diff --git a/.gitignore b/.gitignore index 6c828e6..c9725fe 100644 --- a/.gitignore +++ b/.gitignore @@ -64,3 +64,5 @@ docs intermediate/ *.pbf *.tif +output/output.geojson +output/*.pmtiles diff --git a/README.md b/README.md index b83e949..3da2333 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,31 @@ use in the reproducible code below. # Setup +To run the code below you need to have R and Docker installed. After you +have installed R, you can install the packages we’ll use as follows: + ``` r -# # Install geopandas: -# reticulate::py_install("geopandas") +# Install pak if not already installed: +if (!requireNamespace("pak", quietly = TRUE)) { + install.packages("pak") +} +pkgs = c("sf", "tidyverse", "tmap", "pak") +pkgs_to_install = pkgs[!pkgs %in% installed.packages()] +if (length(pkgs_to_install) > 0) { + pak::pkg_install(pkgs_to_install) +} +# Load the packages with vapply: +vapply(pkgs, require, logical(1), character.only = TRUE) +``` + + sf tidyverse tmap pak + TRUE TRUE TRUE TRUE + +We will also install a couple of package that are not on CRAN: + +``` r +pak::pkg_install("acteng/netgen") +pak::pkg_install("Urban-Analytics-Technology-Platform/od2net/r") ```
@@ -85,19 +107,24 @@ od_geo |> # od2net -Building on code in the `od2net` and -[nptscot/od2net-tests](https://github.com/nptscot/od2net-tests) repos, -the code below prepares the input datasets and runs the -network-generation code, generating output.geojson and output.pmtiles -outputs: +Building on code in the +[od2net](https://urban-analytics-technology-platform.github.io/od2net/r/) +R package, we can generate the files needed for the network generation +stage. + +After you have installed the package, prepare the input datasets and run +the network-generation code to generate output.geojson and +output.pmtiles outputs: ``` r -source("R/setup.R") -make_zones("input/zones_york.geojson") -make_osm() -make_origins() -make_elevation() -destinations = destinations_york # Provided in the R package +origin_zones = netgen::zones_york +names(origin_zones) +names(origin_zones)[1] = "name" +sf::write_sf(zones, "input/zones_york.geojson", delete_dsn = TRUE) +od2net::make_osm(zones_file = "input/zones_york.geojson") +od2net::make_origins() +netgen:::make_elevation() +destinations = netgen::destinations_york # Provided in the R package names(destinations)[1] = "name" destinations = destinations[1] class(destinations$name) = "character" @@ -125,9 +152,22 @@ fs::dir_tree("output") ``` output + ├── counts.csv ├── output.geojson + ├── rnet.pmtiles └── rnet_output_osrm_overline.geojson +From that point you can visualise the pmtiles in the website at +[od2net.org](https://od2net.org) or other front-end application, as +shown below. + +## Adjusting uptake functions + +The example above visualises *all* trips to school, not just active +travel trips. + +## Other approaches to network generation + An advantage of `od2net` is that it can generate the network and routes in a single step. For other approaches, we need to calculate the routes first, as shown below. @@ -181,8 +221,6 @@ osmextract::oe_download( ) ``` - [1] "/home/robin/github/acteng/netgen/geofabrik_osm.pbf" - Then with the system shell: ``` bash @@ -262,5 +300,6 @@ plot(rnet) A disadvantage of this approach is that it’s computational resource-intensive and takes a long time. An in-progress is `od2net`. - + +# Comparing od2net and overline outputs diff --git a/README.qmd b/README.qmd index 421f892..03917f1 100644 --- a/README.qmd +++ b/README.qmd @@ -13,16 +13,32 @@ There are many sources of OD data, see [odgen](odgen.qmd) for a way to generate # Setup +To run the code below you need to have R and Docker installed. +After you have installed R, you can install the packages we'll use as follows: + ```{r} -#| include: false -pkgs = c("sf", "tidyverse", "tmap") +# Install pak if not already installed: +if (!requireNamespace("pak", quietly = TRUE)) { + install.packages("pak") +} +pkgs = c("sf", "tidyverse", "tmap", "pak") pkgs_to_install = pkgs[!pkgs %in% installed.packages()] if (length(pkgs_to_install) > 0) { - install.packages(pkgs_to_install) + pak::pkg_install(pkgs_to_install) } +# Load the packages with vapply: +vapply(pkgs, require, logical(1), character.only = TRUE) ``` +We will also install a couple of package that are not on CRAN: + ```{r} +pak::pkg_install("acteng/netgen") +pak::pkg_install("Urban-Analytics-Technology-Platform/od2net/r") +``` + +```{r} +#| include: false # # Install geopandas: # reticulate::py_install("geopandas") ``` @@ -84,17 +100,21 @@ od_geo |> # od2net -Building on code in the `od2net` and [nptscot/od2net-tests](https://github.com/nptscot/od2net-tests) repos, the code below prepares the input datasets and runs the network-generation code, generating output.geojson and output.pmtiles outputs: +Building on code in the [od2net](https://urban-analytics-technology-platform.github.io/od2net/r/) R package, we can generate the files needed for the network generation stage. + +After you have installed the package, prepare the input datasets and run the network-generation code to generate output.geojson and output.pmtiles outputs: ```{r} #| eval: false -source("R/setup.R") -make_zones("input/zones_york.geojson") -make_osm() -make_origins() -make_elevation() -destinations = destinations_york # Provided in the R package +origin_zones = netgen::zones_york +names(origin_zones) +names(origin_zones)[1] = "name" +sf::write_sf(zones, "input/zones_york.geojson", delete_dsn = TRUE) +od2net::make_osm(zones_file = "input/zones_york.geojson") +od2net::make_origins() +netgen:::make_elevation() +destinations = netgen::destinations_york # Provided in the R package names(destinations)[1] = "name" destinations = destinations[1] class(destinations$name) = "character" @@ -112,15 +132,7 @@ Run the tool with Docker as follows: ```{bash} #| eval: false # On Linux: -sudo docker run -v $(pwd):/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json -# or in Windows: -sudo docker run -v ${pwd}:/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json -``` - -```{r} -#| eval: false -#| echo: false -system("docker run -v $(pwd):/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json") +sudo docker run -v $(pwd):/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json --rng-seed 42 elevation_geotiff /app/input/elevation.tif ``` After that you should see the following in the output folder: @@ -129,6 +141,19 @@ After that you should see the following in the output folder: fs::dir_tree("output") ``` +From that point you can visualise the pmtiles in the website at [od2net.org](https://od2net.org) or other front-end application, as shown below. + +## Minimal od2net example + +For the purposes of testing it's worth + +## Adjusting uptake functions + +The example above visualises *all* trips to school, not just active travel trips. + + +## Other approaches to network generation + An advantage of `od2net` is that it can generate the network and routes in a single step. For other approaches, we need to calculate the routes first, as shown below. @@ -191,6 +216,7 @@ routes_osrm_minimal |> We can spin-up a local OSRM server to calculate routes as [follows](https://github.com/Project-OSRM/osrm-backend#using-docker): ```{r} +#| eval: false location = osmextract::oe_match( od_geo_top_100 |> sf::st_union() ) @@ -285,6 +311,6 @@ plot(rnet) A disadvantage of this approach is that it's computational resource-intensive and takes a long time. An in-progress is `od2net`. - + - \ No newline at end of file +# Comparing od2net and overline outputs diff --git a/config.json b/config.json index 112e624..e979498 100644 --- a/config.json +++ b/config.json @@ -14,6 +14,5 @@ }, "cost": "Distance", "uptake": "Identity", - "lts": "BikeOttawa", - "elevation_geotiff": "elevation.tif" + "lts": "BikeOttawa" } \ No newline at end of file diff --git a/output/output.geojson b/output/output.geojson deleted file mode 100644 index 56c27af..0000000 --- a/output/output.geojson +++ /dev/null @@ -1 +0,0 @@ -{ "type": "FeatureCollection", "metadata": {"config":{"requests":{"description":"Test data for SchoolRoutes project.","pattern":{"ZoneToPoint":{"zones_path":"zones.geojson","csv_path":"od.csv","destinations_path":"destinations.geojson","origin_zone_centroid_fallback":false}},"origins_path":"buildings.geojson","destinations_path":"destinations.geojson"},"cost":"Distance","uptake":"Identity","lts":"BikeOttawa","elevation_geotiff":"elevation.tif"},"num_origins":19261,"num_destinations":62,"num_requests":25515,"num_succeeded_requests":25396,"num_failed_requests":119,"num_edges_with_count":36955,"routing_time_seconds":0.38761106,"total_meters_not_allowed":1744.6075869667354,"total_meters_lts1":13492700.693975307,"total_meters_lts2":18040653.49790111,"total_meters_lts3":24977491.013854958,"total_meters_lts4":6527322.938832796,"total_time_seconds":null,"tippecanoe_time_seconds":null}, "features": 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