Updates to @turf/clusters-dbscan

[DenisCarriere]

Updates to @turf/clusters-dbscan

• Dropped array of Features input
• Create centroids & concave polygons using clusterEach (for testing purposes only)

JSDocs

/**
 * Takes a set of {@link Point|points} and partition them into clusters according to {@link DBSCAN's|https://en.wikipedia.org/wiki/DBSCAN} data clustering algorithm.
 *
 * @name clustersDbscan
 * @param {FeatureCollection<Point>} points to be clustered
 * @param {number} maxDistance Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
 * @param {string} [units=kilometers] in which `maxDistance` is expressed, can be degrees, radians, miles, or kilometers
 * @param {number} [minPoints=3] Minimum number of points to generate a single cluster,
 * points which do not meet this requirement will be classified as an 'edge' or 'noise'.
 * @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')
 * @example
 * // create random points with random z-values in their properties
 * var points = turf.random('point', 100, {
 *   bbox: [0, 30, 20, 50]
 * });
 * var distance = 100;
 * var clustered = turf.clustersDbscan(points, distance);
 *
 * //addToMap
 * var addToMap = clustered;
 */
module.exports = function (points, maxDistance, units, minPoints) {

Example

[DenisCarriere]

Made a fun interactive leaflet map with @turf/clusters-dbscan! 🎉

https://github.com/DenisCarriere/turf-example-clusters-dbscan

@stebogit I can try to do one for @turf/clusters-kmeans too.

[stebogit]

@DenisCarriere I'm astonished. That is super fun, and instructive! 😃

[DenisCarriere]

@stebogit I showed a few friends this tonight and we just kept making clusters for about 20 minutes 😝

If I ever create another example/project with dbscan or any other clustering algorithms I'll make sure to share it here.

The entire source code to make that dbscan example is hosted here:
https://github.com/DenisCarriere/turf-example-clusters-dbscan

Pretty straightforward static website, no Webpack, no Babel, no Typescript, only vanilla Javascript + HTML.