Spatio-Temporal Interpolation using gstat
By Benedikt Gräler, Edzer J. Pebesma, Gerard B. M. Heuvelink. The R Journal, 2016
788 citations recorded by the source corpus.
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
Subfields: Economics and Econometrics, Environmental Engineering, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/6725d723b34a43113007be9d1ccedb80eb8717b2
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In Economics and Econometrics
In Environmental Engineering
- Tomislav Hengl
- Gerard B. M. Heuvelink
- Marvin N. Wright
- Madlene Nussbaum
- Benedikt Gräler
- Dick Brus
- Budiman Minasny
- Alex B. McBratney
In Nature and Landscape Conservation
Citations in the corpus
- Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “Empirical (a), and fitted metric (b, for comparison) and sum-metric (c) spatiotemporal variogram models for daily precipitation data using the spatiotemporal kriging functionality of the gstat package (Gräler et al., 2016).”