A very fast JavaScript library for geospatial point clustering for browsers and Node.
<script src="https://unpkg.com/supercluster@5.0.0/dist/supercluster.min.js"></script>
const index = new Supercluster({
radius: 40,
maxZoom: 16
});
index.load(points);
index.getClusters([-180, -85, 180, 85], 2);
Clustering 6 million points in Leaflet:
Install using NPM (npm install supercluster
) or Yarn (yarn add supercluster
), then:
// import as a ES module
import Supercluster from 'supercluster';
// or require in Node / Browserify
const Supercluster = require('supercluster');
Or use a browser build directly:
<script src="https://unpkg.com/supercluster@5.0.0/dist/supercluster.min.js"></script>
Loads an array of GeoJSON Feature objects. Each feature's geometry
must be a GeoJSON Point. Once loaded, index is immutable.
For the given bbox
array ([westLng, southLat, eastLng, northLat]
) and integer zoom
, returns an array of clusters and points as GeoJSON Feature objects.
For a given zoom and x/y coordinates, returns a geojson-vt-compatible JSON tile object with cluster/point features.
Returns the children of a cluster (on the next zoom level) given its id (cluster_id
value from feature properties).
Returns all the points of a cluster (given its cluster_id
), with pagination support:
limit
is the number of points to return (set to Infinity
for all points),
and offset
is the amount of points to skip (for pagination).
Returns the zoom on which the cluster expands into several children (useful for "click to zoom" feature) given the cluster's cluster_id
.
Option | Default | Description |
---|---|---|
minZoom | 0 | Minimum zoom level at which clusters are generated. |
maxZoom | 16 | Maximum zoom level at which clusters are generated. |
radius | 40 | Cluster radius, in pixels. |
extent | 512 | (Tiles) Tile extent. Radius is calculated relative to this value. |
nodeSize | 64 | Size of the KD-tree leaf node. Affects performance. |
log | false | Whether timing info should be logged. |
In addition to the options above, supercluster supports property aggregation with the following three options:
map
: a function that returns cluster properties corresponding to a single point.reduce
: a reduce function that merges properties of two clusters into one.
Example of setting up a sum
cluster property that accumulates the sum of myValue
property values:
const index = new Supercluster({
map: (props) => ({sum: props.myValue}),
reduce: (accumulated, props) => { accumulated.sum += props.sum; }
});
Note that reduce
must not mutate the second argument (props
).
npm install # install dependencies
npm run build # generate dist/supercluster.js and dist/supercluster.min.js
npm test # run tests