Spatial Interestingness Measures for Co-location Pattern Mining

Co-location pattern mining aims at finding subsets of spatial features frequently located together in spatial proximity. The underlying motivation is to model the spatial correlation structure between the features. This allows to discover interesting co-location rules (feature interactions) for spatial analysis and prediction tasks. As in association rule mining, a major problem is the huge amount of possible patterns and rules. Hence, measures are needed to identify interesting patterns and rules. Existing approaches so far focused on finding frequent patterns, patterns including rare features, and patterns occurring in small (local) regions. In this paper, we present a new general class of interestingness measures that are based on the spatial distribution of co-location patterns. These measures allow to judge the interestingness of a pattern based on properties of the underlying spatial feature distribution. The results are different from standard measures like participation index or confidence. To demonstrate the usefulness of these measures, we apply our approach to the discovery of rules on a subset of the OpenStreetMap point-of-interest data.