Here we will see the R-Trees data structure. The R-Trees are used to store special data indexes in an efficient manner. This structure is very useful to hold special data queries and storages. This R-trees has some real life applications. These are like below −
Indexing multidimensional information
Handling game data
Hold geospatial coordinates
Implementation of virtual maps
One example of R-Tree is like below.
Corresponding R-tree is like below −
Properties of R-Trees
R-Trees are made of with single root, internal and leaf nodes
The root has a pointer to the largest region in the special domain
The parent nodes will hold child nodes where child nodes completely overlap the region of parent nodes
Leaf nodes hold data about MBR to the current object
MBR-Minimum bounding region is the minimum boundary box parameter surrounding the region under consideration
Difference between Quad-trees
| Quad Tree | R-Tree |
| Tiling level optimization is required | R-Tree do not require any optimization |
| Quad-tree can be formed on B-tree | R-tree does not follow the structure of B-tree |
| Spatial Index creation is faster | Spatial Index creation is slower |
| Nearest neighbor querying is slower, but the Window querying is faster. | Nearest neighbor querying is faster, but the Window querying is slower. |