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Geographically Weighted Random Forest

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.

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Geographically Weighted Random Forest (GWRF)

Taksonomisk metoderegister · ml-model / spatial-analysis

Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136. · URL

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Used in the same domainGeographically Weighted Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.Used in the same domainSpatial Lag Modelmachine-suggested · Relational suggestion, not evidence.