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1.0.2 • Public • Published

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

  • points only — no rectangles
  • static — you can't add/remove items
  • indexing is 5-8 times faster
const justUseOneDimensionalPointArrayPlease = true
const points= [55,20,20,10,10,90,90,56,56,58,5,4]
const index = new KDBushOneDimension(points, undefined, undefined,10,Float64Array, justUseOneDimensionalPointArrayPlease);         // 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 using NPM (npm install kdbush-onedimension) or Yarn (yarn add kdbush-onedimension), then:

// import as a ES module
import KDBushOneDimension from 'kdbush-onedimension';
// or require in Node / Browserify
const KDBushOneDimension = require('kdbush-onedimension');


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

Creates an index from the given points.

  • points: Input array of points.
  • getX, getY: Functions to get x and y from an input point. By default, it assumes [x, y] format.
  • 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.
  • justUseOneDimensionalPointArrayPlease: if your point array is one dimensional point array set this option true with this option you can not use getX, getY callbacks
const index = new KDBushOneDimension(points, p => p.x, p => p.y, 64, Int32Array);

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 points input array.

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

bench test:

memory: 85798.464 KB
index 1000000 points: 190.468ms
memory: 85866.384 KB
10000 small bbox queries: 17.985ms
10000 small radius queries: 19.774ms
memory: 104452.88 KB  increase because of first function
index 1000000 points: 101.040ms
memory: 118575.48 KB
10000 small bbox queries: 17.311ms
10000 small radius queries: 16.729ms


npm i kdbush-onedimension

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