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Machine Learning as a generic framework for spatial prediction

Summary: This tutorial explains how to use Random Forest to generate spatial and spatiotemporal predictions (i.e. to make maps from point observations using Random Forest). Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is sub-optimal. To account for this, we use Random Forest (as implemented in the ranger package) in combination with geographical distances to sampling locations to fit models and predict values.

Tutorials: RFsp — Random Forest for spatial data (https://github.com/thengl/GeoMLA)

Reference:

Hengl T, Nussbaum M, Wright MN, Heuvelink GBM, Gräler B. (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518 https://doi.org/10.7717/peerj.5518