Methoden vergelijken

	Geografisch Gewogen Willekeurig Bos	Geografisch Gewogen Regressie (GWR)
Vakgebied	Ruimtelijke analyse	Ruimtelijke analyse
Familie	Machine learning	Regression model
Jaar van ontstaan	2021	2002
Grondlegger	Stefanos Georganos et al.	Fotheringham, Brunsdon & Charlton
Type	Spatially local ensemble learning method	Local spatial regression
Oorspronkelijke bron	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
Aliassen	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Verwant	3	5
Samenvatting	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.