Statistics, Department of

The R Journal
Date of this Version
8-2016
Document Type
Article
Citation
The R Journal (August 2016) 8(1); Editor: Michael Lawrence
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.
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Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2016, The R Foundation. Open access material. License: CC BY 3.0 Unported