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	Analisi delle Componenti Principali Geograficamente Ponderate (GWPCA)	Regressione Geograficamente Ponderata (GWR)
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Machine learning	Regression model
Anno di origine	2011	2002
Ideatore	Paul Harris, Chris Brunsdon & Martin Charlton	Fotheringham, Brunsdon & Charlton
Tipo	Local dimensionality reduction	Local spatial regression
Fonte seminale	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
Alias	Local PCA, Spatially Adaptive PCA, Geographically Weighted Factor Analysis, Yerel Coğrafi Ağırlıklı PCA	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Correlati	2	5
Sintesi	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.