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Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

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RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
10 days ago
[Artigo] "Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables" --- 'Random Forest e técnicas semelhantes de Machine Learning já são usadas para gerar previsões espaciais, mas a localização... https://t.co/WYaL4K4SPX
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
10 days ago
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
11 days ago
RT @tom_hengl: Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publica…
Our paper on using MLA (Random Forest) to generate spatial and spatiotemporal predictions has just been accepted for publication @thePeerJ The most up-to-date preprint of the article can still be accessed from https://t.co/sJfpmLigU3 and the tutorial is at https://t.co/7XLhKmipkB
11 days ago
Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables https://t.co/76x8nI3wec
RT @tom_hengl: Revised version of our paper on using RF as a generic framework for sp and spacetime predictions is now available (the paper…
RT @tom_hengl: Revised version of our paper on using RF as a generic framework for sp and spacetime predictions is now available (the paper…
83 days ago
RT @tom_hengl: Revised version of our paper on using RF as a generic framework for sp and spacetime predictions is now available (the paper…
RT @tom_hengl: Revised version of our paper on using RF as a generic framework for sp and spacetime predictions is now available (the paper…
Revised version of our paper on using RF as a generic framework for sp and spacetime predictions is now available (the paper is still in review) @PeerJPreprints https://t.co/2YtfdzCjUN #Biogeography #SoilScience #ComputationalScience RFsp slides are at: https://t.co/RGQei3ysWS
83 days ago
Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables https://t.co/lYU9EWn018
RT @tom_hengl: @erum2018 my talk on using RF as a generic framework for Spatial and Spatio-temporal predictions now on https://t.co/sdxn5oL…
RT @tom_hengl: @erum2018 my talk on using RF as a generic framework for Spatial and Spatio-temporal predictions now on https://t.co/sdxn5oL…
@erum2018 my talk on using RF as a generic framework for Spatial and Spatio-temporal predictions now on https://t.co/sdxn5oL3uA with detailed results on https://t.co/sJfpmLigU3 #erum2018
RT @tom_hengl: Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables https://t.co/bkqVacp8gG
156 days ago
RT @tom_hengl: Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables https://t.co/bkqVacp8gG
157 days ago
Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables https://t.co/qhFJGdNDfJ
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Supplemental Information

RFsp — Random Forest for spatial data (R tutorial)

DOI: 10.7287/peerj.preprints.26693v3/supp-1

Additional Information

Competing Interests

Tomislav Hengl is employed by the Envirometrix Ltd., Wageningen, Gelderland, Netherlands (http://envirometrix.net; from 1st of February 2018). Marvin N. Wright is employed by the Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen (https://www.bips-institut.de/en/the-institute/departments/biometry-and-data-management/statistical-methods-in-genetics-and-life-course-epidemiology.html).

Author Contributions

Tomislav Hengl conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Madlene Nussbaum conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.

Marvin N Wright performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, approved the final draft.

Gerard B.M. Heuvelink analyzed the data, authored or reviewed drafts of the paper, approved the final draft, mathematical syntax checking.

Benedikt Gräler analyzed the data, prepared figures and/or tables, approved the final draft, fitted spacetime models using kriging.

Data Deposition

The following information was supplied regarding data availability:

All code and data sets used in the paper are also available at: https://github.com/thengl/GeoMLA

Ranger package for R is available under the GPL at: https://github.com/imbs-hl/ranger

Funding

The contributions by Benedikt Gräler have been funded by the German Federal Ministry for Economic Affairs and Energy under grant agreement number 50EE1715C. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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