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# Spatial clustering with PostGIS

Here are a few examples of how you can use PostGIS to perform spatial clustering:

## DBSCAN clustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that clusters points based on density. Points in a high-density region are considered to be part of the same cluster, while points in a low-density region are considered to be noise (i.e., not part of any cluster).

The `ST_ClusterDBSCAN` function in PostGIS takes two parameters: `eps` and `minpoints`. `eps` is the maximum distance that points in a cluster can be from each other, and `minpoints` is the minimum number of points required to form a cluster.

For example, in the following query:

``````SELECT ST_ClusterDBSCAN(geom, eps := 0.1, minpoints := 3) as cluster_id
FROM points
GROUP BY cluster_id;
``````

points that are within 0.1 units of each other and that have at least 3 other points within 0.1 units of them will be considered part of the same cluster. Points that do not meet these criteria will be considered noise and will be assigned a `NULL` value for `cluster_id`.

## K-means clustering:

K-means clustering is an algorithm that divides a set of points into a specified number of clusters based on the mean distance of points from the centroid (mean) of each cluster.

The `ST_ClusterKMeans` function in PostGIS takes a single parameter: the number of clusters to create. For example, in the following query:

```SELECT ST_ClusterKMeans(geom, 2) as cluster_id
FROM points
GROUP BY cluster_id;
```

the points in the `points` table will be divided into 2 clusters based on their distance from the centroids of each cluster.

## Voronoi diagram:

A Voronoi diagram is a partitioning of a plane into regions based on distance to a set of points. Each region represents the area of the plane that is closest to a particular point.

The `ST_VoronoiDiagram` function in PostGIS takes a single parameter: a set of points. For example, in the following query:

```SELECT ST_VoronoiDiagram(geom) as geom
FROM points;
```

The `ST_VoronoiDiagram` function will create a polygon for each point in the input set, with the polygon representing the region of the plane that is closest to that point. The resulting diagram will consist of a set of disjoint polygons, with each polygon representing the region closest to a point.

For example, suppose we have the following set of points:

``````POINT(0 0)
POINT(1 0)

POINT(1 1)
``````

The Voronoi diagram created from these points would look like this:

```POLYGON((0 0, 0.5 0.5, 1 0, 0 0))
POLYGON((1 0, 0.5 0.5, 1 1, 1 0))
POLYGON((1 1, 0.5 0.5, 1 0, 1 1))
```

Each of these polygons represents the region of the plane that is closest to one of the input points.

For more information on PostgreSQL and PostGIS check out these podcast episodes