Detecting clusters in spatially repetitive point event data sets

The analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards exact spatial repetitiveness. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the ‘hot spots’