Clustering and statistics

• Set maxDistance in the units option, where 0.5 to 2 kilometers suits city-scale point sets.
• Iterate groups with turf.clusterEach(clustered, 'cluster', callback) rather than manual grouping.
• Use turf.collect to gather one property per group and then write aggregates back onto points.

var clustered = turf.clustersDbscan(points, 1.5, {
  units: 'kilometers',
  minPoints: 3,
  mutate: true              // 直接改原点集，不额外复制
});

var noise = clustered.features.filter(function (p) {
  return p.properties.cluster === undefined;
});

console.log('组数', new Set(clustered.features
  .map(function (p) { return p.properties.cluster; })
  .filter(function (c) { return c !== undefined; })).size);
console.log('噪声点', noise.length);

var groups = [];

turf.clusterEach(clustered, 'cluster', function (cluster, features) {
  var fc   = turf.featureCollection(features);
  var box  = turf.bboxPolygon(turf.bbox(fc));
  var mid  = turf.center(fc);
  var sum  = features.reduce(function (s, f) {
    return s + (f.properties.value || 0);
  }, 0);

  groups.push({
    cluster: cluster,
    count: features.length,
    total: sum,
    center: turf.getCoord(mid),
    bbox: turf.bbox(fc)
  });
});

console.log(groups);

• mutate: true edits the original point set, so clone first when the raw data is needed elsewhere.
• Convex hulls around KMeans groups can enclose large empty areas that contain no points.