We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat. Various spatio-temporal covariance models have been implemented, such as the separable, product-sum, metric and sum-metric models. In a real-world application we compare spatio temporal interpolations using these models with a purely spatial kriging approach. The target variable of the application is the daily mean PM10 concentration measured at rural air quality monitoring stations across Germany in 2005. R code for variogram fitting and interpolation is presented in this paper to illustrate the workflow of spatio-temporal interpolation using gstat. We conclude that the system works properly and that the extension of gstat facilitates and eases spatio-temporal geostatistical modelling and prediction for R users.
spacetime, gstat, RandomFields, spTimer, spBayes, spate, FNN
SpatioTemporal, Spatial, Bayesian, TimeSeries
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For attribution, please cite this work as
Gräler, et al., "The R Journal: Spatio-Temporal Interpolation using gstat", The R Journal, 2016
BibTeX citation
@article{RJ-2016-014,
author = {Gräler, Benedikt and Pebesma, Edzer and Heuvelink, Gerard},
title = {The R Journal: Spatio-Temporal Interpolation using gstat},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-014},
doi = {10.32614/RJ-2016-014},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {204-218}
}