth fraction (kg/dm3) cec Cation Exchange Capacity soil (cmol(c)/kg) cfvo Volumetric fraction coarse fragments > 2 mm (cm3/100cm3 (volPerc)) clay Proportion clay particles < 0.002 mm fine earth fraction (g/100g) nitrogen Total nitrogen (g/kg) phh2o Soil pH (pH) sand Proportion sand particles > 0.05 mm fine earth fraction (g/100g) silt Proportion silt particles >= 0.002 mm <= 0.05 mm fine earth fraction (g/100g) soc Soil organic carbon content fine earth fraction (g/kg) ocd Organic carbon density (kg/m3) ocs Organic carbon stocks (kg/m²)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SoilGrids data layers — soilgrids","text":"","code":"get_soilgrids(layers, depths, stats)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SoilGrids data layers — soilgrids","text":"https://www.isric.org/explore/soilgrids","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SoilGrids data layers — soilgrids","text":"layers character vector indicating layers download soilgrids depths character vector indicating depths download stats character vector indicating statistics download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SoilGrids data layers — soilgrids","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SoilGrids data layers — soilgrids","text":"Except ocs, available depth \"0-30cm\", parameters available following depths: \"0-5cm\" \"5-15cm\" \"15-30cm\" \"30-60cm\" \"60-100cm\" \"100-200cm\" parameter depth available following statistics: \"Q0.05\" \"Q0.50\" \"mean\" \"Q0.95\"","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SoilGrids data layers — soilgrids","text":"Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2017) SoilGrids250m: Global gridded soil information based machine learning. PLOS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Zonal Soil Properties — soilproperties","title":"Calculate Zonal Soil Properties — soilproperties","text":"indicator allows extraction zonal statistics resource layers previously downloaded SoilGrids, thus total supporting calculation zonal statistics 10 different soil properties 6 different depths total 4 different model outputs (stat). Zonal statistics calculated SoilGrid layers previously made available vie get_resources().","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Zonal Soil Properties — soilproperties","text":"","code":"calc_soilproperties(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Zonal Soil Properties — soilproperties","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Zonal Soil Properties — soilproperties","text":"function returns indicator tibble soilgrid layers statistics variables corresponding statistics value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Zonal Soil Properties — soilproperties","text":"required resource indicator : soilgrids","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Zonal Soil Properties — soilproperties","text":"","code":"if (FALSE) {   library(sf)   library(mapme.biodiversity)    mapme_options(     outdir = NULL,     verbose = FALSE   )    aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_soilgrids(         layers = \"clay\",         depths = \"0-5cm\",         stats = \"mean\"       )     ) %>%     calc_indicators(       calc_soilproperties(engine = \"extract\", stats = c(\"mean\", \"median\"))     ) %>%     portfolio_long()    aoi }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if a spatial data sets exists — spds_exists","title":"Check if a spatial data sets exists — spds_exists","text":"function uses file path readable GDAL check can query information. Note, also work remote files, e.g. S3 bucket. can use function custom resource function query file already present destination. Note, performance dependent connection server. can also used files local file system.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if a spatial data sets exists — spds_exists","text":"","code":"spds_exists(path, oo = character(0), what = c(\"vector\", \"raster\"))"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if a spatial data sets exists — spds_exists","text":"path length 1 character vector GDAL readable file path. oo Either list character vector opening options (-oo) respective GDAL driver. list must equal length input sources, vector recycled. character vector indicating resource vector raster file.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if a spatial data sets exists — spds_exists","text":"logical, TRUE file exists, FALSE .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if a spatial data sets exists — spds_exists","text":"","code":"# a vector resource vec <- system.file(\"shape/nc.shp\", package = \"sf\") spds_exists(vec, what = \"vector\") #> [1] TRUE  # a raster resource ras <- system.file(\"ex/elev.tif\", package = \"terra\") spds_exists(ras, what = \"raster\") #> [1] TRUE  # a non existing file spds_exists(\"not-here.gpkg\", what = \"vector\") #> [1] FALSE"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Species richness based on IUCN raster data — species_richness","title":"Species richness based on IUCN raster data — species_richness","text":"Species richness counts number potential species intersecting polygon grouped IUCN threat categorization. Note, indicator function requires manual download respective raster files.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species richness based on IUCN raster data — species_richness","text":"","code":"calc_species_richness(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Species richness based on IUCN raster data — species_richness","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Species richness based on IUCN raster data — species_richness","text":"function returns indicator tibble IUCN layers specified statistics variable respective species richness (count) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Species richness based on IUCN raster data — species_richness","text":"specific meaning species richness indicator depends supplied raster file. required resources indicator : iucn","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species richness based on IUCN raster data — species_richness","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  iucn_dir <- system.file(\"res\", \"iucn\", package = \"mapme.biodiversity\") sr_rasters <- list.files(iucn_dir, pattern = \"*_SR_*\", full.names = TRUE)  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_iucn(sr_rasters)) %>%   calc_indicators(calc_species_richness(stats = \"median\")) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 species_richness 2023-01-01 00:00:00 amphibi… count    15 #> 2 478140 DOM         1 species_richness 2023-01-01 00:00:00 birds_t… count    27 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate maximum temperature statistics — temperature_max_wc","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"function allows efficiently calculate maximum temperature statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"","code":"calc_temperature_max_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"function returns indicator tibble maximum temperature statistics variables corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"required resources indicator : maximum temperature layer worldclim_max_temperature","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_max_temperature(years = 2018)) %>%   calc_indicators(     calc_temperature_max_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 temperature_ma… 2018-01-01 00:00:00 worldcl… C      20.8 #>  2 478140 DOM         1 temperature_ma… 2018-01-01 00:00:00 worldcl… C      20.5 #>  3 478140 DOM         1 temperature_ma… 2018-02-01 00:00:00 worldcl… C      20.5 #>  4 478140 DOM         1 temperature_ma… 2018-02-01 00:00:00 worldcl… C      20   #>  5 478140 DOM         1 temperature_ma… 2018-03-01 00:00:00 worldcl… C      22.1 #>  6 478140 DOM         1 temperature_ma… 2018-03-01 00:00:00 worldcl… C      22   #>  7 478140 DOM         1 temperature_ma… 2018-04-01 00:00:00 worldcl… C      22.6 #>  8 478140 DOM         1 temperature_ma… 2018-04-01 00:00:00 worldcl… C      22.5 #>  9 478140 DOM         1 temperature_ma… 2018-05-01 00:00:00 worldcl… C      21.5 #> 10 478140 DOM         1 temperature_ma… 2018-05-01 00:00:00 worldcl… C      21   #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"function allows efficiently calculate minimum temperature statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"","code":"calc_temperature_min_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"function returns indicator tibble minimum temperature statistics variables corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"required resources indicator : minimum temperature layer worldclim_min_temperature","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_min_temperature(years = 2018)) %>%   calc_indicators(     calc_temperature_min_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 temperature_mi… 2018-01-01 00:00:00 worldcl… C      10.1 #>  2 478140 DOM         1 temperature_mi… 2018-01-01 00:00:00 worldcl… C      10   #>  3 478140 DOM         1 temperature_mi… 2018-02-01 00:00:00 worldcl… C      10.2 #>  4 478140 DOM         1 temperature_mi… 2018-02-01 00:00:00 worldcl… C      10   #>  5 478140 DOM         1 temperature_mi… 2018-03-01 00:00:00 worldcl… C      10.1 #>  6 478140 DOM         1 temperature_mi… 2018-03-01 00:00:00 worldcl… C      10   #>  7 478140 DOM         1 temperature_mi… 2018-04-01 00:00:00 worldcl… C      11.1 #>  8 478140 DOM         1 temperature_mi… 2018-04-01 00:00:00 worldcl… C      11   #>  9 478140 DOM         1 temperature_mi… 2018-05-01 00:00:00 worldcl… C      12.6 #> 10 478140 DOM         1 temperature_mi… 2018-05-01 00:00:00 worldcl… C      13   #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"resource part publication Olson et al. (2004) \"Terrestrial Ecosystems World (TEOW) WWF-US (Olson)\". depicts 867 terrestrial ecoregions around world classified 14 different terrestrial biomes forests, grasslands, deserts. polygons represent ecoregions, defined relatively large units land inland water sharing large majority biodiversity. datasets made available World Wildlife Fund (WWF) year 2001.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"","code":"get_teow()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. ., Itoua, ., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions world: new map life Earth. Bioscience 51(11):933-938. doi:10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate accessibility statistics — traveltime","title":"Calculate accessibility statistics — traveltime","text":"Accessibility ease larger cities can reached certain location. function allows efficiently calculate accessibility statistics (.e. travel time nearby major cities) polygons. polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate accessibility statistics — traveltime","text":"","code":"calc_traveltime(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate accessibility statistics — traveltime","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate accessibility statistics — traveltime","text":"function returns indicator tibble city ranges statisics variable corresponding values (minutes) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate accessibility statistics — traveltime","text":"required resources indicator : nelson_et_al","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate accessibility statistics — traveltime","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nelson_et_al(ranges = \"100k_200k\")) %>%   calc_indicators(     calc_traveltime(engine = \"extract\", stats = c(\"min\", \"max\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator  datetime            variable       unit  value #>                                         #> 1 478140 DOM         1 traveltime 2015-01-01 00:00:00 100k_200k_tra… minu…   162 #> 2 478140 DOM         1 traveltime 2015-01-01 00:00:00 100k_200k_tra… minu…   528 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate accessibility statistics for the year 2000 — traveltime_2000","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"Accessibility refers ease cities can reached certain location. function allows efficient calculation accessibility statistics (.e., travel time nearest city) polygons.  polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"","code":"calc_traveltime_2000(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"function returns indicator tibble accessibility statistics year 2000 variables corresponding values (minutes) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"required resource indicator : accessibility_2000","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_accessibility_2000()) %>%   calc_indicators(     calc_traveltime_2000(stats = c(\"mean\", \"median\", \"sd\"), engine = \"extract\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 3 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 3 × 9 #>   WDPAID ISO3  assetid indicator       datetime            variable  unit  value #>                                         #> 1 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  387. #> 2 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  420  #> 3 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  204. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate treecover statistics — treecover_area","title":"Calculate treecover statistics — treecover_area","text":"functions allows efficiently calculate treecover statistics polygons. year analysis timeframe, forest losses preceding current years subtracted treecover year 2000 actual treecover figures within polygon returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate treecover statistics — treecover_area","text":"","code":"calc_treecover_area(years = 2000:2023, min_size = 10, min_cover = 35)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate treecover statistics — treecover_area","text":"years numeric vector years calculate treecover area. min_size minimum size forest patch considered forest ha. min_cover minimum cover percentage per pixel considered forest.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate treecover statistics — treecover_area","text":"function returns indicator tibble variable treecover corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate treecover statistics — treecover_area","text":"required resources indicator : gfw_treecover gfw_lossyear","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate treecover statistics — treecover_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\")   ) %>%   calc_indicators(calc_treecover_area(years = 2016:2017, min_size = 1, min_cover = 30)) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator      datetime            variable  unit  value #>                                        #> 1 478140 DOM         1 treecover_area 2016-01-01 00:00:00 treecover ha    2370. #> 2 478140 DOM         1 treecover_area 2017-01-01 00:00:00 treecover ha    2358. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate treeloss statistics — treecover_area_and_emissions","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"functions allows efficiently calculate treecover emissions indicators single function call together. Since pre-processing operations treecover emissions , efficient calculate one run users actually interested statistics. Otherwise users advised use respective single indicator functions.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"","code":"calc_treecover_area_and_emissions(   years = 2000:2023,   min_size = 10,   min_cover = 35 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"years numeric vector years calculate treecover area emissions. min_size minimum size forest patch ha. min_cover minimum threshold stand density pixel considered forest year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"function returns indicator tibble variables treecover emissions ind corresponding values (ha Mg) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"required resources indicator : gfw_treecover gfw_lossyear gfw_emissions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(     calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)   ) %>%   portfolio_long() #> Resource 'gfw_treecover' is already available. #> Resource 'gfw_lossyear' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 emissio… Mg    4296. #> 2 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 treecov… ha    2370. #> 3 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 emissio… Mg    4970. #> 4 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 treecov… ha    2358. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate emission statistics — treecoverloss_emissions","title":"Calculate emission statistics — treecoverloss_emissions","text":"functions allows efficiently calculate emission statistics areas interest. year analysis timeframe, forest losses Hansen et al. (2013) overlayed respective emission layer Harris et al. (2021) area-wise emission statistics calculated year.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate emission statistics — treecoverloss_emissions","text":"","code":"calc_treecoverloss_emissions(years = 2000:2023, min_size = 10, min_cover = 35)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate emission statistics — treecoverloss_emissions","text":"years numeric vector years calculate emissions caused treecover loss. min_size minimum size forest patch ha. min_cover minimum threshold stand density pixel considered forest year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate emission statistics — treecoverloss_emissions","text":"function returns indicator tibble emissions variable emitted CO2 equivalent (Mg)  value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate emission statistics — treecoverloss_emissions","text":"required resources indicator : gfw_treecover gfw_lossyear gfw_emissions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate emission statistics — treecoverloss_emissions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(     calc_treecoverloss_emissions(years = 2016:2017, min_size = 1, min_cover = 30)   ) %>%   portfolio_long() #> Resource 'gfw_treecover' is already available. #> Resource 'gfw_lossyear' is already available. #> Resource 'gfw_emissions' is already available.  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 treecoverloss_e… 2016-01-01 00:00:00 emissio… Mg    4296. #> 2 478140 DOM         1 treecoverloss_e… 2017-01-01 00:00:00 emissio… Mg    4970. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"Terrain Ruggedness Index measurement developed Riley, et al. (1999). elevation difference centre pixel eight immediate pixels squared averaged square root taken get TRI value. function allows calculate terrain ruggedness index (tri) statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"","code":"calc_tri(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"function returns indicator tibble tri variable respective statistic value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"range index values corresponding meaning: 0-80 m - level surface 81-116 m - nearly level surface 117-161 m - slightly rugged surface 162-239 m - intermediately rugged surface 240-497 m - moderately rugged surface 498-958 m - highly rugged surface 959-4367 m  extremely rugged surface required resources indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"Riley, S. J., DeGloria, S. D., & Elliot, R. (1999). Index quantifies topographic heterogeneity. Intermountain Journal Sciences, 5(1-4), 23-27.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_tri(stats = c(\"mean\", \"median\", \"sd\", \"var\"), engine = \"extract\")   ) %>%   portfolio_long() #> Resource 'nasa_srtm' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable   unit  value #>                                    #> 1 478140 DOM         1 tri       2000-02-01 00:00:00 tri_mean   m      33.3 #> 2 478140 DOM         1 tri       2000-02-01 00:00:00 tri_median m      30.8 #> 3 478140 DOM         1 tri       2000-02-01 00:00:00 tri_sd     m      18.7 #> 4 478140 DOM         1 tri       2000-02-01 00:00:00 tri_var    m     349.  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":null,"dir":"Reference","previous_headings":"","what":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"resource distributed Uppsala Conflict Data Program (UCDP) constitutes diaggregated dataset individual events organized violence. encodes different actors involved, spatially disaggregated village levels anc currently covers time period 1989 2021. Older versions data set can downloaded, users recommended download latest data set.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"","code":"get_ucdp_ged(version = \"latest\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"https://ucdp.uu.se/downloads/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"version character vector specifying version download. Defaults \"latest\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"following versions available: 5.0 17.1 17.2 18.1 19.1 20.1 21.1 22.1 23.1 24.1 latest","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"Davies, Shawn, Therese Pettersson & Magnus Öberg (2022). Organized violence 1989-2021 drone warfare. Journal Peace Research 59(4). doi:10.1177/00223433221108428","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"","code":"get_worldclim_max_temperature(   years = 2000:2018,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"resource represents maximum temperature, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (°C), representing maximum temperature per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"","code":"get_worldclim_min_temperature(   years = 2000:2018,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"function returns character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"resource represents minimum temperature, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (°C), representing minimum temperature per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"","code":"get_worldclim_precipitation(   years = 1960:2021,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"resource represents average precipitation, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (mm), representing mean precipitation per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":null,"dir":"Reference","previous_headings":"","what":"Population Count layer for year 2000-2020 — worldpop","title":"Population Count layer for year 2000-2020 — worldpop","text":"resource published open spatial demographic data research organization called WorldPop. resource represents population count, 1 km spatial resolution layers available download year 2000 2020. dataset called WorldPop Unconstrained Global Mosaics. encoded cell value represents total number people particular grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population Count layer for year 2000-2020 — worldpop","text":"","code":"get_worldpop(years = 2000)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Population Count layer for year 2000-2020 — worldpop","text":"https://www.worldpop.org/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population Count layer for year 2000-2020 — worldpop","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population Count layer for year 2000-2020 — worldpop","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population Count layer for year 2000-2020 — worldpop","text":"may required increase timeout option successfully download theses WorldPop layers source location via e.g. options(timeout = 600).","code":""},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-development-version","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity (development version)","text":"get_nasa_srtm() now uses GDAL’s VSI path option pc_url_signing=yes sign URLs Microsoft Planetary Computer (#383)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-092","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.2","title":"mapme.biodiversity 0.9.2","text":"CRAN release: 2024-10-10","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-2","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.2","text":"get_acled() calc_fatalities_acled() calc_fatalities_ucdp() (renamed) calc_exposed_population_acled() calc_exposed_population_ucdp() (renamed) calc_fatalities_ucdp() now returns sparse timeseries, e.g. asset-months now fatalities omitted.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-9-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.9.2","text":"fixes portfolio_wide() throwing error single assets NULL values present calc_mangroves_area() returned NULL invalid geometries encountered Now tries repair geometries return area valid geometries (#375)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-2","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.2","text":".get_intersection() now assumes x tindex represented oriented rings sphere (#378)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-091","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.1","title":"mapme.biodiversity 0.9.1","text":"CRAN release: 2024-09-02","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-1","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.1","text":"get_accessibility_2000() (#365, @fBedecarrats) calc_traveltime_2000() (#365, @fBedecarrats)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.1","text":"adjusts test get_gsw_timseries() calc_gsw_timeseries() write temporal directory R session fix CRAN errors (#370, @karpfen)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-090","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.0","title":"mapme.biodiversity 0.9.0","text":"CRAN release: 2024-08-27","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.0","text":"prep_resources() received additional argument mode get control reading mode (e.g. portfolio asset) resources based WorldClim now support selecting spatial resolution cover historical timeseries starting 1960 (#302) assets now chunked sub-components prior indicator calculation thus parallelization now applied single level (#322) chunk_size now properly set 100,000 ha per documentation (set 10,000 ha) (#324) setting chunk_size=NULL now allowed skips chunking (#331) treecover indicators now trough message landscapemetrics installed (#325) setting outdir via mapme_options() now probes destination trying write GTiff file errors unsuccessful (#335) code previously using httr now uses httr2 (#330) new resources: get_iucn() (#359) get_chelsa() (#318) get_ipbes_biomes() (#345) get_humanfootprint() (#341) get_gsw_time_series() (#354, @karpfen) get_key_biodiversity_areas() (#349, @karpfen) get_biodiversity_intactness_index() (#351, @karpfen) get_vul_carbon(), get_man_carbon(), get_irr_carbon() (#339) new indicators: calc_slope() (#355, @fBedecarrats) calc_ipbes_biomes() (#345) calc_humanfootprint() (#341) calc_gsw_time_series() (#354, @karpfen) calc_species_richness() (#359) calc_exposed_population() (#321) calc_precipitation_chelsa() (#318) calc_key_biodiversity_area() (#349, @karpfen) calc_biodiversity_intactness_index() (#351, @karpfen) calc_vul_carbon(), calc_man_carbon(), calc_irr_carbon() (#339)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.9.0","text":"fixes transforming asset CRS raster dataset calc_deforestation_drivers() (#300) write_portfolio() now drops indicators NULL values instead throwing error (#303) get_ucdp_ged() now adds SRS infos footprints object (#313) uses binary writing mode worldpop resource Windows (#319)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.0","text":".check_portfolio() now checks assetid unique values overrides case (#305) .read_raster() now reads values memory removes VRT files -exit (#311) .fetch_resources() now honors creation opening options (#315) httr calls replaced respective httr2 equivalents (#329)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-080","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.8.0","title":"mapme.biodiversity 0.8.0","text":"CRAN release: 2024-07-03","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-8-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.8.0","text":"updates gfw_lossyear resource v20240402 entails emission data 2000 - 2023 removes nasa_firms resource associated active_fire_counts indicator adds mcd64a1 resource burned_area indicator mapme.biodiveristy now leverages GDAL data /O meaning GDAL readable source data sets writable destinations now supported README.md now includes section set cloud-storages use destination resource data quickstart vignette now uses GFW data example data chunking now applied based area assets bounding box instead area write_portfolio() now serializes two-table GeoPackage re-introduces read_portfolio() (#294) datetime column values now encoded POSIXct","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-8-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.8.0","text":"exports make_footprints() ease process creating footprints resource functions exports spds_exists() resource function check data source exists get_*() functions now required return footprint objects indicating spatial extent elements pointing towards GDAL readable data source source column case user-specified destination found, package now uses gdal_translate write data source destination tests long-running examples tests skipped GA CRAN fixes bug checking portfolio inherits tbl_df","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-070","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.7.0","title":"mapme.biodiversity 0.7.0","text":"CRAN release: 2024-05-31","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.7.0","text":"fixes bug wrong tile paths returned get_gfw_emissions()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.7.0","text":"introduces standardized output format indicators, see #240 information get_chirps() now allows specify years download CHIRPS resources calc_precipitation_chirps() now returns precipitation sums deprecation indicator active_fire_properties since resources can now retrieved using prep_resources() (see )","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-7-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.7.0","text":"exports prep_resources() prepare resources single assets exports portfolio_long() portfolio_wide() automatically un-nest indicator columns change data layout either long wide changes behavior write_portfolio() serialize portfolios GDAL supported spatial formats either long wide format deprecates read_portfolio() introduces option chunk_size mapme_options() control size polygons split processed chunks allows assets type 'MULTIPOLYGON' automatically combines results based aggregation function indicator examples now use portfolio_long() instead tidyr::unnest()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-7-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.7.0","text":"indicator functions must now return tibbles columns named datetime, variable, unit value inner-level indicator functions must now specify statistic aggregation chunks chirps nasa_grace resources updated check internet connectivity can now disabled via environment variable mapme_check_connection (#262) gfw_treecover gfw_lossyear resources updated v1.11 (#277, @fBedecarrats) GFW indicators now automatically detect maximum years based gfw_lossyear layer (#273) drops curl, stringr, tidyselect dependencies moves progressr rvest Imports Suggests drops SPEI Suggests","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-060","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.6.0","title":"mapme.biodiversity 0.6.0","text":"CRAN release: 2024-04-30","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.6.0","text":"introduces new UI based closures resources indicators, see #240 information","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-6-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.6.0","text":"improves output available_resources() available_indicators() introduces mapme_options() add fine-control packages behaviour deprecates init_portfolio() favor mapme_options() check_available_years() check_namespace() download_or_skip() check_engine() check_stats() select_engine() make_global_grid() unzip_and_remove()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.6.0","text":"added Global Surface Water resources respective indicators (#235, @karpfen)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-6-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.6.0","text":"removed st_make_valid() .read_vector().","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-050","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.5.0","title":"mapme.biodiversity 0.5.0","text":"CRAN release: 2024-01-08","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-5-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.5.0","text":"Quickstart vignette uses WorldPop resource instead CHIRPS, relying working internet connection (#230).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.5.0","text":"GFW resources indicators include latest GFC-2022-v1.10 version (#203). Raster resources CRS different WGS84 now supported (#213).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.5.0","text":"argument add_resources init_portfolio() deprecated. means get_resources() run every new R session make resource available processing (#219). Rasters now cropped spatial extent asset setting snap=\"\", thus delivering slightly bigger extent (#212). Speed improvements GFW indicators (x10 larger rasters) now require R package exactextractr installed. Also, advised R package landscapemetrics installed gain full computation speed improvement.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"mapme.biodiversity 0.5.0","text":"calc_indicators() checks 0-length tibbles (#196, #199, #215). Fix bug reading rasters temporal dimensions (#209). raster cells touching polygon now returned (#208).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-5-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.5.0","text":".read_raster_source() now uses simplified logic cover cases (e.g. single tiles, tiled rasters without temporal dimension, single temporal rasters) (#211). Rasters cropped using snap=\"\" default (#212). .read_raster_source() now projects assets case CRS differs portfolio (#213). tile indices raster resources now appended portfolio attributes sf objects instead written disk (#219). .read_raster_source() now applies precision round-trip 5 decimal point match rasters slight changes spatial extent (#217). register_resource() register_indicator() now issue warnings resources/indicators names already registered overwrites (#220).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-040","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.4.0","title":"mapme.biodiversity 0.4.0","text":"CRAN release: 2023-08-28","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.4.0","text":"added new resource called ucdp_ged providing database violent conflict 1989 today added new indicator called fatalities aggregating number deaths type conflict monthly time scale based ucdp_ged resource. Added new resource called fritz_et_al providing raster layer deforestation added new resource called fritz_et_al providing raster layer deforestation drivers tropical forests based Fritz et al. (2022) added new indicator called deforestation_drivers using fritz_et_al resource obtain information absolute relative area driving forest losses assets period 2008-2019 added two new exported functions register_resource() register_indicator() allow users register custom functions resources/indicators added new vignette web-version package informing obtain wide-output indicators added new vignette web-version custom analysis NASA FIRMS resource example section added data years 2017-2020 Global Mangrove Watch resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.4.0","text":"Changed parallel backend future package. Parallel processing now implemented furrr::future_map() asset level within calc_indicators() function. User code now required set plan() enable parallel processing. function call needs wrapped user side progressr::with_progress() show progress bar. mapme.biodiversity longer sets terra’s temporal directory . Instead call terra::terraOptions() manually","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"mapme.biodiversity 0.4.0","text":"esalandcover indicator now returns value per land cover class exactly (#177)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-4-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.4.0","text":"disabled running examples CRAN disabled tests get_* functions CRAN terra engines now use get() resolve requested zonal statistic function applying tidyverse coding style existing code (#156, @karpfen) extensive re-factoring vector-raster zonal statistic engines (#150) extensive re-writing testing infrastructure indicator functions omitting usage snapshot tests far possible (#142) rundir todisk arguments removed indicator functions since practical use instead resource indicator backlog, resources indicators now registered .pkgenv queried runtime. also allows users register custom resources/indicator functions removed deprecation warnings old resource/indicator name","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-030","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.3.0","title":"mapme.biodiversity 0.3.0","text":"CRAN release: 2023-01-21","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.3.0","text":"MacOS s2-based calculations now enabled users can expect package return numerically equivalent results operating system (#131) online source nasa_srtm resource shows expired SSL certificate since November 2022. get_resources() function now includes error instructions disable SSL certification users risk. websites maintainers contacted asked renew certification. (#131)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.3.0","text":"GFW resources now updated use latest version allowing analysis additional year 2021 (#123, @fBedecarrats) GFW indicators now accept numeric min_size argument allowing specify fractional covers (#110) fire indicators now allow simultaneous calculation indicators based MODIS VIIRS. users chose one instruments analysis (#126)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.3.0","text":"case one multiple assets return NA instead tibble now properly tested handled (#101) Rasters longer temporary written disk omit bug caused applying mask/classify already existing raster file (#108, @Jo-Schie) Bug soilproperties set NA caused function return data.frame instead tibble fixed (#116) , treecoverloss_emissions treecover_area_and_emissions now return 0 instead NaN observation years now forest loss occurred (#120)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-3-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.3.0","text":".make_global_grid() now specifies CRS constructing bounding box returns grid specified CRS instead Lat/Lon (#113) .calc_active_fire_properties now uses st_coordinates retrieve locations fires (#119, @DavisVaughan) tests MacOS re-enabled (#131) tests downloading nasa_srtm resource skipped SSL certificate online source expired (#131)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-021","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.2.1","title":"mapme.biodiversity 0.2.1","text":"CRAN release: 2022-09-09","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.2.1","text":"fixes serious bug occurred tiled resources multiple assets within tile resulting returning tile multiple times","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-2-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.2.1","text":"tests catch mentioned bug introduced tiled resources","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-020","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.2.0","title":"mapme.biodiversity 0.2.0","text":"CRAN release: 2022-08-23","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.2.0","text":"extensive renaming resources indicators. handled gracefully next release (.e. warning issued names replaced): resources: treecover2000 -> gfw_treecover lossyear -> gfw_lossyear greenhouse -> gfw_emissions traveltime -> nelson_et_al nasagrace -> nasa_grace mintemperature -> worldclim_min_temperature maxtemperature -> worldclim_max_temperature precipitation -> worldclim_precipitation ecoregions -> teow mangrove -> gmw srtmdem -> nasa_srtm indicators: treecover -> treecover_area emissions -> treecoverloss_emissions treeloss -> treecover_area_and_emissions chirpsprec -> precipitation_chirps accessibility -> traveltime popcount -> population_count wctmin -> temperature_min_wc wctmax -> temperature_max_wc wcprec -> precipitation_wc gmw -> mangroves_area teow -> ecoregion","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.2.0","text":"nasa_firms active_fire_properties active_fire_counts","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-2-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.2.0","text":"adapted download routine GMW v3 (#80) removed data.table imports","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.2.0","text":"fixing issue #84 concerning intersection tiled datasets (#86, @Jo-Schie)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-012","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.2","title":"mapme.biodiversity 0.1.2","text":"CRAN release: 2022-06-24","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-2","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.2","text":"unit tests silenced order informative reverse dependency checks checks tile availability reactivated SRTM fixed notes due uninitialized variables TEOW biome indicators","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-011","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.1","title":"mapme.biodiversity 0.1.1","text":"CRAN release: 2022-05-02","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.1","text":"init_portfolio() now sets testing attribute FALSE default. get_() functions now return filenames early testing set TRUE. calc_() examples now copy files R temporal directory wrapped try() avoid errors/warnings CRAN internet resource available. examples calc_tri() calc_elevation() now disabled CRAN responsiveness CIGAR servers.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-010","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.0","title":"mapme.biodiversity 0.1.0","text":"CRAN release: 2022-04-27","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-1-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.1.0","text":"renamed ‘.assetid’ ‘assetid’ (#22)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.1.0","text":"None","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.0","text":"ensures tests examples adhere CRAN policies writing temporal directory (#22).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-001","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.0.1","title":"mapme.biodiversity 0.0.1","text":"CRAN release: 2022-04-19","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"initial-release-0-0-1","dir":"Changelog","previous_headings":"","what":"Initial release","title":"mapme.biodiversity 0.0.1","text":"Added NEWS.md file track changes package. ecoregions esalandcover greenhouse lossyear mangrove nasagrace soilgrids srtmdem traveltime treecover worldclim worldpop acessibility biome chirpsprec drought_indicator elevation emissions gmw landcover popcount soilproperties teow treecover treeloss tri wcprec wctmax wctmin init_portfolio() used initialize portfolio object. input must sf object geometries type POLYGON users can request download one resources via get_resources() users can request processing indicator via calc_indicators() indicators added portfolio object nested list columns processed portfolio object can exported GeoPackage via write_portfolio() portfolio saved disk GeoPackage can read back R via read_portfolio(). users wish download additional resources calculate indicators, init_portfolio() called . Parallelization using multiple cores host machine disabled Windows MacOS, s2 engine spherical geometric vector operations disabled lwgeom used instead.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-0-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.0.1","text":"Introduced absolute URLS userguide.Rmd pointing online documentation (#59) tags added exported functions explaining output/side effect (#59) using requireNamespace() instead installed.packages() check packages listed SUGGEST loadable (#58)","code":""}]
+[{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. Developers use GNU GPL protect rights two steps: (1) assert copyright software, (2) offer License giving legal permission copy, distribute /modify . developers’ authors’ protection, GPL clearly explains warranty free software. users’ authors’ sake, GPL requires modified versions marked changed, problems attributed erroneously authors previous versions. devices designed deny users access install run modified versions software inside , although manufacturer can . fundamentally incompatible aim protecting users’ freedom change software. systematic pattern abuse occurs area products individuals use, precisely unacceptable. Therefore, designed version GPL prohibit practice products. problems arise substantially domains, stand ready extend provision domains future versions GPL, needed protect freedom users. Finally, every program threatened constantly software patents. 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Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Source Code","title":"GNU General Public License","text":"“source code” work means preferred form work making modifications . “Object code” means non-source form work. “Standard Interface” means interface either official standard defined recognized standards body, , case interfaces specified particular programming language, one widely used among developers working language. “System Libraries” executable work include anything, work whole, () included normal form packaging Major Component, part Major Component, (b) serves enable use work Major Component, implement Standard Interface implementation available public source code form. “Major Component”, context, means major essential component (kernel, window system, ) specific operating system () executable work runs, compiler used produce work, object code interpreter used run . “Corresponding Source” work object code form means source code needed generate, install, (executable work) run object code modify work, including scripts control activities. 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Protecting Users’ Legal Rights From Anti-Circumvention Law","title":"GNU General Public License","text":"covered work shall deemed part effective technological measure applicable law fulfilling obligations article 11 WIPO copyright treaty adopted 20 December 1996, similar laws prohibiting restricting circumvention measures. convey covered work, waive legal power forbid circumvention technological measures extent circumvention effected exercising rights License respect covered work, disclaim intention limit operation modification work means enforcing, work’s users, third parties’ legal rights forbid circumvention technological measures.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_4-conveying-verbatim-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"4. Conveying Verbatim Copies","title":"GNU General Public License","text":"may convey verbatim copies Program’s source code receive , medium, provided conspicuously appropriately publish copy appropriate copyright notice; keep intact notices stating License non-permissive terms added accord section 7 apply code; keep intact notices absence warranty; give recipients copy License along Program. may charge price price copy convey, may offer support warranty protection fee.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_5-conveying-modified-source-versions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"5. Conveying Modified Source Versions","title":"GNU General Public License","text":"may convey work based Program, modifications produce Program, form source code terms section 4, provided also meet conditions: ) work must carry prominent notices stating modified , giving relevant date. b) work must carry prominent notices stating released License conditions added section 7. requirement modifies requirement section 4 “keep intact notices”. c) must license entire work, whole, License anyone comes possession copy. License therefore apply, along applicable section 7 additional terms, whole work, parts, regardless packaged. 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Conveying Non-Source Forms","title":"GNU General Public License","text":"may convey covered work object code form terms sections 4 5, provided also convey machine-readable Corresponding Source terms License, one ways: ) Convey object code , embodied , physical product (including physical distribution medium), accompanied Corresponding Source fixed durable physical medium customarily used software interchange. b) Convey object code , embodied , physical product (including physical distribution medium), accompanied written offer, valid least three years valid long offer spare parts customer support product model, give anyone possesses object code either (1) copy Corresponding Source software product covered License, durable physical medium customarily used software interchange, price reasonable cost physically performing conveying source, (2) access copy Corresponding Source network server charge. c) Convey individual copies object code copy written offer provide Corresponding Source. alternative allowed occasionally noncommercially, received object code offer, accord subsection 6b. d) Convey object code offering access designated place (gratis charge), offer equivalent access Corresponding Source way place charge. need require recipients copy Corresponding Source along object code. place copy object code network server, Corresponding Source may different server (operated third party) supports equivalent copying facilities, provided maintain clear directions next object code saying find Corresponding Source. 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Access network may denied modification materially adversely affects operation network violates rules protocols communication across network. Corresponding Source conveyed, Installation Information provided, accord section must format publicly documented (implementation available public source code form), must require special password key unpacking, reading copying.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_7-additional-terms","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"7. Additional Terms","title":"GNU General Public License","text":"“Additional permissions” terms supplement terms License making exceptions one conditions. 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Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. Termination rights section terminate licenses parties received copies rights License. rights terminated permanently reinstated, qualify receive new licenses material section 10.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_9-acceptance-not-required-for-having-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"9. Acceptance Not Required for Having Copies","title":"GNU General Public License","text":"required accept License order receive run copy Program. Ancillary propagation covered work occurring solely consequence using peer--peer transmission receive copy likewise require acceptance. However, nothing License grants permission propagate modify covered work. actions infringe copyright accept License. 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Automatic Licensing of Downstream Recipients","title":"GNU General Public License","text":"time convey covered work, recipient automatically receives license original licensors, run, modify propagate work, subject License. responsible enforcing compliance third parties License. “entity transaction” transaction transferring control organization, substantially assets one, subdividing organization, merging organizations. propagation covered work results entity transaction, party transaction receives copy work also receives whatever licenses work party’s predecessor interest give previous paragraph, plus right possession Corresponding Source work predecessor interest, predecessor can get reasonable efforts. may impose restrictions exercise rights granted affirmed License. example, may impose license fee, royalty, charge exercise rights granted License, may initiate litigation (including cross-claim counterclaim lawsuit) alleging patent claim infringed making, using, selling, offering sale, importing Program portion .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_11-patents","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"11. Patents","title":"GNU General Public License","text":"“contributor” copyright holder authorizes use License Program work Program based. work thus licensed called contributor’s “contributor version”. contributor’s “essential patent claims” patent claims owned controlled contributor, whether already acquired hereafter acquired, infringed manner, permitted License, making, using, selling contributor version, include claims infringed consequence modification contributor version. purposes definition, “control” includes right grant patent sublicenses manner consistent requirements License. contributor grants non-exclusive, worldwide, royalty-free patent license contributor’s essential patent claims, make, use, sell, offer sale, import otherwise run, modify propagate contents contributor version. following three paragraphs, “patent license” express agreement commitment, however denominated, enforce patent (express permission practice patent covenant sue patent infringement). “grant” patent license party means make agreement commitment enforce patent party. convey covered work, knowingly relying patent license, Corresponding Source work available anyone copy, free charge terms License, publicly available network server readily accessible means, must either (1) cause Corresponding Source available, (2) arrange deprive benefit patent license particular work, (3) arrange, manner consistent requirements License, extend patent license downstream recipients. “Knowingly relying” means actual knowledge , patent license, conveying covered work country, recipient’s use covered work country, infringe one identifiable patents country reason believe valid. , pursuant connection single transaction arrangement, convey, propagate procuring conveyance , covered work, grant patent license parties receiving covered work authorizing use, propagate, modify convey specific copy covered work, patent license grant automatically extended recipients covered work works based . patent license “discriminatory” include within scope coverage, prohibits exercise , conditioned non-exercise one rights specifically granted License. may convey covered work party arrangement third party business distributing software, make payment third party based extent activity conveying work, third party grants, parties receive covered work , discriminatory patent license () connection copies covered work conveyed (copies made copies), (b) primarily connection specific products compilations contain covered work, unless entered arrangement, patent license granted, prior 28 March 2007. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C)     This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see .   Copyright (C)    This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Contributing","text":"reading vignette probably contribute mapme.biodiversity package. great news happy receive Pull-Requests extending package’s functionality! find important -depth information add resources indicators make process seamless possible package’s maintainers. Please make sure read understand guide opening PR. doubt, especially feel framework support use case, always feel free raise issue happily discuss can support ideas. already done , make sure read Terminology vignette get familiar important concepts package. Note use tidyverse style guide package. specifically means function variable names follow snake case pattern. also use arrow assignment operator (<-). submitting PR consistently follow tidyverse style guide, maintainers package might change code adhere code style without notice accepting PR.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Contributing","text":"Ideally, clone GitHub repository via git command command line Linux MacOS systems via GitHub Desktop application Windows. Linux, command look like : accept pushes main, thus first step create specific branch extension. tutorial, pretend re-implement nasa_srtm resource associated elevation indicator, create branch reflecting . Don’t forget check newly created branch! , assume develop extension package R Studio. general guidelines follow also apply choose different tooling development process, however, covered vignette. assume R development dependencies installed. easiest way ensure using devtools:","code":"git clone https://github.com/mapme-initiative/mapme.biodiversity git branch add-elevation git checkout add-elevation devtools::install_dev_deps()"},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"checklist","dir":"Articles","previous_headings":"Adding a resource","what":"Checklist","title":"Contributing","text":"Create file necessary code download resource (R/get_.R) Include roxygen documentation resource following provided template Create outer-level function user facing arguments Check user-specified arguments () correctness Create inner-level function standard arguments Match spatio-temporal extent portfolio resource Create footprint object via make_footprints() resources matching portfolio Include opening (-oo) creation (-co) options resources needed Write testthat script testing newly added functionality write test/testthat/test-get_.R Add small example data set resource inst/res/ Add script producing sample resources data-raw Add useful information resource register via register_resource() Added new dependency? Make sure include supporting statement dependency PR!","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"overview-of-adding-a-resource","dir":"Articles","previous_headings":"Adding a resource","what":"Overview of adding a resource","title":"Contributing","text":"resource supported dataset can made available user’s perspective specifying one functions get_resources(). Currently, package supports raster vector resources. wish submit support new resource, please aware accept new resources associated least one indicator calculation. first step adding resource create new file holding required code. checked new branch project opened R Studio, adapt following command open new resource file:","code":"file.edit(\"R/get_.R\") # e.g. file.edit(\"R/get_soildgrids.R\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"documenting-the-new-resource","dir":"Articles","previous_headings":"Adding a resource","what":"Documenting the new resource","title":"Contributing","text":"first part resource function, make sure include detailed documentation. documentation explain resource represents, comes (including citation), user-facging arguments specified runtime. Importantly, documentation MUST receive roxygen tag @keywords resource, documentation identified resource. Also, add bare name resource @name tag (e.g. case example translates @name nasa_srtm). last two tags important add well. include statement mandatory register functionality () loaded resource function. export tag important resource actually exposed users package.","code":"#' Short title #' #' One or more description paragraphs might follow here. Please describe #' the spatio-temporal structure of your resource here briefly. #' #' @name  #' @param  #' @keywords resource  #' @references  #' @source  #' @returns A function that makes a resource available for a portfolio #' @include register.R #' @export"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---outer-level","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Outer level","title":"Contributing","text":"Resource functions constructed closures, .e. functions return function. outer level exposes arguments set users function fine-control flow function. Note, important check user input outer level correctness warning/error messages case miss specifications thrown immediately. nasa_srtm, outer level look really exciting becuase user-facing arguments checked (see check user-facing arguments constructing indicator ): Note, exported helper functions re-occurring argument checks free use (e.g. check_available_years() case query user temporal time frame). arguments defined outer level resource function ready used inner level, look next.","code":"get_nasa_srtm <- function() {   # .... inner function level }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---inner-level","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Inner level","title":"Contributing","text":"inner level resource function mandatory function signature checked run-time. function required exactly specify signature. nasa_srtm resource, looks like : x argument represents portfolio object handed user calling get_resources() sf-object can thus used derive spatial extent portfolio. Next, comes name type resource required backend correctly handle output log resource made available. arguments default respective output values mapme_options() represent character vector output directory, logical control verbosity. Note, output directory might NULL case user wishes access data directly remote source. look things come together now peak constructing actual body resource function.","code":"function(x,          name = \"nasa_srtm\",          type = \"raster\",          outdir = mapme_options()[[\"outdir\"]],          verbose = mapme_options()[[\"verbose\"]]) {   # ... function body }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-a-resource-function---body","dir":"Articles","previous_headings":"Adding a resource","what":"Constructing a resource function - Body","title":"Contributing","text":"expected output resource function sf-object geometries representing bounding box single resource elements (e.g. tiles case raster resource). provide functionality seamlessly produce footprint object via make_footprints(). Note, function either excepts character vector GDAL readable data sources sf object. case provide charachter vector, bounding box information retrieved automatically. comes performance penalty remote sources, file opened , always opt constructing sf object resource function, feasible. footprints sf object expected contain column called source points GDAL readable data source geometries correspond bounding box single resource element. might opt supply filename argument make_footprints(). defaults basename(srcs[[\"source]]), might supply better suited filename, .e. case source location ends API key value, similar. Next specifying resource wish turn footprint object represents raster vector resource, can specify opening creation options. Opening options relevant opening remote source requires location driver dependent options (e.g. specifying non-standard columns names longitude/latitude CSV driver). Creation options relevant user specified outdir argument refer arguments used gdal_translate. specifically specify data type compression algorithms raster resources, otherwise free optimize data layout efficient access. oo co can specified single character vector, case options applied elements resource, list order accommodate file specific options. Use verbose argument decide informative messages printed, e.g. inform users download progress. Errors warnings emitted either case. intersection x object resource, make sure return NULL early possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-sample-resource-for-package-internal-testing","dir":"Articles","previous_headings":"Adding a resource","what":"Adding sample resource for package internal testing","title":"Contributing","text":"ask provide small subset resource inst/res/resource_name indicators depend resource can tested without need actual download resource. restrictions final size package, ask put substantial effort reducing size files minimum. includes cropping resource samples spatial extent polygon provided inst/extdata/sierra_de_neibe_478140.gpkg polygon similar size supplied case spatial extent intersect resource. raster resources, original raster encoded float, consider changing data type integer introducing scale factor. Also, please use compression algorithm reduce file size. vector resources, consider reducing number vertices case geometries complex. Finally, put processing script resource data-raw ensure reproducibility. , required write unit-test resource function, execute much code possible without actually conducting download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"a-note-on-dependencies-for-resources","dir":"Articles","previous_headings":"Adding a resource","what":"A note on dependencies for resources","title":"Contributing","text":"Note, resource SHALL add additional dependencies package. add dependencies require add supporting statement PR explaining dependencies needed approaches fail. accepting PR, might request change code remove dependencies, feasible achieve functionality without.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-an-indicator","dir":"Articles","previous_headings":"","what":"Adding an indicator","title":"Contributing","text":"process adding indicator similar one resources. However, input-output requirements different. Note, case added new resource also expect new indicator taking advantage resource PR. see, two new important concepts mind adding indicator. processing mode computational engines. briefly explain concepts , however, can also head Terminology vignette interested comprehensive definition two terms.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"checklist-1","dir":"Articles","previous_headings":"Adding an indicator","what":"Checklist","title":"Contributing","text":"Create file necessary code compute indicator (R/calc_.R) Create outer-level function user facing arguments Check user-specified arguments () correctness Create inner-level function standard arguments applicable, implement , asset portfolio based processing modes Return tibble long format standardized column names Write testthat script testing newly added functionality write test/testthat/test-calc_.R Added new dependency? Make sure include supporting statement dependency PR!","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"overview-of-adding-a-new-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Overview of adding a new indicator","title":"Contributing","text":"indicator logical routine depending one resources extracts numeric outputs assets portfolio. user’s perspective, indicators processed via calc_indicators() function. developer construct indicator function closure, e.g. function returns another function. outer level exposes user-facing arguments checks correctly specified, inner level required follow specified signature returns tibble. checked new branch project opened R Studio, adapt following command open new indicator file:","code":"file.edit(\"R/calc_.R\") # e.g. file.edit(\"R/calc_precipitation\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"documenting-the-new-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Documenting the new indicator","title":"Contributing","text":"first part indicator function, make sure include detailed documentation. documentation explain resources required calculate indicator, user-facing arguments specified runtime structure output tibble. Importantly, documentation MUST receive roxygen tag @keywords indicator, documentation identified indicator. Also, add bare name indicator @name tag (e.g. @name elevation). last two tags important add well. include statement mandatory register functionality () loaded indicator function. export tag important resource actually exposed users package.","code":"#' Short title #' #' One or more description paragraphs might follow here. Please describe #' required resource and user arguments here. #' Please document which processing engines are available for your indicator #' and briefly describe how the indicator is derived from its inputs. #' #' @name  #' @param  #' @keywords indicator  #' @returns A function that calculates an indicator for a portfolio #' @include register.R #' @export"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---outer-level","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Outer level","title":"Contributing","text":"Indicator functions constructed closures, .e. functions return function. outer level exposes arguments set users function fine-control flow function. Note, important check user input outer level correctness warning/error messages case miss specifications thrown immediately. elevation, outer level look something like : exported helper functions re-occurring argument checks free use (e.g. check_engine()). Note, arguments defined way outer level indicator function ready used inner level look next.","code":"calc_elevation <- function(engine = \"extract\",                            stats = \"mean\") {   engine <- check_engine(engine)   stats <- check_stats(stats)    # ... inner function level }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---inner-level","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Inner level","title":"Contributing","text":"inner level indicator function mandatory function signature checked run-time. function required exactly specify signature. elevation indicator, looks like : x argument represents portfolio object handed user calling get_resources() sf-object 'POLYGON' features. Next, comes name(s) required resource(s) name indicator. follows computation mode, must one \"asset\" \"portfolio\". realized, large (potentially global) portfolios, depending spatial resolution resource, different processing modes substantially impact time needed computation. high medium resolution raster resources, processing asset level benefits computation time. However, spatially cropping coarse resolution datasets high number assets introduces significant overhead, thus processing resources portfolio level efficient. neither two processing modes lead satisfactory processing times indicator, please leave issue/comment discuss addition another processing mode maintainers package. argument aggregation governs chunked results large polygons combined single indicator. case uses supply polygons larger specified code path assets type MULTIPOLYGON, code path triggered splits assets sub-components. aggregation method specifies statistic used combine values share values remaining indicator columns (.e. datetime, variable, unit). stat keyword special keyword used indicators statistics specified user trigger select respective statistic aggregation statistic (e.g. take sum sums). available statistics : argument verbose defaults corresponding package-wide option control verbosity indicator function.","code":"function(x,          nasa_srtm = NULL,          name = \"elevation\",          mode = \"asset\",          aggregation = \"stat\",          verbose = mapme_options()[[\"verbose\"]]) {   # ... function body } mapme.biodiversity:::available_stats #> [1] \"mean\"   \"median\" \"sd\"     \"min\"    \"max\"    \"sum\"    \"var\""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"constructing-an-indicator-function---body","dir":"Articles","previous_headings":"Adding an indicator","what":"Constructing an indicator function - Body","title":"Contributing","text":"expected output indicator function tibble. Depending mode specified processing, single tibble mode = \"asset\", list tibbles equal rows x case mode = \"portfolio\". may use helper functions provided package common interface e.g. vector-raster zonal statistics (e.g. using select_engine()). encouraged write helper function needed indicator processor. located file main processor, start dot exported. wish include roxygen documentation helpers, make sure add @keywords internal @noRd tags functions. feel one helper functions benefit just one indicator, please comment issue/pull-request discuss package maintainers helper function moved R/utils.R. Use verbose argument decide informative messages printed, e.g. inform users processing progress. Errors warnings emitted either case. intersection x object required resources, reason indicator might calculated given configuration, make sure return NA early possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/contributing.html","id":"adding-units-tests-for-an-indicator","dir":"Articles","previous_headings":"Adding an indicator","what":"Adding units tests for an indicator","title":"Contributing","text":"required add unit tests indicator using package internal example data sets resources. Make sure properly test miss-specification user-facing arguments also check correctness numerical results indicator. might need construct portfolio scratch test indicator function. Instead, can directly call returned function appropriate polygon respective required resource. elevation indicator, looks like :","code":"x <- read_sf(system.file(   \"extdata\", \"sierra_de_neiba_478140.gpkg\",   package = \"mapme.biodiversity\" ))  nasa_srtm <- list.files(   system.file(     \"res\", \"nasa_srtm\",     package = \"mapme.biodiversity\"   ),   pattern = \".tif$\", full.names = TRUE )  nasa_srtm <- rast(nasa_srtm) ce <- calc_elevation(stats = c(\"mean\", \"median\", \"sd\")) result_multi_stat <- ce(shp, nasa_srtm)  expect_equal(   names(result_multi_stat),   c(\"elevation_mean\", \"elevation_median\", \"elevation_sd\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"objectives","dir":"Articles","previous_headings":"","what":"Objectives","title":"Introduction","text":"mapme.biodiversity facilitates statistical data analysis protected areas around globe. supports high number biodiversity related datasets associated indicators can utilized monitor evaluate effectiveness protection efforts. Several indicators available regular intervals almost two decades (2000 2020). allows users analyse spatial temporal dynamics biodiversity portfolios. package abstracts repetitive tasks, temporal spatial selection resources. allows seamless approach quantitative data analysis even large (potentially global) portfolios users enabled focus aims analysis. package tested Microsoft Azure’s cloud infrastructure well local machines. internal framework designed allow easy process provide extensions form custom resources indicators, unlocking potential future growth supported datasets. thus highly appreciate Pull-Requests contributing new resources/indicators. geographic data analysis, package uses sf operation vector data terra raster data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"mapme-biodiversity-package","dir":"Articles","previous_headings":"","what":"mapme.biodiversity package","title":"Introduction","text":"mapme.biodiversity provides standardized interface download analyse great variety biodiversity related spatial datasets allowing users focus aims analysis. sometimes cumbersome process handling different spatial data formats spatial temporal selection handled internally. Many organizations provide value-added datasets related biodiversity. organizations often use different technology stacks distribute data. mapme.biodiversity contains simple routines communicate different backends provide seamless access data. desired resources made available locally, users can decide indicators want calculate fine-control routines provided.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"functionalities","dir":"Articles","previous_headings":"","what":"Functionalities","title":"Introduction","text":"Currently, package offers several functionalities, ideally used consecutive order realize seamless analysis workflow: construct portfolio based sf object get resources spatio-temporal extent portfolio calculate indicators based available resources asset portfolio write results disk GeoPackage use Geo-Spatial software, conduct statistical analysis R","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"inputs-outputs","dir":"Articles","previous_headings":"","what":"Inputs, Outputs","title":"Introduction","text":"sf object containing geometries type 'POLYGON' arbitrary metadata raster vector resources matching spatio-temporal extent portfolio downloaded made available locally. necessary inputs subsequent calculation indicators, raw resource also can used, e.g. custom visualizations analysis. Importantly, resource directory can used different portfolios analysis runs, matching resources figured run time. Thus, need store multiple copies input resources. results indicator calculation added portfolio object nested list columns. approach makes feasible support variety indicators differently shaped outputs (e.g. time variant vs. invariant indicators). analysis done R, pose serious limitations, desired indicator can easily unnested via tidyr::unnest(). However, data shared use geospatial software (e.g.  QGIS), routine write portfolio object GeoPackage disk provided. indicator written independent table unique identifier allows joining attributes geometries later.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/introduction.html","id":"limitations","dir":"Articles","previous_headings":"","what":"Limitations","title":"Introduction","text":"potential limiting factor now processing single large polygons. terra package provides memory-save framework process large raster extents, RAM overflows occur several large polygons processed parallel. advise process large polygons sequentially. took great effort evaluate efficient processing routines indicator. submit new indicator using efficient routine currently implemented package, please contact maintainers via e-mail, issue pull-request happily discuss options integrate routine wider framework planning add new features extend functionality mapme.biodiversity address limitations best possible.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"objectives","dir":"Articles","previous_headings":"","what":"Objectives","title":"How To: Transform indicator output to wide-format","text":"tutorial gives information transform output mapme-biodiversity package wide format exchange (geospatial-)software, QGIS. necessary package uses -called nested-list format default represent indicators. However, format specific R use data software thus requires additional steps taken. vignette shows can change data layout portfolio can easily serialize spatial format choice use software.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"what-are-long-vs--wide-tables","dir":"Articles","previous_headings":"","what":"What are long vs. wide tables?","title":"How To: Transform indicator output to wide-format","text":"Tabular data can structured two different ways, usually referred long wide format. people familiar wide format, format humans naturally structure data work spreadsheets, e.g. Excel. wide-format, identifier observation included exactly repeat (see Table ). long format, identifier well qualifying variables, might repeated several times uniquely identify observation single row (see Table B). long format often required interacting computers, e.g. make plots ggplot2. content two exactly either way, one might just friendly humans computers. familiar R tidyverse, might also heard term tidy data. terms tabular data can imagine tidy data referring data long table naturally fulfills following requirements: variable column observation row value cell Table , sense, tidy since year variable found column instead scattered two different columns. Table B long format variable found exactly one column. sense, individual row represents exactly one observation, meaning observation specific country specific year. structure data long format objects usually larger memory footprint compared wide format. smaller objects data types small memory consumption, might pose serious limitation workflow. However, geometry information, indicated WKT string, might quickly accumulate large proportion available memory, even portfolio consists high number complex geometries copied fit long-format requirement. reason, packages uses nested-list format hold tables indicators single columns within portfolio. remainder tutorial show detail can work R specific data format.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"the-simple-case---single-row-indicators","dir":"Articles","previous_headings":"","what":"The simple case - single-row indicators","title":"How To: Transform indicator output to wide-format","text":"start reading GeoPackage disk. sake argument, split original single polygon 9 distinct polygons simulate realistic portfolio consisting multiple assets. simple example, suppose interested extracting average traveltime cities 20,000 50,000 inhabitants portfolio. usual, make available Nelson et al. resource well requesting calculation respective indicator. can observe output, new column added sf object. called traveltime type list indicating represents nested-list column. means able maintain rectangular shape original data (e.g. one polygon per row), supporting arbitrarily shaped outputs indicators. Let’s observe traveltime indicator looks like instance: syntax , can see can access single object within nested list column (e.g. using list accessor [[). case, shape traveltime indicator single-row two-column tibble average minutes distance category value. can now use either two functions transform portfolio long wide formats: function , default, automatically detect nested-list columns change data layout. case, result still 9 rows just like original data frame indicator traveltime consisted just single row per asset. serialize object disk either format calling write_portfolio() respective format argument:","code":"aoi <- read_sf(   system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",     package = \"mapme.biodiversity\"   ) ) aoi <- st_as_sf(st_make_grid(aoi, n = 3)) print(aoi) #> Simple feature collection with 9 features and 0 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #>                                x #> 1 POLYGON ((-71.80933 18.5766... #> 2 POLYGON ((-71.65022 18.5766... #> 3 POLYGON ((-71.49111 18.5766... #> 4 POLYGON ((-71.80933 18.6175... #> 5 POLYGON ((-71.65022 18.6175... #> 6 POLYGON ((-71.49111 18.6175... #> 7 POLYGON ((-71.80933 18.6584... #> 8 POLYGON ((-71.65022 18.6584... #> 9 POLYGON ((-71.49111 18.6584... outdir <- file.path(tempdir(), \"mapme-resources\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- get_resources(aoi, get_nelson_et_al(ranges = \"100k_200k\")) aoi <- calc_indicators(aoi, calc_traveltime(stats = \"mean\")) print(aoi) #> Simple feature collection with 9 features and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 3 #>   assetid traveltime                                                           x #>                                                          #> 1       1  ((-71.80933 18.57668, -71.65022 18.57668, -71.65022 … #> 2       2  ((-71.65022 18.57668, -71.49111 18.57668, -71.49111 … #> 3       3  ((-71.49111 18.57668, -71.33201 18.57668, -71.33201 … #> 4       4  ((-71.80933 18.61756, -71.65022 18.61756, -71.65022 … #> 5       5  ((-71.65022 18.61756, -71.49111 18.61756, -71.49111 … #> 6       6  ((-71.49111 18.61756, -71.33201 18.61756, -71.33201 … #> 7       7  ((-71.80933 18.65844, -71.65022 18.65844, -71.65022 … #> 8       8  ((-71.65022 18.65844, -71.49111 18.65844, -71.49111 … #> 9       9  ((-71.49111 18.65844, -71.33201 18.65844, -71.33201 … print(aoi$traveltime[[1]]) #> # A tibble: 1 × 4 #>   datetime            variable                  unit    value #>                                          #> 1 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  206. portfolio_long(aoi) #> Simple feature collection with 9 features and 6 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 7 #>   assetid indicator  datetime            variable                  unit    value #>                                                   #> 1       1 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  206. #> 2       2 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  273. #> 3       3 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  283. #> 4       4 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  237. #> 5       5 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  308. #> 6       6 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  265. #> 7       7 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  287. #> 8       8 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  299. #> 9       9 traveltime 2015-01-01 00:00:00 100k_200k_traveltime_mean minutes  235. #> # ℹ 1 more variable: x  portfolio_wide(aoi) #> Simple feature collection with 9 features and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 3 #>   assetid traveltime_2015-01-01_100k_200k_traveltime…¹                         x #>                                                           #> 1       1                                         206. ((-71.80933 18.57668, -7… #> 2       2                                         273. ((-71.65022 18.57668, -7… #> 3       3                                         283. ((-71.49111 18.57668, -7… #> 4       4                                         237. ((-71.80933 18.61756, -7… #> 5       5                                         308. ((-71.65022 18.61756, -7… #> 6       6                                         265. ((-71.49111 18.61756, -7… #> 7       7                                         287. ((-71.80933 18.65844, -7… #> 8       8                                         299. ((-71.65022 18.65844, -7… #> 9       9                                         235. ((-71.49111 18.65844, -7… #> # ℹ abbreviated name: #> #   ¹​`traveltime_2015-01-01_100k_200k_traveltime_mean_minutes` dsn_long <- tempfile(fileext = \".gpkg\") dsn_wide <- tempfile(fileext = \".gpkg\") write_portfolio(aoi, dsn_long, format = \"long\", quiet = TRUE) write_portfolio(aoi, dsn_wide, format = \"wide\", quiet = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/output-wide.html","id":"the-harder-case---indicators-with-multi-row-output","dir":"Articles","previous_headings":"","what":"The harder case - indicators with multi-row output","title":"How To: Transform indicator output to wide-format","text":"Let’s continue query indicator multi-row output, .e. precipitation statistics WorldClim. see addition traveltime indicator, now obtained additional nested-list column called precipitation_wc. Note, however, differences shape indicator tibble take closer look specific asset: single asset, obtain tibble 12 rows (month queried year 2018). Now, let’s look happens transform table long format, time specifically requesting extract precipitation_wc indicators: Instead 9 rows, get tibble 108 rows (9 assets * 12), metadata asset geometry column identifying values repeated 12 times . large portfolios, data layout might memory intensive. cases might favorable transform portfolio wide layout. example output see case, obtain resulting object 9 rows . indicator data now found respective columns named according schema: ___ values found rows unique combination pattern. Note, traveltime still represented nested-list column. serializing disk, present indicators going extracted order able serialize spatial data formats. desired include certain indicators subset portfolio indicated following code block:","code":"aoi <- get_resources(aoi, get_worldclim_precipitation(years = 2018)) aoi <- calc_indicators(aoi, calc_precipitation_wc(stats = \"mean\")) print(aoi) #> Simple feature collection with 9 features and 3 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 4 #>   assetid traveltime       precipitation_wc                                    x #>                                                    #> 1       1   ((-71.80933 18.57668, -71.65022 18… #> 2       2   ((-71.65022 18.57668, -71.49111 18… #> 3       3   ((-71.49111 18.57668, -71.33201 18… #> 4       4   ((-71.80933 18.61756, -71.65022 18… #> 5       5   ((-71.65022 18.61756, -71.49111 18… #> 6       6   ((-71.49111 18.61756, -71.33201 18… #> 7       7   ((-71.80933 18.65844, -71.65022 18… #> 8       8   ((-71.65022 18.65844, -71.49111 18… #> 9       9   ((-71.49111 18.65844, -71.33201 18… print(aoi$precipitation_wc[[1]]) #> # A tibble: 12 × 4 #>    datetime            variable            unit  value #>                                   #>  1 2018-01-01 00:00:00 worldclim_prec_mean mm      NaN #>  2 2018-02-01 00:00:00 worldclim_prec_mean mm      NaN #>  3 2018-03-01 00:00:00 worldclim_prec_mean mm      NaN #>  4 2018-04-01 00:00:00 worldclim_prec_mean mm      NaN #>  5 2018-05-01 00:00:00 worldclim_prec_mean mm      NaN #>  6 2018-06-01 00:00:00 worldclim_prec_mean mm      NaN #>  7 2018-07-01 00:00:00 worldclim_prec_mean mm      NaN #>  8 2018-08-01 00:00:00 worldclim_prec_mean mm      NaN #>  9 2018-09-01 00:00:00 worldclim_prec_mean mm      NaN #> 10 2018-10-01 00:00:00 worldclim_prec_mean mm      NaN #> 11 2018-11-01 00:00:00 worldclim_prec_mean mm      NaN #> 12 2018-12-01 00:00:00 worldclim_prec_mean mm      NaN portfolio_long(aoi, indicators = \"precipitation_wc\") #> Simple feature collection with 108 features and 7 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 108 × 8 #>    assetid traveltime       indicator   datetime            variable unit  value #>                                             #>  1       1  precipitat… 2018-01-01 00:00:00 worldcl… mm      NaN #>  2       1  precipitat… 2018-02-01 00:00:00 worldcl… mm      NaN #>  3       1  precipitat… 2018-03-01 00:00:00 worldcl… mm      NaN #>  4       1  precipitat… 2018-04-01 00:00:00 worldcl… mm      NaN #>  5       1  precipitat… 2018-05-01 00:00:00 worldcl… mm      NaN #>  6       1  precipitat… 2018-06-01 00:00:00 worldcl… mm      NaN #>  7       1  precipitat… 2018-07-01 00:00:00 worldcl… mm      NaN #>  8       1  precipitat… 2018-08-01 00:00:00 worldcl… mm      NaN #>  9       1  precipitat… 2018-09-01 00:00:00 worldcl… mm      NaN #> 10       1  precipitat… 2018-10-01 00:00:00 worldcl… mm      NaN #> # ℹ 98 more rows #> # ℹ 1 more variable: x  portfolio_wide(aoi, indicators = \"precipitation_wc\") #> Simple feature collection with 9 features and 14 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 15 #>   assetid traveltime       precipitation_wc_2018-01-01_…¹ precipitation_wc_201…² #>                                                             #> 1       1                           NaN                    NaN   #> 2       2                            29                     27.2 #> 3       3                            24.4                   24.0 #> 4       4                            31.9                   28.0 #> 5       5                            28.5                   28.2 #> 6       6                            18.9                   21.1 #> 7       7                            24.4                   24.2 #> 8       8                            19.3                   21.0 #> 9       9                           NaN                    NaN   #> # ℹ abbreviated names: ¹​`precipitation_wc_2018-01-01_worldclim_prec_mean_mm`, #> #   ²​`precipitation_wc_2018-02-01_worldclim_prec_mean_mm` #> # ℹ 11 more variables: #> #   `precipitation_wc_2018-03-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-04-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-05-01_worldclim_prec_mean_mm` , #> #   `precipitation_wc_2018-06-01_worldclim_prec_mean_mm` , … dsn <- tempfile(fileext = \".gpkg\") write_portfolio(select(aoi, traveltime), dsn, quiet = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Quickstart","text":"following demonstrate idealized workflow based subset Global Forest Watch (GFW) data set delivered together package. can follow along code snippets reproduce results. Please note reduce time takes process vignette, download resources internet. real use case, thus processing time might substantially increase resources downloaded real portfolios might larger one created example. vignette assumes already followed steps Installation familiarized terminology used package. unfamiliar terminology used , please head Terminology article learn important concepts. idealized workflow using mapme.biodiversity consists following steps: prepare sf-object containing geometries type 'POLYGON' 'MULTIPOLYGON' decide indicator(s) wish calculate make required resource(s) available conduct indicator calculation, adds nested list column portfolio object continue analysis R decide export results spatial data format use geospatial software","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Quickstart","text":"First, load mapme.biodiversity sf package handling spatial vector data. tabular data handling, also load dplyr tidyr packages. , read internal GeoPackage includes part geometry protected area Dominican Republic WDPA database.","code":"library(mapme.biodiversity) library(sf) library(dplyr) library(tidyr)  aoi_path <- system.file(\"extdata\", \"gfw_sample.gpkg\", package = \"mapme.biodiversity\") aoi <- st_read(aoi_path, quiet = TRUE) aoi #> Simple feature collection with 1 feature and 0 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #>                             geom #> 1 POLYGON ((-71.73417 18.6435..."},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"setting-standard-option","dir":"Articles","previous_headings":"","what":"Setting standard option","title":"Quickstart","text":"use mapme_options() function set arguments, output directory, important govern subsequent processing. , create temporary directory. Internally, save time downloading building vignette, copied already existing files output location (code shown ). outdir argument points towards directory local file system machine. downloaded resources written respective directories nested within outdir. request specific resource portfolio, files downloaded missing match spatio-temporal extent. behavior beneficial, e.g. case share outdir different projects ensure resources matching current portfolio returned. verbose logical controls whether package print informative messages calculations. Note, even set FALSE, package inform users potential errors warnings.","code":"outdir <- file.path(tempdir(), \"mapme-resources\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = TRUE )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"getting-the-right-resources","dir":"Articles","previous_headings":"","what":"Getting the right resources","title":"Quickstart","text":"can check indicators available via available_indicators() function: Say, interested treecover_area indicator. can learn indicator required resources using either commands , viewing online version, head treecover_area documentation. inspecting help page learned indicator requires gfw_treecover gfw_lossyear resources requires specify three extra arguments: years calculate treecover, minimum size patches considered forest minimum canopy coverage single pixel considered forested. information hand, can start retrieve required resource. can learn available resources using available_resources() function: purpose vignette, going download , gfw_treecover gfw_lossyear resources. can get detailed information given resource, using either commands open help page. viewing online version documentation, can simply head gfw_treecover resource documentation. can now make required resources available portfolio. use common interface used resources, called get_resources(). specify portfolio object supply one resource functions respective arguments. download matching resources output directory specified earlier.","code":"available_indicators() #> # A tibble: 40 × 3 #>    name                          description                           resources #>                                                                  #>  1 biodiversity_intactness_index Averaged biodiversity intactness ind…   #>  2 biome                         Areal statistics of biomes from TEOW    #>  3 burned_area                   Monthly burned area detected by MODI…   #>  4 deforestation_drivers         Areal statistics of deforestation dr…   #>  5 drought_indicator             Relative wetness statistics based on…   #>  6 ecoregion                     Areal statstics of ecoregions based …   #>  7 elevation                     Statistics of elevation based on NAS…   #>  8 exposed_population_acled      Number of people exposed to conflict…   #>  9 exposed_population_ucdp       Number of people exposed to conflict…   #> 10 fatalities_acled              Number of fatalities by event type b…   #> # ℹ 30 more rows available_indicators(\"treecover_area\") #> # A tibble: 1 × 3 #>   name           description                  resources        #>                                                #> 1 treecover_area Area of forest cover by year  ?treecover_area help(treecover_area) available_resources() #> # A tibble: 35 × 5 #>    name                          description                licence source type  #>                                                         #>  1 accessibility_2000            Accessibility data for th… See JR… https… rast… #>  2 acled                         Armed Conflict Location &… Visit … Visit… vect… #>  3 biodiversity_intactness_index Biodiversity Intactness I… CC-BY-… https… rast… #>  4 chelsa                        Climatologies at High res… Unknow… https… rast… #>  5 chirps                        Climate Hazards Group Inf… CC - u… https… rast… #>  6 esalandcover                  Copernicus Land Monitorin… CC-BY … https… rast… #>  7 fritz_et_al                   Drivers of deforestation … CC-BY … https… rast… #>  8 gfw_emissions                 Global Forest Watch - CO2… CC-BY … https… rast… #>  9 gfw_lossyear                  Global Forest Watch - Yea… CC-BY … https… rast… #> 10 gfw_treecover                 Global Forest Watch - Per… CC-BY … https… rast… #> # ℹ 25 more rows available_resources(\"gfw_treecover\") #> # A tibble: 1 × 5 #>   name          description                                 licence source type  #>                                                         #> 1 gfw_treecover Global Forest Watch - Percentage of canopy… CC-BY … https… rast… ?gfw_treecover help(gfw_treecover) ?gfw_lossyear help(gfw_lossyear) aoi <- get_resources(   x = aoi,   get_gfw_treecover(version = \"GFC-2023-v1.11\"),   get_gfw_lossyear(version = \"GFC-2023-v1.11\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"calculate-specific-indicators","dir":"Articles","previous_headings":"","what":"Calculate specific indicators","title":"Quickstart","text":"next step consists calculating specific indicators. Note indicator requires one resources made available via get_resources() function explained . re-run function every new R session, note data already available re-downloaded. , going calculate treecover_area indicator based resources GFW. Since resources made available previous step, can continue requesting calculation desired indicator. Note command issue error case required resource made available via get_resources() beforehand. Now let’s take look results. addition metadata already familiar , see additional column called treecover_area contains tibble. indicator represented nested-list column sf-object named alike requested indicator. single asset, column contains tibble 6 rows four columns. Let’s closer look object tibble follows standard output format, indicators. indicator represented tibble four columns datetime, variable, unit, value. case treecover_area indicator, variable called treecover expressed ha. Let’s quickly visualize results:  wish change layout portfolio, can use portfolio_long() portfolio_wide() (see respective online tutorial). Especially large portfolios, usually good idea keep geometry information separated variable keep size data object relatively small.","code":"aoi <- calc_indicators(   aoi,   calc_treecover_area(years = 2000:2023, min_size = 1, min_cover = 30) ) aoi #> Simple feature collection with 1 feature and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 3 #>   assetid treecover_area                                                    geom #>                                                          #> 1       1  ((-71.73735 18.64734, -71.71386 18.63179, -71.69 18… aoi$treecover_area #> [[1]] #> # A tibble: 24 × 4 #>    datetime            variable  unit  value #>                         #>  1 2000-01-01 00:00:00 treecover ha    1975. #>  2 2001-01-01 00:00:00 treecover ha    1975. #>  3 2002-01-01 00:00:00 treecover ha    1973. #>  4 2003-01-01 00:00:00 treecover ha    1940. #>  5 2004-01-01 00:00:00 treecover ha    1930. #>  6 2005-01-01 00:00:00 treecover ha    1926. #>  7 2006-01-01 00:00:00 treecover ha    1919. #>  8 2007-01-01 00:00:00 treecover ha    1908. #>  9 2008-01-01 00:00:00 treecover ha    1905. #> 10 2009-01-01 00:00:00 treecover ha    1903. #> # ℹ 14 more rows geoms <- st_geometry(aoi) portfolio_long(aoi, drop_geoms = TRUE) #> # A tibble: 24 × 6 #>    assetid indicator      datetime            variable  unit  value #>                                      #>  1       1 treecover_area 2000-01-01 00:00:00 treecover ha    1975. #>  2       1 treecover_area 2001-01-01 00:00:00 treecover ha    1975. #>  3       1 treecover_area 2002-01-01 00:00:00 treecover ha    1973. #>  4       1 treecover_area 2003-01-01 00:00:00 treecover ha    1940. #>  5       1 treecover_area 2004-01-01 00:00:00 treecover ha    1930. #>  6       1 treecover_area 2005-01-01 00:00:00 treecover ha    1926. #>  7       1 treecover_area 2006-01-01 00:00:00 treecover ha    1919. #>  8       1 treecover_area 2007-01-01 00:00:00 treecover ha    1908. #>  9       1 treecover_area 2008-01-01 00:00:00 treecover ha    1905. #> 10       1 treecover_area 2009-01-01 00:00:00 treecover ha    1903. #> # ℹ 14 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"a-note-on-parallel-computing","dir":"Articles","previous_headings":"Calculate specific indicators","what":"A note on parallel computing","title":"Quickstart","text":"mapme.biodiversity follows parallel computing paradigm {future} package. means user control like set parallel processing. Since {mapme.biodiversity} v0.9, apply pre-chunking assets portfolio. means assets split components roughly size chunk_size. components can iterated parallel speed processing. Indicator values aggregated automatically. another example, code one apply parallel processing 2 assets, 4 workers available process chunks, thus requiring total 8 available cores host machine. sure request workers available machine.","code":"library(future) plan(cluster, workers = 6) library(progressr)  plan(cluster, workers = 2)  with_progress({   aoi <- calc_indicators(     aoi,     calc_treecover_area_and_emissions(       min_size = 1,       min_cover = 30     )   ) })  plan(sequential) # close child processes"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/articles/quickstart.html","id":"exporting-an-portfolio-object","dir":"Articles","previous_headings":"","what":"Exporting an portfolio object","title":"Quickstart","text":"can use write_portfolio() function save processed portfolio object disk GeoPackage. allows sharing data contributors might using R, geospatial software. Simply point towards non-existing file local disk write portfolio. can use read_portfolio() read back GeoPackage written way R:","code":"dsn <- tempfile(fileext = \".gpkg\") write_portfolio(x = aoi, dsn = dsn, quiet = TRUE) from_disk <- read_portfolio(dsn, quiet = TRUE) from_disk #> Simple feature collection with 1 feature and 2 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.73773 ymin: 18.63179 xmax: -71.69 ymax: 18.68691 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 3 #>   assetid treecover_area                                                    geom #>                                                          #> 1       1  ((-71.73735 18.64734, -71.71386 18.63179, -71.69 18… #> [1] TRUE"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Darius . Görgen. Author, maintainer. Om Prakash Bhandari. Author. Andreas Petutschnig. Contributor.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Görgen D, Bhandari O (2024). mapme.biodiversity: Efficient Monitoring Global Biodiversity Portfolios. R package version 0.9.2.9000, https://github.com/mapme-initiative/mapme.biodiversity/, https://mapme-initiative.github.io/mapme.biodiversity/index.html.","code":"@Manual{,   title = {mapme.biodiversity: Efficient Monitoring of Global Biodiversity Portfolios},   author = {Darius A. Görgen and Om Prakash Bhandari},   year = {2024},   note = {R package version 0.9.2.9000,     https://github.com/mapme-initiative/mapme.biodiversity/},   url = {https://mapme-initiative.github.io/mapme.biodiversity/index.html}, }"},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"about","dir":"","previous_headings":"","what":"About","title":"An R package for processing global biodiversity data","text":"Biodiversity areas, especially primary forests, provide multiple ecosystem services local population planet whole. rapid expansion human land use natural ecosystems impacts global climate crisis put natural ecosystems global biodiversity threat. mapme.biodiversity package helps analyse number biodiversity related indicators biodiversity threats based freely available geodata-sources Global Forest Watch. supports computational efficient routines heavy parallel computing cloud-infrastructures AWS Microsoft Azure using statistical programming language R. package allows analysis global biodiversity portfolios thousand millions AOIs normally possible dedicated platforms Google Earth Engine. provides possibility e.g. analyse World Database Protected Areas (WDPA) number relevant indicators. primary use case package support scientific analysis data science individuals organizations seek preserve planet biodiversity. development funded German Development Bank KfW.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"An R package for processing global biodiversity data","text":"package dependencies can installed CRAN via: install development version, use following command:","code":"install.packages(\"mapme.biodiversity\", dependencies = TRUE) remotes::install_github(\"https://github.com/mapme-initiative/mapme.biodiversity\", dependencies = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"available-resources-and-indicators","dir":"","previous_headings":"","what":"Available resources and indicators","title":"An R package for processing global biodiversity data","text":"list resources currently supported mapme.biodiversity. Next, list supported indicators.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"usage-example","dir":"","previous_headings":"","what":"Usage example","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity works constructing portfolio sf object. Specific raster vector resource matching spatio-temporal extent portfolio made available locally. required resources available, indicators can calculated individually asset portfolio. decided indicator interested , can start making required resource available portfolio. Using mapme_options() can set output directory, control maximum size polygons chunked smaller parts, control verbosity package. portfolio represented sf-object. required object contain geometries type POLYGON MULTIPOLYGON assets. can request download resource spatial extent portfolio using get_resources() function. simply supply portfolio one resource functions. resources made available, can query calculation indicator using calc_indicators() function. function also expects portfolio input one indicator functions. indicator calculated assets portfolio, data returned nested list column original portfolio object. output indicator standardized common format, consisting tibble columns datetime, variable, unit, value. can transform data long format using portfolio_long().","code":"library(mapme.biodiversity) library(sf) ## Linking to GEOS 3.13.0, GDAL 3.9.2, PROJ 9.5.0; sf_use_s2() is TRUE mapme_options(   outdir = system.file(\"res\", package = \"mapme.biodiversity\"),   chunk_size = 1e6, # in ha   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\", package = \"mapme.biodiversity\") %>%   sf::read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)) %>%   portfolio_long()  aoi ## Simple feature collection with 4 features and 8 fields ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 ## Geodetic CRS:  WGS 84 ## # A tibble: 4 × 9 ##   WDPAID ISO3  assetid indicator        datetime            variable unit  value ##                                         ## 1 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 emissio… Mg    4296. ## 2 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 treecov… ha    2370. ## 3 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 emissio… Mg    4970. ## 4 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 treecov… ha    2358. ## # ℹ 1 more variable: geom "},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"using-cloud-storages","dir":"","previous_headings":"","what":"Using cloud storages","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity leverages GDAL’s capabilities data /O. users package, means integrating cloud storage easy setting configuration file changing outdir argument mapme_options(). also decide use environment variables, recommend set GDAL config file. can find GDAL’s documentation topic . Suppose want use AWS S3 bucket control write resource data . Let’s assume bucket already set wish refer R code mapme-data. GDAL configuration file look something like : connection handled based GDAL’s virtual file system. can find documentation specific options cloud provider . Ideally, also set following .Renviron file user’s home directory ensure GDAL aware configuration R session started: , scripts set outdir option value specified path variable configuration file:","code":"[credentials]  [.mapme-data] path=/vsis3/mapme-data AWS_SECRET_ACCESS_KEY= AWS_ACCESS_KEY_ID= GDAL_CONFIG_FILE = \"\" mapme_options(outdir = \"/vsis3/mapme-data\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/index.html","id":"a-note-on-parallel-computing","dir":"","previous_headings":"","what":"A note on parallel computing","title":"An R package for processing global biodiversity data","text":"mapme.biodiversity follows parallel computing paradigm {future} package. means user control like set parallel processing. Since {mapme.biodiversity} v0.9, apply pre-chunking assets portfolio. means assets split components roughly size chunk_size. components can iterated parallel speed processing. Indicator values aggregated automatically. another example, code one apply parallel processing 2 assets, 4 workers available process chunks, thus requiring total 8 available cores host machine. sure request workers available machine. Head online documentation find detailed information package.","code":"library(future) plan(cluster, workers = 6) library(progressr)  plan(cluster, workers = 2)  with_progress({   aoi <- calc_indicators(     aoi,     calc_treecover_area_and_emissions(       min_size = 1,       min_cover = 30     )   ) })  plan(sequential) # close child processes"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessibility to Cities in 2000 — accessibility_2000","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"resource provides global maps travel time cities 50,000 people year 2000. Accessibility refers ease larger cities can reached certain location. dataset represents travel time major cities globally year 2000, encoded minutes. data essential historical analyses, understanding impact accessibility land use socio-economic outcomes period.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"","code":"get_accessibility_2000()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"https://forobs.jrc.ec.europa.eu/gam","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/accessibility_2000.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Accessibility to Cities in 2000 — accessibility_2000","text":"European Commission, Joint Research Centre (JRC), Global Accessibility Maps (GAM), 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Armed Conflict Location & Event Data (ACLED) — acled","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"ACLED's homepage: Armed Conflict Location & Event Data Project (ACLED) disaggregated data collection, analysis, crisis mapping project. ACLED collects information dates, actors, locations, fatalities, types reported political violence protest events around world. ACLED team conducts analysis describe, explore, test conflict scenarios, makes data analysis open free use public.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"","code":"get_acled(   years = 2000,   key = Sys.getenv(\"ACLED_ACCESS_KEY\"),   email = Sys.getenv(\"ACLED_ACCESS_EMAIL\"),   accept_terms = FALSE )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"Armed Conflict Location & Event Data Project (ACLED).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"years numeric vector specifying years make ACLED data available (1997 today). Defaults 2000. key ACLED API key obtained registering ACLED (see Details). email Email addressed used register ACLED (see Details). accept_terms logical indicating agree abid ACLED's terms use. Defaults FALSE, thus must manually set TRUE.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"order access data ACLED API, first must register account. Note, ACLED API used provides living database single events altered removed altogether time.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Armed Conflict Location & Event Data (ACLED) — acled","text":"Raleigh, C., Kishi, R. & Linke, . Political instability patterns obscured conflict dataset scope conditions, sources, coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"function calculates mean biodiversity intactness index region.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"","code":"calc_biodiversity_intactness_index()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"function returns indicator tibble variable biodiversity_intactness_index corresponding values (unitless) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"required resources indicator : biodiversity_intactness_index_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Biodiversity Intactness Index — biodiversity_intactness_index_indicator","text":"","code":"# \\dontrun{ library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  lbii <- system.file(\"res\", \"biodiversity_intactness_index\", \"lbii.asc\",                     package = \"mapme.biodiversity\")  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) aoi <- get_resources(aoi, get_biodiversity_intactness_index(lbii)) aoi <- calc_indicators(aoi, calc_biodiversity_intactness_index()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 biodiver… 2005-01-01 00:00:00 biodive… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"variable modeled average abundance originally-present species, relative abundance intact ecosystem. Please refer Newbold et al. (2016) details, please cite using data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"","code":"get_biodiversity_intactness_index(path = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"path character vector biodiversity intactness index ASCII file.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"function returns sf footprints object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biodiversity_intactness_index_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biodiversity Intactness Index — biodiversity_intactness_index_resource","text":"Tim Newbold; Lawrence Hudson; Andy Arnell; Sara Contu et al. (2016). Global map Biodiversity Intactness Index, Newbold et al. (2016) Science [Data set]. Natural History Museum. doi:10.5519/0009936","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate biomes statistics (TEOW) based on WWF — biome","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"function allows efficiently retrieve name biomes compute corresponding area Terrestrial Ecoregions World (TEOW) - World Wildlife Fund (WWF) polygons. polygon, name area biomes (hectare) returned. required resources indicator : teow","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"","code":"calc_biome()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"function returns indicator tibble variable biome type corresponding area (ha) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/biome.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate biomes statistics (TEOW) based on WWF — biome","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_teow()) %>%   calc_indicators(calc_biome()) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 biome     2001-01-01 00:00:00 tropical_subt… ha    18349. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"Calculates Monthly Burned Area based Terra Aqua combined MCD64A1 Version 6.1. s monthly, global gridded 500 meter (m) product containing per-pixel burned-area information.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"","code":"calc_burned_area(engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"function returns indicator tibble variable burned area corresponding area (ha) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"required resources indicator : mcd64a1","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/burned_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Monthly Burned Area based on MODIS (MCD64A1) — burned_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_mcd64a1(years = 2010)) %>%   calc_indicators(calc_burned_area(engine = \"extract\")) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator   datetime            variable    unit  value #>                                        #>  1 478140 DOM         1 burned_area 2010-12-01 00:00:00 burned_area ha      0   #>  2 478140 DOM         1 burned_area 2010-11-01 00:00:00 burned_area ha      0   #>  3 478140 DOM         1 burned_area 2010-10-01 00:00:00 burned_area ha      0   #>  4 478140 DOM         1 burned_area 2010-09-01 00:00:00 burned_area ha      0   #>  5 478140 DOM         1 burned_area 2010-08-01 00:00:00 burned_area ha      0   #>  6 478140 DOM         1 burned_area 2010-07-01 00:00:00 burned_area ha      0   #>  7 478140 DOM         1 burned_area 2010-06-01 00:00:00 burned_area ha      0   #>  8 478140 DOM         1 burned_area 2010-05-01 00:00:00 burned_area ha      0   #>  9 478140 DOM         1 burned_area 2010-04-01 00:00:00 burned_area ha      0   #> 10 478140 DOM         1 burned_area 2010-03-01 00:00:00 burned_area ha     42.8 #> 11 478140 DOM         1 burned_area 2010-02-01 00:00:00 burned_area ha      0   #> 12 478140 DOM         1 burned_area 2010-01-01 00:00:00 burned_area ha      0   #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"indicator calculates population exposed conflict events within specified buffer distance around events ACLED. Per default, first available WorldPop layer used estimate exposed populations years respective year, recent layer used years .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"","code":"calc_exposed_population_acled(   distance = 5000,   filter_category = c(\"event_type\", \"sub_event_type\", \"disorder_type\"),   filter_types = NULL,   years = c(1997:2024),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"distance numeric vector indicating buffer radius meters. length 1, buffer size around included conflict events drawn. Otherwise, must equal length included categories selected filter_types. filter_category character indicating categories used calculate exposed population . Defaults event_type meaning one estimation per event type returned. filter_types character vector event types respective category specified filter_category retain. Defaults NULL, meaning filter applied types retained. years numeric vector indicating years calculate exposed population. Restricted available years ACLED. years intersecting available WorldPop layers, first layer used earlier years last layer recent years. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"function returns indicator tibble conflict exposure variable precentage population value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"indicator inspired Conflict Exposure tool ACLED (see citation ), differs regard simply flatten buffered event layer instead applying voronoi tessellation. required resources indicator : acled worldpop Events ACLED classified according schema described extensively codebook. may filter certain types events. categories filter can applied either \"event_type\", \"event_sub_type\", \"disorder_type\". translated following categories: event_type: battles protests riots explosions/remote_violence violence_against_civilians strategic_developments event_sub_type: government_regains_territory non-state_actor_overtakes_territory armed_clash excessive_force_against_protesters protest_with_intervention peaceful_protest violent_demonstration mob_violence chemical_weapon air/drone_strike suicide_bomb shelling/artillery/missile_attack remote_explosive/landmine/ied grenade sexual_violence attack abduction/forced_disappearance agreement arrests change_to_group/activity disrupted_weapons_use headquarters_or_base_established looting/property_destruction non-violent_transfer_of_territory disorder_type: political_violence political_violence;_demonstrations demonstrations political_violence strategic_developments may supply buffer distances event categories. Custom buffers drawn per category. Supply single value wish differentiate categories. Otherwise, supply vector distances equal length included categories. may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 3 decreasing accuracy: value 1: source reporting indicates particular town, coordinates available town value 2: source material indicates activity took place small part region, mentions general area activity occurs near town city, event coded town geo-referenced coordinates represent area value 3: larger region mentioned, closest natural location noted reporting (like “border area,” “forest,” “sea,” among others) – provincial capital used information available temporal precision levels 1 3 decreasing precision: value 1: source material includes actual date event value 2: source material indicates event happened sometime week within similar period time value 3: source material indicates event took place sometime month (.e. past two three weeks, January), without reference particular date, month mid-point chosen","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"Raleigh, C; C Dowd; Tatem; Linke; N Tejedor-Garavito; M Bondarenko K Kishi. 2023. Assessing Mapping Global Local Conflict Exposure. Working Paper.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/calc_exposed_population_acled.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population exposed to violent conflict from ACLED — calc_exposed_population_acled","text":"","code":"# \\dontrun{ if (FALSE) {   library(sf)   library(mapme.biodiversity)    outdir <- file.path(tempdir(), \"mapme-data\")   dir.create(outdir, showWarnings = FALSE)    mapme_options(     outdir = outdir,     verbose = FALSE,     chunk_size = 1e8   )    aoi <- system.file(\"extdata\", \"burundi.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_acled(year = 2000),       get_worldpop(years = 2000)     ) %>%     calc_indicators(       conflict_exposure_acled(         distance = 5000,         years = 2000,         precision_location = 1,         precision_time = 1       )     ) %>%     portfolio_long()    aoi } # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate carbon statistics — carbon_indicators","title":"Calculate carbon statistics — carbon_indicators","text":"functions allow calculated statistics based harmonized carbon layers 2010 2018 Noon et al. (2022).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate carbon statistics — carbon_indicators","text":"","code":"calc_irr_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )  calc_man_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )  calc_vul_carbon(   type = c(\"total\", \"soil\", \"biomass\", \"all\"),   engine = \"extract\",   stats = \"mean\" )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate carbon statistics — carbon_indicators","text":"type One \"total\", \"soil\", \"biomass\", \"\". Determines data layer statistics calculated. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate carbon statistics — carbon_indicators","text":"function returns indicator tibble (type)_carbon_(stat) variable respective statistic (Mg) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate carbon statistics — carbon_indicators","text":"required resources indicators : carbon_resources Irrecoverable carbon amount carbon , lost today, recovered 2050. can calculated - -ground carbon, total amount carbon, layers. Manageable carbon amount carbon , principle, manageable human activities, e.g. release atmosphere can prevented. can calculated - -ground carbon, total amount carbon, layers. Vulnerable carbon amount carbon released typical land conversion activity. can calculated - -ground carbon, total amount carbon, layers.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_indicators.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate carbon statistics — carbon_indicators","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_man_carbon(),     get_vul_carbon(),     get_irr_carbon()   ) %>%   calc_indicators(     calc_man_carbon(stats = \"sum\"),     calc_vul_carbon(stats = \"sum\"),     calc_irr_carbon(stats = \"sum\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 6 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #>   WDPAID                       NAME                 DESIG_ENG ISO3 assetid #> 1  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 2  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 3  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 4  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 5  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #> 6  41057 Shell Beach Protected Area Managed Resource Use Area  GUY       1 #>    indicator   datetime             variable unit    value #> 1 man_carbon 2010-01-01 man_carbon_total_sum   Mg 819413.5 #> 2 man_carbon 2018-01-01 man_carbon_total_sum   Mg 819413.5 #> 3 vul_carbon 2010-01-01 vul_carbon_total_sum   Mg 696439.2 #> 4 vul_carbon 2018-01-01 vul_carbon_total_sum   Mg 696191.6 #> 5 irr_carbon 2010-01-01 irr_carbon_total_sum   Mg 406579.3 #> 6 irr_carbon 2018-01-01 irr_carbon_total_sum   Mg 407012.9 #>                             geom #> 1 POLYGON ((-59.84334 8.36199... #> 2 POLYGON ((-59.84334 8.36199... #> 3 POLYGON ((-59.84334 8.36199... #> 4 POLYGON ((-59.84334 8.36199... #> 5 POLYGON ((-59.84334 8.36199... #> 6 POLYGON ((-59.84334 8.36199... # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":null,"dir":"Reference","previous_headings":"","what":"Carbon Layers — carbon_resources","title":"Carbon Layers — carbon_resources","text":"resources publication Noon et al. (2022) \"Mapping irrecoverable carbon Earth’s ecosystems\". publication differentiates 3 different kinds carbon varying degrees manageability humans. three layers available ground carbon, well layer combining two.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carbon Layers — carbon_resources","text":"","code":"get_irr_carbon()  get_vul_carbon()  get_man_carbon()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Carbon Layers — carbon_resources","text":"https://zenodo.org/records/4091029","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carbon Layers — carbon_resources","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Carbon Layers — carbon_resources","text":"may required increase timeout option successfully download theses layers source location via e.g. options(timeout = 600). Irrecoverable carbon defined amount carbon, , lost today, recovered mid 21st century (within 30 years, considering publication date). Vulnerable carbon defined amount carbon lost hypothetical typical conversion event (without including information probability event actually occurring). Manageable carbon defined land areas, expect cyrosols, carbon loss driven direct land-use conversion halted climate change impacts affecting area can potentially directly mitigated adaptive management.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/carbon_resources.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Carbon Layers — carbon_resources","text":"Noon, M.L., Goldstein, ., Ledezma, J.C. et al. Mapping irrecoverable carbon Earth’s ecosystems. Nat Sustain 5, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to check yearly availability — check_available_years","title":"Helper to check yearly availability — check_available_years","text":"Use function check specifed vector years intersects yearly availablity resource.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to check yearly availability — check_available_years","text":"","code":"check_available_years(target_years, available_years, indicator)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_available_years.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to check yearly availability — check_available_years","text":"target_years Numeric indicating target year. available_years Numeric indicating available years. indicator character vector target resource/indicator name.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if namespace is available — check_namespace","title":"Checks if namespace is available — check_namespace","text":"Use function resource/indicator function requires namespace certain package available. informative error/warning message printed case.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if namespace is available — check_namespace","text":"","code":"check_namespace(pkg, error = TRUE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if namespace is available — check_namespace","text":"pkg character vector length one indicating package name namespace tested error logical indicating whether promote missing namespace error. FALSE, warning emitted.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/check_namespace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks if namespace is available — check_namespace","text":"TRUE, invisible, namespace available. error message error = TRUE, FALSE warning otherwise.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"CHELSA data (Karger et al. 2017) consists downscaled model output temperature precipitation estimates horizontal resolution 30 arc sec. precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, boundary layer height, subsequent bias correction. spatial resolution 1-arc second (~1km equator). resource makes V2 available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"","code":"get_chelsa(years = 1979:2018)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"https://envicloud.wsl.ch/#/?prefix=chelsa/chelsa_V2/GLOBAL/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"years numeric vector years make CHELSA monthly precipitation layers available . Must greater 1979, defaults c(1979:2018).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chelsa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Climatologies at High resolution for the Earth Land Surface Areas (CHELSA) — chelsa","text":"Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2021) Climatologies high resolution earth’s land surface areas. EnviDat. doi:10.16904/envidat.228.v2.1 Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies high resolution Earth land surface areas. Scientific Data. 4 170122. doi:10.1038/sdata.2017.122","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":null,"dir":"Reference","previous_headings":"","what":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"resource published Funk et al. (2015) represents quasi-global (50°S-50°S) rainfall estimation monthly resolution starting year 1981 near-present. spatial resolution 0.05°. data can used retrieve information amount rainfall. Due availability +30 years, anomaly detection long-term average analysis also possible. routine download complete archive order support long-term average anomaly calculations respect 1981 - 2010 climate normal period. Thus additional arguments need specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"","code":"get_chirps(years = 1981:2020)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/cogs/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"years numeric vector years download CHIRPS precipitation layers. Must greater 1981, defaults c(1981:2020).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/chirps.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) — chirps","text":"Funk, C., Peterson, P., Landsfeld, M. et al. climate hazards infrared precipitation stations—new environmental record monitoring extremes. Sci Data 2, 150066 (2015). doi:10.1038/sdata.2015.66","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate deforestation drivers — deforestation_drivers","title":"Calculate deforestation drivers — deforestation_drivers","text":"function extracts areal statistics drivers deforestation based data source produced Fritz et al (2022).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate deforestation drivers — deforestation_drivers","text":"","code":"calc_deforestation_drivers()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate deforestation drivers — deforestation_drivers","text":"function returns indicator tibble deforestation drivers variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate deforestation drivers — deforestation_drivers","text":"required resource indicator : fritz_et_al","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/deforestation_drivers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate deforestation drivers — deforestation_drivers","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_fritz_et_al(resolution = 100)) %>%   calc_indicators(calc_deforestation_drivers()) %>%   portfolio_long()  aoi #> Simple feature collection with 10 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 10 × 9 #>    WDPAID ISO3  assetid indicator      datetime            variable unit   value #>                                         #>  1 478140 DOM         1 deforestation… 2008-01-01 00:00:00 commerc… ha        0  #>  2 478140 DOM         1 deforestation… 2008-01-01 00:00:00 commerc… ha        0  #>  3 478140 DOM         1 deforestation… 2008-01-01 00:00:00 managed… ha        0  #>  4 478140 DOM         1 deforestation… 2008-01-01 00:00:00 mining   ha        0  #>  5 478140 DOM         1 deforestation… 2008-01-01 00:00:00 natural… ha        0  #>  6 478140 DOM         1 deforestation… 2008-01-01 00:00:00 pasture  ha        0  #>  7 478140 DOM         1 deforestation… 2008-01-01 00:00:00 roads    ha        0  #>  8 478140 DOM         1 deforestation… 2008-01-01 00:00:00 wildfire ha        0  #>  9 478140 DOM         1 deforestation… 2008-01-01 00:00:00 other_s… ha    16809. #> 10 478140 DOM         1 deforestation… 2008-01-01 00:00:00 shiftin… ha        0  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate drought indicator statistics — drought_indicator","title":"Calculate drought indicator statistics — drought_indicator","text":"function allows efficiently calculate relative wetness shallow groundwater section regard 1948-2012 reference period. values represent wetness percentile given area achieves given point time regard reference period. polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate drought indicator statistics — drought_indicator","text":"","code":"calc_drought_indicator(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate drought indicator statistics — drought_indicator","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"mean\", \"median\" \"sd\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate drought indicator statistics — drought_indicator","text":"function returns indicator tibble specified drought indicator statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate drought indicator statistics — drought_indicator","text":"required resources indicator : nasa_grace","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/drought_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate drought indicator statistics — drought_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_grace(years = 2022)) %>%   calc_indicators(     calc_drought_indicator(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 40 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 40 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 drought_indica… 2022-01-03 00:00:00 wetness… perc…  57.5 #>  2 478140 DOM         1 drought_indica… 2022-01-03 00:00:00 wetness… perc…  57.5 #>  3 478140 DOM         1 drought_indica… 2022-01-10 00:00:00 wetness… perc…  55.5 #>  4 478140 DOM         1 drought_indica… 2022-01-10 00:00:00 wetness… perc…  55.5 #>  5 478140 DOM         1 drought_indica… 2022-01-17 00:00:00 wetness… perc…  54   #>  6 478140 DOM         1 drought_indica… 2022-01-17 00:00:00 wetness… perc…  54   #>  7 478140 DOM         1 drought_indica… 2022-01-24 00:00:00 wetness… perc…  53   #>  8 478140 DOM         1 drought_indica… 2022-01-24 00:00:00 wetness… perc…  53   #>  9 478140 DOM         1 drought_indica… 2022-01-31 00:00:00 wetness… perc…  43.5 #> 10 478140 DOM         1 drought_indica… 2022-01-31 00:00:00 wetness… perc…  43.5 #> # ℹ 30 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"function allows efficiently retrieve name ecoregions compute corresponding area Terrestrial Ecoregions World (TEOW) - World Wildlife Fund (WWF) polygons. polygon, name area ecoregions (hectare) returned. required resources indicator : teow","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"","code":"calc_ecoregion()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"function returns indicator tibble ecoregion type variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ecoregion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate terrestrial ecoregions statistics (TEOW) based on WWF — ecoregion","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_teow()) %>%   calc_indicators(calc_ecoregion()) %>%   portfolio_long() #> Resource 'teow' is already available.  aoi #> Simple feature collection with 1 feature and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 ecoregion 2001-01-01 00:00:00 hispaniolan_p… ha    18349. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate elevation statistics — elevation","title":"Calculate elevation statistics — elevation","text":"function allows calculate elevation statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate elevation statistics — elevation","text":"","code":"calc_elevation(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate elevation statistics — elevation","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"mean\", \"median\" \"sd\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate elevation statistics — elevation","text":"function returns indicator tibble specified elevation statistics variable corresponding values (meters) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate elevation statistics — elevation","text":"required resources indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/elevation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate elevation statistics — elevation","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_elevation(engine = \"extract\", stats = c(\"mean\", \"median\", \"sd\", \"var\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable       unit   value #>                                         #> 1 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_mean m      1704. #> 2 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_med… m      1702  #> 3 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_sd   m       219. #> 4 478140 DOM         1 elevation 2000-02-01 00:00:00 elevation_var  m     48085. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to select processing engines — engine","title":"Function to select processing engines — engine","text":"check_engine() checks extraction engine zonal vector-raster operations supported backend. check_stats checks one multiple statistics supported zonal vector-raster extraction backend. select_engine extracts zonal vector-raster statistics supported engine one statistics. Columns named according argument name plus respective stat. portfolio asset modes supported.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to select processing engines — engine","text":"","code":"check_engine(queried_engine)  check_stats(queried_stats)  select_engine(x, raster, stats, engine, name = NULL, mode = \"asset\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to select processing engines — engine","text":"queried_engine character vector length one indicating engine check . queried_stats character vector statistic names checked supported backend x sf object representing portfolio. raster terra SpatRaster values extracted. stats character vector statistics aggregate raster values . engine character vector length one specifying engine used extraction. name character vector indicating name append columns names. mode character vector indicating mode conduct extraction (e.g. asset-wise whole portfolio ).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/engine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to select processing engines — engine","text":"check_engine() returns character queried engine, supported. Throws error otherwise. check_stats returns character vector supported statistics. Throws error queried statistics supported. select_engine returns tibble.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":null,"dir":"Reference","previous_headings":"","what":"ESA Copernicus Global Land Cover layer — esalandcover","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"100 meter spatial resolution land cover resource published Buchhorn et al. (2020) \"Copernicus Global Land Cover Layers—Collection 2\". resource represents actual surface cover ground available annually period 2015 2019. cell values range 0 200, representing total 23 discrete classifications ESA.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"","code":"get_esalandcover(years = 2015:2019)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"https://lcviewer.vito./download","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/esalandcover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ESA Copernicus Global Land Cover layer — esalandcover","text":"© European Union, Copernicus Land Monitoring Service (year), European Environment Agency (EEA)\", f.ex. 2018: “© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"indicator calculates population exposed conflict events within specified buffer distance around violent events UCDP GED. Per default, first available WorldPop layer used estimate exposed populations years respective year, recent layer used years .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"","code":"calc_exposed_population_ucdp(   distance = 5000,   violence_types = 1:3,   years = c(1989:2023),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"distance numeric vector indicating buffer size around included conflict events calculate exposed population. Either length 1 apply types events, discrete values category included violence_types. violence_types numeric vector indicating types violence included (see Details). years numeric vector indicating years calculate exposed population. Restricted available years UCDP GED. years intersecting available WorldPop layers, first layer used earlier years last layer recent years. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"function returns indicator tibble conflict exposure variable precentage population value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"indicator inspired Conflict Exposure tool ACLED (see citation ), differs regard simply flatten buffered event layer instead applying voronoi tessellation. required resources indicator : ucdp_ged worldpop may filter certain types violence. coded types according UCDP codebook : value 1: state-based conflict value 2: non-state conflict value 3: one-sided conflict may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 7 decreasing accuracy: value 1: location information corresponds exactly geographical coordinates available value 2: location information refers limited area around specified location value 3: source refers can specified larger location level second order administrative divisions (ADM2), district municipality, GED uses centroid point coordinates ADM2. value 4: location information refers first order administrative division, province (ADM1), GED uses coordinates centroid point ADM1 value 5: used different cases source refers parts country larger ADM1, smaller entire country; two locations mentioned representiative point selected; location mentioned non-independend island; location specifically mentioned relation another location value 6: location mentioned refers entire country centroid used value 7: event takes place water international airspace, geographical coordinates dataset either represent centroid point certain water area estimated coordinates temporal precision levels 1 5 decreasing precision: value 1: exact date event known value 2: start enddates events unspecified character, spanning one calendar day though longer six days value 3: start end dates events specified certain week, specific dates provided value 4: start end dates events specified certain month value 5: start enddates events specified certain year, specific dates provided","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"Raleigh, C; C Dowd; Tatem; Linke; N Tejedor-Garavito; M Bondarenko K Kishi. 2023. Assessing Mapping Global Local Conflict Exposure. Working Paper.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/exposed_population_ucdp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population exposed to violent conflict from UCDP GED — exposed_population_ucdp","text":"","code":"# \\dontrun{ if (FALSE) {   library(sf)   library(mapme.biodiversity)    outdir <- file.path(tempdir(), \"mapme-data\")   dir.create(outdir, showWarnings = FALSE)    mapme_options(     outdir = outdir,     verbose = FALSE,     chunk_size = 1e8   )    aoi <- system.file(\"extdata\", \"burundi.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_ucdp_ged(version = \"22.1\"),       get_worldpop(years = 2000)     ) %>%     calc_indicators(       conflict_exposure(         distance = 5000,         violence_types = 1:3,         years = 2000,         precision_location = 1,         precision_time = 1       )     ) %>%     portfolio_long()    aoi } # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"indicator aggregated number fatalities within given asset monthly cadence stratified either event type, sub-event type disorder type. learn different categorisation ACLED uses encode events please consult ACLED's codebook.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"","code":"calc_fatalities_acled(   years = 2000,   stratum = c(\"event_type\", \"sub_event_type\", \"disorder_type\"),   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"years numeric vector indicating years summarize fatalities. stratum character vector indicating stratification applied. one \"event_type\", \"sub_event_type\", \"disorder_type\". Defaults \"event_type\". precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"function returns indicator tibble type violence variable counts civilian fatalities value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"required resources indicator : acled may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 3 decreasing accuracy: value 1: source reporting indicates particular town, coordinates available town value 2: source material indicates activity took place small part region, mentions general area activity occurs near town city, event coded town geo-referenced coordinates represent area value 3: larger region mentioned, closest natural location noted reporting (like “border area,” “forest,” “sea,” among others) – provincial capital used information available temporal precision levels 1 3 decreasing precision: value 1: source material includes actual date event value 2: source material indicates event happened sometime week within similar period time value 3: source material indicates event took place sometime month (.e. past two three weeks, January), without reference particular date, month mid-point chosen","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"Raleigh, C., Kishi, R. & Linke, . Political instability patterns obscured conflict dataset scope conditions, sources, coding choices. Humanit Soc Sci Commun 10, 74 (2023). doi:10.1057/s41599-023-01559-4","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_acled.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate number of fatalities of conflict events from ACLED — fatalities_acled","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE,   chunk_size = 1e8 )  aoi <- system.file(\"extdata\", \"burundi.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_acled(years = 2020)) %>%   calc_indicators(     calc_fatalities_acled(       years = 2020,       precision_location = 1,       precision_time = 1     )   ) %>%   portfolio_long() #> Error in get_acled(years = 2020): Please read and agree to ACLED's Terms of Use here: #> https://acleddata.com/terms-of-use/  aoi #> Error: object 'aoi' not found # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"indicator aggregated number fatalities within given asset monthly cadence stratified type conflict. different types conflicts encoded UCDP GED database : state-based conflict non-state conflict one-sided violence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"","code":"calc_fatalities_ucdp(   years = 1989:2023,   precision_location = 1,   precision_time = 1 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"years numeric vector indicating years summarize fatalities. precision_location numeric indicating precision value geolocation events included. Defaults 1. precision_time numeric indicating precision value temporal coding events included. Defaults 1.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"function returns indicator tibble type violence variable counts civilian fatalities value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"required resources indicator : ucdp_ged may apply quality filters based precision geolocation events temporal precision. default, set include events highest precision scores. geo-precision levels 1 7 decreasing accuracy: value 1: location information corresponds exactly geographical coordinates available value 2: location information refers limited area around specified location value 3: source refers can specified larger location level second order administrative divisions (ADM2), district municipality, GED uses centroid point coordinates ADM2. value 4: location information refers first order administrative division, province (ADM1), GED uses coordinates centroid point ADM1 value 5: used different cases source refers parts country larger ADM1, smaller entire country; two locations mentioned representiative point selected; location mentioned non-independend island; location specifically mentioned relation another location value 6: location mentioned refers entire country centroid used value 7: event takes place water international airspace, geographical coordinates dataset either represent centroid point certain water area estimated coordinates temporal precision levels 1 5 decreasing precision: value 1: exact date event known value 2: start enddates events unspecified character, spanning one calendar day though longer six days value 3: start end dates events specified certain week, specific dates provided value 4: start end dates events specified certain month value 5: start enddates events specified certain year, specific dates provided","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"Sundberg, Ralph, Erik Melander, 2013, “Introducing UCDP Georeferenced Event Dataset”, Journal Peace Research, vol.50, .4, 523-532","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fatalities_ucpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate number of fatalities of violent conflict from UCDP GED — fatalities_ucpd","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE,   chunk_size = 1e8 )  aoi <- system.file(\"extdata\", \"burundi.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_ucdp_ged(version = \"22.1\")) %>%   calc_indicators(     calc_fatalities(       years = 1991:1992,       precision_location = 1,       precision_time = 1     )   ) %>%   portfolio_long() #> Error in calc_fatalities(years = 1991:1992, precision_location = 1, precision_time = 1): could not find function \"calc_fatalities\"  aoi #> Error: object 'aoi' not found # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":null,"dir":"Reference","previous_headings":"","what":"Drivers of deforestation for tropical forests — fritz_et_al","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"resource produced neirest-neighbour matching crowd-sourced campaign map dominant driver forest loss based visual interpretation VHR images matched Global Forest Loss data Hansen (2013) version 1.7 forest loss layer re sampled resolution 100 1.000 meters. Dominant drivers determined period 2008 2009.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"","code":"get_fritz_et_al(resolution = 100)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"https://zenodo.org/record/7997885","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"resolution integer indicating resolution download. Defaults 100.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"indicates 9 different classes: commercial agriculture commercial oil palm plantations managed forests mining natural disturbances pasture roads wildfire subsistence agriculture shifting cultivation","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/fritz_et_al.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Drivers of deforestation for tropical forests — fritz_et_al","text":"Steffen, F., Carlos, J.C.L., See. L., Schepaschenko D., Hofhansl F., Jung M., Dürauer M., Georgieva ., Danylo O., Lesiv M., McCallum . (2022) Continental Assessment Drivers Tropical Deforestation Focus Protected Areas. F.Cos.Sc.(3) doi:10.3389/fcosc.2022.830248","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest greenhouse gas emissions — gfw_emissions","title":"Forest greenhouse gas emissions — gfw_emissions","text":"resource part publication Harris et al. (2021) \"Global maps twenty-first century forest carbon fluxes.\". represents \"greenhouse gas emissions arising stand-replacing forest disturbances occurred modelled year (megagrams CO2 emissions/ha, 2001 2023). Emissions include relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil) greenhouse gases (CO2, CH4, N2O).\" area unit downloaded corresponds \"megagrams CO2 emissions/pixel\" layer, order support calculation area-wise emissions.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest greenhouse gas emissions — gfw_emissions","text":"","code":"get_gfw_emissions()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Forest greenhouse gas emissions — gfw_emissions","text":"https://data.globalforestwatch.org/datasets/gfw::forest-greenhouse-gas-emissions/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest greenhouse gas emissions — gfw_emissions","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest greenhouse gas emissions — gfw_emissions","text":"arguments users need specify. However, users note spatial extent dataset totally cover extent treecover2000 lossyear resources Hansen et al. (2013). missing value (NA) inserted greenhouse gas emissions areas data available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_emissions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest greenhouse gas emissions — gfw_emissions","text":"Harris, N.L., Gibbs, D.., Baccini, . et al. Global maps twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021). https://doi.org/10.1038/s41558-020-00976-6","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":null,"dir":"Reference","previous_headings":"","what":"Year of forest loss occurrence — gfw_lossyear","title":"Year of forest loss occurrence — gfw_lossyear","text":"resource part publication Hansen et al. (2013) \"High-Resolution Global Maps 21st-Century Forest Cover Change\". represents \"Forest loss period 2000–2021, defined stand-replacement disturbance, change forest non-forest state. Encoded either 0 (loss) else value range 1–20, representing loss detected primarily year 2001–2021, respectively.\" Due changes satellites products used compilation tree loss product, results year 2011 afterwards directly comparable reprocessing finished. Users aware limitation, especially timeframe analysis spans two periods delimited year 2011.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Year of forest loss occurrence — gfw_lossyear","text":"","code":"get_gfw_lossyear(version = \"GFC-2023-v1.11\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Year of forest loss occurrence — gfw_lossyear","text":"https://data.globalforestwatch.org/documents/tree-cover-loss/explore","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Year of forest loss occurrence — gfw_lossyear","text":"version version dataset download. Defaults \"GFC-2023-v1.11\". Check mapme.biodiversity:::.available_gfw_versions() get list available versions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Year of forest loss occurrence — gfw_lossyear","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_lossyear.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Year of forest loss occurrence — gfw_lossyear","text":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. . Turubanova, . Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, . Kommareddy, . Egorov, L. Chini, C. O. Justice, J. R. G. Townshend. 2013. “High-Resolution Global Maps 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":null,"dir":"Reference","previous_headings":"","what":"Treecover for the year 2000 — gfw_treecover","title":"Treecover for the year 2000 — gfw_treecover","text":"resource part publication Hansen et al. (2013) represents \"tree cover year 2000, defined canopy closure vegetation taller 5m height. Encoded percentage per output grid cell, range 0–100.\" Due changes satellites products used compilation treecover product, results year 2011 afterwards directly comparable reprocessing finished. Users aware limitation, especially timeframe analysis spans two periods delimited year 2011.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Treecover for the year 2000 — gfw_treecover","text":"","code":"get_gfw_treecover(version = \"GFC-2023-v1.11\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Treecover for the year 2000 — gfw_treecover","text":"https://data.globalforestwatch.org/documents/tree-cover-2000/explore","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Treecover for the year 2000 — gfw_treecover","text":"version version dataset download. Defaults \"GFC-2023-v1.11\". Check mapme.biodiversity:::.available_gfw_versions() get list available versions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Treecover for the year 2000 — gfw_treecover","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gfw_treecover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Treecover for the year 2000 — gfw_treecover","text":"Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. . Turubanova, . Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, . Kommareddy, . Egorov, L. Chini, C. O. Justice, J. R. G. Townshend. 2013. “High-Resolution Global Maps 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Change — global_surface_water_change","title":"Global Surface Water Change — global_surface_water_change","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Change — global_surface_water_change","text":"","code":"get_global_surface_water_change(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Change — global_surface_water_change","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Change — global_surface_water_change","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Change — global_surface_water_change","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Change — global_surface_water_change","text":"change water occurrence intensity two periods derived homologous pairs months (.e. months containing valid observations periods). difference occurrence surface water calculated homologous pair months. average differences constitutes Surface Water Occurrence change intensity. raster files integer cell values [0, 200] 0 represents surface water loss 200 represents surface water gain.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Change — global_surface_water_change","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Occurrence — global_surface_water_occurrence","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"","code":"get_global_surface_water_occurrence(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"GSW occurrence raw data comes raster files integer cell values [0, 100]. value gives percentage time given pixel classified water entire observation period. 0 denotes pixel never classified water, 100 denotes pixel permanent water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_occurrence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Occurrence — global_surface_water_occurrence","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Recurrence — global_surface_water_recurrence","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"","code":"get_global_surface_water_recurrence(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Water Recurrence measurement degree variability presence water year year. describes frequency water returned particular location one year another, expressed percentage. raster files integer cell values [0, 100], 100 represents water reoccurs predictably every year, whereas lower values indicate water occurs episodically.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_recurrence.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Recurrence — global_surface_water_recurrence","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Seasonality — global_surface_water_seasonality","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"","code":"get_global_surface_water_seasonality(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"GSW seasonality describes intra-annual distribution surface water pixel. raster files integer cell values [0, 12], indicating many months per year pixel classified water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_seasonality.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Seasonality — global_surface_water_seasonality","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Surface Water Transitions — global_surface_water_transitions","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"Global Surface Water dataset developed European Commission's Joint Research Centre framework Copernicus Programme. maps location temporal distribution water surfaces global scale past 3.8 decades provides statistics extent change. provisioned global tiled raster resource available land areas. reported data represent aggregated observations 1984 - 2021.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"","code":"get_global_surface_water_transitions(version = \"v1_4_2021\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"https://global-surface-water.appspot.com/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"version character vector indicating version GSW data set make available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"GSW transition data contains information type surface water change pixel. raster files integer cell values [0, 10] code different transition classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/global_surface_water_transitions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Surface Water Transitions — global_surface_water_transitions","text":"Pekel, JF., Cottam, ., Gorelick, N. et al. High-resolution mapping global surface water long-term changes. Nature 540, 418–422 (2016). https://doi.org/10.1038/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Mangrove Extent Polygon — gmw","title":"Global Mangrove Extent Polygon — gmw","text":"resource part publication Bunting et al. (2018) \"Global Mangrove Watch—New 2010 Global Baseline Mangrove Extent\". polygons represent mangrove, tropical coastal vegetation considered significant part marine ecosystem. resource available selected years period 1996- 2020 Global Mangrove Watch (GMW), providing geospatial information global mangrove extent.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Mangrove Extent Polygon — gmw","text":"","code":"get_gmw(years = c(1996, 2007:2010, 2015:2020))"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Mangrove Extent Polygon — gmw","text":"https://data.unep-wcmc.org/datasets/45","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Mangrove Extent Polygon — gmw","text":"years numeric vector years make GMW available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Mangrove Extent Polygon — gmw","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gmw.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Mangrove Extent Polygon — gmw","text":"Bunting P., Rosenqvist ., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy ., Itoh T., Shimada M. Finlayson C.M. (2018). Global Mangrove Watch – New 2010 Global Baseline Mangrove Extent. Remote Sensing 10(10): 1669. doi:10.3390/rs10101669.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Change — gsw_change","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"change water occurrence intensity two periods derived homologous pairs months (.e. months containing valid observations periods). difference occurrence surface water calculated homologous pair months. average differences constitutes Surface Water Occurrence change intensity. raster files integer cell values [0, 200] 0 represents surface water loss 200 represents surface water gain.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"","code":"calc_gsw_change(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". stats Aggregation function data combined. Default: \"mean\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"function returns indicator tibble change intensity variable corresponding (unitless) values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"pixel values aggregated using method provided via stats parameter using specified engine. required resources indicator : global_surface_water_change","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Change — gsw_change","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_change()) %>%   calc_indicators(     calc_gsw_change(engine = \"extract\", stats = \"mean\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_chan… 2021-01-01 00:00:00 gsw_cha… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"GSW occurrence raw data comes raster files integer cell values [0, 100]. value gives percentage time given pixel classified water entire observation period. 0 denotes pixel never classified water, 100 denotes pixel permanent water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"","code":"calc_gsw_occurrence(engine = \"extract\", min_occurrence = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". min_occurrence Threshold define pixels count towards GSW occurrence area [0, 100].","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"function returns indicator tibble occurrence variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"raw data values aggregated based provided threshold parameter min_occurrence, function returns area covered values greater equal threshold. required resources indicator : global_surface_water_occurrence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Occurrence — gsw_occurrence","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_occurrence()) %>%   calc_indicators(     calc_gsw_occurrence(engine = \"extract\", min_occurrence = 10)   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_occu… 2021-01-01 00:00:00 gsw_occ… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"Water Recurrence measurement degree variability presence water year year. describes frequency water returned particular location one year another, expressed percentage. raster files integer cell values [0, 100], 100 represents water reoccurs predictably every year, whereas lower values indicate water occurs episodically.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"","code":"calc_gsw_recurrence(engine = \"extract\", min_recurrence = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\". Default: \"extract\". min_recurrence Threshold define pixels count towards GSW recurrence area [0, 100].","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"function returns indicator tibble recurrence variable corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"raw data values aggregated based provided threshold parameter min_recurrence, function returns area covered values greater equal threshold. required resources indicator : global_surface_water_recurrence","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_recurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Recurrence — gsw_recurrence","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_recurrence()) %>%   calc_indicators(     calc_gsw_recurrence(engine = \"extract\", min_recurrence = 10)   ) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_recu… 2021-01-01 00:00:00 gsw_rec… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"GSW seasonality describes intra-annual distribution surface water pixel. raster files integer cell values [0, 12], indicating many months per year pixel classified water.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"","code":"calc_gsw_seasonality()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"function returns indicator tibble seasonality categories variables corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"pixel values aggregated using method provided via stats parameter. required resources indicator : global_surface_water_seasonality","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_seasonality.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Seasonality — gsw_seasonality","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_seasonality()) %>%   calc_indicators(calc_gsw_seasonality()) %>%   portfolio_long()  aoi #> Simple feature collection with 13 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 13 × 11 #>    WDPAID NAME    DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #>  1  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  2  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  3  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  4  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  5  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  6  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  7  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  8  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #>  9  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 10  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 11  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 12  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> 13  41057 Shell … Managed … GUY         1 gsw_seas… 2021-01-01 00:00:00 gsw_sea… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"function calculates total area global surface water time series data, separated following classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"","code":"calc_gsw_time_series()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"function returning tibble time series global surface water data classes.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"Observation: possible determine whether pixel water (may case frozen areas polar night extreme latitudes). Permanent Water: Water detected twelve months per year combination permanent observation. Seasonal Water: Water water detected. Water: Water detected. required resources indicator : gsw_time_series_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water Time Series — gsw_time_series_indicator","text":"","code":"# \\dontrun{ library(mapme.biodiversity) library(sf)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) aoi <- get_resources(aoi, get_gsw_time_series (years = 2000:2001)) aoi <- calc_indicators(aoi, calc_gsw_time_series()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 8 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 8 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 no_obse… #> 2  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 no_obse… #> 3  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 not_wat… #> 4  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 not_wat… #> 5  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 seasona… #> 6  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 seasona… #> 7  41057 Shell B… Managed … GUY         1 gsw_time… 2000-01-01 00:00:00 permane… #> 8  41057 Shell B… Managed … GUY         1 gsw_time… 2001-01-01 00:00:00 permane… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"function constructs  necessary data URLs given data set, version polygon downloads processing mapme.biodiversity package.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"","code":"get_gsw_time_series(years, version = \"LATEST\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"Raw Data: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GSWE/YearlyClassification/LATEST/tiles/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"years Numeric vector years process 1984 2021. Default: 1984:2021. version Version data set process. Available options (VER1-0, VER2-0, VER3-0, VER4-0, VER5-0 LATEST) Default: LATEST. Choosing LATEST result latest available version.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"function returns character vector file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"available surface water classes given pixel following: Observation: possible determine whether pixel water (may case frozen areas polar night extreme latitudes). Permanent Water: Water detected twelve months per year combination permanent observation. Seasonal Water: Water water detected. Water: Water detected.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_time_series_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper function to download Global Surface Water (GSW) yearly time series data — gsw_time_series_resource","text":"Global Surface Water Explorer: https://global-surface-water.appspot.com/ Data Users Guide: https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2021.pdf Research Article: https://www.nature.com/articles/nature20584","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"GSW transition data contains information type surface water change pixel. raster files integer cell values [0, 10] code different transition classes:","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"","code":"calc_gsw_transitions()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"function returns indicator tibble transition classes variable corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"aggregate, sum area transition class given region. required resources indicator : global_surface_water_transitions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/gsw_transitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Global Surface Water (GSW) Transitions — gsw_transitions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_global_surface_water_transitions()) %>%   calc_indicators(calc_gsw_transitions()) %>%   portfolio_long()  aoi #> Simple feature collection with 9 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 9 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_per… #> 2  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_new… #> 3  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_los… #> 4  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_sea… #> 5  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_new… #> 6  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_sea… #> 7  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_per… #> 8  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_eph… #> 9  41057 Shell B… Managed … GUY         1 gsw_tran… 2021-01-01 00:00:00 gsw_eph… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate human footprint statistics — humanfootprint_indicator","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"Human footprint data measures pressure imposed natural environment different dimensions human actions. theoretical maximum value, representing highest level human pressure, 50. routine allows extract zonal statistics human footprint data.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"","code":"calc_humanfootprint(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"function returns indicator tibble humanfootprint variable associated value (unitless) per year.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"required resources indicator : humanfootprint_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate human footprint statistics — humanfootprint_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_humanfootprint(years = 2010)) %>%   calc_indicators(calc_humanfootprint(stats = \"median\")) %>%   portfolio_long()  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 humanfoo… 2010-01-01 00:00:00 humanfo… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial Human Foootprint — humanfootprint_resource","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"resource part publication Mu et al. (2022) \"global record annual terrestrial Human Footprint dataset 2000 2018\". calculated based 8 variables representing human pressures natural ecosystems collected yearly cadence 2000 2020 sampled 1km spatial resolution. variables used expansion built environments (expressed percentage built-areas within grid cell), population density (aggregated gridd cell), nighttime lights, crop pasture lands, roads railways (excluding trails minor roads), navigable waterways (compares waterways nighttime lights dataset). human footprint calculated based weighting scheme proposed Venter et al. (2016), assigning pixel value 0 50, 50 representing theoretical value highest human pressure.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"","code":"get_humanfootprint(years = 2000:2020)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"https://figshare.com/articles/figure/An_annual_global_terrestrial_Human_Footprint_dataset_from_2000_to_2018/16571064","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"years numeric vector indicating years download human footprint data, defaults 2000:2020.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"may required increase timeout option successfully download theses layers source location via e.g. options(timeout = 600). case 403 error occurs, can create account Figshare create personal access token. set FIGSHARE_PAT environment variable, used authenticate.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/humanfootprint_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial Human Foootprint — humanfootprint_resource","text":"Mu, H., Li, X., Wen, Y. et al. global record annual terrestrial Human Footprint dataset 2000 2018. Sci Data 9, 176 (2022). doi:10.1038/s41597-022-01284-8","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":null,"dir":"Reference","previous_headings":"","what":"Register or list indicators in mapme.biodiversity — indicators","title":"Register or list indicators in mapme.biodiversity — indicators","text":"register_indicator() used register new indicator function base information package's internal environment used inform users available indicators. Note, registering custom indicator effect current R session. available_indicators() returns tibble registered indicators basic information required resources.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Register or list indicators in mapme.biodiversity — indicators","text":"","code":"register_indicator(name = NULL, description = NULL, resources = NULL)  available_indicators(indicators = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Register or list indicators in mapme.biodiversity — indicators","text":"name character vector indicating name indicator. description character vector basic description resources character vector required resources need available calculate indicator. names must correspond already registered resources. indicators NULL returns list registered indicators (default). Otherwise ones specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Register or list indicators in mapme.biodiversity — indicators","text":"register_indicator() called side-effect registering indicator available_resources() returns tibble listing available indicators.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/indicators.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Register or list indicators in mapme.biodiversity — indicators","text":"","code":"# \\dontrun{ register_indicator(   name = \"treecover_area\",   description = \"Area of forest cover by year\",   resources = c(     \"gfw_treecover\",     \"gfw_lossyear\"   ) ) # } available_indicators() #> # A tibble: 41 × 3 #>    name                          description                           resources #>                                                                  #>  1 biodiversity_intactness_index Averaged biodiversity intactness ind…   #>  2 biome                         Areal statistics of biomes from TEOW    #>  3 burned_area                   Monthly burned area detected by MODI…   #>  4 deforestation_drivers         Areal statistics of deforestation dr…   #>  5 drought_indicator             Relative wetness statistics based on…   #>  6 ecoregion                     Areal statstics of ecoregions based …   #>  7 elevation                     Statistics of elevation based on NAS…   #>  8 exposed_population_acled      Number of people exposed to conflict…   #>  9 exposed_population_ucdp       Number of people exposed to conflict…   #> 10 fatalities_acled              Number of fatalities by event type b…   #> # ℹ 31 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"indicator calculates areal distribution different biome classes within asset based IBPES biomes dataset.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"","code":"calc_ipbes_biomes()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"function returns indicator tibble biome class variable respective area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"required resources indicator : ipbes_biomes","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biome_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate areal statistics for IBPES Biomes — ipbes_biome_stats","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_ipbes_biomes()) %>%   calc_indicators(calc_ipbes_biomes()) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 ipbes_bi… 2019-01-01 00:00:00 tropica… #> 2  41057 Shell B… Managed … GUY         1 ipbes_bi… 2019-01-01 00:00:00 shelf_e… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial and Aquatic Biomes — ipbes_biomes","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"resource part Global Assessment Report Biodiversity Ecosystem Services represents division Earth's surface several subcategories. classification differentiates biomes anthromes. Biomes differentiated terrestrial aquatic biomes.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"","code":"get_ipbes_biomes()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"https://zenodo.org/records/3975694","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"Terrestrial biomes include: Tropical subtropical dry humid forests Temperate boreal forests woodlands Mediterranean forests, woodlands scrub Tundra High Mountain habitats Tropical subtropical savannas grasslands Temperate Grasslands Deserts xeric shrublands Wetlands – peatlands, mires, bogs Aquatic biomes include: Cryosphere Aquaculture areas Inland surface waters water bodies/freshwater Shelf ecosystems (neritic intertidal/littoral zone) Open ocean pelagic systems (euphotic zone)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ipbes_biomes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial and Aquatic Biomes — ipbes_biomes","text":"IPBES (2019): Summary policymakers global assessment report biodiversity ecosystem services Intergovernmental Science-Policy Platform Biodiversity Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio, H. T. Ngo, M. Guèze, J. Agard, . Arneth, P. Balvanera, K. . Brauman, S. H. M. Butchart, K. M. . Chan, L. . Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, . Pfaff, S. Polasky, . Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, . J. Visseren-Hamakers, K. J. Willis, C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":null,"dir":"Reference","previous_headings":"","what":"IUCN Red List of Threatened Species — iucn","title":"IUCN Red List of Threatened Species — iucn","text":"resource part spatial data set Red List Threatened Species released IUCN. free use non-commercial licence. commercial uses, request sent Integrated Biodiversity Assessment Tool (IBAT).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IUCN Red List of Threatened Species — iucn","text":"","code":"get_iucn(paths = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IUCN Red List of Threatened Species — iucn","text":"https://www.iucnredlist.org/resources/-spatial-downloads","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IUCN Red List of Threatened Species — iucn","text":"paths character vector respective species range files GTiff format. Note, theses files downloaded manually.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"IUCN Red List of Threatened Species — iucn","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"IUCN Red List of Threatened Species — iucn","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/iucn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"IUCN Red List of Threatened Species — iucn","text":"IUCN (2024). IUCN Red List Threatened Species. https://www.iucnredlist.org doi:10.1038/s41597-022-01284-8","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"function calculates total area key biodiversity areas given input polygon.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"","code":"calc_key_biodiversity_area()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"function returning indicator tibble key_biodiversity_area variable total overlap area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"required resources indicator : key_biodiversity_areas_resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_indicator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Key Biodiversity Areas — key_biodiversity_areas_indicator","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- read_sf(   system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",               package = \"mapme.biodiversity\" )) kbas <- system.file(\"res\", \"key_biodiversity_areas\", \"kbas.gpkg\",                     package = \"mapme.biodiversity\") aoi <- get_resources(aoi, get_key_biodiversity_areas(kbas)) aoi <- calc_indicators(aoi, calc_key_biodiversity_area()) aoi <- portfolio_long(aoi)  aoi #> Simple feature collection with 1 feature and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 key_biod… 2024-01-01 00:00:00 key_bio… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":null,"dir":"Reference","previous_headings":"","what":"Key Biodiversity Areas — key_biodiversity_areas_resource","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"resource contains outlines key biodiversity areas, areas representing sites specific importance nature conservation.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"","code":"get_key_biodiversity_areas(path = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"https://www.keybiodiversityareas.org/kba-data","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"path character vector key biodiversity areas GPKG file. Note, file downloaded manually.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"function returns sf footprints object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"use data mapme workflows, manually download global data set point towards file path local machine. Please find available data source link given .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/key_biodiversity_areas_resource.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Key Biodiversity Areas — key_biodiversity_areas_resource","text":"BirdLife International (2024). World Database Key Biodiversity Areas. Developed KBA Partnership: BirdLife International, International Union Conservation Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Re:wild, NatureServe, Rainforest Trust, Royal Society Protection Birds, Wildlife Conservation Society World Wildlife Fund. Available www.keybiodiversityareas.org.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate area of different landcover classes — landcover","title":"Calculate area of different landcover classes — landcover","text":"land cover data shows us much region covered forests, rivers, wetlands, barren land, urban infrastructure thus allowing observation land cover dynamics period time. function allows efficiently calculate area different landcover classes polygons. polygon, area classes hectare(ha) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate area of different landcover classes — landcover","text":"","code":"calc_landcover()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate area of different landcover classes — landcover","text":"function returns indicator tibble landcover classes variables corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate area of different landcover classes — landcover","text":"required resources indicator : esalandcover","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/landcover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate area of different landcover classes — landcover","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_esalandcover(years = 2016:2017)) %>%   calc_indicators(calc_landcover()) %>%   portfolio_long()  aoi #> Simple feature collection with 22 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 22 × 9 #>    WDPAID ISO3  assetid indicator datetime            variable      unit   value #>                                         #>  1 478140 DOM         1 landcover 2016-01-01 00:00:00 shrubs        ha    5.06e2 #>  2 478140 DOM         1 landcover 2016-01-01 00:00:00 herbaceous_v… ha    1.84e3 #>  3 478140 DOM         1 landcover 2016-01-01 00:00:00 cropland      ha    1.15e0 #>  4 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    4.65e3 #>  5 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    1.03e1 #>  6 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    4.98e3 #>  7 478140 DOM         1 landcover 2016-01-01 00:00:00 closed_fores… ha    1.46e2 #>  8 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    1.90e3 #>  9 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    8.85e1 #> 10 478140 DOM         1 landcover 2016-01-01 00:00:00 open_forest_… ha    1.49e1 #> # ℹ 12 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create footprints for vector or raster data sets — make_footprints","title":"Create footprints for vector or raster data sets — make_footprints","text":"function can create footprints vector raster datasets. Specify character vector GDAL readable sources either vector raster type. Internally, GDAL used create sf object single column indicating source geometry indicating bounding box respective source. Note, performance remote sources dependent connection server. means create footprints resource function (e.g. using output {rstac::items_bbox()}) prefer means function remote files.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create footprints for vector or raster data sets — make_footprints","text":"","code":"make_footprints(   srcs = NULL,   filenames = if (inherits(srcs, \"sf\")) basename(srcs[[\"source\"]]) else basename(srcs),   what = c(\"vector\", \"raster\"),   oo = NULL,   co = NULL,   precision = 1e+05 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create footprints for vector or raster data sets — make_footprints","text":"srcs character vector GDAL readable paths either vector raster sources, internal footprint functions called, sf object appended filenames potential options. filenames character vector indicating filenames source data sets written destionation. Defaults basename(srcs) case character type basename(srcs[[\"source\"]]) case sf object. character vector indicating files vector raster files. oo Either list character vector opening options (-oo) respective GDAL driver. list must equal length input sources, vector recycled. co Either list character vector creation options (-co) respective GDAL driver. list must equal length input sources, vector recycled. precision numeric indicating precision coordinates binary round-trip done (see ?sf::st_as_binary()).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create footprints for vector or raster data sets — make_footprints","text":"sf object files sources geometry indicating spatial footprint.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_footprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create footprints for vector or raster data sets — make_footprints","text":"","code":"# a vector resource # requires GDAL >= 3.7.0 if (FALSE) {   vec <- system.file(\"shape/nc.shp\", package = \"sf\")   make_footprints(vec, what = \"vector\") }  # a raster resource ras <- system.file(\"ex/elev.tif\", package = \"terra\") make_footprints(ras, what = \"raster\") #> Simple feature collection with 1 feature and 6 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 5.7417 ymin: 49.4417 xmax: 6.5333 ymax: 50.1917 #> Geodetic CRS:  WGS 84 #> # A tibble: 1 × 7 #>   filename location         type  oo     co     source                  geometry #>                                     #> 1 elev.tif /home/runner/wo… rast…   /home… ((5.7417 50.1917, 5.7417…"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper to create a grid of regular resolution and CRS — make_global_grid","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"Use function create regular grid custom CRS. used e.g. create tile grid Global Forest Watch order retrieve intersecting tiles given portfolio.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"","code":"make_global_grid(   xmin = -180,   xmax = 170,   dx = 10,   ymin = -50,   ymax = 80,   dy = 10,   proj = NULL )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"xmin minimum longitude value (E/W) xmax maximum longitude value (E/W) dx difference longitude value per grid ymin minimum latitude value (S/N) ymax maximum latitude value (E/W) dy difference latitude value per grid proj projection system","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/make_global_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper to create a grid of regular resolution and CRS — make_global_grid","text":"sf object defined grid.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"function allows efficiently calculate area mangrove Global Mangrove Watch - World Conservation Monitoring Centre (WCMC) polygons. polygon, area mangrove (hectare) desired year returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"","code":"calc_mangroves_area()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"function returns indicator tibble mangroves variable corresponding areas (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"required resources indicator : gmw","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mangroves_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mangrove extent based on Global Mangrove Watch (GMW) — mangroves_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"shell_beach_protected_area_41057_B.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_gmw(years = c(1996, 2016))) %>%   calc_indicators(calc_mangroves_area()) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 10 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -59.84866 ymin: 8.307999 xmax: -59.71 ymax: 8.364002 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 11 #>   WDPAID NAME     DESIG_ENG ISO3  assetid indicator datetime            variable #>                                         #> 1  41057 Shell B… Managed … GUY         1 mangrove… 1996-01-01 00:00:00 mangrov… #> 2  41057 Shell B… Managed … GUY         1 mangrove… 2016-01-01 00:00:00 mangrov… #> # ℹ 3 more variables: unit , value , geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":null,"dir":"Reference","previous_headings":"","what":"Portfolio methods for mapme.biodiversity — mapme","title":"Portfolio methods for mapme.biodiversity — mapme","text":"mapme_options() sets default options mapme.biodiversity control behavior downstream functions. Mainly, output path well chunk size (ha), can set. Additionally, verbosity can set path log directory can controlled. Might extended options future. get_resources() data sets required calculation indicators can made available. function supports specification several resource functions. determine output path, temporary directory verbosity, output mapme_options() used. calc_indicators() calculates specific biodiversity indicators. requirement resources mandatory inputs requested indicators available locally. Multiple indicators respective additional arguments can supplied. function reads crops available resources extent single asset. Specific resources can queried. supplied (default), available resources prepared.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Portfolio methods for mapme.biodiversity — mapme","text":"","code":"mapme_options(..., outdir, chunk_size, retries, verbose, log_dir)  get_resources(x, ...)  calc_indicators(x, ...)  prep_resources(   x,   avail_resources = NULL,   resources = NULL,   mode = c(\"portfolio\", \"asset\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Portfolio methods for mapme.biodiversity — mapme","text":"... One functions resources/indicators outdir length one character indicating output path. chunk_size numeric length one giving maximum chunk area ha. Defaults 100,000 ha. refers area asset's bounding box. lies value chunk_size, splitting chunking considered. asset processes -bounding box area specified value. retries numeric length one indicating number re-tries package attempt make resource available. Defaults 3. verbose logical, indicating informative messages printed. log_dir character path pointing toward GDAL-writable destination used log erroneous assets. Defaults NULL, meaning erroneous assets serialized disk. specified, GPKG named file.path(log_dir, paste0(Sys.Date(), \"_mapme-error-assets.gpkg\")) created appended case erroneous assets. x sf object features type \"POLYGON\" avail_resources list object available resources. NULL (default), available resources automatically determined. resources character vector resources prepared. NULL (default) available resources prepared. mode character indicating reading mode, e.g. either \"portfolio\" (default) \"asset\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Portfolio methods for mapme.biodiversity — mapme","text":"mapme_options() returns list options arguments specified. Otherwise sets matching arguments new values package's internal environment. get_resources() called side effect making resources available package environment. Returns x, invisibly. calc_indicators() returns x, invisibly, additional nested list column per requested indicator. prep_resources() returns list prepared vector raster resources sf SpatRaster-objects.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mapme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Portfolio methods for mapme.biodiversity — mapme","text":"","code":"library(mapme.biodiversity) mapme_options() #> $outdir #> [1] \"/tmp/RtmpjQFEKr/mapme-data\" #>  #> $chunk_size #> [1] 1e+08 #>  #> $retries #> [1] 3 #>  #> $verbose #> [1] FALSE #>  #> $log_dir #> NULL #>"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":null,"dir":"Reference","previous_headings":"","what":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"Terra Aqua combined MCD64A1 Version 6.1 Burned Area data product monthly, global gridded 500 meter (m) product containing per-pixel burned-area quality information. MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled 1 kilometer (km) MODIS active fire observations.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"","code":"get_mcd64a1(years = 2000:2022)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"https://planetarycomputer.microsoft.com/dataset/modis-64A1-061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"years Numeric vector years make MCD64A1 product available . Must greater year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"algorithm uses burn sensitive Vegetation Index (VI) create dynamic thresholds applied composite data. VI derived MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 7 measure temporal texture. algorithm identifies date burn 500 m grid cells within individual MODIS tile. date encoded single data layer ordinal day calendar year burn occurred values assigned unburned land pixels additional special values reserved missing data water grid cells.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/mcd64a1.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MODIS Burned Area Monthly (MCD64A1) — mcd64a1","text":"Giglio, L., C. Justice, L. Boschetti, D. Roy. MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061. 2021, distributed NASA EOSDIS Land Processes Distributed Active Archive Center. doi:10.5067/MODIS/MCD64A1.061","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":null,"dir":"Reference","previous_headings":"","what":"NASA GRACE-based Drought Indicator layer — nasa_grace","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"resource published NASA GRACE Tellus. data set reflects potential drought conditions shallow groundwater section relative reference period spanning 1948 2012. available global raster weekly temporal resolution starting year 2003. value indicates wetness percentile given pixel regard reference period.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"","code":"get_nasa_grace(years = 2003:2022)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_grace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NASA GRACE-based Drought Indicator layer — nasa_grace","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":null,"dir":"Reference","previous_headings":"","what":"NASADEM HGT v001 — nasa_srtm","title":"NASADEM HGT v001 — nasa_srtm","text":"resource processed Land Processes Distributed Active Archive Center (LP DAAC) made available Microsoft Planetery Computer. NASADEM distributed 1 degree latitude 1 degree longitude tiles consist land 60° N 56° S latitude. accounts 80% Earth’s total landmass.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NASADEM HGT v001 — nasa_srtm","text":"","code":"get_nasa_srtm()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NASADEM HGT v001 — nasa_srtm","text":"https://planetarycomputer.microsoft.com/dataset/nasadem","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NASADEM HGT v001 — nasa_srtm","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nasa_srtm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NASADEM HGT v001 — nasa_srtm","text":"NASA JPL (2020). NASADEM Merged DEM Global 1 arc second V001. NASA EOSDIS Land Processes DAAC. Accessed 2023-07-01 doi:10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":null,"dir":"Reference","previous_headings":"","what":"Accessibility to Cities layer — nelson_et_al","title":"Accessibility to Cities layer — nelson_et_al","text":"resource published Weiss et al. (2018) \"global map travel time cities assess inequalities accessibility 2015\" journal nature. Accessibility ease larger cities can reached certain location. resource represents travel time major cities year 2015. Encoded minutes, representing time needed reach particular cell nearby city target population range. following ranges nearby cities available: \"5k_10k\" \"10k_20k\" \"20k_50k\" \"50k_100k\" \"100k_200k\" \"200k_500k\" \"500k_1mio\" \"1mio_5mio\" \"50k_50mio\" \"5k_110mio\" \"20k_110mio\" \"5mio_50mio\"","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Accessibility to Cities layer — nelson_et_al","text":"","code":"get_nelson_et_al(ranges = \"20k_50k\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Accessibility to Cities layer — nelson_et_al","text":"https://figshare.com/articles/dataset/Travel_time_to_cities_and_ports_in_the_year_2015/7638134/3","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Accessibility to Cities layer — nelson_et_al","text":"ranges character vector indicating one ranges download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Accessibility to Cities layer — nelson_et_al","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Accessibility to Cities layer — nelson_et_al","text":"Note, figshare server applies rather restrictive rate limit thus frequently resulting opaque error codes (see https://github.com/mapme-initiative/mapme.biodiversity/issues/308). Please set GDAL configuration options sensible values case running issue, e.g.: Sys.setenv(\"GDAL_HTTP_MAX_RETRY\" = \"5\", \"GDAL_HTTP_RETRY_DELAY\" = \"15\").","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/nelson_et_al.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Accessibility to Cities layer — nelson_et_al","text":"Weiss, D. J., Nelson, ., Gibson, H. S., Temperley, W., Peedell, S., Lieber, ., … & Gething, P. W. (2018). global map travel time cities assess inequalities accessibility 2015. Nature, 553(7688), 333-336.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate population count statistics — population_count","title":"Calculate population count statistics — population_count","text":"WorldPop, initiated 2013, offers easy access spatial demographic datasets, claiming use peer-reviewed fully transparent methods create global mosaics years 2000 2020. function allows efficiently calculate population count statistics (e.g. total number population) polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate population count statistics — population_count","text":"","code":"calc_population_count(engine = \"extract\", stats = \"sum\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate population count statistics — population_count","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character \"min\", \"max\", \"sum\", \"mean\", \"median\" \"sd\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate population count statistics — population_count","text":"function returns indicator tibble specified populations statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate population count statistics — population_count","text":"required resources indicator : worldpop","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/population_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate population count statistics — population_count","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldpop(years = 2010:2020)) %>%   calc_indicators(     calc_population_count(engine = \"extract\", stats = c(\"sum\", \"median\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 22 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 22 × 9 #>    WDPAID ISO3  assetid indicator      datetime            variable unit   value #>                                         #>  1 478140 DOM         1 population_co… 2010-01-01 00:00:00 populat… count 4016.  #>  2 478140 DOM         1 population_co… 2010-01-01 00:00:00 populat… count   15.5 #>  3 478140 DOM         1 population_co… 2011-01-01 00:00:00 populat… count 3991.  #>  4 478140 DOM         1 population_co… 2011-01-01 00:00:00 populat… count   13.8 #>  5 478140 DOM         1 population_co… 2012-01-01 00:00:00 populat… count 4068.  #>  6 478140 DOM         1 population_co… 2012-01-01 00:00:00 populat… count   15.8 #>  7 478140 DOM         1 population_co… 2013-01-01 00:00:00 populat… count 3958.  #>  8 478140 DOM         1 population_co… 2013-01-01 00:00:00 populat… count   15.2 #>  9 478140 DOM         1 population_co… 2014-01-01 00:00:00 populat… count 3981.  #> 10 478140 DOM         1 population_co… 2014-01-01 00:00:00 populat… count   15.3 #> # ℹ 12 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":null,"dir":"Reference","previous_headings":"","what":"Portfolio methods — portfolio","title":"Portfolio methods — portfolio","text":"write_portfolio() writes processed biodiversity portfolio disk. Portfolio data serialized disk GeoPackage including two tables: metadata indicators. metadata tables includes, among simple variables geometries primary key called assetid. 'indicators' tables includes foreign key assetid, column called indicator giving name original indicator well standard indicator columns datetime, variable, unit, value. convenience, use read_portfolio() read portfolio GeoPackage back R. portfolio_long() transforms portfolio long-format, potentially dropping geometries process. portfolio_wide() transforms portfolio wide-format, potentially dropping geometries process.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Portfolio methods — portfolio","text":"","code":"write_portfolio(x, dsn, ...)  read_portfolio(src, ...)  portfolio_long(x, indicators = NULL, drop_geoms = FALSE)  portfolio_wide(x, indicators = NULL, drop_geoms = FALSE)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Portfolio methods — portfolio","text":"x portfolio object processed mapme.biodiversity. dsn file path output file (must end gpkg). ... Additional arguments supplied write_sf() read_sf() src character vector pointing GeoPackage previously written disk via write_portfolio() indicators NULL (default), indicator columns detected transformed automatically. character vector supplied, indicators transformed. drop_geoms logical, indicating geometries dropped.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/portfolio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Portfolio methods — portfolio","text":"write_portfolio() returns dsn, invisibly. read_portfolio() returns sf object object nested list columns every indicator found GeoPackage source file. portfolio_long() returns portfolio object long-format. portfolio_wide() returns portfolio object wide-format.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation average based on CHELSA — precipitation_chelsa","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"functions allows calculate averaged precipitation CHELSA downscaled precipitation layers. Based user-selected years, monthly averages precipitation calculated.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"","code":"calc_precipitation_chelsa(years = 1979:2018, engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"years numeric vector indicating years calculate precipitation statistics. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"function returns indicator tibble variable precipitation sum precipitation (mm/m^2) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"required resources indicator : chelsa","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chelsa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation average based on CHELSA — precipitation_chelsa","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_chelsa(years = 2010)) %>%   calc_indicators(     calc_precipitation_chelsa(       years = 2010,       engine = \"extract\"     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2010-01-01 00:00:00 precipi… mm/m…  52.5 #>  2 478140 DOM         1 precipitation_… 2010-02-01 00:00:00 precipi… mm/m…  10.4 #>  3 478140 DOM         1 precipitation_… 2010-03-01 00:00:00 precipi… mm/m…  32.7 #>  4 478140 DOM         1 precipitation_… 2010-04-01 00:00:00 precipi… mm/m… 104.  #>  5 478140 DOM         1 precipitation_… 2010-05-01 00:00:00 precipi… mm/m… 218.  #>  6 478140 DOM         1 precipitation_… 2010-06-01 00:00:00 precipi… mm/m… 142.  #>  7 478140 DOM         1 precipitation_… 2010-07-01 00:00:00 precipi… mm/m… 191.  #>  8 478140 DOM         1 precipitation_… 2010-08-01 00:00:00 precipi… mm/m… 153.  #>  9 478140 DOM         1 precipitation_… 2010-09-01 00:00:00 precipi… mm/m… 161.  #> 10 478140 DOM         1 precipitation_… 2010-10-01 00:00:00 precipi… mm/m… 116.  #> 11 478140 DOM         1 precipitation_… 2010-11-01 00:00:00 precipi… mm/m… 209.  #> 12 478140 DOM         1 precipitation_… 2010-12-01 00:00:00 precipi… mm/m…  39.5 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"functions allows calculate precipitation sums based CHIRPS rainfall estimates. Corresponding time-frame analysis portfolio, monthly precipitation sums calculated.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"","code":"calc_precipitation_chirps(years = 1981:2020, engine = \"extract\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"years numeric vector indicating years calculate precipitation statistics. engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"function returns indicator tibble variable precipitation sum precipitation (mm) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"required resources indicator : chirps","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_chirps.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation sums based on CHIRPS — precipitation_chirps","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_chirps(years = 2010)) %>%   calc_indicators(     calc_precipitation_chirps(       years = 2010,       engine = \"extract\"     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 12 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 12 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2010-01-01 00:00:00 precipi… mm      102 #>  2 478140 DOM         1 precipitation_… 2010-02-01 00:00:00 precipi… mm      129 #>  3 478140 DOM         1 precipitation_… 2010-03-01 00:00:00 precipi… mm      199 #>  4 478140 DOM         1 precipitation_… 2010-04-01 00:00:00 precipi… mm      827 #>  5 478140 DOM         1 precipitation_… 2010-05-01 00:00:00 precipi… mm     1067 #>  6 478140 DOM         1 precipitation_… 2010-06-01 00:00:00 precipi… mm     1220 #>  7 478140 DOM         1 precipitation_… 2010-07-01 00:00:00 precipi… mm      878 #>  8 478140 DOM         1 precipitation_… 2010-08-01 00:00:00 precipi… mm      588 #>  9 478140 DOM         1 precipitation_… 2010-09-01 00:00:00 precipi… mm      582 #> 10 478140 DOM         1 precipitation_… 2010-10-01 00:00:00 precipi… mm      560 #> 11 478140 DOM         1 precipitation_… 2010-11-01 00:00:00 precipi… mm      683 #> 12 478140 DOM         1 precipitation_… 2010-12-01 00:00:00 precipi… mm       59 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate precipitation statistics — precipitation_wc","title":"Calculate precipitation statistics — precipitation_wc","text":"function allows efficiently calculate precipitation statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate precipitation statistics — precipitation_wc","text":"","code":"calc_precipitation_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate precipitation statistics — precipitation_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate precipitation statistics — precipitation_wc","text":"function returns indicator tibble precipition statistics variable corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate precipitation statistics — precipitation_wc","text":"required resources indicator : precipitation layer worldclim_precipitation","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/precipitation_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate precipitation statistics — precipitation_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_precipitation(years = 2018)) %>%   calc_indicators(     calc_precipitation_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 precipitation_… 2018-01-01 00:00:00 worldcl… mm     26.7 #>  2 478140 DOM         1 precipitation_… 2018-01-01 00:00:00 worldcl… mm     26.7 #>  3 478140 DOM         1 precipitation_… 2018-02-01 00:00:00 worldcl… mm     26.1 #>  4 478140 DOM         1 precipitation_… 2018-02-01 00:00:00 worldcl… mm     26.5 #>  5 478140 DOM         1 precipitation_… 2018-03-01 00:00:00 worldcl… mm     66.7 #>  6 478140 DOM         1 precipitation_… 2018-03-01 00:00:00 worldcl… mm     68.6 #>  7 478140 DOM         1 precipitation_… 2018-04-01 00:00:00 worldcl… mm     82.0 #>  8 478140 DOM         1 precipitation_… 2018-04-01 00:00:00 worldcl… mm     82.1 #>  9 478140 DOM         1 precipitation_… 2018-05-01 00:00:00 worldcl… mm    330.  #> 10 478140 DOM         1 precipitation_… 2018-05-01 00:00:00 worldcl… mm    338.  #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":null,"dir":"Reference","previous_headings":"","what":"Register or list resources in mapme.biodiversity — resources","title":"Register or list resources in mapme.biodiversity — resources","text":"register_resource() used register new resource function base information package's internal environment used inform users available resources. Note, registering custom resource effect current R session. available_resources() returns tibble registered resources basic information source licence.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Register or list resources in mapme.biodiversity — resources","text":"","code":"register_resource(   name = NULL,   description = NULL,   licence = NULL,   source = NULL,   type = NULL )  available_resources(resources = NULL)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Register or list resources in mapme.biodiversity — resources","text":"name character vector indicating name resource. description character vector basic description licence character vector indicating licence resource. case custom licence, put link licence text. source Optional, preferably URL data found. type character vector indicating type resource. Either 'vector' 'raster'. resources NULL returns list resources (default). Otherwise ones specified.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Register or list resources in mapme.biodiversity — resources","text":"register_resource() called side-effect registering resource. available_resources() returns tibble listing available resources.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/resources.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Register or list resources in mapme.biodiversity — resources","text":"","code":"# \\dontrun{ register_resource(   name = \"gfw_treecover\",   description = \"Global Forest Watch - Percentage of canopy closure in 2000\",   licence = \"CC-BY 4.0\",   source = \"https://data.globalforestwatch.org/documents/tree-cover-2000/explore\",   type = \"raster\" ) # } available_resources() #> # A tibble: 36 × 5 #>    name                          description                licence source type  #>                                                         #>  1 accessibility_2000            Accessibility data for th… See JR… https… rast… #>  2 acled                         Armed Conflict Location &… Visit … Visit… vect… #>  3 biodiversity_intactness_index Biodiversity Intactness I… CC-BY-… https… rast… #>  4 chelsa                        Climatologies at High res… Unknow… https… rast… #>  5 chirps                        Climate Hazards Group Inf… CC - u… https… rast… #>  6 esalandcover                  Copernicus Land Monitorin… CC-BY … https… rast… #>  7 fritz_et_al                   Drivers of deforestation … CC-BY … https… rast… #>  8 gfw_emissions                 Global Forest Watch - CO2… CC-BY … https… rast… #>  9 gfw_lossyear                  Global Forest Watch - Yea… CC-BY … https… rast… #> 10 gfw_treecover                 Global Forest Watch - Per… CC-BY … https… rast… #> # ℹ 26 more rows"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate slope statistics — slope","title":"Calculate slope statistics — slope","text":"function allows calculate slope statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate slope statistics — slope","text":"","code":"calc_slope(engine = \"exactextract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate slope statistics — slope","text":"engine preferred processing function either one \"zonal\", \"extract\" \"exactextract\" character string. stats Function applied compute statistics polygons. Accepts either single string vector strings, \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate slope statistics — slope","text":"function returns indicator tibble specified slope statistics variables corresponding values (degrees).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate slope statistics — slope","text":"required resource indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/slope.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate slope statistics — slope","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_slope(stats = c(\"mean\", \"median\", \"sd\", \"var\"), engine = \"extract\")   ) %>%   portfolio_long() #> Resource 'nasa_srtm' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable     unit    value #>                                        #> 1 478140 DOM         1 slope     2000-02-01 00:00:00 slope_mean   degrees 17.8  #> 2 478140 DOM         1 slope     2000-02-01 00:00:00 slope_median degrees 17.0  #> 3 478140 DOM         1 slope     2000-02-01 00:00:00 slope_sd     degrees  9.93 #> 4 478140 DOM         1 slope     2000-02-01 00:00:00 slope_var    degrees 98.6  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":null,"dir":"Reference","previous_headings":"","what":"SoilGrids data layers — soilgrids","title":"SoilGrids data layers — soilgrids","text":"SoilGrids project combining global observation data machine learning map spatial distribution soil properties across globe. produced spatial resolution 250 meters parameters mapped different depths. order able assess prediction uncertainty, besides mean median prediction, 0.05 0.95 percentile predictions available. following parameters available: bdod Bulk density fine earth fraction (kg/dm3) cec Cation Exchange Capacity soil (cmol(c)/kg) cfvo Volumetric fraction coarse fragments > 2 mm (cm3/100cm3 (volPerc)) clay Proportion clay particles < 0.002 mm fine earth fraction (g/100g) nitrogen Total nitrogen (g/kg) phh2o Soil pH (pH) sand Proportion sand particles > 0.05 mm fine earth fraction (g/100g) silt Proportion silt particles >= 0.002 mm <= 0.05 mm fine earth fraction (g/100g) soc Soil organic carbon content fine earth fraction (g/kg) ocd Organic carbon density (kg/m3) ocs Organic carbon stocks (kg/m²)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SoilGrids data layers — soilgrids","text":"","code":"get_soilgrids(layers, depths, stats)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SoilGrids data layers — soilgrids","text":"https://www.isric.org/explore/soilgrids","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SoilGrids data layers — soilgrids","text":"layers character vector indicating layers download soilgrids depths character vector indicating depths download stats character vector indicating statistics download.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SoilGrids data layers — soilgrids","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SoilGrids data layers — soilgrids","text":"Except ocs, available depth \"0-30cm\", parameters available following depths: \"0-5cm\" \"5-15cm\" \"15-30cm\" \"30-60cm\" \"60-100cm\" \"100-200cm\" parameter depth available following statistics: \"Q0.05\" \"Q0.50\" \"mean\" \"Q0.95\"","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilgrids.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SoilGrids data layers — soilgrids","text":"Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2017) SoilGrids250m: Global gridded soil information based machine learning. PLOS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Zonal Soil Properties — soilproperties","title":"Calculate Zonal Soil Properties — soilproperties","text":"indicator allows extraction zonal statistics resource layers previously downloaded SoilGrids, thus total supporting calculation zonal statistics 10 different soil properties 6 different depths total 4 different model outputs (stat). Zonal statistics calculated SoilGrid layers previously made available vie get_resources().","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Zonal Soil Properties — soilproperties","text":"","code":"calc_soilproperties(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Zonal Soil Properties — soilproperties","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Zonal Soil Properties — soilproperties","text":"function returns indicator tibble soilgrid layers statistics variables corresponding statistics value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Zonal Soil Properties — soilproperties","text":"required resource indicator : soilgrids","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/soilproperties.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Zonal Soil Properties — soilproperties","text":"","code":"if (FALSE) {   library(sf)   library(mapme.biodiversity)    mapme_options(     outdir = NULL,     verbose = FALSE   )    aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",     package = \"mapme.biodiversity\"   ) %>%     read_sf() %>%     get_resources(       get_soilgrids(         layers = \"clay\",         depths = \"0-5cm\",         stats = \"mean\"       )     ) %>%     calc_indicators(       calc_soilproperties(engine = \"extract\", stats = c(\"mean\", \"median\"))     ) %>%     portfolio_long()    aoi }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if a spatial data sets exists — spds_exists","title":"Check if a spatial data sets exists — spds_exists","text":"function uses file path readable GDAL check can query information. Note, also work remote files, e.g. S3 bucket. can use function custom resource function query file already present destination. Note, performance dependent connection server. can also used files local file system.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if a spatial data sets exists — spds_exists","text":"","code":"spds_exists(path, oo = character(0), what = c(\"vector\", \"raster\"))"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if a spatial data sets exists — spds_exists","text":"path length 1 character vector GDAL readable file path. oo Either list character vector opening options (-oo) respective GDAL driver. list must equal length input sources, vector recycled. character vector indicating resource vector raster file.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if a spatial data sets exists — spds_exists","text":"logical, TRUE file exists, FALSE .","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/spds_exists.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if a spatial data sets exists — spds_exists","text":"","code":"# a vector resource vec <- system.file(\"shape/nc.shp\", package = \"sf\") spds_exists(vec, what = \"vector\") #> [1] TRUE  # a raster resource ras <- system.file(\"ex/elev.tif\", package = \"terra\") spds_exists(ras, what = \"raster\") #> [1] TRUE  # a non existing file spds_exists(\"not-here.gpkg\", what = \"vector\") #> [1] FALSE"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Species richness based on IUCN raster data — species_richness","title":"Species richness based on IUCN raster data — species_richness","text":"Species richness counts number potential species intersecting polygon grouped IUCN threat categorization. Note, indicator function requires manual download respective raster files.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Species richness based on IUCN raster data — species_richness","text":"","code":"calc_species_richness(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Species richness based on IUCN raster data — species_richness","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either one multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Species richness based on IUCN raster data — species_richness","text":"function returns indicator tibble IUCN layers specified statistics variable respective species richness (count) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Species richness based on IUCN raster data — species_richness","text":"specific meaning species richness indicator depends supplied raster file. required resources indicator : iucn","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/species_richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Species richness based on IUCN raster data — species_richness","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  iucn_dir <- system.file(\"res\", \"iucn\", package = \"mapme.biodiversity\") sr_rasters <- list.files(iucn_dir, pattern = \"*_SR_*\", full.names = TRUE)  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_iucn(sr_rasters)) %>%   calc_indicators(calc_species_richness(stats = \"median\")) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 species_richness 2023-01-01 00:00:00 amphibi… count    15 #> 2 478140 DOM         1 species_richness 2023-01-01 00:00:00 birds_t… count    27 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate maximum temperature statistics — temperature_max_wc","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"function allows efficiently calculate maximum temperature statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"","code":"calc_temperature_max_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"function returns indicator tibble maximum temperature statistics variables corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"required resources indicator : maximum temperature layer worldclim_max_temperature","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_max_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate maximum temperature statistics — temperature_max_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_max_temperature(years = 2018)) %>%   calc_indicators(     calc_temperature_max_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 temperature_ma… 2018-01-01 00:00:00 worldcl… C      20.8 #>  2 478140 DOM         1 temperature_ma… 2018-01-01 00:00:00 worldcl… C      20.5 #>  3 478140 DOM         1 temperature_ma… 2018-02-01 00:00:00 worldcl… C      20.5 #>  4 478140 DOM         1 temperature_ma… 2018-02-01 00:00:00 worldcl… C      20   #>  5 478140 DOM         1 temperature_ma… 2018-03-01 00:00:00 worldcl… C      22.1 #>  6 478140 DOM         1 temperature_ma… 2018-03-01 00:00:00 worldcl… C      22   #>  7 478140 DOM         1 temperature_ma… 2018-04-01 00:00:00 worldcl… C      22.6 #>  8 478140 DOM         1 temperature_ma… 2018-04-01 00:00:00 worldcl… C      22.5 #>  9 478140 DOM         1 temperature_ma… 2018-05-01 00:00:00 worldcl… C      21.5 #> 10 478140 DOM         1 temperature_ma… 2018-05-01 00:00:00 worldcl… C      21   #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"function allows efficiently calculate minimum temperature statistics Worldclim polygons. polygon, desired statistic/s (min, max, sum, mean, median, sd var) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"","code":"calc_temperature_min_wc(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"function returns indicator tibble minimum temperature statistics variables corresponding values value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"required resources indicator : minimum temperature layer worldclim_min_temperature","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/temperature_min_wc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate minimum temperature statistics based on WorldClim — temperature_min_wc","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_worldclim_min_temperature(years = 2018)) %>%   calc_indicators(     calc_temperature_min_wc(       engine = \"extract\",       stats = c(\"mean\", \"median\")     )   ) %>%   portfolio_long()  aoi #> Simple feature collection with 24 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 24 × 9 #>    WDPAID ISO3  assetid indicator       datetime            variable unit  value #>                                         #>  1 478140 DOM         1 temperature_mi… 2018-01-01 00:00:00 worldcl… C      10.1 #>  2 478140 DOM         1 temperature_mi… 2018-01-01 00:00:00 worldcl… C      10   #>  3 478140 DOM         1 temperature_mi… 2018-02-01 00:00:00 worldcl… C      10.2 #>  4 478140 DOM         1 temperature_mi… 2018-02-01 00:00:00 worldcl… C      10   #>  5 478140 DOM         1 temperature_mi… 2018-03-01 00:00:00 worldcl… C      10.1 #>  6 478140 DOM         1 temperature_mi… 2018-03-01 00:00:00 worldcl… C      10   #>  7 478140 DOM         1 temperature_mi… 2018-04-01 00:00:00 worldcl… C      11.1 #>  8 478140 DOM         1 temperature_mi… 2018-04-01 00:00:00 worldcl… C      11   #>  9 478140 DOM         1 temperature_mi… 2018-05-01 00:00:00 worldcl… C      12.6 #> 10 478140 DOM         1 temperature_mi… 2018-05-01 00:00:00 worldcl… C      13   #> # ℹ 14 more rows #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":null,"dir":"Reference","previous_headings":"","what":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"resource part publication Olson et al. (2004) \"Terrestrial Ecosystems World (TEOW) WWF-US (Olson)\". depicts 867 terrestrial ecoregions around world classified 14 different terrestrial biomes forests, grasslands, deserts. polygons represent ecoregions, defined relatively large units land inland water sharing large majority biodiversity. datasets made available World Wildlife Fund (WWF) year 2001.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"","code":"get_teow()"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/teow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Terrestrial Ecoregions of the World (TEOW) Polygon — teow","text":"Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. ., Itoua, ., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions world: new map life Earth. Bioscience 51(11):933-938. doi:10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate accessibility statistics — traveltime","title":"Calculate accessibility statistics — traveltime","text":"Accessibility ease larger cities can reached certain location. function allows efficiently calculate accessibility statistics (.e. travel time nearby major cities) polygons. polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate accessibility statistics — traveltime","text":"","code":"calc_traveltime(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate accessibility statistics — traveltime","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate accessibility statistics — traveltime","text":"function returns indicator tibble city ranges statisics variable corresponding values (minutes) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate accessibility statistics — traveltime","text":"required resources indicator : nelson_et_al","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate accessibility statistics — traveltime","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nelson_et_al(ranges = \"100k_200k\")) %>%   calc_indicators(     calc_traveltime(engine = \"extract\", stats = c(\"min\", \"max\"))   ) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator  datetime            variable       unit  value #>                                         #> 1 478140 DOM         1 traveltime 2015-01-01 00:00:00 100k_200k_tra… minu…   162 #> 2 478140 DOM         1 traveltime 2015-01-01 00:00:00 100k_200k_tra… minu…   528 #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate accessibility statistics for the year 2000 — traveltime_2000","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"Accessibility refers ease cities can reached certain location. function allows efficient calculation accessibility statistics (.e., travel time nearest city) polygons.  polygon, desired statistic/s (mean, median sd) /returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"","code":"calc_traveltime_2000(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\", \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"function returns indicator tibble accessibility statistics year 2000 variables corresponding values (minutes) values.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"required resource indicator : accessibility_2000","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/traveltime_2000.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate accessibility statistics for the year 2000 — traveltime_2000","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_accessibility_2000()) %>%   calc_indicators(     calc_traveltime_2000(stats = c(\"mean\", \"median\", \"sd\"), engine = \"extract\")   ) %>%   portfolio_long()  aoi #> Simple feature collection with 3 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 3 × 9 #>   WDPAID ISO3  assetid indicator       datetime            variable  unit  value #>                                         #> 1 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  387. #> 2 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  420  #> 3 478140 DOM         1 traveltime_2000 2000-01-01 00:00:00 travelti… minu…  204. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate treecover statistics — treecover_area","title":"Calculate treecover statistics — treecover_area","text":"functions allows efficiently calculate treecover statistics polygons. year analysis timeframe, forest losses preceding current years subtracted treecover year 2000 actual treecover figures within polygon returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate treecover statistics — treecover_area","text":"","code":"calc_treecover_area(years = 2000:2023, min_size = 10, min_cover = 35)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate treecover statistics — treecover_area","text":"years numeric vector years calculate treecover area. min_size minimum size forest patch considered forest ha. min_cover minimum cover percentage per pixel considered forest.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate treecover statistics — treecover_area","text":"function returns indicator tibble variable treecover corresponding area (ha) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate treecover statistics — treecover_area","text":"required resources indicator : gfw_treecover gfw_lossyear","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate treecover statistics — treecover_area","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\")   ) %>%   calc_indicators(calc_treecover_area(years = 2016:2017, min_size = 1, min_cover = 30)) %>%   portfolio_long()  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator      datetime            variable  unit  value #>                                        #> 1 478140 DOM         1 treecover_area 2016-01-01 00:00:00 treecover ha    2370. #> 2 478140 DOM         1 treecover_area 2017-01-01 00:00:00 treecover ha    2358. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate treeloss statistics — treecover_area_and_emissions","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"functions allows efficiently calculate treecover emissions indicators single function call together. Since pre-processing operations treecover emissions , efficient calculate one run users actually interested statistics. Otherwise users advised use respective single indicator functions.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"","code":"calc_treecover_area_and_emissions(   years = 2000:2023,   min_size = 10,   min_cover = 35 )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"years numeric vector years calculate treecover area emissions. min_size minimum size forest patch ha. min_cover minimum threshold stand density pixel considered forest year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"function returns indicator tibble variables treecover emissions ind corresponding values (ha Mg) value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"required resources indicator : gfw_treecover gfw_lossyear gfw_emissions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecover_area_and_emissions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate treeloss statistics — treecover_area_and_emissions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(     calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)   ) %>%   portfolio_long() #> Resource 'gfw_treecover' is already available. #> Resource 'gfw_lossyear' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 emissio… Mg    4296. #> 2 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 treecov… ha    2370. #> 3 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 emissio… Mg    4970. #> 4 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 treecov… ha    2358. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate emission statistics — treecoverloss_emissions","title":"Calculate emission statistics — treecoverloss_emissions","text":"functions allows efficiently calculate emission statistics areas interest. year analysis timeframe, forest losses Hansen et al. (2013) overlayed respective emission layer Harris et al. (2021) area-wise emission statistics calculated year.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate emission statistics — treecoverloss_emissions","text":"","code":"calc_treecoverloss_emissions(years = 2000:2023, min_size = 10, min_cover = 35)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate emission statistics — treecoverloss_emissions","text":"years numeric vector years calculate emissions caused treecover loss. min_size minimum size forest patch ha. min_cover minimum threshold stand density pixel considered forest year 2000.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate emission statistics — treecoverloss_emissions","text":"function returns indicator tibble emissions variable emitted CO2 equivalent (Mg)  value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate emission statistics — treecoverloss_emissions","text":"required resources indicator : gfw_treecover gfw_lossyear gfw_emissions","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/treecoverloss_emissions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate emission statistics — treecoverloss_emissions","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(     get_gfw_treecover(version = \"GFC-2023-v1.11\"),     get_gfw_lossyear(version = \"GFC-2023-v1.11\"),     get_gfw_emissions()   ) %>%   calc_indicators(     calc_treecoverloss_emissions(years = 2016:2017, min_size = 1, min_cover = 30)   ) %>%   portfolio_long() #> Resource 'gfw_treecover' is already available. #> Resource 'gfw_lossyear' is already available. #> Resource 'gfw_emissions' is already available.  aoi #> Simple feature collection with 2 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 2 × 9 #>   WDPAID ISO3  assetid indicator        datetime            variable unit  value #>                                         #> 1 478140 DOM         1 treecoverloss_e… 2016-01-01 00:00:00 emissio… Mg    4296. #> 2 478140 DOM         1 treecoverloss_e… 2017-01-01 00:00:00 emissio… Mg    4970. #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"Terrain Ruggedness Index measurement developed Riley, et al. (1999). elevation difference centre pixel eight immediate pixels squared averaged square root taken get TRI value. function allows calculate terrain ruggedness index (tri) statistics polygons. polygon, desired statistic(s) returned.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"","code":"calc_tri(engine = \"extract\", stats = \"mean\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"engine preferred processing functions either one \"zonal\", \"extract\" \"exactextract\" character. stats Function applied compute statistics polygons either single multiple inputs character. Supported statistics : \"mean\", \"median\", \"sd\", \"min\", \"max\", \"sum\" \"var\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"function returns indicator tibble tri variable respective statistic value.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"range index values corresponding meaning: 0-80 m - level surface 81-116 m - nearly level surface 117-161 m - slightly rugged surface 162-239 m - intermediately rugged surface 240-497 m - moderately rugged surface 498-958 m - highly rugged surface 959-4367 m  extremely rugged surface required resources indicator : nasa_srtm","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"Riley, S. J., DeGloria, S. D., & Elliot, R. (1999). Index quantifies topographic heterogeneity. Intermountain Journal Sciences, 5(1-4), 23-27.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/tri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Terrain Ruggedness Index (TRI) statistics — tri","text":"","code":"# \\dontrun{ library(sf) library(mapme.biodiversity)  outdir <- file.path(tempdir(), \"mapme-data\") dir.create(outdir, showWarnings = FALSE)  mapme_options(   outdir = outdir,   verbose = FALSE )  aoi <- system.file(\"extdata\", \"sierra_de_neiba_478140_2.gpkg\",   package = \"mapme.biodiversity\" ) %>%   read_sf() %>%   get_resources(get_nasa_srtm()) %>%   calc_indicators(     calc_tri(stats = c(\"mean\", \"median\", \"sd\", \"var\"), engine = \"extract\")   ) %>%   portfolio_long() #> Resource 'nasa_srtm' is already available.  aoi #> Simple feature collection with 4 features and 8 fields #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931 #> Geodetic CRS:  WGS 84 #> # A tibble: 4 × 9 #>   WDPAID ISO3  assetid indicator datetime            variable   unit  value #>                                    #> 1 478140 DOM         1 tri       2000-02-01 00:00:00 tri_mean   m      33.3 #> 2 478140 DOM         1 tri       2000-02-01 00:00:00 tri_median m      30.8 #> 3 478140 DOM         1 tri       2000-02-01 00:00:00 tri_sd     m      18.7 #> 4 478140 DOM         1 tri       2000-02-01 00:00:00 tri_var    m     349.  #> # ℹ 1 more variable: geom  # }"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":null,"dir":"Reference","previous_headings":"","what":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"resource distributed Uppsala Conflict Data Program (UCDP) constitutes diaggregated dataset individual events organized violence. encodes different actors involved, spatially disaggregated village levels anc currently covers time period 1989 2021. Older versions data set can downloaded, users recommended download latest data set.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"","code":"get_ucdp_ged(version = \"latest\")"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"https://ucdp.uu.se/downloads/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"version character vector specifying version download. Defaults \"latest\".","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"following versions available: 5.0 17.1 17.2 18.1 19.1 20.1 21.1 22.1 23.1 24.1 latest","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/ucdp_ged.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"UCDP Georeferenced Event Dataset (UCDP GED) — ucdp_ged","text":"Davies, Shawn, Therese Pettersson & Magnus Öberg (2022). Organized violence 1989-2021 drone warfare. Journal Peace Research 59(4). doi:10.1177/00223433221108428","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"","code":"get_worldclim_max_temperature(   years = 2000:2018,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_max_temperature.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Maximum Temperature layer — worldclim_max_temperature","text":"resource represents maximum temperature, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (°C), representing maximum temperature per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"","code":"get_worldclim_min_temperature(   years = 2000:2018,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"function returns character file paths.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_min_temperature.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Minimum Temperature layer — worldclim_min_temperature","text":"resource represents minimum temperature, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (°C), representing minimum temperature per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":null,"dir":"Reference","previous_headings":"","what":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"resource published Fick et al. (2017) \"WorldClim 2: new 1-km spatial resolution climate surfaces global land areas\" represents multiple climatic variables requiring minimum temperature, maximum temperature, mean precipitation layers. layers available download period 1960 - 2021 monthly basis WorldClim.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"","code":"get_worldclim_precipitation(   years = 1960:2021,   resolution = c(\"2.5m\", \"5m\", \"10m\") )"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"https://www.worldclim.org/data/index.html","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"years numeric vector indicating years make resource available. resolution character vector indicating desired resolution.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldclim_precipitation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Downloads WorldClim Mean Precipitation layer — worldclim_precipitation","text":"resource represents average precipitation, layers available download period 1960 - 2021 monthly basis WorldClim. Encoded (mm), representing mean precipitation per output grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":null,"dir":"Reference","previous_headings":"","what":"Population Count layer for year 2000-2020 — worldpop","title":"Population Count layer for year 2000-2020 — worldpop","text":"resource published open spatial demographic data research organization called WorldPop. resource represents population count, 1 km spatial resolution layers available download year 2000 2020. dataset called WorldPop Unconstrained Global Mosaics. encoded cell value represents total number people particular grid cell.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population Count layer for year 2000-2020 — worldpop","text":"","code":"get_worldpop(years = 2000)"},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Population Count layer for year 2000-2020 — worldpop","text":"https://www.worldpop.org/","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Population Count layer for year 2000-2020 — worldpop","text":"years numeric vector indicating years make resource available.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Population Count layer for year 2000-2020 — worldpop","text":"function returns sf footprint object.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/reference/worldpop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Population Count layer for year 2000-2020 — worldpop","text":"may required increase timeout option successfully download theses WorldPop layers source location via e.g. options(timeout = 600).","code":""},{"path":[]},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-development-version","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity (development version)","text":"get_nasa_srtm() now uses GDAL’s VSI path option pc_url_signing=yes sign URLs Microsoft Planetary Computer (#383)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-development-version","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity (development version)","text":"test .read_vector() now copies input GPKG directory write permissions avoid CRAN check failures included read directory","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-092","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.2","title":"mapme.biodiversity 0.9.2","text":"CRAN release: 2024-10-10","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-2","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.2","text":"get_acled() calc_fatalities_acled() calc_fatalities_ucdp() (renamed) calc_exposed_population_acled() calc_exposed_population_ucdp() (renamed) calc_fatalities_ucdp() now returns sparse timeseries, e.g. asset-months now fatalities omitted.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-9-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.9.2","text":"fixes portfolio_wide() throwing error single assets NULL values present calc_mangroves_area() returned NULL invalid geometries encountered Now tries repair geometries return area valid geometries (#375)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-2","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.2","text":".get_intersection() now assumes x tindex represented oriented rings sphere (#378)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-091","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.1","title":"mapme.biodiversity 0.9.1","text":"CRAN release: 2024-09-02","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-1","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.1","text":"get_accessibility_2000() (#365, @fBedecarrats) calc_traveltime_2000() (#365, @fBedecarrats)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.1","text":"adjusts test get_gsw_timseries() calc_gsw_timeseries() write temporal directory R session fix CRAN errors (#370, @karpfen)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-090","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.9.0","title":"mapme.biodiversity 0.9.0","text":"CRAN release: 2024-08-27","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-9-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.9.0","text":"prep_resources() received additional argument mode get control reading mode (e.g. portfolio asset) resources based WorldClim now support selecting spatial resolution cover historical timeseries starting 1960 (#302) assets now chunked sub-components prior indicator calculation thus parallelization now applied single level (#322) chunk_size now properly set 100,000 ha per documentation (set 10,000 ha) (#324) setting chunk_size=NULL now allowed skips chunking (#331) treecover indicators now trough message landscapemetrics installed (#325) setting outdir via mapme_options() now probes destination trying write GTiff file errors unsuccessful (#335) code previously using httr now uses httr2 (#330) new resources: get_iucn() (#359) get_chelsa() (#318) get_ipbes_biomes() (#345) get_humanfootprint() (#341) get_gsw_time_series() (#354, @karpfen) get_key_biodiversity_areas() (#349, @karpfen) get_biodiversity_intactness_index() (#351, @karpfen) get_vul_carbon(), get_man_carbon(), get_irr_carbon() (#339) new indicators: calc_slope() (#355, @fBedecarrats) calc_ipbes_biomes() (#345) calc_humanfootprint() (#341) calc_gsw_time_series() (#354, @karpfen) calc_species_richness() (#359) calc_exposed_population() (#321) calc_precipitation_chelsa() (#318) calc_key_biodiversity_area() (#349, @karpfen) calc_biodiversity_intactness_index() (#351, @karpfen) calc_vul_carbon(), calc_man_carbon(), calc_irr_carbon() (#339)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.9.0","text":"fixes transforming asset CRS raster dataset calc_deforestation_drivers() (#300) write_portfolio() now drops indicators NULL values instead throwing error (#303) get_ucdp_ged() now adds SRS infos footprints object (#313) uses binary writing mode worldpop resource Windows (#319)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-9-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.9.0","text":".check_portfolio() now checks assetid unique values overrides case (#305) .read_raster() now reads values memory removes VRT files -exit (#311) .fetch_resources() now honors creation opening options (#315) httr calls replaced respective httr2 equivalents (#329)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-080","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.8.0","title":"mapme.biodiversity 0.8.0","text":"CRAN release: 2024-07-03","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-8-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.8.0","text":"updates gfw_lossyear resource v20240402 entails emission data 2000 - 2023 removes nasa_firms resource associated active_fire_counts indicator adds mcd64a1 resource burned_area indicator mapme.biodiveristy now leverages GDAL data /O meaning GDAL readable source data sets writable destinations now supported README.md now includes section set cloud-storages use destination resource data quickstart vignette now uses GFW data example data chunking now applied based area assets bounding box instead area write_portfolio() now serializes two-table GeoPackage re-introduces read_portfolio() (#294) datetime column values now encoded POSIXct","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-8-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.8.0","text":"exports make_footprints() ease process creating footprints resource functions exports spds_exists() resource function check data source exists get_*() functions now required return footprint objects indicating spatial extent elements pointing towards GDAL readable data source source column case user-specified destination found, package now uses gdal_translate write data source destination tests long-running examples tests skipped GA CRAN fixes bug checking portfolio inherits tbl_df","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-070","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.7.0","title":"mapme.biodiversity 0.7.0","text":"CRAN release: 2024-05-31","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-7-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.7.0","text":"fixes bug wrong tile paths returned get_gfw_emissions()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.7.0","text":"introduces standardized output format indicators, see #240 information get_chirps() now allows specify years download CHIRPS resources calc_precipitation_chirps() now returns precipitation sums deprecation indicator active_fire_properties since resources can now retrieved using prep_resources() (see )","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-7-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.7.0","text":"exports prep_resources() prepare resources single assets exports portfolio_long() portfolio_wide() automatically un-nest indicator columns change data layout either long wide changes behavior write_portfolio() serialize portfolios GDAL supported spatial formats either long wide format deprecates read_portfolio() introduces option chunk_size mapme_options() control size polygons split processed chunks allows assets type 'MULTIPOLYGON' automatically combines results based aggregation function indicator examples now use portfolio_long() instead tidyr::unnest()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-7-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.7.0","text":"indicator functions must now return tibbles columns named datetime, variable, unit value inner-level indicator functions must now specify statistic aggregation chunks chirps nasa_grace resources updated check internet connectivity can now disabled via environment variable mapme_check_connection (#262) gfw_treecover gfw_lossyear resources updated v1.11 (#277, @fBedecarrats) GFW indicators now automatically detect maximum years based gfw_lossyear layer (#273) drops curl, stringr, tidyselect dependencies moves progressr rvest Imports Suggests drops SPEI Suggests","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-060","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.6.0","title":"mapme.biodiversity 0.6.0","text":"CRAN release: 2024-04-30","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.6.0","text":"introduces new UI based closures resources indicators, see #240 information","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-6-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.6.0","text":"improves output available_resources() available_indicators() introduces mapme_options() add fine-control packages behaviour deprecates init_portfolio() favor mapme_options() check_available_years() check_namespace() download_or_skip() check_engine() check_stats() select_engine() make_global_grid() unzip_and_remove()","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.6.0","text":"added Global Surface Water resources respective indicators (#235, @karpfen)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-6-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.6.0","text":"removed st_make_valid() .read_vector().","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-050","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.5.0","title":"mapme.biodiversity 0.5.0","text":"CRAN release: 2024-01-08","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"general-0-5-0","dir":"Changelog","previous_headings":"","what":"General","title":"mapme.biodiversity 0.5.0","text":"Quickstart vignette uses WorldPop resource instead CHIRPS, relying working internet connection (#230).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.5.0","text":"GFW resources indicators include latest GFC-2022-v1.10 version (#203). Raster resources CRS different WGS84 now supported (#213).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.5.0","text":"argument add_resources init_portfolio() deprecated. means get_resources() run every new R session make resource available processing (#219). Rasters now cropped spatial extent asset setting snap=\"\", thus delivering slightly bigger extent (#212). Speed improvements GFW indicators (x10 larger rasters) now require R package exactextractr installed. Also, advised R package landscapemetrics installed gain full computation speed improvement.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"mapme.biodiversity 0.5.0","text":"calc_indicators() checks 0-length tibbles (#196, #199, #215). Fix bug reading rasters temporal dimensions (#209). raster cells touching polygon now returned (#208).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-5-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.5.0","text":".read_raster_source() now uses simplified logic cover cases (e.g. single tiles, tiled rasters without temporal dimension, single temporal rasters) (#211). Rasters cropped using snap=\"\" default (#212). .read_raster_source() now projects assets case CRS differs portfolio (#213). tile indices raster resources now appended portfolio attributes sf objects instead written disk (#219). .read_raster_source() now applies precision round-trip 5 decimal point match rasters slight changes spatial extent (#217). register_resource() register_indicator() now issue warnings resources/indicators names already registered overwrites (#220).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-040","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.4.0","title":"mapme.biodiversity 0.4.0","text":"CRAN release: 2023-08-28","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.4.0","text":"added new resource called ucdp_ged providing database violent conflict 1989 today added new indicator called fatalities aggregating number deaths type conflict monthly time scale based ucdp_ged resource. Added new resource called fritz_et_al providing raster layer deforestation added new resource called fritz_et_al providing raster layer deforestation drivers tropical forests based Fritz et al. (2022) added new indicator called deforestation_drivers using fritz_et_al resource obtain information absolute relative area driving forest losses assets period 2008-2019 added two new exported functions register_resource() register_indicator() allow users register custom functions resources/indicators added new vignette web-version package informing obtain wide-output indicators added new vignette web-version custom analysis NASA FIRMS resource example section added data years 2017-2020 Global Mangrove Watch resource","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.4.0","text":"Changed parallel backend future package. Parallel processing now implemented furrr::future_map() asset level within calc_indicators() function. User code now required set plan() enable parallel processing. function call needs wrapped user side progressr::with_progress() show progress bar. mapme.biodiversity longer sets terra’s temporal directory . Instead call terra::terraOptions() manually","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"mapme.biodiversity 0.4.0","text":"esalandcover indicator now returns value per land cover class exactly (#177)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-4-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.4.0","text":"disabled running examples CRAN disabled tests get_* functions CRAN terra engines now use get() resolve requested zonal statistic function applying tidyverse coding style existing code (#156, @karpfen) extensive re-factoring vector-raster zonal statistic engines (#150) extensive re-writing testing infrastructure indicator functions omitting usage snapshot tests far possible (#142) rundir todisk arguments removed indicator functions since practical use instead resource indicator backlog, resources indicators now registered .pkgenv queried runtime. also allows users register custom resources/indicator functions removed deprecation warnings old resource/indicator name","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-030","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.3.0","title":"mapme.biodiversity 0.3.0","text":"CRAN release: 2023-01-21","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.3.0","text":"MacOS s2-based calculations now enabled users can expect package return numerically equivalent results operating system (#131) online source nasa_srtm resource shows expired SSL certificate since November 2022. get_resources() function now includes error instructions disable SSL certification users risk. websites maintainers contacted asked renew certification. (#131)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.3.0","text":"GFW resources now updated use latest version allowing analysis additional year 2021 (#123, @fBedecarrats) GFW indicators now accept numeric min_size argument allowing specify fractional covers (#110) fire indicators now allow simultaneous calculation indicators based MODIS VIIRS. users chose one instruments analysis (#126)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.3.0","text":"case one multiple assets return NA instead tibble now properly tested handled (#101) Rasters longer temporary written disk omit bug caused applying mask/classify already existing raster file (#108, @Jo-Schie) Bug soilproperties set NA caused function return data.frame instead tibble fixed (#116) , treecoverloss_emissions treecover_area_and_emissions now return 0 instead NaN observation years now forest loss occurred (#120)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-3-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.3.0","text":".make_global_grid() now specifies CRS constructing bounding box returns grid specified CRS instead Lat/Lon (#113) .calc_active_fire_properties now uses st_coordinates retrieve locations fires (#119, @DavisVaughan) tests MacOS re-enabled (#131) tests downloading nasa_srtm resource skipped SSL certificate online source expired (#131)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-021","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.2.1","title":"mapme.biodiversity 0.2.1","text":"CRAN release: 2022-09-09","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.2.1","text":"fixes serious bug occurred tiled resources multiple assets within tile resulting returning tile multiple times","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-2-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.2.1","text":"tests catch mentioned bug introduced tiled resources","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-020","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.2.0","title":"mapme.biodiversity 0.2.0","text":"CRAN release: 2022-08-23","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.2.0","text":"extensive renaming resources indicators. handled gracefully next release (.e. warning issued names replaced): resources: treecover2000 -> gfw_treecover lossyear -> gfw_lossyear greenhouse -> gfw_emissions traveltime -> nelson_et_al nasagrace -> nasa_grace mintemperature -> worldclim_min_temperature maxtemperature -> worldclim_max_temperature precipitation -> worldclim_precipitation ecoregions -> teow mangrove -> gmw srtmdem -> nasa_srtm indicators: treecover -> treecover_area emissions -> treecoverloss_emissions treeloss -> treecover_area_and_emissions chirpsprec -> precipitation_chirps accessibility -> traveltime popcount -> population_count wctmin -> temperature_min_wc wctmax -> temperature_max_wc wcprec -> precipitation_wc gmw -> mangroves_area teow -> ecoregion","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.2.0","text":"nasa_firms active_fire_properties active_fire_counts","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-2-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.2.0","text":"adapted download routine GMW v3 (#80) removed data.table imports","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"mapme.biodiversity 0.2.0","text":"fixing issue #84 concerning intersection tiled datasets (#86, @Jo-Schie)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-012","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.2","title":"mapme.biodiversity 0.1.2","text":"CRAN release: 2022-06-24","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-2","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.2","text":"unit tests silenced order informative reverse dependency checks checks tile availability reactivated SRTM fixed notes due uninitialized variables TEOW biome indicators","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-011","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.1","title":"mapme.biodiversity 0.1.1","text":"CRAN release: 2022-05-02","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.1","text":"init_portfolio() now sets testing attribute FALSE default. get_() functions now return filenames early testing set TRUE. calc_() examples now copy files R temporal directory wrapped try() avoid errors/warnings CRAN internet resource available. examples calc_tri() calc_elevation() now disabled CRAN responsiveness CIGAR servers.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-010","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.1.0","title":"mapme.biodiversity 0.1.0","text":"CRAN release: 2022-04-27","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"breaking-changes-0-1-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"mapme.biodiversity 0.1.0","text":"renamed ‘.assetid’ ‘assetid’ (#22)","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"mapme.biodiversity 0.1.0","text":"None","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-1-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.1.0","text":"ensures tests examples adhere CRAN policies writing temporal directory (#22).","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"mapmebiodiversity-001","dir":"Changelog","previous_headings":"","what":"mapme.biodiversity 0.0.1","title":"mapme.biodiversity 0.0.1","text":"CRAN release: 2022-04-19","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"initial-release-0-0-1","dir":"Changelog","previous_headings":"","what":"Initial release","title":"mapme.biodiversity 0.0.1","text":"Added NEWS.md file track changes package. ecoregions esalandcover greenhouse lossyear mangrove nasagrace soilgrids srtmdem traveltime treecover worldclim worldpop acessibility biome chirpsprec drought_indicator elevation emissions gmw landcover popcount soilproperties teow treecover treeloss tri wcprec wctmax wctmin init_portfolio() used initialize portfolio object. input must sf object geometries type POLYGON users can request download one resources via get_resources() users can request processing indicator via calc_indicators() indicators added portfolio object nested list columns processed portfolio object can exported GeoPackage via write_portfolio() portfolio saved disk GeoPackage can read back R via read_portfolio(). users wish download additional resources calculate indicators, init_portfolio() called . Parallelization using multiple cores host machine disabled Windows MacOS, s2 engine spherical geometric vector operations disabled lwgeom used instead.","code":""},{"path":"https://mapme-initiative.github.io/mapme.biodiversity/dev/news/index.html","id":"internal-0-0-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"mapme.biodiversity 0.0.1","text":"Introduced absolute URLS userguide.Rmd pointing online documentation (#59) tags added exported functions explaining output/side effect (#59) using requireNamespace() instead installed.packages() check packages listed SUGGEST loadable (#58)","code":""}]