k-nearest neighbors search for RBush. Implements a simple depth-first kNN search algorithm using a priority queue.

import RBush from 'rbush';
import knn from 'rbush-knn';

const tree = new RBush(); // create RBush tree
tree.load(data); // bulk insert

const neighbors = knn(tree, 40, 40, 10); // return 10 nearest items around point [40, 40]

You can optionally pass a filter function to find k neighbors that satisfy a certain condition:

const neighbors = knn(tree, 40, 40, 10, function (item) {
    return item.foo === 'bar';
});

knn(tree, x, y, [k, filterFn, maxDistance])

• tree: an RBush tree
• x, y: query coordinates
• k: number of neighbors to search for (Infinity by default)
• filterFn: optional filter function; k nearest items where filterFn(item) === true will be returned.
• maxDistance (optional): maximum distance between neighbors and the query coordinates (Infinity by default)