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THE SSES PROGRAM
Research


Research projects in the SSES Program have emphasized the development of statistical methodology and computational aspects of spatial and spatial-temporal statistics. Some research projects have matured into products that involve less technical presentations of the research. This may be in the form of what we call Web-Projects or in the form of web-based tutorials. Occasionally the product is freeware.

Web-Projects:
       El Nino Southern Oscillation (ENSO)
       Total Column Ozone (TCO)
       Dynamic Modeling of Ice Streams (Ice Streams)

Tutorials:
       Tutorial on Bayesian Statistics for Geophysicists
       Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic

Freeware:
       Covariance Matching Constrained Kriging (CMCK)

A number of SSES research projects are in areas of "big science", such as remote sensing of the earth on a global scale, Bayesian statistical exposure modeling from sources to biomarkers, and regional and global climate modeling in space and time. Other research areas include spatial command and control, disease mapping, medical imaging, ice-stream dynamics, and air quality.

Massive Spatial Datasets; ONR

The Office of Naval Research (ONR) has awarded Noel Cressie a three-year grant (2005-2007) titled "Optimal Mapping when Datasets are Massive". The research is concerned with optimal spatial prediction for very large to massive datasets. Computational challenges are met by using tree-structured spatial models and spatial-random-effects (SRE) models. A methodology termed Fixed Rank Kriging (FRK) offers considerable promise. Spatial datasets evolving in time are also considered.

Sources to Biomarkers (STB); EPA/ACC

A team of SSES researchers from OSU (Catherine Calder, Peter Craigmile, Noel Cressie, and Tom Santner) and Battelle Memorial Institute (Bruce Buxton, Nancy McMillan, and Michele Morara) was awarded a four-year (2004-2008) STAR Grant funded by the Environmental Protection Agency's (EPA) National Center for Environmental Research and the American Chemistry Council's (ACC) Long-Range Research Initiative and titled "From Sources to Biomarkers: A Hierarchical Bayesian Approach for Human Exposure Modeling". This work 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.

West Nile Virus Dynamics in Avian Populations; NSF

Shannon LaDeau, Postdoctoral Fellow, NSF Program in Bioinformatics (Smithsonian Institution and OSU), and Kate Calder (OSU) are developing statistical models to characterize the spread of West Nile Virus (WNV) in avian populations across North America.

Covariance Matching Constrained Kriging (CMCK)

The SSES Program has developed software for implementing covariance matching constrained kriging. Programs written in R are available for download at this webpage.