Spatial indexing

Geospatial datasets are often very large files easily reaching hundreds of megabytes or even several gigabytes in size. Geospatial software can be quite slow in trying to repeatedly access large files when performing analysis. As discussed briefly in Chapter 1, Learning Geospatial Analysis with Python, spatial indexing creates a guide, which allows software to quickly locate query results without examining every single feature in the dataset. Spatial indexes allow software to eliminate possibilities and perform more detailed searches or comparisons on a much smaller subset of the data.

Indexing algorithms

Many spatial indexing algorithms are derivatives of well-established algorithms used for decades on nonspatial information. The two most common spatial indexing algorithms are Quadtree index and R-tree index.

Quadtree index

The Quadtree algorithm actually represents a series of different algorithms based on a common theme. Each node in a Quadtree index contains four children...