Published January 23, 2022 | Version v0.1
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Spatial and Spatiotemporal Interpolation / Prediction using Ensemble Machine Learning

  • 1. OpenGeoHub / EnvirometriX
  • 2. OpenGeoHub


This R tutorial explains step-by-step how to use Ensemble Machine Learning to generate predictions (maps) from 2D, 3D, 2D+T training (point) datasets. We show functionality to do automated benchmarking for spatial/spatiotemporal prediction problems, and for which we use primarily the mlr framework and spatial packages terra, rgdal and similar. In addition, we explain how to plot spatial/spatiotemporal prediction inputs and outputs, including how to do accuracy plots and predictograms. We focus engineering the predictive mapping around three main areas: (a) accuracy performance, (b) computing time, (c) robustness of the algorithms (sensitivity to noise, artifacts etc).

Online version of the book is available at:


Acknowledgement: CEF Telecom project 2018-EU-IA-0095. This project is co-financed by the by the European Union.



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Book: 10.5281/zenodo.5513826 (DOI)
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Book: 10.5281/zenodo.5886677 (DOI)