Spatial Data Indexing and Point Location

  • Jakob Andreas Bærentzen
  • Jens Gravesen
  • François Anton
  • Henrik Aanæs


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.


Spatial Data Voronoi Diagram Random Access Memory Spatial Database Minimum Bound Rectangle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Jakob Andreas Bærentzen
    • 1
  • Jens Gravesen
    • 2
  • François Anton
    • 1
  • Henrik Aanæs
    • 1
  1. 1.Department of Informatics and Mathematical ModellingTechnical University of DenmarkKongens LyngbyDenmark
  2. 2.Department of MathematicsTechnical University of DenmarkKongens LyngbyDenmark

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