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rtree-ldf

v0.0.9

Published

Disk based rtree implementation based on rbush mainly built to create fragmented linked data rtrees

Downloads

20

Readme

Rtree LDF

Rtree-ldf is based on rbush, a high-performance JavaScript library for 2D spatial indexing of points and rectangles. It is completely disk based and uses a nosql-database for storage and a lru-cache for improved performance. Please note that this implementation isn't optimal and an implementation in another language such as c++ will be much faster. Performance will also greatly depend on the cache size given to the tree.

Install

Install with NPM (npm install --save rtree-ldf).

Usage

const Rtree = require('rtree-ldf')

Creating a Tree

const tree = new Rtree({
	dir: './db',
	openExisting: true,
	cacheSize: 100000,
	maxEntries: 16,
});
  • dir: Directory where the tree will be saved on disk
  • openExisting (Opt.): Open an existing tree located in dir (default: false)
  • cacheSize (Opt.): Amount of nodes that will can be cached (max 1.000.000, default: 100.000)
  • maxEntries (Opt.): defines the maximum number of entries in a tree node (default: 9)

Closing a tree

If you want to make sure your tree is completely saved to the disk, make sure to call tree.close() when you are done.

Adding Data

Insert an item:

const item = {
    minX: 20, 
    minY: 40,
    maxX: 30,
    maxY: 50,
    "@id": "gtfs:station",
    foo: bar
};
tree.insert(item);

minX, minY, maxX and maxY are required. You can also add extra data properties.

Removing Data

Remove a previously inserted item:

tree.remove(item);

You can also pass a custom equals function.

tree.remove(itemCopy, function (a, b) {
    return a.id === b.id;
});

Remove all items:

tree.clear();

Bulk-Inserting Data

Load an array of data into the tree.

tree.load([item1, item2, ...]);

Search

var result = tree.search({
    minX: 40,
    minY: 20,
    maxX: 80,
    maxY: 70
});

Returns an array of data items (points or rectangles) that the given bounding box intersects.

var allItems = tree.all();

Returns all items of the tree.

Collisions

var result = tree.collides({minX: 40, minY: 20, maxX: 80, maxY: 70});

Returns true if there are any items intersecting the given bounding box, otherwise false.

Export to JSON

// export data as JSON object
var treeData = tree.toJSON();

Export to linked data fragments

tree.toFragments({
	outDir: './fragments/', 
	treeDir: 'tree', 
	dataDir: 'data', 
	collection: 'stations' , 
	manages: 'http://vocab.gtfs.org/terms#station'
});

The tree will be exported into fragments conform to the TreeOntology. The fragments are formatted in JSON-LD and most will be around 500 kB which should give a fragment size of around 50 kB after compression.

  • outDir: base directory where the fragments will be exported to
  • collection: name of the fragment describing the collection. This fragment will be placed in the out directory
  • manages: type of data the collection manages
  • treeDir (opt.): directory starting from outDir where the treeFragments will be exported to. The fragments are exported to the outDir by default.
  • dataDir (opt.): directory starting from outDir where the dataFragments will be exported to. The fragments are exported to the outDir by default.