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 Representation:

Properties of R-tree:

Consists of a single root, internals nodes, and leaf nodes.
The root contains the pointer to the largest region in the spatial domain.
Parent nodes contains pointers to their child nodes where the 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 an 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.
Spatial 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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