Clustering to reduce regional heterogeneity: A spanish case-study

Statistical methods of dimension reduction and classification are used to obtain homogeneous local-area clustering with regard to the most relevant demographic parameters. The dimension reduction is conducted in two stages using Principal Component Analysis and a modified k-mean procedure is proposed to determine the final clusters. This clustering will be useful in future demographic studies at a local level, in particular to obtain forecasts of demographic rates and population projections. The region of Castile and León in Spain is used to illustrate the method. A Poisson model is used to explore the advantages of the new clustering over the more conventional classification based on provinces.