Methoden vergleichen

	Geographically Weighted Random Forest	Geographisch gewichtete Regression (GWR)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Machine learning	Regression model
Entstehungsjahr	2021	2002
Urheber	Stefanos Georganos et al.	Fotheringham, Brunsdon & Charlton
Typ	Spatially local ensemble learning method	Local spatial regression
Wegweisende Quelle	Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Aliasnamen	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Verwandt	3	5
Zusammenfassung	Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues in 2019 (published 2021) as an extension of Breiman's Random Forest to handle spatial non-stationarity — the phenomenon where predictor–response relationships vary across geographic space.	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.