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Clustering Functions

ST_ClusterDBSCAN

Window function that returns a cluster id for each input geometry using the DBSCAN algorithm.

Synopsis

integer ST_ClusterDBSCAN(geometry winset
            geom, float8
            eps, integer
            minpoints)

Description

A window function that returns a cluster number for each input geometry, using the 2D Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Unlike ST_ClusterKMeans, it does not require the number of clusters to be specified, but instead uses the desired distance (eps) and density (minpoints) parameters to determine each cluster.

An input geometry is added to a cluster if it is either:

  • A "core" geometry, that is within eps distance of at least minpoints input geometries (including itself); or
  • A "border" geometry, that is within eps distance of a core geometry.

Note that border geometries may be within eps distance of core geometries in more than one cluster. Either assignment would be correct, so the border geometry will be arbitrarily asssigned to one of the available clusters. In this situation it is possible for a correct cluster to be generated with fewer than minpoints geometries. To ensure deterministic assignment of border geometries (so that repeated calls to ST_ClusterDBSCAN will produce identical results) use an ORDER BY clause in the window definition. Ambiguous cluster assignments may differ from other DBSCAN implementations.

Note

Geometries that do not meet the criteria to join any cluster are assigned a cluster number of NULL.

Availability: 2.3.0

Examples

Clustering polygon within 50 meters of each other, and requiring at least 2 polygons per cluster.

image

Clusters within 50 meters with at least 2 items per cluster. Singletons have NULL for cid


SELECT name, ST_ClusterDBSCAN(geom, eps => 50, minpoints => 2) over () AS cid
FROM boston_polys
WHERE name > '' AND building > ''
    AND ST_DWithin(geom,
        ST_Transform(
            ST_GeomFromText('POINT(-71.04054 42.35141)', 4326), 26986),
           500);
 

                name                 | bucket
-------------------------------------+--------
 Manulife Tower                      |      0
 Park Lane Seaport I                 |      0
 Park Lane Seaport II                |      0
 Renaissance Boston Waterfront Hotel |      0
 Seaport Boston Hotel                |      0
 Seaport Hotel & World Trade Center  |      0
 Waterside Place                     |      0
 World Trade Center East             |      0
 100 Northern Avenue                 |      1
 100 Pier 4                          |      1
 The Institute of Contemporary Art   |      1
 101 Seaport                         |      2
 District Hall                       |      2
 One Marina Park Drive               |      2
 Twenty Two Liberty                  |      2
 Vertex                              |      2
 Vertex                              |      2
 Watermark Seaport                   |      2
 Blue Hills Bank Pavilion            |   NULL
 World Trade Center West             |   NULL
(20 rows)

A example showing combining parcels with the same cluster number into geometry collections.

SELECT cid, ST_Collect(geom) AS cluster_geom, array_agg(parcel_id) AS ids_in_cluster FROM (
    SELECT parcel_id, ST_ClusterDBSCAN(geom, eps => 0.5, minpoints => 5) over () AS cid, geom
    FROM parcels) sq
GROUP BY cid;

See Also

ST_DWithin, ST_ClusterKMeans, ST_ClusterIntersecting, ST_ClusterIntersectingWin, ST_ClusterWithin, ST_ClusterWithinWin

ST_ClusterIntersecting

Aggregate function that clusters input geometries into connected sets.

Synopsis

geometry[] ST_ClusterIntersecting(geometry set g)

Description

An aggregate function that returns an array of GeometryCollections partitioning the input geometries into connected clusters that are disjoint. Each geometry in a cluster intersects at least one other geometry in the cluster, and does not intersect any geometry in other clusters.

Availability: 2.2.0

Examples

WITH testdata AS
  (SELECT unnest(ARRAY['LINESTRING (0 0, 1 1)'::geometry,
           'LINESTRING (5 5, 4 4)'::geometry,
           'LINESTRING (6 6, 7 7)'::geometry,
           'LINESTRING (0 0, -1 -1)'::geometry,
           'POLYGON ((0 0, 4 0, 4 4, 0 4, 0 0))'::geometry]) AS geom)

SELECT ST_AsText(unnest(ST_ClusterIntersecting(geom))) FROM testdata;

--result

st_astext
---------
GEOMETRYCOLLECTION(LINESTRING(0 0,1 1),LINESTRING(5 5,4 4),LINESTRING(0 0,-1 -1),POLYGON((0 0,4 0,4 4,0 4,0 0)))
GEOMETRYCOLLECTION(LINESTRING(6 6,7 7))

See Also

ST_ClusterIntersectingWin, ST_ClusterWithin, ST_ClusterWithinWin

ST_ClusterIntersectingWin

Window function that returns a cluster id for each input geometry, clustering input geometries into connected sets.

Synopsis

integer ST_ClusterIntersectingWin(geometry winset  geom)

Description

A window function that builds connected clusters of geometries that intersect. It is possible to traverse all geometries in a cluster without leaving the cluster. The return value is the cluster number that the geometry argument participates in, or null for null inputs.

Availability: 3.4.0

Examples

WITH testdata AS (
  SELECT id, geom::geometry FROM (
  VALUES  (1, 'LINESTRING (0 0, 1 1)'),
          (2, 'LINESTRING (5 5, 4 4)'),
          (3, 'LINESTRING (6 6, 7 7)'),
          (4, 'LINESTRING (0 0, -1 -1)'),
          (5, 'POLYGON ((0 0, 4 0, 4 4, 0 4, 0 0))')) AS t(id, geom)
)
SELECT id,
  ST_AsText(geom),
  ST_ClusterIntersectingWin(geom) OVER () AS cluster
FROM testdata;

 id |           st_astext            | cluster
----+--------------------------------+---------
  1 | LINESTRING(0 0,1 1)            |       0
  2 | LINESTRING(5 5,4 4)            |       0
  3 | LINESTRING(6 6,7 7)            |       1
  4 | LINESTRING(0 0,-1 -1)          |       0
  5 | POLYGON((0 0,4 0,4 4,0 4,0 0)) |       0

See Also

ST_ClusterIntersecting, ST_ClusterWithin, ST_ClusterWithinWin

ST_ClusterKMeans

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

Synopsis

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

Description

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

Availability: 2.3.0

Examples

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;

image

Parcels color-coded by cluster number (cid)

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;

image

World population clustered to above specs produces 46 clusters. Clusters are centered at well-populated regions (New York, Moscow). Greenland is one cluster. There are island clusters that span across the antimeridian. Cluster edges follow Earth's curvature.

See Also

ST_ClusterDBSCAN, ST_ClusterIntersectingWin, ST_ClusterWithinWin, ST_ClusterIntersecting, ST_ClusterWithin, ST_Subdivide, ST_Force_3D, ST_Force_4D,

ST_ClusterWithin

Aggregate function that clusters geometries by separation distance.

Synopsis

geometry[] ST_ClusterWithin(geometry set  g, float8  distance)

Description

An aggregate function that returns an array of GeometryCollections, where each collection is a cluster containing some input geometries. Clustering partitions the input geometries into sets in which each geometry is within the specified distance of at least one other geometry in the same cluster. Distances are Cartesian distances in the units of the SRID.

ST_ClusterWithin is equivalent to running ST_ClusterDBSCAN with minpoints => 0.

Availability: 2.2.0

Examples

WITH testdata AS
  (SELECT unnest(ARRAY['LINESTRING (0 0, 1 1)'::geometry,
               'LINESTRING (5 5, 4 4)'::geometry,
               'LINESTRING (6 6, 7 7)'::geometry,
               'LINESTRING (0 0, -1 -1)'::geometry,
               'POLYGON ((0 0, 4 0, 4 4, 0 4, 0 0))'::geometry]) AS geom)

SELECT ST_AsText(unnest(ST_ClusterWithin(geom, 1.4))) FROM testdata;

--result

st_astext
---------
GEOMETRYCOLLECTION(LINESTRING(0 0,1 1),LINESTRING(5 5,4 4),LINESTRING(0 0,-1 -1),POLYGON((0 0,4 0,4 4,0 4,0 0)))
GEOMETRYCOLLECTION(LINESTRING(6 6,7 7))

See Also

ST_ClusterWithinWin, ST_ClusterDBSCAN, ST_ClusterIntersecting, ST_ClusterIntersectingWin

ST_ClusterWithinWin

Window function that returns a cluster id for each input geometry, clustering using separation distance.

Synopsis

integer ST_ClusterWithinWin(geometry winset  geom, float8  distance)

Description

A window function that returns a cluster number for each input geometry. Clustering partitions the geometries into sets in which each geometry is within the specified distance of at least one other geometry in the same cluster. Distances are Cartesian distances in the units of the SRID.

ST_ClusterWithinWin is equivalent to running ST_ClusterDBSCAN with minpoints => 0.

Availability: 3.4.0

Examples

WITH testdata AS (
  SELECT id, geom::geometry FROM (
  VALUES  (1, 'LINESTRING (0 0, 1 1)'),
          (2, 'LINESTRING (5 5, 4 4)'),
          (3, 'LINESTRING (6 6, 7 7)'),
          (4, 'LINESTRING (0 0, -1 -1)'),
          (5, 'POLYGON ((0 0, 4 0, 4 4, 0 4, 0 0))')) AS t(id, geom)
)
SELECT id,
  ST_AsText(geom),
  ST_ClusterWithinWin(geom, 1.4) OVER () AS cluster
FROM testdata;


 id |           st_astext            | cluster
----+--------------------------------+---------
  1 | LINESTRING(0 0,1 1)            |       0
  2 | LINESTRING(5 5,4 4)            |       0
  3 | LINESTRING(6 6,7 7)            |       1
  4 | LINESTRING(0 0,-1 -1)          |       0
  5 | POLYGON((0 0,4 0,4 4,0 4,0 0)) |       0

See Also

ST_ClusterWithin, ST_ClusterDBSCAN, ST_ClusterIntersecting, ST_ClusterIntersectingWin,