A Decision Tree for Multi-Layered Spatial Data

Spatial data mining fulfils real needs of many geomatic applications. It allows taking advantage of the growing availability of geographically referenced data and their potential richness. Nowadays, spatial data mining is a clearly identified field of data mining. This article deals with the spatial data classification using a decision tree. We propose a new method called SCART. This method differs from conventional decision trees by considering the specificities of geographical data, namely their organization in thematic layers, and the spatial relationships. SCART is an extension of CART method in two directions. From one hand, the algorithm considers several thematic layers as in the so-called relational data mining area, and from the other hand, it extends discriminating criteria to criteria on the neighborhood. For this purpose, it determines which combination of attribute value and spatial relationship of neighboring objects provides the best criterion.