Topics and scope
- Geostatistical applications in spatial and spatio-temporal modeling.
- Spatio-temporal methods for the analysis of hydrological, environmental and climate phenomena.
- Spatial analysis and prediction using geostatistical models.
- Geostatistical characterization of uncertainties. Conditional simulations.
- Selected topics of spatial data analysis: machine (computer) learning and data mining.
- Spartan spatial random fields and stochastic local interaction (SLI) models.
Scope of the Workshop
- Concepts of geostatistics.
- Research methodology based on the geostatistical approach.
- Empirical measures of spatial variability and respective theoretical models.
- Variogram estimation and modeling.
- Cross-validation and model selection.
- Kriging estimators (kriging, cokriging).
- Quality and effectiveness of kriging techniques.
- Conditional simulations and risk mapping.
- Methods of fast interpolation (e.g., kriging linear model, inverse distances, nearest neighbours).
- Case studies and best practices.
- Introduction to environmental data mining using machine (computer) learning algorithms.
- Sparse precision matrices, stochastic local interactions and quick spatial interpolation.