Spatio-Temporal Interpolation using gstat

Authors

• Benedikt Gräler, University of Münster
• Edzer Pebesma, University of Münster
• Gerard Heuvelink, Wageningen University

Date of this Version

8-2016

Document Type

Article

Citation

The R Journal (August 2016) 8(1); Editor: Michael Lawrence

Comments

Copyright 2016, The R Foundation. Open access material. License: CC BY 3.0 Unported

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. Inareal-world application we comparespatio 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.