# Random Spatial Forests

arXiv: https://arxiv.org/abs/2006.00150 · HTML: https://mlsanity.com/paper/2006.00150/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 2 of 2)

- [Variable importance measure for spatial machine learning models with application to air pollution exposure prediction](https://mlsanity.com/paper/2406.01982/) (2024)
- [Principal component analysis balancing prediction and approximation accuracy for spatial data](https://mlsanity.com/paper/2408.01662/) (2024)

## Abstract (arXiv)

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.