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Flatbush

A really fast static spatial index for 2D points and rectangles in JavaScript.

An efficient implementation of the packed Hilbert R-tree algorithm. Enables fast spatial queries on a very large number of objects (e.g. millions), which is very useful in maps, data visualizations and computational geometry algorithms.

Similar to RBush, with the following key differences:

  • Static: you can't add/remove items after initial indexing.
  • Faster indexing and search, with much lower memory footprint.
  • Index is stored as a single array buffer (so you can transfer it between threads or store it as a compact binary file).

Supports geographic locations with the geoflatbush extension.

Build Status minzipped size Simply Awesome

Usage

// initialize Flatbush for 1000 items
const index = new Flatbush(1000);

// fill it with 1000 rectangles
for (const p of items) {
    index.add(p.minX, p.minY, p.maxX, p.maxY);
}

// perform the indexing
index.finish();

// make a bounding box query
const found = index.search(minX, minY, maxX, maxY).map((i) => items[i]);

// make a k-nearest-neighbors query
const neighborIds = index.neighbors(x, y, 5);

// instantly transfer the index from a worker to the main thread
postMessage(index.data, [index.data]);

// reconstruct the index from a raw array buffer
const index = Flatbush.from(e.data);

Install

Install using NPM (npm install flatbush) or Yarn (yarn add flatbush), then:

// import as an ES module
import Flatbush from 'flatbush';

// or require as a CommonJS module
const Flatbush = require('flatbush');

Or use a browser build directly:

<script src="https://unpkg.com/[email protected]/flatbush.min.js"></script>

API

new Flatbush(numItems[, nodeSize, ArrayType])

Creates a Flatbush index that will hold a given number of items (numItems). Additionally accepts:

  • nodeSize: size of the tree node (16 by default); experiment with different values for best performance (increasing this value makes indexing faster and queries slower, and vise versa).
  • ArrayType: the array type used for coordinates storage (Float64Array by default); other types may be faster in certain cases (e.g. Int32Array when your data is integer).

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

Adds a given rectangle to the index. Returns a zero-based, incremental number that represents the newly added rectangle.

index.finish()

Performs indexing of the added rectangles. Their number must match the one provided when creating a Flatbush object.

index.search(minX, minY, maxX, maxY[, filterFn])

Returns an array of indices of items intersecting or touching a given bounding box. Item indices refer to the value returned by index.add().

const ids = index.search(10, 10, 20, 20);

If given a filterFn, calls it on every found item (passing an item index) and only includes it if the function returned a truthy value.

const ids = index.search(10, 10, 20, 20, (i) => items[i].foo === 'bar');

index.neighbors(x, y[, maxResults, maxDistance, filterFn])

Returns an array of item indices in order of distance from the given x, y (known as K nearest neighbors, or KNN). Item indices refer to the value returned by index.add().

const ids = index.neighbors(10, 10, 5); // returns 5 ids

maxResults and maxDistance are Infinity by default. Also accepts a filterFn similar to index.search.

Flatbush.from(data)

Recreates a Flatbush index from raw ArrayBuffer data (that's exposed as index.data on a previously indexed Flatbush instance). Very useful for transferring indices between threads or storing them in a file.

Properties

  • data: array buffer that holds the index.
  • minX, minY, maxX, maxY: bounding box of the data.
  • numItems: number of stored items.
  • nodeSize: number of items in a node tree.
  • ArrayType: array type used for internal coordinates storage.
  • IndexArrayType: array type used for internal item indices storage.

Performance

Running npm run bench with Node v10.11.0:

bench flatbush rbush
index 1,000,000 rectangles 263ms 1208ms
1000 searches 10% 594ms 1105ms
1000 searches 1% 68ms 213ms
1000 searches 0.01% 9ms 27ms
1000 searches of 100 neighbors 29ms 58ms
1 search of 1,000,000 neighbors 148ms 781ms
100,000 searches of 1 neighbor 870ms 1548ms