diff --git a/README.md b/README.md index 224f3d3..cd97dad 100644 --- a/README.md +++ b/README.md @@ -3,33 +3,28 @@ Overline is a function that takes overlapping linestrings and converts them into a route network (Morgan and Lovelace 2020) as illustrated in a -minimal reproducible example below. +minimal reproducible example created with +[ATIP](https://github.com/acteng/atip/). -``` r -library(sf) -library(stplanr) -library(tidyverse) -library(tmap) -sl = routes_fast_sf[2:3, 0] -sl$n = 1:2 -plot(sl) -``` +The input is a dataset in which some segments overlap: -![](README_files/figure-commonmark/unnamed-chunk-1-1.png) + Reading layer `minimal_2_lines' from data source + `/home/robin/github/acteng/overline/test-data/minimal_2_lines.txt' + using driver `GeoJSON' + Simple feature collection with 2 features and 5 fields + Geometry type: LINESTRING + Dimension: XY + Bounding box: xmin: -1.29569 ymin: 50.69972 xmax: -1.295337 ymax: 50.69987 + Geodetic CRS: WGS 84 -``` r -rnet = overline(sl, attrib = "n") -plot(rnet) -``` +![](README_files/figure-commonmark/unnamed-chunk-1-1.png) -![](README_files/figure-commonmark/unnamed-chunk-1-2.png) +The output is a dataset in which the overlapping segments have been +combined: -``` r -sf::write_sf(sl, "minimal-example-input.geojson", delete_dsn = TRUE) -sf::write_sf(rnet, "minimal-example-output.geojson", delete_dsn = TRUE) -``` +![](README_files/figure-commonmark/unnamed-chunk-2-1.png) -The function has been implemented in the [`overline()` +The functionality has been implemented in the [`overline()` function](https://docs.ropensci.org/stplanr/reference/overline.html) in the R package `stplanr`. The function works fine for city sized datasets but for national datasets is slow, buggy and not feature complete, as it @@ -41,15 +36,18 @@ In Python, the input and outputs can be visualised as follows: ``` python import geopandas as gpd input = gpd.read_file("input.geojson") -input.plot() +# Plot with colour by value: +input.plot(column="value") ``` +![](README_files/figure-commonmark/unnamed-chunk-3-1.png) + ``` python output = gpd.read_file("output.geojson") -output.plot() +output.plot(column="value") ``` -![](README_files/figure-commonmark/unnamed-chunk-3-3.png) +![](README_files/figure-commonmark/unnamed-chunk-4-3.png) # Example with road names @@ -57,113 +55,12 @@ The example below takes routes at the segment level and calculates average gradient for each segment. Road names are NOT currently implemented in `overline()` in R. -``` r -sl_desire_lines = stplanr::flowlines_sf[2:3, ] -qtm(sl_desire_lines) + - qtm(sl) -``` - -![](README_files/figure-commonmark/unnamed-chunk-2-1.png) - -``` r -route_segments_minimal = stplanr::route( - l = sl_desire_lines, - route_fun = cyclestreets::journey - ) -``` - - Most common output is sf - -``` r -names(route_segments_minimal) -``` - - [1] "Area.of.residence" - [2] "Area.of.workplace" - [3] "All" - [4] "Work.mainly.at.or.from.home" - [5] "Underground..metro..light.rail..tram" - [6] "Train" - [7] "Bus..minibus.or.coach" - [8] "Taxi" - [9] "Motorcycle..scooter.or.moped" - [10] "Driving.a.car.or.van" - [11] "Passenger.in.a.car.or.van" - [12] "Bicycle" - [13] "On.foot" - [14] "Other.method.of.travel.to.work" - [15] "id" - [16] "route_number" - [17] "name" - [18] "distances" - [19] "time" - [20] "busynance" - [21] "elevations" - [22] "start_longitude" - [23] "start_latitude" - [24] "finish_longitude" - [25] "finish_latitude" - [26] "crow_fly_distance" - [27] "event" - [28] "whence" - [29] "speed" - [30] "itinerary" - [31] "plan" - [32] "note" - [33] "length" - [34] "quietness" - [35] "west" - [36] "south" - [37] "east" - [38] "north" - [39] "leaving" - [40] "arriving" - [41] "grammesCO2saved" - [42] "calories" - [43] "edition" - [44] "gradient_segment" - [45] "elevation_change" - [46] "provisionName" - [47] "gradient_smooth" - [48] "geometry" - -``` r -tm_shape(route_segments_minimal) + - tm_lines("name") -``` - -![](README_files/figure-commonmark/unnamed-chunk-2-2.png) - -``` r -rnet_from_cyclestreets = overline( - route_segments_minimal, - attrib = c("All", "gradient_smooth", "quietness"), - fun = c(sum = sum, mean = mean) - ) -rnet_from_cyclestreets = rnet_from_cyclestreets %>% - transmute(All = All_sum, Gradient = gradient_smooth_mean, Quietness = quietness_mean) -plot(rnet_from_cyclestreets) -``` - -![](README_files/figure-commonmark/unnamed-chunk-2-3.png) - -``` r -sf::write_sf(route_segments_minimal, "route_segments_minimal.geojson", delete_dsn = TRUE) -sf::write_sf(rnet_from_cyclestreets, "rnet_from_cyclestreets.geojson", delete_dsn = TRUE) -``` +![](README_files/figure-commonmark/rnet_from_cyclestreets-5.png) # Large example A large example plus benchmark is shown below: -``` r -# list.files() -cycle_routes_london = pct::get_pct_routes_fast("london") -sf::write_sf(cycle_routes_london, "cycle_routes_london.geojson") -zip("cycle_routes_london.zip", "cycle_routes_london.geojson") -system("gh release upload v0 cycle_routes_london.zip") -``` - ``` r system.time({ cycle_routes_london = geojsonsf::geojson_sf("cycle_routes_london.geojson") diff --git a/README.qmd b/README.qmd index b23730b..021cfe5 100644 --- a/README.qmd +++ b/README.qmd @@ -5,54 +5,63 @@ bibliography: references.bib # overline -Overline is a function that takes overlapping linestrings and converts them into a route network [@morgan2020] as illustrated in a minimal reproducible example below. +Overline is a function that takes overlapping linestrings and converts them into a route network [@morgan2020] as illustrated in a minimal reproducible example created with [ATIP](https://github.com/acteng/atip/). -```{r, message=FALSE} -library(sf) -library(stplanr) +The input is a dataset in which some segments overlap: + +```{r, message=FALSE, echo=FALSE} library(tidyverse) library(tmap) -sl = routes_fast_sf[2:3, 0] -sl$n = 1:2 -plot(sl) -rnet = overline(sl, attrib = "n") -plot(rnet) -sf::write_sf(sl, "minimal-example-input.geojson", delete_dsn = TRUE) -sf::write_sf(rnet, "minimal-example-output.geojson", delete_dsn = TRUE) +library(stplanr) +layers = sf::st_layers("test-data/minimal_2_lines.txt") +minimal_2_lines = sf::st_read("test-data/minimal_2_lines.txt", layer = layers[[1]]) +minimal_2_lines = minimal_2_lines %>% + transmute(value = c(2, 1), name = c("A", "B")) +sf::write_sf(minimal_2_lines, "input.geojson", delete_dsn = TRUE) +tm_shape(minimal_2_lines) + + tm_lines("name", palette = "Set1", lwd = "value", scale = 9) +``` + +The output is a dataset in which the overlapping segments have been combined: + +```{r, message=FALSE, echo=FALSE} +minimal_2_rnet = overline(minimal_2_lines, attrib = "value") +tm_shape(minimal_2_rnet) + + tm_lines("value", palette = "Set1", lwd = "value", scale = 15) +sf::write_sf(minimal_2_rnet, "output.geojson", delete_dsn = TRUE) ``` -The function has been implemented in the [`overline()` function](https://docs.ropensci.org/stplanr/reference/overline.html) in the R package `stplanr`. The function works fine for city sized datasets but for national datasets is slow, buggy and not feature complete, as it does not retain OSM IDs. This repo provides a place to discuss and develop example code to solve this problem. +The functionality has been implemented in the [`overline()` function](https://docs.ropensci.org/stplanr/reference/overline.html) in the R package `stplanr`. The function works fine for city sized datasets but for national datasets is slow, buggy and not feature complete, as it does not retain OSM IDs. This repo provides a place to discuss and develop example code to solve this problem. In Python, the input and outputs can be visualised as follows: -```python +```{python} import geopandas as gpd input = gpd.read_file("input.geojson") -input.plot() +# Plot with colour by value: +input.plot(column="value") ``` -```python +```{python} output = gpd.read_file("output.geojson") -output.plot() +output.plot(column="value") ``` -![](README_files/figure-commonmark/unnamed-chunk-3-3.png) - # Example with road names The example below takes routes at the segment level and calculates average gradient for each segment. Road names are NOT currently implemented in `overline()` in R. -```{r} +```{r rnet_from_cyclestreets, message=FALSE, echo=FALSE} sl_desire_lines = stplanr::flowlines_sf[2:3, ] -qtm(sl_desire_lines) + - qtm(sl) +# qtm(sl_desire_lines) + +# qtm(sl) route_segments_minimal = stplanr::route( l = sl_desire_lines, route_fun = cyclestreets::journey ) -names(route_segments_minimal) -tm_shape(route_segments_minimal) + - tm_lines("name") +# names(route_segments_minimal) +route_segments_minimal = route_segments_minimal %>% + transmute(All = All, name = name, gradient_smooth = gradient_smooth, quietness = quietness) rnet_from_cyclestreets = overline( route_segments_minimal, attrib = c("All", "gradient_smooth", "quietness"), @@ -60,9 +69,15 @@ rnet_from_cyclestreets = overline( ) rnet_from_cyclestreets = rnet_from_cyclestreets %>% transmute(All = All_sum, Gradient = gradient_smooth_mean, Quietness = quietness_mean) -plot(rnet_from_cyclestreets) sf::write_sf(route_segments_minimal, "route_segments_minimal.geojson", delete_dsn = TRUE) sf::write_sf(rnet_from_cyclestreets, "rnet_from_cyclestreets.geojson", delete_dsn = TRUE) +m1 = tm_shape(route_segments_minimal) + + tm_lines("name", lwd = "All", palette = "Set1", scale = 15) + + tm_layout(legend.outside = TRUE) +m2 = tm_shape(rnet_from_cyclestreets) + + tm_lines(lwd = "All", palette = "viridis", scale = 20) + + tm_layout(legend.outside = TRUE) +tmap_arrange(m1, m2) ``` # Large example @@ -71,6 +86,7 @@ A large example plus benchmark is shown below: ```{r} #| eval: false +#| echo: false # list.files() cycle_routes_london = pct::get_pct_routes_fast("london") sf::write_sf(cycle_routes_london, "cycle_routes_london.geojson") diff --git a/README_files/figure-commonmark/rnet_from_cyclestreets-5.png b/README_files/figure-commonmark/rnet_from_cyclestreets-5.png new file mode 100644 index 0000000..f4201f8 Binary files /dev/null and b/README_files/figure-commonmark/rnet_from_cyclestreets-5.png differ diff --git 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