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 spatiotemporal interpolations using these models with a purely spatial kriging approach.The target variable of the application is the daily mean PM 10 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.