Facultade de Fisioterapia

Testing spatial heterogeneity in geographically weighted principal components analysis

Roca Pardiñas, Javier; Ordonez, C.; Cotos Yáñez, Tomás R.; Pérez-Álvarez, Rubén
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.
Year:
2017
Type of Publication:
Article
Keywords:
Principal components; kernel smoothing; bandwidth selection; soil contamination
Journal:
International Journal of Geographical Information Science
Volume:
31
Number:
4
Pages:
676-693
Month:
April
Note:
Q1 14/79 h-index 2,502 (JCR2016)
Comments:
Fundacion para el Fomento en Asturias de la Investigacion Cientifica Aplicada y la Tecnologia (FICYT) (Spain) FC-15-GRUPIN14-033 Spanish Ministry of Science and Innovation MTM2014-55699-P
DOI:
https://doi.org/10.1080/13658816.2016.1224886
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