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    4.0.0 • Public • Published


    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


    // 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
    // make a bounding box query
    const found =, 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(, []);
    // reconstruct the index from a raw array buffer
    const index = Flatbush.from(;


    Install with NPM: npm install flatbush, then import as a module:

    import Flatbush from 'flatbush';

    Or use as a module directly in the browser with jsDelivr:

    <script type="module">
        import Flatbush from '';

    Alternatively, there's a browser bundle with a Flatbush global variable:

    <script src=""></script>


    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.


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


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


    • 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.


    Running node bench.js with Node v14:

    bench flatbush rbush
    index 1,000,000 rectangles 273ms 1143ms
    1000 searches 10% 575ms 781ms
    1000 searches 1% 63ms 155ms
    1000 searches 0.01% 6ms 17ms
    1000 searches of 100 neighbors 24ms 43ms
    1 search of 1,000,000 neighbors 133ms 280ms
    100,000 searches of 1 neighbor 710ms 1170ms


    npm i flatbush

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