@turf/clusters-dbscan
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    6.5.0 • Public • Published

    @turf/clusters-dbscan

    clustersDbscan

    Takes a set of points and partition them into clusters according to https://en.wikipedia.org/wiki/DBSCAN data clustering algorithm.

    Parameters

    • points FeatureCollection<Point> to be clustered
    • maxDistance number Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
    • options Object Optional parameters (optional, default {})
      • options.units string in which maxDistance is expressed, can be degrees, radians, miles, or kilometers (optional, default "kilometers")
      • options.mutate boolean Allows GeoJSON input to be mutated (optional, default false)
      • options.minPoints number Minimum number of points to generate a single cluster, points which do not meet this requirement will be classified as an 'edge' or 'noise'. (optional, default 3)

    Examples

    // create random points with random z-values in their properties
    var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
    var maxDistance = 100;
    var clustered = turf.clustersDbscan(points, maxDistance);
    
    //addToMap
    var addToMap = [clustered];

    Returns FeatureCollection<Point> Clustered Points with an additional two properties associated to each Feature:- {number} cluster - the associated clusterId

    • {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise')

    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 module individually:

    $ npm install @turf/clusters-dbscan

    Or install the Turf module that includes it as a function:

    $ npm install @turf/turf

    Install

    npm i @turf/clusters-dbscan

    DownloadsWeekly Downloads

    279,253

    Version

    6.5.0

    License

    MIT

    Unpacked Size

    15.2 kB

    Total Files

    7

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    Collaborators

    • twelch
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    • tmcw
    • morganherlocker
    • tcql
    • mdfedderly