Summary
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.
Outcomes reported
Referenced by Nature Communications British biodiversity scenarios as citation 101; likely supports topic area: biodiversity / conservation. Topics: biodiversity / conservation Evidence type: Research article / other Source report: Nature Communications British biodiversity scenarios Ref#: Nature Communications British biodiversity scenarios #101 Original: Gräler, B., Pebesma, E. & Heuvelink, G. Spatio-Temporal Interpolation using gstat. R J 8, 204 (2016).
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