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index.Rmd
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---
knit: "bookdown::render_book"
title: "Spatial and spatiotemporal interpolation using Ensemble Machine Learning"
author: "Tom Hengl, Leandro Parente, Carmelo Bonannella and contributors"
description: "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). More chapters will be added in the future. Contributions are welcome. To discuss issues or report a bug please use the repository homepage."
url: 'https\://opengeohub.github.io/spatial-prediction-eml/'
bibliography: ./tex/refs.bib
csl: ./tex/apa.csl
documentclass: svmono
classoption: graybox,natbib,nospthms
geometry: "paperwidth=18.90cm, paperheight=24.58cm, top=2.1cm, bottom=2.1cm, inner=2cm, outer=2cm"
monofont: "Source Code Pro"
monofontoptions: "Scale=0.7"
biblio-style: spbasic
fig_caption: yes
link-citations: yes
gihub-repo: OpenGeoHub/spatial-prediction-eml/
twitter-handle: opengeohub
cover-image: cover.png
site: bookdown::bookdown_site
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction {.unnumbered}
## Ensemble Machine Learning {.unnumbered}
[](https://doi.org/10.5281/zenodo.5894878)
[{.cover width="250"}](https://opengeohub.github.io/spatial-prediction-eml/) This [Rmarkdown tutorial](https://opengeohub.github.io/spatial-prediction-eml/) provides practical instructions, illustrated with sample
dataset, on how to use Ensemble Machine Learning to generate predictions (maps) from
2D, 3D, 2D+T (spatiotemporal) 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..
Ensembles are predictive models that combine predictions from two or more learners
[@seni2010ensemble; @zhang2012ensemble]. The specific benefits of using Ensemble learners are:
- **Performance**: they can help improve the average prediction performance over any individual contributing learner in the ensemble.
- **Robustness**: they can help reduce extrapolation / overshooting effects of individual learners.
- **Unbiasness**: they can help determine a model-free estimate of prediction errors.
Even the most flexible and best performing learners such as Random Forest or neural
networks always carry a bias in the sense that the fitting produces recognizable
patterns and these are limited by the properties of the algorithm. In the case of
ensembles, the modeling algorithm becomes secondary, and even though the improvements
in accuracy are often minor as compared to the best individual learner, there is
a good chance that the final EML model will be less prone to overshooting and
extrapolation problems.
There are in principle three ways to apply ensembles [@zhang2012ensemble]:
- _bagging_: learn in parallel, then combine using some deterministic principle (e.g. weighted averaging),
- _boosting_: learn sequentially in an adaptive way, then combine using some deterministic principle,
- _stacking_: learn in parallel, then fit a meta-model to predict ensemble estimates,
The _“meta-model”_ is an additional model that basically combines all individual
or _“base learners”_. In this tutorial we focus only on the stacking approach to Ensemble ML.
There are several packages in R that implement Ensemble ML, for example:
- [SuperLearner](https://cran.r-project.org/web/packages/SuperLearner/vignettes/Guide-to-SuperLearner.html) package,
- [caretEnsemble](https://cran.r-project.org/web/packages/caretEnsemble/vignettes/caretEnsemble-intro.html) package,
- [h2o.stackedEnsemble](http://docs.h2o.ai/h2o-tutorials/latest-stable/tutorials/ensembles-stacking/index.html) package,
- [mlr](https://mlr.mlr-org.com/reference/makeStackedLearner.html) and [mlr3](https://mlr3gallery.mlr-org.com/posts/2020-04-27-tuning-stacking/) packages,
Ensemble ML is also available in Python through the [scikit-learn](https://scikit-learn.org/stable/modules/ensemble.html) library.
In this tutorial we focus primarily on using the [mlr package](https://mlr.mlr-org.com/),
i.e. a wrapper functions to mlr implemented in the landmap package.
## Using geographical distances to improve spatial interpolation {.unnumbered}
Machine Learning was for long time been considered suboptimal for spatial
interpolation problems, in comparison to classical geostatistical techniques
such as kriging, because it basically ignores spatial dependence structure in
the data. To incorporate spatial dependence structures in machine learning, one
can now add the so-called "geographical features": buffer distance, oblique
distances, and/or distances in the watershed, as features. This has shown to
improve prediction performance and produce maps that visually appear as they
have been produced by kriging [@hengl2018random].
Use of geographical as features in machine learning for spatial predictions is explained in detail in:
- Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., & MacMillan, R. A. (2018). [Spatial modelling with Euclidean distance fields and machine learning](https://doi.org/10.1111/ejss.12687). European journal of soil science, 69(5), 757-770.
- Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). [Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables](https://doi.org/10.7717/peerj.5518). PeerJ, 6, e5518. <https://doi.org/10.7717/peerj.5518>
- Møller, A. B., Beucher, A. M., Pouladi, N., and Greve, M. H. (2020). [Oblique geographic coordinates as covariates for digital soil mapping](https://doi.org/10.5194/soil-6-269-2020). SOIL, 6, 269–289, https://doi.org/10.5194/soil-6-269-2020
- Sekulić, A., Kilibarda, M., Heuvelink, G.B., Nikolić, M., Bajat, B. (2020). [Random Forest Spatial Interpolation](https://doi.org/10.3390/rs12101687). Remote Sens. 12, 1687. <https://doi.org/10.3390/rs12101687>
In the case the number of covariates / features becomes large, and assuming the
covariates are diverse, and that the points are equally spread in an area of
interest, there is probably no need for using geographical distances in model
training because unique combinations of features become so large that they can
be used to represent _geographical position_ [@hengl2018random].
## Installing the landmap package {.unnumbered}
To install the most recent landmap package from Github use:
```{r, eval=FALSE}
library(devtools)
install_github("envirometrix/landmap")
```
## Important literature {.unnumbered}
For an introduction to Spatial Data Science and Machine Learning with R we
recommend studying first:
- Becker, M. et al.: **[“mlr3 book”](https://mlr3book.mlr-org.com/)**;
- Bivand, R., Pebesma, E. and Gómez-Rubio, V.: **[“Applied Spatial Data Analysis with R”](https://asdar-book.org/)**;
- Irizarry, R.A.: **[“Introduction to Data Science: Data Analysis and Prediction Algorithms with R”](https://rafalab.github.io/dsbook/)**;
- Kuhn, M.: **[“The caret package”](https://topepo.github.io/caret/)**;
- Molnar, C.: **[“Interpretable Machine Learning: A Guide for Making Black Box Models Explainable”](https://christophm.github.io/interpretable-ml-book/)**;
- Lovelace, R., Nowosad, J. and Muenchow, J.: **[“Geocomputation with R”](https://geocompr.robinlovelace.net/)**;
For an introduction to **Predictive Soil Mapping** using R refer to <https://soilmapper.org>.
Machine Learning in **python** with resampling can be best implemented via the
[scikit-learn library](https://scikit-learn.org/stable/), which matches in
functionality what is available via the mlr package in R.
## License {.unnumbered}
[<img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" />](http://creativecommons.org/licenses/by-sa/4.0/)
This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
```{r, include=FALSE, message=FALSE, results='hide'}
ls <- c("rgdal", "raster", "plotKML", "ranger", "mlr", "ggridges", "viridis",
"xgboost", "fastSave", "glmnet", "matrixStats", "kernlab", "deepnet",
"ggplot2", "landmap")
new.packages <- ls[!(ls %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
lapply(ls, require, character.only = TRUE)
#options(warn=-1)
#load.pigz("eml_data.RData")
source("PSM_functions.R")
```
## Acknowledgements {.unnumbered}
 This tutorial is based on the **[“R for Data Science”](https://r4ds.had.co.nz/)**
book by Hadley Wickham and contributors.
**[OpenLandMap](https://openlandmap.org)** is a collaborative effort and many people
have contributed data, software, fixes and improvements via pull request.
[OpenGeoHub](https://opengeohub.org) is an independent not-for-profit research
foundation promoting Open Source and Open Data solutions. These tools were developed
primarily for the need of the Geo-harmonizer project and to enable creation of
next-generation environmental layers for continental Europe [@witjes2021spatiotemporal; @Bonannella2022].
**[EnvirometriX Ltd.](https://envirometrix.nl)** is the commercial branch of the group
responsible for designing soil sampling designs for the **[AgriCapture](https://agricaptureco2.eu/)**
and similar soil monitoring projects.
[<img src="tex/opengeohub_logo_ml.png" alt="OpenGeoHub logo" width="350"/>](https://opengeohub.org)
**[EcoDataCube.eu](https://EcoDataCube.eu/)** project is co-financed by the European Union (**[CEF Telecom project 2018-EU-IA-0095](https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095)**).
**[EarthMonitor.org](https://EarthMonitor.org/)** project has received funding from the European Union's Horizon Europe research an innovation programme under grant agreement **[No. 101059548](https://cordis.europa.eu/project/id/101059548)**.