Awesome
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
}
Rect
s are data structures for representing spatial objects, while Point
s
represent spatial locations. Creating Point
s is easy--they're just slices
of float64
s:
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 dhconnelly@gmail.com.
This fork is maintained by Patrick Higgins (patrick.allen.higgins@gmail.com).
License
rtreego is released under a BSD-style license; see LICENSE for more details.