Fast mining of spatial collocations

TLDR

Spatial collocation patterns link non‑spatial features within a spatial neighborhood, such as contaminated water reservoirs associated with nearby diseases, and prior work has treated these neighborhoods as itemsets for transactional data mining. The authors propose a method that integrates spatial neighborhood discovery with the mining process. The technique extends a spatial join algorithm to handle multiple inputs and count long pattern instances. Experiments show significant performance improvements over prior approaches.

Abstract

Spatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.