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---
title: "Analyzing space-time satellite data for disease ecology applications with GRASS GIS and R"
subtitle: "OpenGeoHub Summer School 2021"
author:
- "Veronica Andreo"
date: '`r Sys.Date()`'
output:
xaringan::moon_reader:
css: assets/css/xaringan-themer.css
seal: false
nature:
slideNumberFormat: "%current%"
highlightStyle: dark
highlightLines: true
ratio: 16:9
countIncrementalSlides: true
---
```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(
fig.width=9, fig.height=6, fig.retina=3,
out.width = "80%",
cache = FALSE,
echo = TRUE,
message = FALSE,
warning = FALSE,
hiline = TRUE
)
```
```{r libraries, include=FALSE}
library(fontawesome)
library(emo)
```
```{r xaringanExtra, echo=FALSE}
# https://github.com/gadenbuie/xaringanExtra
xaringanExtra::use_xaringan_extra("tile_view")
xaringanExtra::use_panelset()
xaringanExtra::use_logo(image_url = "https://grass.osgeo.org/images/logos/grasslogo.svg",
width = "40px")
xaringanExtra::use_fit_screen()
xaringanExtra::use_progress_bar(color = "#ee4912", location = "bottom", height = "3px")
```
<br><br><br>
.center[
#### Slides:
https://veroandreo.github.io/grass_opengeohub2021/presentation.html
<br><br>
#### Repo:
[`r fa("github", width="3em")`](https://github.com/veroandreo/grass_opengeohub2021)
]
---
class: grass-slide, hide_logo, center
## Analyzing space-time satellite data for disease ecology applications with GRASS GIS and R
#### Verónica Andreo
.message-box-white[
<br>
<img src="assets/img/banner_conicet_conae_letrasnegras.png" width="65%">
OpenGeoHub Summer School - September, 2021]
---
layout: false
## About me
.pull-left-wide[
- PhD in Biology and MSc in Remote Sensing and GIS
- Researcher and lecturer at [Gulich Institute](https://ig.conae.unc.edu.ar/) from the [Argentinean Space Agency - CONAE](https://www.argentina.gob.ar/ciencia/conae), working on applications of RS & GIS for disease ecology
- [GRASS GIS](https://grass.osgeo.org/) dev team and PSC Chair
- [OSGeo](https://www.osgeo.org/) Charter member
- [FOSS4G](https://2021.foss4g.org/) enthusiast and advocate]
.pull-right-narrow[
.bio-img[]
https://veroandreo.gitlab.io/
]
---
class: grass-slide
## How is this session organized?
.large[
- GRASS GIS basic intro
- Temporal GRASS GIS basic intro
- Demo session
]
---
## What GRASS?
.pull-left-wide[
- **GRASS GIS** (Geographic Resources Analysis Support System), a FOSS suite used for geospatial data management and analysis, image processing, graphics and maps, spatial modeling, and visualization.
- Originally developed by the U.S. Army Construction Engineering Research Laboratories for land management and environmental planning (1982-1995).
- More history: https://grass.osgeo.org/about/history/]
.pull-right-narrow[
]
---
class: grass-slide center
A bit of (geek) GRASS GIS history...
<iframe width="560" height="315" scrolling="no" src="//av.tib.eu/player/12963" frameborder="0" allowfullscreen></iframe>
<br>
.message-box-white[
In case you didn't notice, the video is narrated by William Shatner `r emo::ji("nerd")` `r emo::ji("rocket")`
]
---
class: grass-slide
## GRASS GIS general stuff
- Free and open source, you can use, modify, improve, share
- Strong user community, commercial support
- Large amount of tools: 500+ [core modules](#modules), 300+ [addons](#addons)
- GUI and CLI interfaces
- Python API and libraries - new `grass.jupyter` library can be tried [online](https://mybinder.org/v2/gh/OSGeo/grass/c173461?urlpath=lab%2Ftree%2Fdoc%2Fnotebooks%2Fgrass_jupyter.ipynb)
- Connection with R, QGIS, WPS, etc.
- Different data types supported: [raster](https://grass.osgeo.org/grass-stable/manuals/rasterintro.html) (including [satellite imagery](https://grass.osgeo.org/grass-stable/manuals/imageryintro.html)), [3D raster or voxel](https://grass.osgeo.org/grass-stable/manuals/raster3dintro.html), [vector](https://grass.osgeo.org/grass-stable/manuals/vectorintro.html) and [space-time datasets](https://grass.osgeo.org/grass-stable/manuals/temporalintro.html)
---
name: modules
### `r fa("tools")` More than [500 core modules](https://grass.osgeo.org/grass-stable/manuals/full_index.html) `r fa("tools")`
| Prefix | Function class | Type of command | Example
|--------------------------------------------------------------------- |:---------------- |:----------------------------------- |:-------------------------------------------------------------------------------------------------------------------
| [g.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#g) | general | general data management | [g.rename](https://grass.osgeo.org/grass-stable/manuals/g.rename.html): renames map
| [d.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#d) | display | graphical output | [d.rast](https://grass.osgeo.org/grass-stable/manuals/d.rast.html): display raster map
| [r.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#r) | raster | raster processing | [r.mapcalc](https://grass.osgeo.org/grass-stable/manuals/r.mapcalc.html): map algebra
| [v.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#r) | vector | vector processing | [v.clean](https://grass.osgeo.org/grass-stable/manuals/v.clean.html): topological cleaning
| [i.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#i) | imagery | imagery processing | [i.pca](https://grass.osgeo.org/grass-stable/manuals/i.pca.html): Principal Component Analysis
| [r3.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#r3) | voxel | 3D raster processing | [r3.stats](https://grass.osgeo.org/grass-stable/manuals/r3.stats.html): voxel statistics
| [db.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#db) | database | database management | [db.select](https://grass.osgeo.org/grass-stable/manuals/db.select.html): select value(s) from table
| [ps.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#ps) | postscript | PostScript map creation | [ps.map](https://grass.osgeo.org/grass-stable/manuals/ps.map.html): PostScript map creation
| [t.\*](https://grass.osgeo.org/grass-stable/manuals/full_index.html#t) | temporal | space-time datasets | [t.rast.aggregate](https://grass.osgeo.org/grass-stable/manuals/t.rast.aggregate.html): raster time series aggregation
---
class: grass-slide
name: addons
## `r fa("tools")` More than [300 add-ons](https://grass.osgeo.org/grass-stable/manuals/addons/) `r fa("tools")`
Plugins or **Add-ons** can be installed from
a centralized [OSGeo repository](https://grass.osgeo.org/grass-stable/manuals/addons/)
or from github (or similar repositories) using
[g.extension](https://grass.osgeo.org/grass-stable/manuals/g.extension.html) command.
```bash
# install extension from GRASS GIS Add-on repository
g.extension extension=r.hants
# install extension from github repository
g.extension extension=r3.slice \
url=https://github.com/petrasovaa/r3.slice
```
---
### Graphical User Interface (GUI)
<center><img src="assets/img/GUI_description.png" width="870px"></center>
---
### `r fa("terminal")` Command line
<center><img src="assets/img/grass_command_line.png" width="65%"></center>
---
### GRASS + `r fa("r-project", fill="#276dc2")` through rgrass7 package
We can use [R within a GRASS GIS session](https://grasswiki.osgeo.org/wiki/R_statistics/rgrass7#R_within_GRASS) or use [GRASS GIS within an R session](https://grasswiki.osgeo.org/wiki/R_statistics/rgrass7#GRASS_within_R)
<center><img src="assets/img/RwithinGRASS_and_Rstudio_from_grass.png" width="75%"></center>
---
class: grass-slide center
# Temporal support in GRASS GIS: .black[TGRASS]
---
## The TGRASS framework
.left[GRASS GIS was the first FOSS GIS that incorporated capabilities to *manage, analyze, process and visualize spatio-temporal data*, as well as the temporal relationships among time series.]
- TGRASS is fully **based on metadata** and does not duplicate any dataset
- **Snapshot** approach, i.e., adds time stamps to maps
- A collection of time stamped maps (snapshots) of the same variable are called **space-time datasets** or STDS
- Maps in a STDS can have different spatial and temporal extents
---
class: grass-slide
## Space-time datasets
- Space time raster datasets (**STRDS**)
- Space time 3D raster datasets (**STR3DS**)
- Space time vector datasets (**STVDS**)
<br>
> `r emo::ji("announcement")` Upcoming GRASS 8 will support image collections by adding a band reference
---
class: grass-slide
.pull-right-narrow[
<img src="assets/img/temporal_modules.png" width="110%" align="center">
]
## Temporal modules
.pull-left-wide[
- **t.\***: General modules to handle STDS of all types
- **t.rast.\***: Modules that deal with STRDS
- **t.rast3d.\***: Modules that deal with STR3DS
- **t.vect.\***: Modules that deal with STVDS
]
---
layout: true
### Other TGRASS notions
---
.pull-left[
- Time can be defined as **intervals** (start and end time) or **instances** (only start time)
- Time can be **absolute** (e.g., 2017-04-06 22:39:49) or **relative** (e.g., 4 years, 90 days)
- **Granularity** is the greatest common divisor of the temporal extents (and possible gaps) of all maps in the space-time cube
]
.pull-right[
<img src="https://grass.osgeo.org/grass78/manuals/timeline_2D.jpg">
]
---
- **Topology** refers to temporal relations between time intervals in a STDS.
.center[<img src="assets/img/temp_relation.png">]
---
layout: false
.left-column[
### TGRASS:
#### framework and workflow]
.right-column[<img src="assets/img/tgrass_flowchart.png" width="91%">]
---
class: grass-slide center hide_logo
## .black[Walk through space-time analysis for disease ecology with GRASS GIS and] `r fa("r-project", fill="#276dc2", width = "2em")`
---
class: grass-slide
### Demo session overview
> .center[Habitat suitability mapping for the Asian tiger mosquito in Northern Italy based on variables derived from daily LST data]
.pull-left[
- **GRASS**
- Import species records
- Create random background points
- Create different environmental layers from daily LST data
]
.pull-right[
- **R**
- Read data from GRASS into R
- Model species distribution
- Model evaluation
- Visualization of results
]
---
.footnote[Jarnevich et al. 2015. doi:[10.1016/j.ecoinf.2015.06.007](https://doi.org/10.1016/j.ecoinf.2015.06.007)]
.center[<img src="assets/img/sdm_workflow_edited.png" width="72%">]
---
### Data for the session
.pull-left[
- Records of *Aedes albopictus* (Asian tiger mosquito) in Northern Italy downloaded from [GBIF](https://www.gbif.org/)
- Daily MODIS LST reconstructed by [mundialis](https://www.mundialis.de/en/) based on [Metz et al. 2017](https://www.mdpi.com/2072-4292/9/12/1333/htm)
- 1 km spatial resolution
- Converted to Celsius degrees
]
.pull-right[
<img src="assets/img/aedes_albopictus.jpg" width="47%" align="right">
<br>
<img src="assets/img/lst_north_italy_2014.png" width="78%" align="left">
]
---
### `r fa("download")` Get sample location, mosquito records and code `r fa("download")`
- Create a folder named `grassdata_ogh`
- Download and unzip [eu_laea location with LST mapset](https://drive.google.com/file/d/1z1b2NLC4Z6yzz_57RddTdRRK_gUkd7fU/view?usp=sharing) and unzip within your `grassdata_ogh` folder
- Download the asian tiger [mosquito occurrences](https://github.com/veroandreo/grass_opengeohub2021/raw/master/data/aedes_albopictus.gpkg) as `.gpkg` file
- Download the [GRASS script](https://github.com/veroandreo/grass_opengeohub2021/raw/master/code/grass_R_disease_ecology_code.sh)
- Download the [R script](https://github.com/veroandreo/grass_opengeohub2021/raw/master/code/grass_R_disease_ecology_code.r)
The `grassdata_ogh` folder's tree should look like this:
```
grassdata_ogh/
└── eu_laea
├── italy_LST_daily
└── PERMANENT
```
---
.left-column[
## Let's start GRASS GIS!
`r emo::ji("hushed")`
### Fear not!
]
.right-column[
.center[<img src="assets/img/start_grass.png" width="60%">
`r emo::ji("announcement")` This start-up screen will be gone in GRASS 8 !!
]
]
---
## What was that? <a href="https://grass.osgeo.org/grass76/manuals/grass_database.html">GRASS database</a>
.pull-left[
- The **GRASS DATABASE** (or "GISDBASE") is an existing directory containing all GRASS GIS LOCATIONs.
- A **LOCATION** is defined by its coordinate system and geographical boundaries.
- **MAPSET** is a subdirectory within Locations. In a **MAPSET** you can organize GIS maps thematically, geographically, by project, etc.
- When GRASS GIS starts, it connects to `Database/Location/Mapset`:
]
.pull-right[
<img src="assets/img/grass_database.png" width="950px">
.right[More info at [grass database](#grass-database-slide)]
]
---
class: grass-slide center
# Now we are ready to start
# `r emo::ji("runner")`
---
#### Importing species records
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Import records
v.import input=aedes_albopictus.gpkg output=aedes_albopictus
# List raster maps
g.list type=raster
r.colors map=lst_2014.150_avg color=celsius
# Display records
d.mon wx0
d.rast lst_2014.150_avg
d.vect aedes_albopictus icon=basic/circle \
size=7 fill_color=black
```
]
.panel[.panel-name[Map]
.center[<img src="assets/img/wx0_aedes_lst.png" width="570px">]
]
]
.footnote[You can also get the occurrences directly from GBIF into GRASS. See this [extra slide](#gbif-example).]
---
#### Creating random background points
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Create buffer around Aedes albopictus records
v.buffer input=aedes_albopictus output=aedes_buffer distance=2000
# Set computational region
g.region -p raster=lst_2014.001_avg
# Create a vector mask to limit background points
r.mapcalc expression="MASK = if(lst_2014.001_avg, 1, null())"
r.to.vect input=MASK output=vect_mask type=area
# Subtract buffers from vector mask
v.overlay ainput=vect_mask binput=aedes_buffer operator=xor output=mask_bg
# Generate random background points
v.random output=background_points npoints=1000 restrict=mask_bg seed=3749
```
]
.panel[.panel-name[Map]
.center[<img src="assets/img/points_aedes_background.png" width="600px">]
]
]
.footnote[See extra slides for details about [*computational region*](#region) and [*masks*](#mask) in GRASS GIS.]
---
#### Create daily LST STRDS
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Create time series
t.create type=strds temporaltype=absolute \
output=lst_daily title="Average Daily LST" \
description="Average daily LST in degree C - 2014-2018"
# Get list of maps
g.list type=raster pattern="lst_201*" output=list_lst.csv
# Register maps in strds
t.register -i input=lst_daily file=list_lst.csv \
increment="1 days" start="2014-01-01"
# Get info about the strds
t.info input=lst_daily
```
]
.panel[.panel-name[Output]
.center[<img src="assets/img/t_info_output.png" width="50%">]
]
]
.footnote[See [t.create](https://grass.osgeo.org/grass-stable/manuals/t.create.html) & [t.register](https://grass.osgeo.org/grass-stable/manuals/t.register.html)]
---
### Generate environmental variables from LST STRDS
#### Long term monthly avg, min and max LST
.panelset[
.panel[.panel-name[GRASS code]
```bash
for i in $(seq -w 1 12) ; do
t.rast.series input=lst_daily method=average \
where="strftime('%m', start_time)='${i}'" \
output=lst_average_${i}
t.rast.series input=lst_daily method=minimum \
where="strftime('%m', start_time)='${i}'" \
output=lst_minimum_${i}
t.rast.series input=lst_daily method=maximum \
where="strftime('%m', start_time)='${i}'" \
output=lst_maximum_${i}
done
```
]
.panel[.panel-name[Windows cmd]
```vbscript
FOR %i IN (01,02,03,04,05,06,07,08,09,10,11,12) DO (
t.rast.series input=lst_daily method=average where="strftime('%m', start_time)='%i'" output=lst_average_%i
t.rast.series input=lst_daily method=minimum where="strftime('%m', start_time)='%i'" output=lst_minimum_%i
t.rast.series input=lst_daily method=maximum where="strftime('%m', start_time)='%i'" output=lst_maximum_%i
)
```
]
.panel[.panel-name[Output]
.center[<img src="assets/img/list_long_term_avg_lst.png" width="70%">]
]
]
.footnote[See [t.rast.series](https://grass.osgeo.org/grass-stable/manuals/t.rast.series.html) manual for further details]
---
#### Bioclimatic variables
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Install extension
g.extension extension=r.bioclim
# Estimate temperature related bioclimatic variables
r.bioclim \
tmin=$(g.list type=raster pattern="lst_minimum_??" separator=",") \
tmax=$(g.list type=raster pattern="lst_maximum_??" separator=",") \
tavg=$(g.list type=raster pattern="lst_average_??" separator=",") \
output=worldclim_
# List output maps
g.list type=raster pattern="worldclim*"
```
]
.panel[.panel-name[Output]
.center[<img src="assets/img/list_worldclim_lst.png" width="80%">]
]
.panel[.panel-name[Map]
.center[<img src="assets/img/bio1.png" width="50%">]
]
]
.footnote[See [r.bioclim](https://grass.osgeo.org/grass-stable/manuals/addons/r.bioclim.html) manual for further details]
---
#### Spring warming
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Annual spring warming: slope(daily Tmean february-march-april)
t.rast.aggregate input=lst_daily output=annual_spring_warming \
basename=spring_warming suffix=gran \
method=slope granularity="1 years" \
where="strftime('%m',start_time)='02' or \
strftime('%m',start_time)='03' or \
strftime('%m', start_time)='04'"
# Average spring warming
t.rast.series input=annual_spring_warming \
output=avg_spring_warming \
method=average
```
]
.panel[.panel-name[Map]
.center[<img src="assets/img/spring_warming.png" width="50%">]
]
]
.footnote[See [t.rast.aggregate](https://grass.osgeo.org/grass-stable/manuals/t.rast.aggregate.html) manual]
---
#### Autumnal cooling
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Annual autumnal cooling: slope(daily Tmean august-september-october)
t.rast.aggregate input=lst_daily output=annual_autumnal_cooling \
basename=autumnal_cooling suffix=gran \
method=slope granularity="1 years" \
where="strftime('%m',start_time)='08' or \
strftime('%m',start_time)='09' or \
strftime('%m', start_time)='10'"
# Average autumnal cooling
t.rast.series input=annual_autumnal_cooling \
output=avg_autumnal_cooling \
method=average
```
]
.panel[.panel-name[Map]
.center[<img src="assets/img/autumnal_cooling.png" width="51%">]
]
]
---
#### Number of days with LSTmean >= 20 and <= 30
.panelset[
.panel[.panel-name[GRASS code]
```bash
# Keep only pixels meeting the condition
t.rast.algebra -n \
expression="tmean_higher20_lower30 = if(lst_daily >= 20.0 && lst_daily <= 30.0, 1, null())" \
basename=tmean_higher20_lower30 suffix=gran nproc=7
# Count how many times per year the condition is met
t.rast.aggregate input=tmean_higher20_lower30 \
output=count_tmean_higher20_lower30 \
basename=tmean_higher20_lower30 suffix=gran \
method=count granularity="1 years"
# Average number of days with LSTmean >= 20 and <= 30
t.rast.series input=count_tmean_higher20_lower30 \
output=avg_count_tmean_higher20_lower30 method=average
```
]
.panel[.panel-name[Map]
.center[<img src="assets/img/count_days_higher20_lower30.png" width="50%">]
]
]
.footnote[See [t.rast.algebra](https://grass.osgeo.org/grass-stable/manuals/t.rast.algebra.html) manual for further details]
---
layout: true
#### Number of consecutive days with LSTmean <= -2.0
---
```bash
# Create annual mask
t.rast.aggregate input=lst_daily output=annual_mask \
basename=annual_mask suffix=gran \
granularity="1 year" method=count
# Replace values by zero
t.rast.mapcalc input=annual_mask output=annual_mask_0 \
expression="if(annual_mask, 0)" \
basename=annual_mask_0
# Calculate consecutive days with LST <= -2.0
t.rast.algebra \
expression="lower_m2_consec_days = annual_mask_0 {+,contains,l} \
if(lst_daily <= -2.0 && lst_daily[-1] <= -2.0 || \
lst_daily[1] <= -2.0 && lst_daily <= -2.0, 1, 0)" \
basename=lower_m2_ suffix=gran nproc=7
```
---
```bash
# Inspect values
t.rast.list input=lower_m2_consec_days \
columns=name,start_time,end_time,min,max
# Median number of consecutive days with LST <= -2
t.rast.series input=lower_m2_consec_days \
output=median_lower_m2_consec_days method=median
```
---
.center[<img src="assets/img/median_days_lower_m2.png" width="60%">]
---
layout: false
class: grass-slide hide_logo center
## We have all these maps in GRASS, how do we connect with R now?
# `r emo::ji("hushed")`
---
## [**rgrass7**](https://cran.r-project.org/web/packages/rgrass7/index.html)
- `initGRASS()`: starts a GRASS GIS session from R
- `execGRASS()`: executes GRASS GIS commands
- `gmeta()`: shows GRASS location metadata
- `readVECT()` and `readRAST()`: read vector and raster maps from GRASS into *sf* or *sp* objects
- `writeVECT()` and `writeRAST()`: write *sf* or *sp* objects into GRASS GIS database
.footnote[`r emo::ji("announcement")` Volunteers needed!!]
---
layout: true
### GRASS GIS and `r fa("r-project", fill="#276dc2")` can be used together in two ways:
---
.pull-left[
A. Using [R within a GRASS GIS session](https://grasswiki.osgeo.org/wiki/R_statistics/rgrass7#R_within_GRASS), i.e. starting R (or RStudio) from GRASS terminal
<br>
- type `R` or `rstudio &` in the GRASS GIS terminal
- load `rgrass7` library
- use `readVECT()`, `readRAST()` to read data from GRASS into R
- access GRASS GIS modules and database through `execGRASS()`
- write data (back) to GRASS database with `writeVECT()` and `writeRAST()`
]
.pull-right[
<img src="assets/img/grass_terminal_calling_R.png" width="87%">
]
---
.pull-left[
B. Using [GRASS GIS within an R session](https://grasswiki.osgeo.org/wiki/R_statistics/rgrass7#GRASS_within_R), i.e. we connect to GRASS GIS database from within R (or RStudio).
<br>
- we need to start GRASS GIS with `initGRASS()`
- we access GRASS GIS modules through `execGRASS()`
> Originally intended to apply GRASS functions on data outside GRASS DB; hence some prefer to create throw away locations
]
.pull-right[
<img src="assets/img/grass_within_rstudio_session.png" width="100%">
]
---
layout: false
class: grass-slide center hide_logo
# Let's move to `r fa("r-project", fill="#276dc2")`
---
#### Option B: Close GRASS, open Rstudio and run:
.panelset[
.panel[.panel-name[R code]
```{r grass_init, results='hide', message=FALSE}
library(rgrass7)
# path to GRASS binaries (run `grass78 --config path`)
myGRASS <- "/usr/lib64/grass78"
# path to GRASS database
myGISDbase <- "/home/veroandreo/grassdata_ogh/"
# path to location
myLocation <- "eu_laea"
# path to mapset
myMapset <- "italy_LST_daily"
# start GRASS GIS from R
initGRASS(gisBase = myGRASS, home = tempdir(), gisDbase = myGISDbase,
location = myLocation, mapset = myMapset, override = TRUE)
```
]
.panel[.panel-name[Output]
```{r ref.label='grass_init', echo=FALSE, size='tiny'}
# start GRASS GIS from R
initGRASS(gisBase = myGRASS, home = tempdir(), gisDbase = myGISDbase,
location = myLocation, mapset = myMapset, override = TRUE)
```
]
]
---
#### Load other packages
```{r load_libraries}
library(raster)
library(sf)
library(mapview)
library(biomod2)
```
---
#### Read vector data
.panelset[
.panel[.panel-name[R code]
```{r read_vectors, message=FALSE, warning=FALSE, results='hide'}
# Use sf for vectors
use_sf()
# Read vector layers
Aa_pres <- readVECT("aedes_albopictus")
background <- readVECT("background_points")
```
```{r plot, message=FALSE, warning=FALSE, results='hide'}
# Quick visualization in mapview
mapview(Aa_pres) +
mapview(background, col.regions=NA, cex=2)
```
]
.panel[.panel-name[Map]
```{r ref.label='plot', warning=FALSE, echo=FALSE}
mapview(Aa_pres) +
mapview(background, color="black", col.regions="black", cex=2)
```
]
]
---
layout: true
#### Read raster data
---
```{r read_rasters, message=FALSE, warning=FALSE, results='hide'}
# Use sp for rasters
use_sp()
# List rasters by pattern
worldclim <- execGRASS("g.list", parameters = list(type = "raster", pattern = "worldclim*"))
avg <- execGRASS("g.list", parameters = list(type = "raster", pattern = "avg*"))
median <- execGRASS("g.list", parameters = list(type = "raster", pattern = "median*", exclude = "*[1-5]"))
# Concatenate map lists
to_import <- c(attributes(worldclim)$resOut,
attributes(avg)$resOut,
attributes(median)$resOut)
# Read raster layers
predictors <- list()
for (i in to_import){ predictors[i] <- raster(readRAST(i)) }
```
---
```{r quick_view, message=FALSE, warning=FALSE}
# Quick visualization in mapview
mapview(predictors[['worldclim_bio01']]) + Aa_pres
```
---
layout: false
#### Data preparation and formatting
```{r data_prep1, message=FALSE, warning=FALSE, results='hide'}
# Response variable
n_pres <- dim(Aa_pres)[1]
n_backg <- dim(background)[1]
spp_name <- 'Aedes.albopictus'
pres <- rep(1, n_pres)
backg <- rep(0, n_backg)
myResp <- c(pres, backg)
myRespXY <- rbind(st_coordinates(Aa_pres),
st_coordinates(background))
# Explanatory variables
myExpl <- raster::stack(predictors)
names(myExpl)
```
---
#### Data preparation and formatting
.panelset[
.panel[.panel-name[R code]
```{r data_prep2, message=FALSE, warning=FALSE, results='hide', fig.show='hide'}
# Format data as required by biomod
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = spp_name)
# Inspect data
myBiomodData
# Plot data
plot(myBiomodData)
```
]
.panel[.panel-name[Output]
```{r print_data_object, warning=FALSE, echo=FALSE}
# Inspect data
myBiomodData
```
]
.panel[.panel-name[Plot]
```{r plot_data_object, warning=FALSE, echo=FALSE, out.width='72%'}
par(mar=c(2,2,2,2))
plot(myBiomodData)
```
]
]
---
#### Set model(s) options
.panelset[
.panel[.panel-name[R code]
```{r model_options, message=FALSE, warning=FALSE, results='hide'}
# Set model options
myBiomodOption <- BIOMOD_ModelingOptions(
MAXENT.Phillips =
list(path_to_maxent.jar = "/home/veroandreo/software/maxent/maxent.jar",
maximumiterations = 200,
lq2lqptthreshold = 100,
l2lqthreshold = 100))
# Inspect all configs for MaxEnt
myBiomodOption@MAXENT.Phillips
```
]
.panel[.panel-name[Output]
```{r print_model_options, message=FALSE, warning=FALSE, echo=FALSE}
myBiomodOption@MAXENT.Phillips
```
]
]
---
#### Run model
```{r model_run, message=FALSE, warning=FALSE, results='hide'}
# Run model
myBiomodModelOut <- BIOMOD_Modeling(
myBiomodData,
models = c('MAXENT.Phillips'),
models.options = myBiomodOption,
NbRunEval=5,
DataSplit=80,
VarImport=10,
models.eval.meth = c('ROC','ACCURACY'),
SaveObj = TRUE,
rescal.all.models = FALSE,
do.full.models = FALSE,
modeling.id = paste(spp_name,"Habitat_Suitability",sep="_"))
# Inspect the model
myBiomodModelOut
```
---
#### Model evaluation
.panelset[
.panel[.panel-name[R code]
```{r model_eval, message=FALSE, warning=FALSE, results='hold'}
# Extract evaluation data
myBiomodModelEval <- get_evaluations(myBiomodModelOut)
# Accuracy
myBiomodModelEval["ACCURACY","Testing.data",,,]
# ROC: Receiver-operator curve
myBiomodModelEval["ROC","Testing.data",,,]
# Save run with max ROC
max_roc <- which.max(myBiomodModelEval["ROC","Testing.data",,,])
```
]
.panel[.panel-name[Plot]
```{r plot_model_eval, message=FALSE, warning=FALSE, echo=FALSE, out.width='60%'}
models_scores_graph(myBiomodModelOut,
by = "cv_run",
metrics = c("ROC", "ACCURACY"))
```
]
]
---
#### Variable importance
.panelset[
.panel[.panel-name[R code]
```{r vi, message=FALSE, warning=FALSE, results='hide'}
# Variable importance
vi <- get_variables_importance(myBiomodModelOut)
# Let's see the first part
head(vi[1:13,1, ,], n = 10L) %>%
knitr::kable(format = 'html')
# ... and estimate the mean