A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests

Stefanos Georganos · Stamatis Kalogirou

109 citations0/0 files storedISPRS International Journal of Geo-Information

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Spatial and Panel Data Analysis Soil Geostatistics and Mapping demographic modeling and climate adaptation

Abstract

The aim of this paper is to present developments of an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data. We applied the methodology to a simple model of mean household income in the European Union regions to allow easy understanding and reproducibility of the analysis. The results are encouraging and suggest an improvement in the prediction power compared to previous techniques. The algorithm has been implemented in R and is available in the updated version of the SpatialML package in the CRAN repository.

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2015Geographical and Temporal Weighted Regression (GTWR)2019Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction

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2025Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees 2023Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA 2023The spatiotemporal socio-demography of the Tokyo capital region: a data-driven explorative approach

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