clustering

This module contains functions that perform clustering on geographies.

ST_CLUSTERKMEANS

ST_CLUSTERKMEANS(geog, numberOfClusters)

Description

Takes a set of points as input and partitions them into clusters using the k-means algorithm. Returns an array of tuples with the cluster index for each of the input features and the input geometry.

• geog: ARRAY<GEOGRAPHY> points to be clustered.

• numberOfClusters: INT64|NULL numberOfClusters that will be generated. If NULL the default value Math.sqrt(<NUMBER OF POINTS>/2) is used. The output number of cluster cannot be greater to the number of distinct points of the geog.

Return type

ARRAY<STRUCT<cluster INT64, geom GEOGRAPHY>>

Example

carto-un

SELECT `carto-un`.carto.ST_CLUSTERKMEANS([ST_GEOGPOINT(0, 0), ST_GEOGPOINT(0, 1), ST_GEOGPOINT(5, 0), ST_GEOGPOINT(1, 0)], 2);
-- {cluster: 1, geom: POINT(0 0)}
-- {cluster: 1, geom: POINT(0 1)}
-- {cluster: 0, geom: POINT(5 0)}
-- {cluster: 1, geom: POINT(1 0)}

carto-un-eu

SELECT `carto-un-eu`.carto.ST_CLUSTERKMEANS([ST_GEOGPOINT(0, 0), ST_GEOGPOINT(0, 1), ST_GEOGPOINT(5, 0), ST_GEOGPOINT(1, 0)], 2);
-- {cluster: 1, geom: POINT(0 0)}
-- {cluster: 1, geom: POINT(0 1)}
-- {cluster: 0, geom: POINT(5 0)}
-- {cluster: 1, geom: POINT(1 0)}

manual

SELECT carto.ST_CLUSTERKMEANS([ST_GEOGPOINT(0, 0), ST_GEOGPOINT(0, 1), ST_GEOGPOINT(5, 0), ST_GEOGPOINT(1, 0)], 2);
-- {cluster: 1, geom: POINT(0 0)}
-- {cluster: 1, geom: POINT(0 1)}
-- {cluster: 0, geom: POINT(5 0)}
-- {cluster: 1, geom: POINT(1 0)}

Additional examples

• New police stations based on Chicago crime location clusters

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