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I have been reading several SO posts regarding K-D Trees vs. R-Trees but I still have some questions regarding my specific application.

For my Java application, I want to maintain a relatively small number of spatial data points (a few hundred thousand). The key is that data insertion will not be bulk loaded, but rather, frequently and incrementally inserted. I should also mention that I will be performing a good number of periodic range queries on sub-regions of the spatial domain.

I have read that K-D Trees do not typically support incremental building and that R-trees are more suitable for this since they maintain a balanced state.

However, after looking into the solutions suggested here: Java commercial-friendly R-tree implementation?

I did not find that the implementations were easy to work with for returning a list of points in range searches. However, I have found: http://java-ml.sourceforge.net/ to have a very nice implementation of a K-D Tree that works quickly and outperforms standard array storage for a test set of points (~25K). Additionally, I have read that R-trees store redundant information when dealing with points (since a point is a rectangle with min=max).

Since I am working with a smaller number of points, are the differences between the two structures less important than, say, if I was working with a database application storing millions of points?

halfer
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Nathaniel Wendt
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2 Answers2

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It is incorrect that R-trees can't store points. They are designed to support rectangles, and will need to do so at inner nodes. But a good implementation should store points at the leaf level, and roughly have the double data capacity there.

You can trivially store point, and expose them as a "rectangles" with min=max to the tree management code.

Your data isn't small. Small would be like 100 objects. For 100 objects, an R-tree won't make much sense, as it would likely consists of a single leaf only. For good performance, an R-tree needs a good fan-out. k-d-tree always have a fan-out of 2; they are binary trees. At 100k objects, a k-d-tree will be pretty deep. Assuming that you have a fanout of 100 (for dynamic r-trees, you then should allow up to 200 objects per page), you can store 1 million points in a 3-level tree.

I've used the ELKI R*-tree, and it is really fast. But it's not commercial friendly, unless you get a different license: it's AGPL-3 licensed, which is a copyleft license.

Furthermore, the API isn't designed for standalone use. If you want to use them, the best way is to work with the full ELKI framework, instead of trying to rip out the R*-tree.

If your data is low dimensional (say, 3-dimensional) and has a finite bound, don't underestimate the performance of simple grid-based approaches. In particular for in-memory operations. In many cases, I wouldn't even go to an Octree, but just define the optimal grid for my use case, and then implement it using object lists. Keep sorted by one coordinate within each grid cell to further accelerate performance.

Has QUIT--Anony-Mousse
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  • The bounds of my region are not know a priori and are subject to change with inserted data. Do you think that grid-based approaches are worth investigating if they must adapt to growth in the available grid? – Nathaniel Wendt Feb 03 '14 at 05:51
  • As long as you have some idea of the bounds, grid approaches can still work well, if you design your grid so it can grow in any direction (e.g. by allowing negative indexes, and only storing occupied grid cells). – Has QUIT--Anony-Mousse Feb 03 '14 at 13:08
  • what are some simple grid-based approaches one can use? – nicodjimenez Nov 17 '14 at 04:33
  • Depends on your data. Try 10-100 grid bins in each dimension. Linear scan within the points grid bin and then neighboring grid bins until you can prune them completely. – Has QUIT--Anony-Mousse Nov 17 '14 at 13:16
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If you want to frequently add/remove/update data points, you may want to look at the PH-Tree. The is on open source Java version available: www.phtree.org

It works a bit like a quadtree, but is much more efficient by using binary hypercubes and prefix-sharing.

It has excellent update performance (no rebalancing required) and is quite memory efficient. It works better with larger datasets, but 100K should be fine for 2 or 3 dimensions.

TilmannZ
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