Install

npm install @gridworkjs/rtree

Usage

A map application indexing building footprints and park boundaries. Bulk-load the dataset for optimal tree quality, then query by viewport:

import { createRTree } from '@gridworkjs/rtree'
import { rect, bounds } from '@gridworkjs/core'

const map = createRTree(feature => bounds(feature.geometry))

// load all features at once - STR bulk loading produces a much
// tighter tree than inserting one by one
map.load([
  { name: 'City Hall', geometry: rect(200, 300, 260, 370) },
  { name: 'Central Park', geometry: rect(100, 400, 500, 600) },
  { name: 'Library', geometry: rect(210, 310, 240, 340) },
  { name: 'Warehouse', geometry: rect(800, 100, 900, 200) }
])

// user pans the map - what's visible in this viewport?
map.search(rect(150, 250, 550, 650))
// => [City Hall, Central Park, Library]

// user taps a spot - what's closest?
map.nearest({ x: 230, y: 250 }, 1)
// => [City Hall]

// new construction - add it dynamically
map.insert({ name: 'Cafe', geometry: rect(215, 350, 230, 365) })

R-trees are the best general-purpose spatial index for rectangles and regions. Use load() when you have the full dataset upfront, then mix in insert() and remove() as things change.

Bulk Loading

For static or mostly-static datasets, load() produces a much better tree than repeated insert() calls. It uses the Sort-Tile-Recursive (STR) algorithm to pack items into nodes with minimal overlap.

const tree = createRTree(item => item.geo)
tree.load(items)  // replaces any existing data

After bulk loading, you can still insert() and remove() items dynamically.

Options

const tree = createRTree(accessor, {
  maxEntries: 9  // max entries per node (default: 9)
})

Higher maxEntries values create shallower trees with wider nodes. Lower values create deeper trees with tighter bounding boxes. The default of 9 works well for most use cases.

API

createRTree(accessor, options?)

Creates a new R-tree. The accessor function receives an item and returns its geometry (a point, rect, circle, or raw { minX, minY, maxX, maxY } bounds).

tree.insert(item)

Adds an item to the index. The tree automatically splits nodes and rebalances as needed.

tree.remove(item)

Removes an item by reference identity (===). Returns true if the item was found and removed, false otherwise. Underflowing nodes are dissolved and their entries reinserted.

tree.search(query)

Returns all items whose bounding boxes intersect the query. The query can be a raw bounds object or any geometry supported by @gridworkjs/core.

tree.nearest(point, k?)

Returns the k nearest items to the given point, ordered by distance. Defaults to k = 1. Uses a priority-queue traversal for efficient pruning.

tree.clear()

Removes all items from the index.

tree.load(items)

Bulk loads an array of items using the STR algorithm. Replaces any existing data in the tree. Produces better tree quality than sequential inserts.

tree.size

The number of items in the index.

tree.bounds

The bounding box of all items, or null if the tree is empty.

License

MIT