Topics and scope


  1. Geostatistical applications in spatial and spatio-temporal modeling.
  2. Spatio-temporal methods for the analysis of hydrological, environmental and climate phenomena.
  3. Spatial analysis and prediction using geostatistical models.
  4. Geostatistical characterization of uncertainties. Conditional simulations.
  5. Selected topics of spatial data analysis: machine (computer) learning and data mining.
  6. Spartan spatial random fields and stochastic local interaction (SLI) models.


Scope of the Workshop 

  1. Concepts of geostatistics.
  2. Research methodology based on the geostatistical approach.
  3. Empirical measures of spatial variability and respective theoretical models.
  4. Variogram estimation and modeling.
  5. Cross-validation and model selection.
  6. Kriging estimators (kriging, cokriging).
  7. Quality and effectiveness of kriging techniques.
  8. Conditional simulations and risk mapping.
  9. Methods of fast interpolation (e.g., kriging linear model, inverse distances, nearest neighbours).
  10. Case studies and best practices.
  11. Introduction to environmental data mining using machine (computer) learning algorithms.
  12. Sparse precision matrices, stochastic local interactions and quick spatial interpolation.


About the author: Katarzyna Tadaszak