Introduction to Spatial Statistics (MS-level course)
The course gives an introduction to statistical methods for geostatistical data, regional data, and spatial point patterns. Theoretical properties are illustrated with simulation, and applications are emphasized in take-home tutorials.

Spatial Statistics (PhD-level course)
The course presents topics that include exploratory spatial data analysis, (multivariate) spatial prediction, spatial hierarchical modeling (empirical Bayesian and fully Bayesian), and the incorporation of a temporal component in spatial models.

Environmental Statistics (MS-level course)
The course presents topics that include standard statistics used in environmental settings, sampling design, causality, limits of detection, toxicology, risk analysis, time series, spatial statistics, and hierarchical modeling.

Discussions In Spatial and Environmental Statistics (DISES)
The SSES Program organizes DISES on an occasional basis. It consists of Brown Bag Seminars and of discussion groups in spatial, spatio-temporal, or environmental statistics. If you would like more information, please e-mail us at sses@stat.osu.edu.

Tutorial on Bayesian Statistics for Geophysicists
A web-based instructional tool, Tutorial on Bayesian Statistics for Geophysicists, has been developed in conjunction with members of the Byrd Polar Research Center; it is part of the Web-Project, Ice Streams.

Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic
A web-based instructional tool, Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic, has been developed in conjunction with a research project joint with Battelle; it is part of the Web-Project, Sources to Biomarkers.

Tutorial on Fixed Rank Kriging (FRK) of CO2 Data
This instructional tool is part of the Web-Project, CO2.

Some of the science and engineering problems to which this approach has been applied are given below.

Remote Sensing
Enormous amounts of time and money have been spent on obtaining multi-resolution satellite data with pinpoint accuracy. Data-production algorithms currently use rudimentary statistical methods, such as the sample mean and the sample variance, to interpolate these "level 2" data (irregularly distributed in space and time), in order to obtain regularly distributed "level 3" data. In doing so, the obvious spatial and temporal dependencies present in remote-sensing data gathered from satellites have been largely ignored. We address this problem through an empirical Bayesian methodology that yields a dynamic, spatial, change-of-resolution Kalman filter. The methodology has been applied to Total Column Ozone (TCO) data. Further details are available on the Web-Projects page; see TCO. A more recent empirical Bayesian methodology, called Fixed Rank Kriging, has been applied to large remote-sensing datasets in research funded by the National Aeronautics and Space Administration (NASA). Further details are available on the Web-Projects page; see CO2. Fully Bayesian versions of this methodology have also been developed.

Geophysical Modeling
The dramatic world-wide shifts in weather patterns and associated societal impacts caused by the 1997-98 El Nino brought unprecedented attention to the role of inter-annual climate variability in our daily lives. Climate processes exhibit substantial spatio-temporal variability that is often non-stationary, non-linear, and dependent. Although it can be quite difficult to model such variability with traditional spatio-temporal approaches, we have demonstrated that it is possible using highly structured (hierarchical), Bayesian, spatial-temporal models. Physical knowledge of the system is used wherever possible in constructing the models, resulting in what has been called physical statistical modeling. Two projects that take this approach are the study of sea surface temperature in the equatorial Pacific Ocean in order to forecast El Nino Southern Oscillation (ENSO) events, and a study of ice-stream dynamics in Antarctica and Greenland. Further details are available on the Web-Projects page; see ENSO and Ice Streams, respectively.

Human Exposure to Metals
The project, "From Sources to Biomarkers: A Hierarchical Bayesian Approach for Human Exposure Modeling," seeks to characterize multi-pollutant (arsenic, lead, cadmium, and chromium) human exposures by linking sources to biomarkers using a multi-scale hierarchical Bayesian statistical model. Further details are available on the Web-Projects page; see STB.

Uncertainty Quantification of Regional Climate Model Projection
Further details are available on the Web-Projects page; see Warming.