Abstract
This chapter is concerned with spatial databases. Anyone designing algorithms for geometry processing will invariably need to store data in spatial databases. The chapter begins with an introduction to spatial databases and moves on to discuss particular data structures.
The kD tree for instance is a simple and very popular data structure for storing points in space. Essentially, a kD tree is a binary tree that recursively divides space into smaller regions. The same is true of a binary space partitioning tree, but the planes that divide space can be arbitrarily oriented, and a BSP tree is often used for triangles. Quadtrees and octrees divide space into four and eight sub-regions at each node and thus have a higher branching factor.
The chapter closes with a discussion of object-driven spatial access methods. The distinguishing characteristic of these is that objects are grouped as opposed to space being divided.
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Bærentzen, J.A., Gravesen, J., Anton, F., Aanæs, H. (2012). Spatial Data Indexing and Point Location. In: Guide to Computational Geometry Processing. Springer, London. https://doi.org/10.1007/978-1-4471-4075-7_12
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