The difference (algorithmically) is: in quadtrees, the data reaching a node is split into a fixed (2^d), equal size cells, whereas in kdtrees, the data is split into two regions based on some data analysis (e.g. the median of some coordinate). Quadtrees do not scale well to high dimensions, due to the exponential dependency in … Read more
Thanks to John Vinyard for suggesting scipy. After some good research and testing, here is the solution to this question: Prerequisites: Install Numpy and SciPy Import the SciPy and Numpy Modules Make a copy of the 5 dimensional array including just the X and Y values. Create an instance of a cKDTree as such: YourTreeName … Read more
I have just implemented the Thing + ThingF + Base instance variant of the tree. And guess what … this one is amazingly fast. I was under the impression that this one would be the slowest of all variants. I really should have read my own post … the line where I write: there is … Read more
cKDTree is a subset of KDTree, implemented in C++ wrapped in Cython, so therefore faster. Each of them is a binary trie, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than … Read more
In the book Algorithms in a Nutshell there is a kd tree implementation in java along with a few variations. All of the code is on oreilly.com and the book itself also walk you through the algorithm so you could build one yourself.
They are actually quite different. They serve similar purpose (region queries on spatial data), and they both are trees (and both belong to the family of bounding volume hierarchy indexes), but that is about all they have in common. R-Trees are balanced, k-d-trees are not (unless bulk-loaded). This is why R-trees are preferred for changing … Read more