Discovery of spatial association rules in geo-referenced census data: A relational mining approach

Article type: Research Article

Authors: Appice, Annalisa | Ceci, Michelangelo | Lanza, Antonietta | Lisi, Francesca A. | Malerba, Donato

Affiliations: Dipartimento di Informatica, Università degli Studi di Bari, via Orabona 4, 70126 Bari, Italy. E-mail: appice@di.uniba.it, ceci@di.uniba.it, lanza@di.uniba.it, lisi@di.uniba.it, malerba@di.uniba.it

Abstract: Census data mining has great potential both in business development and in good public policy, but still must be solved in this field a number of research issues. In this paper, problems related to the geo-referenciation of census data are considered. In particular, the accommodation of the spatial dimension in census data mining is investigated for the task of discovering spatial association rules, that is, association rules involving spatial relations among (spatial) objects. The formulation of a new method based on a multi-relational data mining approach is proposed. It takes advantage of the representation and inference techniques developed in the field of Inductive Logic Programming (ILP). In particular, the expressive power of predicate logic is profitably used to represent both spatial relations and background knowledge, such as spatial hierarchies and rules for spatial qualitative reasoning. The logical notions of generality order and of the downward refinement operator on the space of patterns are profitably used to define both the search space and the search strategy. The proposed method has been implemented in the ILP system SPADA (Spatial Pattern Discovery Algorithm). SPADA has been interfaced both to a module for the extraction of spatial features from a spatial database and to a module for numerical attribute discretization. The three modules have been used in an application to urban accessibility of a hospital in Stockport, Greater Manchester. Results obtained through a spatial analysis of geo-referenced census data are illustrated.

DOI: 10.3233/IDA-2003-7604

Journal: Intelligent Data Analysis, vol. 7, no. 6, pp. 541-566, 2003