An application of Cyclic Signature (CS) clustering for spatial-temporal pattern analysis to support public safety work

In this paper, we propose a novel approach, Cyclic Signature (CS) clustering, to analyze spatial-temporal pattern. CS clustering is based on the calendar regularities of events to analyze spatial-temporal patterns. An experiment, based on a set of reported crime data for a district in Hong Kong, was performed to compare CS clustering against traditional clustering approaches. The results show that CS clustering can provide information which differs greatly from traditional clustering approaches. In addition, the groups created by CS clustering have higher intra-cluster similarities and lower inter-cluster similarities than traditional clustering approaches.