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- The comparison results suggest that GWPCA provides better fitness than the standard PCA model by considering spatial heterogeneity.
cihh.utp.ac.pa/sites/default/files/documentos/2021/pdf/geographically_weighted_principal_component.pdf
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Jun 26, 2020 · The performance of GWPCA (Figure 6), in terms of the variance explained by the first five components, is better in 680 out of 797 localities (i.e., 85% of the units under study) compared with that obtained with standard PCA.
- Alfredo Cartone, Paolo Postiglione
- 2021
In geographical settings, standard PCA, in which the components do not depend on location, may be replaced with a GWPCA (Fotheringham et al. 2002; Lloyd 2010a; Harris et al. 2011a), to account for spatial heterogeneity in the structure of the multivariate data.
Aug 23, 2011 · Here we initially consider the basics of (global) principal components, then consider the development of a locally weighted PCA (for the exploration of local subsets in attribute-space) and finally GWPCA.
- Paul Harris, Chris Brunsdon, Martin Charlton
- 2011
Jun 1, 2021 · More critically, the loading data generated by GWPCA could be used to derive local SFQI that depends on local environmental circumstances. Therefore, GWPCA may be a better tool than the traditionally-used PCA in the SFQI assessment in large-scale areas.
- Jian Chen, Mingkai Qu, Jianlin Zhang, Enze Xie, Biao Huang, Yongcun Zhao
- 2021
Oct 1, 2011 · In this respect, standard PCA can be (a) replaced with a geographically weighted PCA (GWPCA), when we want to account for a certain spatial heterogeneity; (b) adapted to account for spatial...
The comparison results suggest that GWPCA provides better fitness than the standard PCA model by considering spatial heterogeneity. Keywords: Geographically Weighted Principal Components Analysis, multivariate analysis, sustainable development, electricity consumption.
This paper proposes a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations, and shows that sPCA performed better than PCA to reveal spatial genetic patterns. Expand.
