Abstract

We present efficient algorithms to compute simple and complex map algebra operations over raster data stored in main memory, using the k(2)-acc compact data structure. Raster data correspond to numerical data that represent attributes of spatial objects, such as temperature or elevation measures. Compact data structures allow efficient data storage in main memory and query them in their compressed form. A k(2)-acc is a set of k(2)-trees, one for every distinct numeric value in the raster matrix. We demonstrate that map algebra operations can be computed efficiently using this compact data structure. In fact, some map algebra operations perform over five orders of magnitude faster compared with algorithms working over uncompressed datasets.