SSES
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THE SSES PROGRAM
Program in Spatial Statistics and
Environmental Statistics

The SSES Program
Department of Statistics
408 Cockins Hall
1958 Neil Avenue
Columbus, OH 43210-1247

e-mail: sses@stat.osu.edu
telephone: 614-292-5194
fax: 614-292-2096

Announcements:

A Graduate Interdisciplinary Specialization in Geospatial Data and Analysis (GSDA) is being offered by the Departments of Geography and Statistics.

Overview of the SSES Program

The Program in Spatial Statistics and Environmental Statistics (SSES) was established in 1999 in the Department of Statistics at The Ohio State University (OSU). The Program's vision is to promote the Department of Statistics as an international leader in research and education in Spatial Statistics and Environmental Statistics.

The Program's mission is in teaching, research, and science collaboration. In teaching, the SSES Program offers courses in both Spatial Statistics (at the MS and PhD level) and Environmental Statistics (at the MS level) through the Department of Statistics, Masters and PhD supervision, campus-wide interdisciplinary seminar series, and discussion opportunities in spatial statistics and environmental statistics.

In research, the SSES Program has emphasized the development of statistical methodology and computational aspects of spatial and spatio-temporal statistics. This methodology has been applied in areas of "big science", such as remote sensing of the earth on a global scale, regional climate modeling in space and time, and Bayesian statistical exposure modeling from sources to biomarkers. Other research areas include spatial command and control, disease mapping, medical imaging, ice-stream dynamics, and air quality. Funding from grants in these areas is used to support Research Assistants, Postdoctoral Fellows, and Visitors in the Program.

In science collaboration, investigators in the SSES Program work with scientists and engineers at The Ohio State University (OSU) and elsewhere on problems that are of an environmental or spatial nature. Several of the Program's collaborative projects are featured on the Web-Projects page. The Program's expertise is available to OSU faculty and staff for consulting on an occasional basis in areas that are substantially environmental or spatial. The Program also organizes seminar series with campus-wide appeal in the areas of environmental and spatial science.

Faculty with direct responsibilities in the Program are Noel Cressie (Director) and Kate Calder (Associate Director). Other participants in the Program are involved in various research and collaborative projects.

Spatial Statistics
All data have a more-or-less precise spatial and temporal label associated with them. Data that are close together in space (and time) are often more alike than those that are far apart. A spatial statistical model incorporates this spatial variation into the stochastic generating mechanism. Temporal information allows this mechanism to be dynamic. Prediction of unobserveds from observeds and estimation of unknown model parameters are the principal forms of statistical inference. The search for well defined statistical criteria and a quantification of the variability inherent in the (optimal) predictor or estimator are intrinsic to a statistical approach.

Environmental Statistics
Variability is inherent in the earth's environment at every scale. The traditional views of the scientific method use experimental design to control that variability in the presence of responses of interest and explanatory variables. Although this research paradigm applies to some narrowly defined problems in the environmental sciences, the processes of major interest typically exhibit strong spatial, temporal, and exogenous variability for which control may not be possible. This leads naturally to statistical methodology based fundamentally on hierarchical modeling. At each level of the hierarchy, simple conditional models are built (local modeling); the result is a joint model that can be very complex but for which analysis is possible (global analysis). That is, model locally, analyze globally.