Geographical databases are available to date containing detailed and georeferenced data on population, commercial activities, business, transport and services at urban level. Such data allow examining urban phenomena at very detailed scale but also require new methods for analysis, comprehension and visualization of the spatial phenomena. In this paper a density-based method for extracting spatial information from large geographical databases is examined and first results of its application at the urban scale are presented. Kernel Density Estimation is used as a density based technique to detect clusters in spatial data distributions. GIS and spatial analytical methods are examined to detect areas of high services’ supply in an urban environment. The analysis aims at identifying clusters of services in the urban environment and at verifying the correspondence between urban centres and high levels of service.
