Methoden vergleichen

	Geographically Weighted Random Forest	Spatial-Lag-Modell (SAR / Spatial Autoregressive)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Machine learning	Regression model
Entstehungsjahr	2021	1988
Urheber	Stefanos Georganos et al.	Anselin (textbook formalisation); LeSage & Pace
Typ	Spatially local ensemble learning method	Spatial autoregressive 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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Aliasnamen	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.