Spatial data mining for clustering: from the literature review to an application using RedCap

The aim of the paper is both to review the scientific literature about spatial data mining methods - in particular spatial clustering methods developed in recent years - and to present an original application of the recently proposed RedCap method (Guo, 2008) of spatial clustering and regionalization on Florentine Metropolitan Area (FMA). Demographic indicators computed on official data provided by the Italian Institute of Statistics (Istat), are the input of a spatial clustering and regionalization model in order to get a classification of the FMA municipalities into a number of homogeneous (with respect to demographic structure) and spatially contiguous zones. In the optics of a progressive decentralization of the governance activities we believe that the FMA represents a very interesting case of study. This due to the fact that the individuation of new spatial areas built considering both the demographic characteristics of the resident population and the spatial dimension of the territory where this population insists could become a useful tool for local governance.