Testing spatial heterogeneity in geographically weighted principal components analysis
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/1145
EDITED VERSION: http://dx.doi.org/10.1080/13658816.2016.1224886
DOCUMENT TYPE: article
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.
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