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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
Leer publicación
ROCA PARDIÑAS, JAVIER
ORDOÑEZ, CELESTINO
COTOS YÁÑEZ, TOMÁS R.
RUBEN PEREZ ALVAREZ
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