Efficient clustering technique for regionalisation of a spatial database

Regionalisation, a prominent problem from social geography, could be solved by a classification algorithm for grouping spatial objects. A typical task is to find spatially compact and dense regions of arbitrary shape with a homogeneous internal distribution of social variables. Grouping a set of homogeneous spatial units to compose a larger region can be useful for sampling procedures as well as many applications, e.g. direct mailing. It would be helpful to have specific purpose regions, depending on the kind of homogeneity one is interested in. In this paper, we propose an algorithm combining the 'spatial density' clustering approach and a covariance based method to inductively find spatially dense and non-spatially homogeneous clusters of arbitrary shape.