In this example we are going to identify hotspots of amenity POIs in Stockholm using OpenStreetMap data and the GETIS_ORD_H3_TABLE 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_TABLE function, which returns the Getis-Ord Gi* statistic for each H3 cell, calculated over n_amenity_pois (number of amenity POIs in the cell).
-- Create table with POI counts by grid cellCREATETABLEproject.dataset.stockholm_poi_count_grid ASSELECT h3, COUNT(*) AS n_amenity_poisFROM (SELECT`carto-un`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3,FROM cartobq.docs.osm_pois_stockholmWHERE amenity IS NOT NULL )GROUP BY h3;-- Compute Getis-Ord Gi*CALL`carto-un`.carto.GETIS_ORD_H3_TABLE('project.dataset.stockholm_poi_count_grid', 'project.dataset.stockholm_poi_count_grid_gi','h3', 'n_amenity_pois', 4, 'triangular');
-- Create table with POI counts by grid cellCREATETABLEproject.dataset.stockholm_poi_count_grid ASSELECT h3, COUNT(*) AS n_amenity_poisFROM (SELECT`carto-un-eu`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3,FROM cartobq.docs.osm_pois_stockholmWHERE amenity IS NOT NULL )GROUP BY h3;-- Compute Getis-Ord Gi*CALL`carto-un-eu`.carto.GETIS_ORD_H3_TABLE('project.dataset.stockholm_poi_count_grid', 'project.dataset.stockholm_poi_count_grid_gi','h3', 'n_amenity_pois', 4, 'triangular');
-- Create table with POI counts by grid cellCREATETABLEproject.dataset.stockholm_poi_count_grid ASSELECT h3, COUNT(*) AS n_amenity_poisFROM (SELECT carto.H3_FROMGEOGPOINT(geom, 9) AS h3,FROM cartobq.docs.osm_pois_stockholmWHERE amenity IS NOT NULL )GROUP BY h3;-- Compute Getis-Ord Gi*CALL carto.GETIS_ORD_H3_TABLE('project.dataset.stockholm_poi_count_grid', 'project.dataset.stockholm_poi_count_grid_gi','h3', 'n_amenity_pois', 4, 'triangular');
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.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.