This course will introduce a suite of geostatistical methods for the spatial analysis of environmental data. Participants will learn how to apply geostatistics for the description of spatial patterns and identification of scales of variability, spatial interpolation and stochastic modeling of environmental attributes, creation of risk maps and their use in decision-making. Lectures will alternate with analysis of environmental data using the Stanford Geostatistical Modeling Software 
(S-GeMS) and the TerraSeer Space-time Information System (STIS).Test datasets will be prepared so that results of geostatistical prediction and impacts on decision-making can be compared and discussed during the course. Each participant will receive a set of lecture notes and have the opportunity to purchase a copy of  Dr. Goovaerts’ textbook. A copy of the public domain S-GeMS software will be provided. Ample time will be allocated to discussion, and participants are invited to bring their own case studies to seek 
Dr. Goovaerts opinion. The course provides 30 hours of training and instruction, and a Certificate of Completion will  be provided upon conclusion. Enrollment is limited, so register early to secure a seat in the course. Advance registration is required.

A key feature of environmental information is that each observation relates to a particular location in space. Knowledge of an attribute value, say a pollutant concentration or a soil property, is of little interest  unless the location of the measurement is known and accounted for in the analysis. Another feature is that  the information available is usually sparse which, in combination with the imperfect knowledge of underlying processes, leads to a large uncertainty about the actual spatial distribution of values. Such an uncertainty needs to be quantified and accounted for in decision-making, hence probabilistic (statistical) tools are increasingly preferred to a deterministic approach where a single (error-free) representation is sought. Geostatistics provides a set of statistical tools for the analysis of data distributed in space and time. It allows the description of spatial patterns in the data, the incorporation of multiple sources of information in the mapping of environmental attributes, the modeling of the spatial uncertainty and its propagation through decision-making. Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, environmental studies, and more recently exposure assessment and environmental epidemiology. The recognition of the importance of geostatistical analysis is illustrated by the inclusion of geostatistical functions in a growing number of products, including ArcGIS Geostatistical Analyst and TerraSeer Space-time Information System (STIS).