A data-driven approach for the definition of metropolitan regions

Abstract

The objective of this paper is to present a data-driven approach for the definition of metropolitan regions. The proposed approach, which constitutes an option to avoid the endless confrontations that may be derived from the essentially subjective political criteria, explores two branches of Spatial Analyses: Spatial Statistics and Spatial Modeling. Spatial Statistics tools are used to identify the characteristics of local association and combined with Cellular Automata techniques in order to build prediction models. The analyses conducted with Exploratory Spatial Data Analyses (ESDA) tools and census data give a clear indication of clusters of zones with similar characteristics, which can be seen as uniform regions. Spatial dynamic models can then be used to foresee the global behavior of regions in terms of growth, although based on local (and historical) relationships among zones. The proposed approach is tested in a case study carried out in Portugal, where this is a timely issue.