Mining Spatial Association Rules in Census Data: A Relational Approach

In this paper we propose a method for the discovery of spatial association rules, that is, association rules involving spatial relations among (spatial) objects. The method is based on a multi-relational data mining approach and takes advantages of the representation and reasoning techniques developed in the field of Inductive Logic Programming (ILP). In particular, the expressive power of predicate logic is profitably used to represent spatial relations and background knowledge (such as spatial hierarchies and rules for spatial qualitative reasoning) in a very elegant, natural way. The integration of computational logics with efficient spatial database indexing and querying procedures permits applications that cannot be tackled by traditional statistical techniques in spatial data analysis. The proposed method has been implemented in the ILP system SPADA (Spatial Pattern Discovery Algorithm). We report the preliminary results on the application of SPADA to Stockport census data.