The objective of this paper is to present a combined, two-step spatial analysis 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 analysis: spatial statistics and spatial modelling. Spatial statistics tools are used to identify the characteristics of local association and are combined with a neural network in order to build prediction models. The analyses conducted with exploratory spatial data analysis tools and census data give a clear indication of clusters of zones with similar characteristics, which can be seen as uniform regions. Spatial models can then be used to foresee the global behaviour of regions in terms of growth, albeit the basis of 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.
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