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Analyzing spatio-temporal data with R: Everything you always wanted to know - but were afraid to ask

Abstract : We present an overview of (geo-)statistical models, methods and techniques for the analysis and prediction of continuous spatio-temporal processes residing in continuous space. Various approaches exist for building statistical models for such processes, estimating their parameters and performing predictions. We cover the Gaussian process approach, very common in spatial statistics and geostatistics, and we focus on R-based implementations of numerical procedures. To illustrate and compare the use of some of the most relevant packages, we treat a real-world application with high-dimensional data. The target variable is the daily mean PM10 concentration predicted thanks to a chemistry-transport model and observation series collected at monitoring stations across France in 2014. We give R code covering the full work-flow from importing data sets to the prediction of PM10 concentrations with a fitted parametric model, including the visualization of data, estimation of the parameters of the spatio-temporal covariance function and model selection. We conclude with some elements of comparison between the packages that are available today and some discussion for future developments.
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Submitted on : Monday, May 25, 2020 - 3:13:02 PM
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  • HAL Id : hal-02618656, version 1
  • PRODINRA : 406701


Denis Allard, Maxime Beauchamp, Liliane Bel, Nicolas Desassis, Ghislain Geniaux, et al.. Analyzing spatio-temporal data with R: Everything you always wanted to know - but were afraid to ask. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2017, 158 (3), pp.124-158. ⟨hal-02618656⟩



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