@turf/clusters

@turf/clusters

getCluster

Get Cluster

Parameters

• geojson FeatureCollection GeoJSON Features
• filter any Filter used on GeoJSON properties to get Cluster

Examples

var geojson = turf.featureCollection([
    turf.point([0, 0], {'marker-symbol': 'circle'}),
    turf.point([2, 4], {'marker-symbol': 'star'}),
    turf.point([3, 6], {'marker-symbol': 'star'}),
    turf.point([5, 1], {'marker-symbol': 'square'}),
    turf.point([4, 2], {'marker-symbol': 'circle'})
]);

// Create a cluster using K-Means (adds `cluster` to GeoJSON properties)
var clustered = turf.clustersKmeans(geojson);

// Retrieve first cluster (0)
var cluster = turf.getCluster(clustered, {cluster: 0});
//= cluster

// Retrieve cluster based on custom properties
turf.getCluster(clustered, {'marker-symbol': 'circle'}).length;
//= 2
turf.getCluster(clustered, {'marker-symbol': 'square'}).length;
//= 1

Returns FeatureCollection Single Cluster filtered by GeoJSON Properties

clusterEachCallback

Callback for clusterEach

Type: Function

Parameters

• cluster FeatureCollection? The current cluster being processed.
• clusterValue any? Value used to create cluster being processed.
• currentIndex number? The index of the current element being processed in the array.Starts at index 0

Returns void

clusterEach

clusterEach

Parameters

• geojson FeatureCollection GeoJSON Features
• property (string | number) GeoJSON property key/value used to create clusters
• callback clusterEachCallback a method that takes (cluster, clusterValue, currentIndex)

Examples

var geojson = turf.featureCollection([
    turf.point([0, 0]),
    turf.point([2, 4]),
    turf.point([3, 6]),
    turf.point([5, 1]),
    turf.point([4, 2])
]);

// Create a cluster using K-Means (adds `cluster` to GeoJSON properties)
var clustered = turf.clustersKmeans(geojson);

// Iterate over each cluster
turf.clusterEach(clustered, 'cluster', function (cluster, clusterValue, currentIndex) {
    //= cluster
    //= clusterValue
    //= currentIndex
})

// Calculate the total number of clusters
var total = 0
turf.clusterEach(clustered, 'cluster', function () {
    total++;
});

// Create an Array of all the values retrieved from the 'cluster' property
var values = []
turf.clusterEach(clustered, 'cluster', function (cluster, clusterValue) {
    values.push(clusterValue);
});

Returns void

clusterReduceCallback

Callback for clusterReduce

The first time the callback function is called, the values provided as arguments depend on whether the reduce method has an initialValue argument.

If an initialValue is provided to the reduce method:

• The previousValue argument is initialValue.
• The currentValue argument is the value of the first element present in the array.

If an initialValue is not provided:

• The previousValue argument is the value of the first element present in the array.
• The currentValue argument is the value of the second element present in the array.

Type: Function

Parameters

• previousValue any? The accumulated value previously returned in the last invocation of the callback, or initialValue, if supplied.
• cluster FeatureCollection? The current cluster being processed.
• clusterValue any? Value used to create cluster being processed.
• currentIndex number? The index of the current element being processed in the array. Starts at index 0, if an initialValue is provided, and at index 1 otherwise.

Returns void

clusterReduce

Reduce clusters in GeoJSON Features, similar to Array.reduce()

Parameters

• geojson FeatureCollection GeoJSON Features
• property (string | number) GeoJSON property key/value used to create clusters
• callback clusterReduceCallback a method that takes (previousValue, cluster, clusterValue, currentIndex)
• initialValue any? Value to use as the first argument to the first call of the callback.

Examples

var geojson = turf.featureCollection([
    turf.point([0, 0]),
    turf.point([2, 4]),
    turf.point([3, 6]),
    turf.point([5, 1]),
    turf.point([4, 2])
]);

// Create a cluster using K-Means (adds `cluster` to GeoJSON properties)
var clustered = turf.clustersKmeans(geojson);

// Iterate over each cluster and perform a calculation
var initialValue = 0
turf.clusterReduce(clustered, 'cluster', function (previousValue, cluster, clusterValue, currentIndex) {
    //=previousValue
    //=cluster
    //=clusterValue
    //=currentIndex
    return previousValue++;
}, initialValue);

// Calculate the total number of clusters
var total = turf.clusterReduce(clustered, 'cluster', function (previousValue) {
    return previousValue++;
}, 0);

// Create an Array of all the values retrieved from the 'cluster' property
var values = turf.clusterReduce(clustered, 'cluster', function (previousValue, cluster, clusterValue) {
    return previousValue.concat(clusterValue);
}, []);

Returns any The value that results from the reduction.

This module is part of the Turfjs project, an open source module collection dedicated to geographic algorithms. It is maintained in the Turfjs/turf repository, where you can create PRs and issues.

Installation

Install this single module individually:

$ npm install @turf/clusters

Or install the all-encompassing @turf/turf module that includes all modules as functions:

$ npm install @turf/turf

Similar packages

Other packages similar to @turf/clusters

supercluster

Supercluster is a fast geospatial point clustering library for browsers and Node. It is designed for visualizing large datasets on maps by clustering points into groups. Compared to @turf/clusters, Supercluster is optimized for performance and is particularly useful for rendering clusters on interactive maps.

kmeans-js

kmeans-js is a simple implementation of the K-Means clustering algorithm in JavaScript. It is not specifically designed for geospatial data but can be used for general clustering tasks. Compared to @turf/clusters, kmeans-js is more general-purpose and lacks the geospatial-specific optimizations and features.

hdbscan

hdbscan is a high-performance implementation of the HDBSCAN clustering algorithm. It is designed for hierarchical density-based clustering and is suitable for large datasets. Compared to @turf/clusters, hdbscan offers more advanced clustering capabilities but requires more computational resources and is more complex to use.