The SpatialARMED Framework: Handling Complex Spatial Components in Spatial Association Rule Mining

Recent research has identified spatial association rule (SAR) mining as a promising technique for geographic pattern mining and knowledge discovery. Nevertheless, important spatial components embedded in the studied phenomenon, in particular complex spatial functional relations such as neighborhood effects and spatial spillover effects have largely been neglected. This article unravels this specific problem to enhance the effective application of SAR mining practices in spatial data analytics. The main discussion focuses on the specification of complex spatial components by means of spatial dependence properties of the data and on how to integrate them in the process of SAR mining. A comprehensive framework dubbed SpatialARMED is proposed for the effective extraction of spatial patterns. The framework is then showcased through its application to crime analysis.