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
cartobq.docs.osm_pois_berlin 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 function, which yields a result of
0.698 by applying the query below:carto-un
carto-un-eu
manual
WITH berlin AS (
SELECT
h3, CAST(COUNT(*) AS FLOAT64) AS n_pois
FROM (
SELECT `carto-un`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3
FROM `cartobq.docs.osm_pois_berlin` )
GROUP BY h3)
SELECT `carto-un`.carto.MORANS_I_H3(ARRAY_AGG(STRUCT(h3, n_pois)),
1, 'exponential')
FROM berlin;
WITH berlin AS (
SELECT
h3, CAST(COUNT(*) AS FLOAT64) AS n_pois
FROM (
SELECT `carto-un-eu`.carto.H3_FROMGEOGPOINT(geom, 9) AS h3
FROM `cartobq.docs.osm_pois_berlin` )
GROUP BY h3)
SELECT `carto-un-eu`.carto.MORANS_I_H3(ARRAY_AGG(STRUCT(h3, n_pois)),
1, 'exponential')
FROM berlin;
WITH berlin AS (
SELECT
h3, CAST(COUNT(*) AS FLOAT64) AS n_pois
FROM (
SELECT carto.H3_FROMGEOGPOINT(geom, 9) AS h3
FROM `cartobq.docs.osm_pois_berlin` )
GROUP BY h3)
SELECT carto.MORANS_I_H3(ARRAY_AGG(STRUCT(h3, n_pois)),
1, 'exponential')
FROM berlin;
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.