ST_ClusterKMeans — Window function that returns a cluster id for each input geometry using the K-means algorithm.

`integer `

geometry winset **ST_ClusterKMeans**(`geom`, integer `number_of_clusters`, float `max_radius``)`

;

Returns K-means cluster number for each input geometry. The distance used for clustering is the distance between the centroids for 2D geometries, and distance between bounding box centers for 3D geometries. For POINT inputs, M coordinate will be treated as weight of input and has to be larger than 0.

`max_radius`

, if set, will cause ST_ClusterKMeans to generate more clusters than `k`

ensuring that no cluster in output has radius larger than `max_radius`

. This is useful in reachability analysis.

Enhanced: 3.2.0 Support for `max_radius`

Enhanced: 3.1.0 Support for 3D geometries and weights

Disponibilidade: 2.3.0

Generate dummy set of parcels for examples:

CREATE TABLE parcels AS SELECT lpad((row_number() over())::text,3,'0') As parcel_id, geom, ('{residential, commercial}'::text[])[1 + mod(row_number()OVER(),2)] As type FROM ST_Subdivide(ST_Buffer('SRID=3857;LINESTRING(40 100, 98 100, 100 150, 60 90)'::geometry, 40, 'endcap=square'),12) As geom;

SELECT ST_ClusterKMeans(geom, 3) OVER() AS cid, parcel_id, geom FROM parcels;

cid | parcel_id | geom -----+-----------+--------------- 0 | 001 | 0103000000... 0 | 002 | 0103000000... 1 | 003 | 0103000000... 0 | 004 | 0103000000... 1 | 005 | 0103000000... 2 | 006 | 0103000000... 2 | 007 | 0103000000...

Partitioning parcel clusters by type:

SELECT ST_ClusterKMeans(geom, 3) over (PARTITION BY type) AS cid, parcel_id, type FROM parcels;

cid | parcel_id | type -----+-----------+------------- 1 | 005 | commercial 1 | 003 | commercial 2 | 007 | commercial 0 | 001 | commercial 1 | 004 | residential 0 | 002 | residential 2 | 006 | residential

Example: Clustering a preaggregated planetary-scale data population dataset using 3D clusering and weighting. Identify at least 20 regions based on Kontur Population Data that do not span more than 3000 km from their center:

create table kontur_population_3000km_clusters as select geom, ST_ClusterKMeans( ST_Force4D( ST_Transform(ST_Force3D(geom), 4978), -- cluster in 3D XYZ CRS mvalue => population -- set clustering to be weighed by population ), 20, -- aim to generate at least 20 clusters max_radius => 3000000 -- but generate more to make each under 3000 km radius ) over () as cid from kontur_population;