Add weighted subpoints for #77

examples/york_minimal/README.md

@@ -3,20 +3,101 @@
 The setup information is contained within the `setup.py` file, which
 generates minimal input files.
 
-``` {bash}
+``` bash
 python setup.py
 ```
 
+We’ll get a sample of 2 schools in York (York High School and Huntington
+School) using the `osmextract` package.
+
+``` r
+library(osmextract)
+```
+
+    Data (c) OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright.
+    Check the package website, https://docs.ropensci.org/osmextract/, for more details.
+
+``` r
+q = "SELECT * FROM multipolygons WHERE amenity='school'"
+schools_york = osmextract::oe_get("York", query = q, extra_tags = "amenity")
+```
+
+    No exact match found for place = York and provider = geofabrik. Best match is Corse. 
+    Checking the other providers.
+
+    No exact match found in any OSM provider data. Searching for the location online.
+
+    The input place was matched with North Yorkshire. 
+
+    The chosen file was already detected in the download directory. Skip downloading.
+
+    The corresponding gpkg file was already detected. Skip vectortranslate operations.
+
+    Reading query `SELECT * FROM multipolygons WHERE amenity='school''
+    from data source `/home/robin/data/osm/geofabrik_north-yorkshire-latest.gpkg' 
+      using driver `GPKG'
+    Simple feature collection with 603 features and 25 fields
+    Geometry type: MULTIPOLYGON
+    Dimension:     XY
+    Bounding box:  xmin: -2.546044 ymin: 53.6425 xmax: -0.2912398 ymax: 54.61681
+    Geodetic CRS:  WGS 84
+
+``` r
+# schools_york$name
+schools_york_minimal = dplyr::filter(
+    schools_york,
+    name %in% c("York High School", "Huntington School")
+)
+schools_york_minimal$name
+```
+
+    [1] "York High School"  "Huntington School"
+
+``` r
+# Remove columns that only contain NA:
+schools_york_minimal = schools_york_minimal[, colSums(is.na(schools_york_minimal)) < nrow(schools_york_minimal)]
+sf::write_sf(schools_york_minimal, "input/destinations.geojson", delete_dsn = TRUE)
+```
+
+We’ll also create a sample of subpoints in York, taking 3 random points
+from each zone.
+
+``` r
+zones = sf::st_read("input/zones.geojson")
+```
+
+    Reading layer `zones' from data source 
+      `/home/robin/github/Urban-Analytics-Technology-Platform/od2net/examples/york_minimal/input/zones.geojson' 
+      using driver `GeoJSON'
+    Simple feature collection with 3 features and 1 field
+    Geometry type: POLYGON
+    Dimension:     XY
+    Bounding box:  xmin: -1.146752 ymin: 53.92474 xmax: -1.025942 ymax: 54.01074
+    Geodetic CRS:  WGS 84
+
+``` r
+set.seed(123)
+subpoints = sf::st_sample(zones, size = rep(3, nrow(zones))) |>
+    sf::st_sf()
+# Let's add provide the subpoints with values representing their importance:
+subpoints$size = runif(nrow(subpoints), 1, 10) |>
+    round(1)
+sf::write_sf(subpoints, "input/subpoints.geojson", delete_dsn = TRUE)
+```
+
 We can visualise these as follows:
 
 ``` python
 import geopandas as gpd
 import pandas as pd
 zones = gpd.read_file("input/zones.geojson")
 destinations = gpd.read_file("input/destinations.geojson")
+subpoints = gpd.read_file("input/subpoints.geojson")
 od = pd.read_csv("input/od.csv")
 ax = zones.plot()
 destinations.plot(ax=ax, color='red')
+subpoints.plot(ax=ax, color='blue', markersize=subpoints['size'] * 3)
+ax.set_title("Origins and Destinations")
 ```
 
-![](README_files/figure-commonmark/cell-2-output-1.png)
+![](README_files/figure-commonmark/origins_destinations_plot-1.png)

examples/york_minimal/README.qmd

@@ -5,18 +5,52 @@ format: gfm
 The setup information is contained within the `setup.py` file, which generates minimal input files.
 
 ```{bash}
+#| eval: false
 python setup.py
 ```
 
-We can visualise these as follows:
+We'll get a sample of 2 schools in York (York High School and Huntington School) using the `osmextract` package.
+
+```{r}
+library(osmextract)
+q = "SELECT * FROM multipolygons WHERE amenity='school'"
+schools_york = osmextract::oe_get("York", query = q, extra_tags = "amenity")
+# schools_york$name
+schools_york_minimal = dplyr::filter(
+    schools_york,
+    name %in% c("York High School", "Huntington School")
+)
+schools_york_minimal$name
+# Remove columns that only contain NA:
+schools_york_minimal = schools_york_minimal[, colSums(is.na(schools_york_minimal)) < nrow(schools_york_minimal)]
+sf::write_sf(schools_york_minimal, "input/destinations.geojson", delete_dsn = TRUE)
+```
+
+We'll also create a sample of subpoints in York, taking 3 random points from each zone.
 
+```{r}
+zones = sf::st_read("input/zones.geojson")
+set.seed(123)
+subpoints = sf::st_sample(zones, size = rep(3, nrow(zones))) |>
+    sf::st_sf()
+# Let's add provide the subpoints with values representing their importance:
+subpoints$size = runif(nrow(subpoints), 1, 10) |>
+    round(1)
+sf::write_sf(subpoints, "input/subpoints.geojson", delete_dsn = TRUE)
+```
+
+We can visualise these as follows:
 
 ```{python}
+#| label: origins_destinations_plot
 import geopandas as gpd
 import pandas as pd
 zones = gpd.read_file("input/zones.geojson")
 destinations = gpd.read_file("input/destinations.geojson")
+subpoints = gpd.read_file("input/subpoints.geojson")
 od = pd.read_csv("input/od.csv")
 ax = zones.plot()
 destinations.plot(ax=ax, color='red')
+subpoints.plot(ax=ax, color='blue', markersize=subpoints['size'] * 3)
+ax.set_title("Origins and Destinations")
 ```

examples/york_minimal/setup.py

@@ -27,13 +27,6 @@ def makeOrigins():
     )
 
 
-def makeDestinations():
-    writeFixedOutputFile(
-        "input/destinations.geojson",
-        """{"type":"FeatureCollection","features":[{"type":"Feature","properties":{"name":"poppleton"},"geometry":{"coordinates":[-2.141025,53.988014],"type":"Point"}},{"type":"Feature","properties":{"name":"corndogs"},"geometry":{"coordinates":[-1.080074,53.959366],"type":"Point"}}]}""",
-    )
-
-
 def makeZones():
     writeFixedOutputFile(
         "input/zones.geojson",
@@ -45,12 +38,12 @@ def makeOD():
     writeFixedOutputFile(
         "input/od.csv",
         """from,to,count
-south,poppleton,500
-center,poppleton,100
-north,poppleton,200
-south,corndogs,800
-center,corndogs,300
-north,corndogs,600""",
+south,York High School,500
+center,York High School,100
+north,York High School,200
+south,Huntington School,800
+center,Huntington School,300
+north,Huntington School,600""",
     )