A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests
Stefanos Georganos, Stamatis Kalogirou
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.
Topics & Concepts
Geospatial analysisRandom forestComputer scienceEuropean unionAnalyticsSimple (philosophy)Geographically Weighted RegressionData miningSpatial analysisEconometricsData scienceGeographyCartographyStatisticsMathematicsMachine learningRemote sensingBusinessEconomic policyPhilosophyEpistemologyLand Use and Ecosystem ServicesSpatial and Panel Data AnalysisEconomic and Environmental Valuation