Computing the spatial autocorrelation of POIs locations in Berlin

In this example we are going to analyze the spatial correlation of POIs locations in Berlin using OpenStreetMap data and the Moran’s I function available in the statistics module. POIs data can be found in the publicly available table.

First, we are going to visually analyze the distribution of the POIs in the Berlin area by plotting the aggregation of POIs in each H3 cell of resolution 9. This can be done simply by applying the H3_FROMGEOGPOINT function to compute the H3 cell that each POI belongs to and then performing a group by to count the number of POIs inside each cell (n_pois).

By looking at the resulting map below, it is clear that there is a level of spatial autocorrelation in the distribution of the POIs:

We can measure this spatial autocorrelation using the MORANS_I_H3_TABLE function, which yields a result of 0.673 by applying the query below:

-- Create table with POI counts by grid cell
CREATE OR REPLACE TABLE project.dataset.berlin_poi_count_grid AS
    h3, COUNT(*) AS n_pois
    SELECT `carto-un`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3
    FROM )

-- Compute Moran's I
CALL `carto-un`.carto.MORANS_I_H3_TABLE(

-- Read computed value
SELECT * FROM project.dataset.berlin_poi_count_grid_mi;

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