The first part of the course is on statistical and geostatistical inference of data with known locations presenting interpolation methods. The theory of spatial variability is explained presenting the structure of spatial variability-dependence with covariance functions and variograms. Data normalization techniques and trend analysis are presented for appropriate geostatistical applications. Comparison of geostatistical methodologies is applied, optimal sampling is discussed and attention is given to multivariate geostatistics.
The second part of the course considers modelling spatiotemporal phenomena. The extension from pure spatial to spatiotemporal approaches is not trivial. Spatiotemporal covariance models (separable, nonseparable) are presented and explored on real datasets. The practical part deals with the exploration of different spatiotemporal variogram models and space-time kriging approaches applied to an own data set or a provided example.
Finally, the third part of the course presents simulation techniques combined with estimations of uncertainty.