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Testing spatial heterogeneity in geographically weighted principal components analysis

Abstract: We propose a method to evaluate the existence of spatial variability in the covariance structure in a geographically weighted principal components analysis (GWPCA). The method, that is extensive to locally weighted principal components analysis, is based on performing a statistical hypothesis test using the eigenvectors of the PCA scores covariance matrix. The application of the method to simulated data shows that it has a greater statistical power than the current statistical test that uses the eigenvalues of the raw data covariance matrix. Finally, the method was applied to a real problem whose objective is to find spatial distribution patterns in a set of soil pollutants. The results show the utility of GWPCA versus PCA.

 Fuente: International Journal of Geographical Information Science, 2017, vol. 31, nº 4, 676-693

Editorial: Taylor & Francis

 Año de publicación: 2017

Nº de páginas: 18

Tipo de publicación: Artículo de Revista

DOI: 10.1080/13658816.2016.1224886

ISSN: 1365-8816,1362-3087

Url de la publicación: https://www.tandfonline.com/toc/tgis20/current

Autores/as

ROCA PARDIÑAS, JAVIER

ORDOÑEZ, CELESTINO

COTOS YÁÑEZ, TOMÁS R.