RT Journal Article
T1 Testing spatial heterogeneity in geographically weighted principal components analysis
A1 Roca Pardiñas, Javier
A1 Ordóñez Galán, Celestino
A1 Cotos Yáñez, Tomas Raimundo
A1 Pérez Álvarez, Rubén
AB 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.
PB International Journal of Geographical Information Science
SN 13658816
YR 2016
FD 2016-09-05
LK http://hdl.handle.net/11093/1145
UL http://hdl.handle.net/11093/1145
LA eng
NO International Journal of Geographical Information Science, 31(4): 676-693 (2016)
NO Fundación para el Fomento en Asturias de la Investigación Científica | Ref. FC-15-GRUPIN14-033
DS Investigo
RD 08-oct-2024