Spatial Clustering Methods for the Detection of Homogenous Areas

Local statistical offices often dispose of very rich databases of spatially referenced socio–economic data. These datasets frequently exhibit strong clustering due to the fact that the degree of detail of the information is greater than the extent of many socio– economic phenomena: for instance, high income block of houses are often located in high income neighborhoods. In this work we apply statistical techniques for the detection of spatial clusters using data from the statistical office of the city of Florence. The aim of the paper lies in a review of methods for the analysis and detection of spatial clusters and in the application of a variation of a recently proposed clustering method. The empirical application shows that ignoring spatial clustering can lead to misleading inference and that, on the other hand, the use of appropriate methods for the detection of spatial clusters leads to meaningful inference of urban socio–economic phenomena.