Discovering spatial interaction patterns

Abstract

Advances in sensing and satellite technologies and the growth of Internet have resulted in the easy accessibility of vast amount of spatial data. Extracting useful knowledge from these data is an important and challenging task, in particular, finding interaction among spatial features. Existing works typically adopt a grid-like approach to transform the continuous spatial space to a discrete space. 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. 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. © 2008 Springer-Verlag Berlin Heidelberg.