The R Journal: article published in 2016, volume 8:1

Spatio-Temporal Interpolation using gstat
Benedikt Gräler, Edzer Pebesma and Gerard Heuvelink , The R Journal (2016) 8:1, pages 204-218.

Abstract 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.

Received: 2015-07-24; online 2016-06-13
CRAN packages: spacetime, gstat, RandomFields, spTimer, spBayes, spate, FNN
CRAN Task Views implied by cited CRAN packages: SpatioTemporal, Spatial, Bayesian, TimeSeries

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-014,
  author = {Benedikt Gräler and Edzer Pebesma and Gerard Heuvelink},
  title = {{Spatio-Temporal Interpolation using gstat}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-014},
  url = {https://doi.org/10.32614/RJ-2016-014},
  pages = {204--218},
  volume = {8},
  number = {1}
}