The dissemination of methods of applied (spatial) statistics, i.e., geostatistical methods, to representatives of various scientific disciplines (geology, mining, environmental engineering, climatology, renewable energy resources, etc.). The attendees are expected to have knowledge and expertise in diverse areas of significant interest for the national economy.
The creation of opportunities to acquire both basic and applied knowledge, founded on a firm theoretical background, of geostatistical methods.
The development of practical skills for the use of geostatistics and specialized software during the Workshop sessions.
An original and innovative (modern) research methodology which involves the use of geostatistical methods, and it is applied to the spatial analysis of the variability of regionalized variables (i.e. variables whose position in space is described by two (plane) or three coordinate values and possibly time as well for dynamic phenomena.
The scope of applications of the geostatistical methodology is increasing following the creation and availability of spatial databases in various scientific areas, and due to the increasing capacity (content) of existing databases. This increasing availability of spatial data generates new possibilities for many fields of research, and the geostatistical methodology provides a practical suite of tools that can be used to process and extract information from spatial data.

Topics:

• 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

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