Random Spatial Forests
Travis Hee Wai, Michael T. Young, Adam A. Szpiro

TL;DR
This paper presents random spatial forests, an efficient method combining random forests with spatial correlation modeling, improving prediction accuracy for spatial data in environmental applications.
Contribution
The paper introduces a novel computationally efficient algorithm for spatially correlated tree-based regression, integrating kriging and non-parametric spatial smoothers.
Findings
Improved prediction accuracy over existing methods
Effective modeling of spatial correlation in regression trees
Validated on environmental measurement datasets
Abstract
We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree adjusting for spatial correlation. We evaluate two different approaches for estimation of random spatial forests, a pseudo-likelihood approach combining random forests with kriging and a non-parametric version for a general class of spatial smoothers. We show improved prediction accuracy of our method compared to existing two-step approaches combining random forests and kriging across a range of numerical simulations and demonstrate its performance on elemental carbon, organic carbon, silicon, and sulfur measurements across the continental United States from 2009-2010.
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Taxonomy
TopicsSoil Geostatistics and Mapping · Spatial and Panel Data Analysis · Forest ecology and management
