77 weighted points

examples/york_minimal/.gitignore

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+README_files/
+input/north-yorkshire-latest.osm.pbf
+input/inputs.html

examples/york_minimal/README.md

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+
+
+The setup information is contained within the `setup.py` file, which
+generates minimal input files.
+
+``` 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$name
+destinations = dplyr::filter(
+    schools_york,
+    name %in% c("York High School", "Huntington School")
+) |>
+  dplyr::select(name, everything())
+destinations$name
+```
+
+    [1] "York High School"  "Huntington School"
+
+``` r
+# Remove columns that only contain NA:
+destinations = destinations[, colSums(is.na(destinations)) < nrow(destinations)]
+destinations = sf::st_centroid(destinations)
+```
+
+    Warning: st_centroid assumes attributes are constant over geometries
+
+``` r
+sf::write_sf(destinations, "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/origins_destinations_plot-1.png)
+
+Let’s visualise the flows between the origins and destinations:
+
+``` r
+library(ggplot2)
+od = readr::read_csv("input/od.csv")
+```
+
+    Rows: 6 Columns: 3
+    ── Column specification ────────────────────────────────────────────────────────
+    Delimiter: ","
+    chr (2): from, to
+    dbl (1): count
+
+    ℹ Use `spec()` to retrieve the full column specification for this data.
+    ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
+
+``` r
+od_geo = od::od_to_sf(od, zones, destinations)
+```
+
+    0 origins with no match in zone ids
+    0 destinations with no match in zone ids
+     points not in od data removed.
+
+``` r
+ggplot() +
+  geom_sf(data = zones, fill = "grey") +
+  geom_sf(data = subpoints, aes(size = size), color = "blue") +
+  geom_sf(data = destinations, color = "red") +
+  geom_sf(data = od_geo, aes(size = count), color = "black")
+```
+
+![](README_files/figure-commonmark/flows_plot-3.png)
+
+We can then run the od2net command as follows:
+
+``` bash
+docker run -v $(pwd):/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json
+```

examples/york_minimal/README.qmd

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+---
+format: gfm
+---
+
+The setup information is contained within the `setup.py` file, which generates minimal input files.
+
+```{bash}
+#| eval: false
+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)
+q = "SELECT * FROM multipolygons WHERE amenity='school'"
+schools_york = osmextract::oe_get("York", query = q, extra_tags = "amenity")
+# schools_york$name
+destinations = dplyr::filter(
+    schools_york,
+    name %in% c("York High School", "Huntington School")
+) |>
+  dplyr::select(name, everything())
+destinations$name
+# Remove columns that only contain NA:
+destinations = destinations[, colSums(is.na(destinations)) < nrow(destinations)]
+destinations = sf::st_centroid(destinations)
+sf::write_sf(destinations, "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")
+```
+
+Let's visualise the flows between the origins and destinations:
+
+```{r}
+#| label: flows_plot
+library(ggplot2)
+od = readr::read_csv("input/od.csv")
+od_geo = od::od_to_sf(od, zones, destinations)
+ggplot() +
+  geom_sf(data = zones, fill = "grey") +
+  geom_sf(data = subpoints, aes(size = size), color = "blue") +
+  geom_sf(data = destinations, color = "red") +
+  geom_sf(data = od_geo, aes(size = count), color = "black")
+```
+
+```{r}
+#| eval: false
+#| echo: false
+library(tmap)
+tmap_mode("view")
+m = qtm(zones) +
+  tm_shape(subpoints) +
+  tm_dots(size = "size", col = "blue") +
+  tm_shape(destinations) +
+  tm_dots(fill = "red", size = 5) +
+  tm_shape(od_geo) +
+  tm_lines(lwd = "count", col = "black", scale = 9)
+tmap_save(m, "input/inputs.html")
+browseURL("input/inputs.html")
+```
+
+We can then run the od2net command as follows:
+
+
+```{bash}
+#| eval: false
+docker run -v $(pwd):/app ghcr.io/urban-analytics-technology-platform/od2net:main /app/config.json
+```

examples/york_minimal/config.json

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+{
+  "requests": {
+    "description": "Manually drawn zones and flows to schools, demonstrating weighted subpoints",
+    "pattern": {
+      "ZoneToPoint": {
+        "zones_path": "zones.geojson",
+        "destinations_path": "destinations.geojson",
+        "csv_path": "od.csv",
+        "origin_zone_centroid_fallback": false
+      }
+    },
+    "origins_path": "subpoints.geojson",
+    "destinations_path": "destinations.geojson"
+  },
+  "uptake": "Identity",
+  "cost": {
+    "ExternalCommand": "python3 cost.py"
+  },
+  "lts": {
+    "ExternalCommand": "python3 lts.py"
+  }
+}

examples/york_minimal/cost.py

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+import json
+import sys
+
+# Output a numeric edge cost
+def calculate(edge):
+    tags = edge["osm_tags"]
+    length_meters = edge["length_meters"]
+    lts = edge["lts"]
+    nearby_amenities = edge["nearby_amenities"]
+    slope = edge["slope"]
+
+    # Return None to not use the edge at all
+
+    if tags["highway"] == "residential":
+        return [round(length_meters), round(length_meters)]
+    else:
+        # Strongly avoid non-residential roads
+        return [round(10 * length_meters), round(10 * length_meters)]
+
+
+# Read an array of JSON dictionaries from STDIN
+input_batch = json.loads(sys.stdin.read())
+# Calculate an edge cost for each one
+results = list(map(calculate, input_batch))
+# Write a JSON array of the resulting numbers
+print(json.dumps(results))

examples/york_minimal/input/destinations.geojson

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+{
+"type": "FeatureCollection",
+"name": "destinations",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "name": "York High School", "osm_way_id": "29110900", "amenity": "school", "other_tags": "\"addr:city\"=>\"York\",\"addr:country\"=>\"GB\",\"addr:postcode\"=>\"YO24 3WZ\",\"addr:street\"=>\"Cornlands Road\",\"addr:suburb\"=>\"Acomb\",\"capacity\"=>\"991\",\"email\"=>\"[email protected]\",\"isced:level\"=>\"2\",\"max_age\"=>\"16\",\"min_age\"=>\"11\",\"phone\"=>\"+44 1904 555500\",\"ref:GB:uprn\"=>\"100052163540\",\"ref:edubase\"=>\"144652\",\"ref:edubase:group\"=>\"16073\",\"school:trust\"=>\"yes\",\"school:trust:name\"=>\"South Bank Multi Academy Trust\",\"school:trust:type\"=>\"multi_academy\",\"school:type\"=>\"academy\",\"website\"=>\"https://www.yorkhighschool.co.uk/\",\"wikidata\"=>\"Q8055461\",\"wikipedia\"=>\"en:York High School, York\"" }, "geometry": { "type": "Point", "coordinates": [ -1.128900930965928, 53.947613356147485 ] } },
+{ "type": "Feature", "properties": { "name": "Huntington School", "osm_way_id": "122135723", "amenity": "school", "other_tags": "\"addr:country\"=>\"GB\",\"addr:postcode\"=>\"YO32 9WT\",\"addr:street\"=>\"Huntington Road\",\"capacity\"=>\"1545\",\"email\"=>\"[email protected]\",\"isced:level\"=>\"2;3\",\"max_age\"=>\"18\",\"min_age\"=>\"11\",\"phone\"=>\"+44 1904 752100\",\"ref:GB:uprn\"=>\"100052170371\",\"ref:edubase\"=>\"121673\",\"school:trust\"=>\"no\",\"school:type\"=>\"community\",\"website\"=>\"https://huntingtonschool.co.uk/\"" }, "geometry": { "type": "Point", "coordinates": [ -1.063642017028779, 53.990093958328686 ] } }
+]
+}

examples/york_minimal/input/od.csv

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+from,to,count
+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

examples/york_minimal/input/subpoints.geojson

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+{
+"type": "FeatureCollection",
+"name": "subpoints",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "size": 7.2 }, "geometry": { "type": "Point", "coordinates": [ -1.084948428827924, 53.959860019753116 ] } },
+{ "type": "Feature", "properties": { "size": 8.2 }, "geometry": { "type": "Point", "coordinates": [ -1.065675923641903, 53.968255508127534 ] } },
+{ "type": "Feature", "properties": { "size": 1.2 }, "geometry": { "type": "Point", "coordinates": [ -1.08027588725639, 53.960397590493173 ] } },
+{ "type": "Feature", "properties": { "size": 5.3 }, "geometry": { "type": "Point", "coordinates": [ -1.048145775760174, 53.988466436780065 ] } },
+{ "type": "Feature", "properties": { "size": 7.8 }, "geometry": { "type": "Point", "coordinates": [ -1.055372173407674, 54.009228087733909 ] } },
+{ "type": "Feature", "properties": { "size": 2.9 }, "geometry": { "type": "Point", "coordinates": [ -1.077859414261401, 53.998822595720604 ] } },
+{ "type": "Feature", "properties": { "size": 3.9 }, "geometry": { "type": "Point", "coordinates": [ -1.11062851182091, 53.93422678953317 ] } },
+{ "type": "Feature", "properties": { "size": 3.1 }, "geometry": { "type": "Point", "coordinates": [ -1.107718347978171, 53.929568710302227 ] } },
+{ "type": "Feature", "properties": { "size": 2.3 }, "geometry": { "type": "Point", "coordinates": [ -1.116778858632627, 53.956331735484653 ] } }
+]
+}

examples/york_minimal/input/zones.geojson

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+{"type":"FeatureCollection","features":[{"type":"Feature","properties":{"name":"center"},"geometry":{"coordinates":[[[-1.08285,53.970735],[-1.096017,53.958917],[-1.075591,53.947693],[-1.057528,53.967309],[-1.08285,53.970735]]],"type":"Polygon"}},{"type":"Feature","properties":{"name":"north"},"geometry":{"coordinates":[[[-1.094806,53.998733],[-1.068685,53.977605],[-1.025942,53.9965],[-1.056812,54.010736],[-1.094806,53.998733]]],"type":"Polygon"}},{"type":"Feature","properties":{"name":"south"},"geometry":{"coordinates":[[[-1.146752,53.957545],[-1.139312,53.924745],[-1.091661,53.9281],[-1.1067,53.956427],[-1.146752,53.957545]]],"type":"Polygon"}}]}

examples/york_minimal/lts.py

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+import json
+import sys
+
+# Output 0 (not allowed), 1 (suitable for children), 2 (low stress), 3 (low stress), or 4 (high stress)
+def calculate(tags):
+    if tags["highway"] == "residential":
+        return 2
+    else:
+        return 4
+
+
+# Read an array of JSON dictionaries from STDIN
+tags_batch = json.loads(sys.stdin.read())
+# Calculate LTS for each one
+lts_results = list(map(calculate, tags_batch))
+# Write a JSON array of the resulting numbers
+print(json.dumps(lts_results))