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Clustering to reduce regional heterogeneity: a Spanish case-study

  • Year: 2004
  • Author: Sabata, Cristina Rueda; Esteban, Pedro C. Alvarez; Iscar, Agustin Mayo; Diez, Ana Lopez
  • Journal Name: Journal of Population Research
  • Journal Number: Vol.21, No.1
  • Country: Spain

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 Leon 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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