Abstract
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.
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Sabater, C.R., Alvarez Esteban, P.C., Iscar, A.M. et al. Clustering to reduce regional heterogeneity: A spanish case-study. Journal of Population Research 21, 73–93 (2004). https://doi.org/10.1007/BF03032211
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DOI: https://doi.org/10.1007/BF03032211