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Introduction to R-tree
  • Difficulty Level : Hard
  • Last Updated : 03 Jan, 2020

R-tree is a tree data structure used for storing spatial data indexes in an efficient manner. R-trees are highly useful for spatial data queries and storage. Some of the real life applications are mentioned below:

  • Indexing multi-dimensional information.
  • Handling geospatial coordinates.
  • Implementation of virtual maps.
  • Handling game data.

Example:
R-Tree
R-Tree Represesntation:
Tree representation

Properties of R-tree:

  • Consists of a single root, internals nodes and leaf nodes.
  • Root contains the pointer to the largest region in the spatial domain.
  • Parent nodes contains pointers to their child nodes where region of child nodes completely overlaps the regions of parent nodes.
  • Leaf nodes contains data about the MBR to the current objects.
  • MBR-Minimum bounding region refers to the minimal bounding box parameter surrounding the region/object under consideration.

Comparison with Quad-trees:

  • Tiling level optimization is required in Quad-trees whereas in R-tree doesn’t require any such optimization.
  • Quad-tree can be implemented on top of existing B-tree whereas R-tree follow a different structure from a B-tree.
  • Spatal index creation in Quad-trees is faster as compared to R-trees.
  • R-trees are faster than Quad-trees for Nearest Neighbour queries while for window queries, Quad-trees are faster than R-trees.

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