Spatio-Temporal Interpolation using gstat

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.

Benedikt Gräler , Edzer Pebesma , Gerard Heuvelink

CRAN packages used

spacetime, gstat, RandomFields, spTimer, spBayes, spate, FNN

CRAN Task Views implied by cited packages

SpatioTemporal, Spatial, Bayesian, TimeSeries


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Gräler, et al., "The R Journal: Spatio-Temporal Interpolation using gstat", The R Journal, 2016

BibTeX citation

  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 = {},
  doi = {10.32614/RJ-2016-014},
  volume = {8},
  issue = {1},
  issn = {2073-4859},
  pages = {204-218}