...one of the most highly
regarded and expertly designed C++ library projects in the
world.
— Herb Sutter and Andrei
Alexandrescu, C++
Coding Standards
The Boost.Geometry.Index is intended to gather data structures called spatial indexes which may be used to accelerate searching for objects in space. In general, spatial indexes stores geometric objects' representations and allows searching for objects occupying some space or close to some point in space.
Currently, only one spatial index is implemented  Rtree.
Rtree is a tree data structure used for spatial searching. It was proposed by Antonin Guttman in 1984 ^{[1]} as an expansion of Btree for multidimensional data. It may be used to store points or volumetric data in order to perform a spatial query. This query may for example return objects that are inside some area or are close to some point in space ^{[2]}. It's possible to insert new objects or to remove the ones already stored.
The Rtree structure is presented on the image below. Each Rtree's node store a box describing the space occupied by its children nodes. At the bottom of the structure, there are leafnodes which contains values (geometric objects representations).
The Rtree is a selfbalanced data structure. The key part of balancing algorithm is node splitting algorithm ^{[3]} ^{[4]}. Each algorithm produces different splits so the internal structure of a tree may be different for each one of them. In general, more complex algorithms analyses elements better and produces less overlapping nodes. In the searching process less nodes must be traversed in order to find desired objects. On the other hand more complex analysis takes more time. In general faster inserting will result in slower searching and vice versa. The performance of the Rtree depends on balancing algorithm, parameters and data inserted into the container.
Additionally there are also algorithms creating Rtree containing some, number of objects. This technique is called bulk loading and is done by use of packing algorithm ^{[5]} ^{[6]}. This method is faster and results in Rtrees with better internal structure. This means that the query performance is increased.
The examples of structures of trees created by use of different algorithms and exemplary operations times are presented below.
Linear algorithm 
Quadratic algorithm 
R*tree 
Packing algorithm 


Example structure 




1M Values inserts 
1.76s 
2.47s 
6.19s 
1.67s 
100k spatial queries 
2.21s 
0.51s 
0.12s 
0.07s 
100k knn queries 
6.37s 
2.09s 
0.64s 
0.52s 
The performance of the Rtree for different values of Max parameter and Min=0.5*Max is presented in the table below. The configuration of the machine used for testing is: Intel(R) Core(TM) i7 870 @ 2.93GHz, 8GB RAM, MS Windows 7 x64. In the two upper figures you can see the performance of the Rtree storing random, relatively small, nonoverlapping, 2d boxes. In the lower ones, the performance of the Rtree also storing random, 2d boxes, but this time quite big and possibly overlapping. As you can see, the Rtree performance is different in both cases.
building 
querying 


non overlapping 


overlapping 


Key features of this implementation of the Rtree are:
Below you can find features that will (or probably will) be added in the future releases:
Rtree depends on Boost.Move, Boost.Container, Boost.Tuple, Boost.Utility, Boost.MPL.
The spatial index was originally started by Federico J. Fernandez during the Google Summer of Code 2008 program, mentored by Hartmut Kaiser.
I'd like to thank Barend Gehrels, Bruno Lalande, Mateusz Łoskot, Lucanus J. Simonson for their support and ideas.
^{[1] } Guttman, A. (1984). RTrees: A Dynamic Index Structure for Spatial Searching
^{[2] } Cheung, K.; Fu, A. (1998). Enhanced Nearest Neighbour Search on the Rtree
^{[3] } Greene, D. (1989). An implementation and performance analysis of spatial data access methods
^{[4] } Beckmann, N.; Kriegel, H. P.; Schneider, R.; Seeger, B. (1990). The R*tree: an efficient and robust access method for points and rectangles
^{[5] } Leutenegger, Scott T.; Edgington, Jeffrey M.; Lopez, Mario A. (1997). STR: A Simple and Efficient Algorithm for RTree Packing
^{[6] } Garcia, Yvan J.; Lopez, Mario A.; Leutenegger, Scott T. (1997). A Greedy Algorithm for Bulk Loading Rtrees