问题
What kind of data structure could be used for an efficient nearest neighbor search in a large set of geo coordinates? With "regular" spatial index structures like R-Trees that assume planar coordinates, I see two problems (Are there others I have overlooked?):
- Wraparound at the poles and the International Date Line
- Distortion of distances near the poles
How can these factors be allowed for? I guess the second one could compensated by transforming the coordinates. Can an R-Tree be modified to take wraparound into account? Or are there specialized geo-spatial index structures?
回答1:
Take a look at Geohash.
Also, to compensate for wraparound, simply use not one but three orthogonal R-trees, so that there does not exist a point on the earth surface such that all three trees have a wraparound at that point. Then, two points are close if they are close according to at least one of these trees.
回答2:
Could you use a locality-sensitive hashing (LSH) algorithm in 3 dimensions? That would quickly give you an approximate neighboring group which you could then sanity-check by calculating great-circle distances.
Here's a paper describing an algorithm for efficient LSH on the surface of a unit d-dimensional hypersphere. Presumably it works for d=3.
来源：https://stackoverflow.com/questions/2398954/spatial-index-for-geo-coordinates