Event pattern analysis: the point density around the appearance and disappearance of points

Abstract

ABSTRACT Point pattern analysis is a fundamental analytical tool in various academic fields related to spatial information science. Basic but important patterns discussed in existing studies are spatially clustered and dispersed points. Analysis of these static patterns aims to reveal their underlying structure, i.e. why and how they are formed. However, to answer this research question, the direct tracking of the pattern formation process is more effective, though we have not yet fully conducted such analysis along the temporal axis. To fill the research gap, this paper develops a new method of spatiotemporal analysis. We focus on the point density around the appearance and disappearance of points. A research question is whether points appear/disappear in dense or sparse space. Extending Ripley’s K-function, we develop four measures statistically evaluating the point density. We applied the proposed method to the analysis of the competition among convenience store chains in Shibuya-ku, Tokyo.