@julien.cousineau/kdbush

A very fast static spatial index for 2D points based on a flat KD-tree. Compared to RBush:

• points only — no rectangles
• static — you can't add/remove items
• indexing is 5-8 times faster

const index = new KDBush(x,y);         // make an index
const ids1 = index.range(10, 10, 20, 20); // bbox search - minX, minY, maxX, maxY
const ids2 = index.within(10, 10, 5);     // radius search - x, y, radius

Install

Install using NPM (npm install @julien.cousineau/kdbush), then:

// import as a ES module
import KDBush from '@julien.cousineau/kdbush';

// or require in Node / Browserify
const KDBush = require('kdbush');

API

new KDBush(points[, getX, getY, nodeSize, arrayType])

Creates an index from the given points.

• x: Input array of x.
• y: Input array of y.
• nodeSize: Size of the KD-tree node, 64 by default. Higher means faster indexing but slower search, and vise versa.
• arrayType: Array type to use for storing coordinate values. Float64Array by default, but if your coordinates are integer values, Int32Array makes things a bit faster.

const x = new Float32Array(n);
const y = new Float32Array(n);
const index = new KDBush(x, y, 64, Float32Array);

index.range(minX, minY, maxX, maxY)

Finds all items within the given bounding box and returns an array of indices that refer to the items in the original x and y input array.

const results = index.range(10, 10, 20, 20).map(id => [x[id],y[id]]);

index.within(x, y, radius)

Finds all items within a given radius from the query point and returns an array of indices.

const results = index.within(10, 10, 5).map(id => [x[id],y[id]]);