Salīdzināt metodes

	Ģeogrāfiski svērtā galveno komponentu analīze (GWPCA)	Ģeogrāfiski svērtā regresija (GWR)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Machine learning	Regression model
Izcelsmes gads	2011	2002
Autors	Paul Harris, Chris Brunsdon & Martin Charlton	Fotheringham, Brunsdon & Charlton
Tips	Local dimensionality reduction	Local spatial regression
Pirmavots	Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Citi nosaukumi	Local PCA, Spatially Adaptive PCA, Geographically Weighted Factor Analysis, Yerel Coğrafi Ağırlıklı PCA	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Saistītās	2	5
Kopsavilkums	Geographically Weighted Principal Component Analysis (GWPCA) is a local dimensionality-reduction method introduced by Harris, Brunsdon, and Charlton in 2011. It extends classical PCA by fitting a separate weighted PCA at every location in a dataset, allowing eigenstructures — the principal components and their loadings — to vary continuously across geographic space rather than being constrained to a single global solution. GWPCA is suited to researchers in environmental science, public health, and regional economics who suspect that multivariate relationships among variables differ by location.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.