Discovering Spatial Interaction Patterns

Advances in sensing and satellite technologies and the growth of Internet have resulted in a vast amount of uncertain spatial data. Extracting interaction patterns from these uncertain data is a challenging task. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the distribution of the feature instances. Superimposing the influence maps allows the interaction of the feature types to be quickly determined. We design an efficient algorithm called PROBER to find the maximal spatial interaction patterns. Experiments on both synthetic and real world datasets indicate that the proposed approach is scalable and is able to discover patterns that have been missed by existing methods.