rtreego

A library for efficiently storing and querying spatial data in the Go programming language.

Forked from github.com/dhconnelly/rtreego to specialize for 3 dimensions and tune for fewer memory allocations.

About

The R-tree is a popular data structure for efficiently storing and querying spatial objects; one common use is implementing geospatial indexes in database management systems. The variant implemented here, known as the R*-tree, improves performance and increases storage utilization. Both bounding-box queries and k-nearest-neighbor queries are supported.

R-trees are balanced, so maximum tree height is guaranteed to be logarithmic in the number of entries; however, good worst-case performance is not guaranteed. Instead, a number of rebalancing heuristics are applied that perform well in practice. For more details please refer to the references.

Status

Geometric primitives (points, rectangles, and their relevant geometric algorithms) are implemented and tested. The R-tree data structure and algorithms are currently under development.

Install

With Go 1 installed, just run go get github.com/patrick-higgins/rtreego.

Usage

Make sure you import github.com/patrick-higgins/rtreego in your Go source files.

Storing, updating, and deleting objects

To create a new tree, specify the number of spatial dimensions and the minimum and maximum branching factor:

rt := rtreego.NewTree(2, 25, 50)

Any type that implements the Spatial interface can be stored in the tree:

type Spatial interface {
	Bounds() *Rect
}

Rects are data structures for representing spatial objects, while Points represent spatial locations. Creating Points is easy--they're just slices of float64s:

p1 := rtreego.Point{0.4, 0.5}
p2 := rtreego.Point{6.2, -3.4}

To create a Rect, specify a location and the lengths of the sides:

r1 := rtreego.NewRect(p1, []float64{1, 2})
r2 := rtreego.NewRect(p2, []float64{1.7, 2.7})

To demonstrate, let's create and store some test data.

type Thing struct {
	where *Rect
	name string
}

func (t *Thing) Bounds() *Rect {
	return t.where
}

rt.Insert(&Thing{r1, "foo"})
rt.Insert(&Thing{r2, "bar"})

size := rt.Size() // returns 2

We can insert and delete objects from the tree in any order.

rt.Delete(thing2)
// do some stuff...
rt.Insert(anotherThing)

If you want to store points instead of rectangles, you can easily convert a point into a rectangle using the ToRect method:

var tol = 0.01

type Somewhere struct {
	location rtreego.Point
	name string
	wormhole chan int
}

func (s *Somewhere) Bounds() *Rect {
	// define the bounds of s to be a rectangle centered at s.location
	// with side lengths 2 * tol:
	return s.location.ToRect(tol)
}

rt.Insert(&Somewhere{rtreego.Point{0, 0}, "Someplace", nil})

If you want to update the location of an object, you must delete it, update it, and re-insert. Just modifying the object so that the *Rect returned by Location() changes, without deleting and re-inserting the object, will corrupt the tree.

Queries

Bounding-box and k-nearest-neighbors queries are supported.

Bounding-box queries require a search *Rect argument and come in two flavors: containment search and intersection search. The former returns all objects that fall strictly inside the search rectangle, while the latter returns all objects that touch the search rectangle.

bb := rtreego.NewRect(rtreego.Point{1.7, -3.4}, []float64{3.2, 1.9})

// Get a slice of the objects in rt that intersect bb:
results, _ := rt.SearchIntersect(bb)

// Get a slice of the objects in rt that are contained inside bb:
results, _ = rt.SearchContained(bb)

Nearest-neighbor queries find the objects in a tree closest to a specified query point.

q := rtreego.Point{6.5, -2.47}
k := 5

// Get a slice of the k objects in rt closest to q:
results, _ = rt.SearchNearestNeighbors(q, k)

More information

See http://github.com/patrick-higgins/rtreego for full API documentation.

References

• A. Guttman. R-trees: A Dynamic Index Structure for Spatial Searching. Proceedings of ACM SIGMOD, pages 47-57, 1984. http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Guttman84.pdf

• N. Beckmann, H .P. Kriegel, R. Schneider and B. Seeger. The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. Proceedings of ACM SIGMOD, pages 323-331, May 1990. http://infolab.usc.edu/csci587/Fall2011/papers/p322-beckmann.pdf

• N. Roussopoulos, S. Kelley and F. Vincent. Nearest Neighbor Queries. ACM SIGMOD, pages 71-79, 1995. http://www.postgis.org/support/nearestneighbor.pdf

Author

rtreego is written and maintained by Daniel Connelly. You can find my stuff at dhconnelly.com or email me at [email protected].

This fork is maintained by Patrick Higgins ([email protected]).

License

rtreego is released under a BSD-style license; see LICENSE for more details.