Analytics Toolbox for BigQuery

Analytics Toolbox for BigQuery

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Identifying amenity hotspots in Stockholm

In this example we are going to identify hotspots of amenity POIs in Stockholm using OpenStreetMap data and the GETIS_ORD_H3 function of the statistics module. POIs data can be found in the publicly available cartobq.docs.osm_pois_stockholm table.

The process consists of three simple steps:

  • First, we retrieve all POIs from OpenstreetMaps which belong to the category “amenity”.
  • Next, we find the H3 cell of resolution 9 to which each POI belongs and count the number of amenity POIs inside each cell.
  • Finally, we call the GETIS_ORD_H3 function, which returns the Getis-Ord Gi* statistic for each H3 cell, calculated over n_amenity_pois (number of amenity POIs in the cell).
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WITH
  stockholm AS (
  SELECT h3, CAST(COUNT(*) AS FLOAT64) AS n_amenity_pois,
  FROM (
    SELECT `carto-un`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3,
    FROM cartobq.docs.osm_pois_stockholm
    WHERE amenity IS NOT NULL )
  GROUP BY h3),

  getis_ord AS (
  SELECT `carto-un`.carto.GETIS_ORD_H3(ARRAY_AGG(STRUCT(h3, n_amenity_pois)),
      4, 'triangular') AS output
  FROM stockholm )

SELECT unnested.INDEX AS h3id, unnested.gi
FROM getis_ord, UNNEST(getis_ord.output) AS unnested

The results can be explored in the map below, where we can use the histogram widget to narrow down the cells with the highest Gi* values, which correspond to the location of the hotspots of amenity POIs in Stockholm.

EU flag

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.