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Journal article

Clustering to reduce regional heterogeneity: a Spanish case-study

1 Jan 2004
Description

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.

Publication Details
Volume: 
Vol.21
Issue: 
No.1
Language: 
Published year only: 
2004
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