手法を比較

	地理加重ランダムフォレスト	空間ラグモデル（SAR / 空間自己回帰）
分野	空間分析	空間分析
系統	Machine learning	Regression model
提唱年	2021	1988
提唱者	Stefanos Georganos et al.	Anselin (textbook formalisation); LeSage & Pace
種類	Spatially local ensemble learning method	Spatial autoregressive regression
原典	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.
別名	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
関連	3	5
概要	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.