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Research projects in the SSES Program have emphasized the development
of statistical methodology and computational aspects of spatial and
spatio-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.
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The National Science Foundation
(NSF) has awarded Noel Cressie a two-year grant
(2007-2009) titled "Spatial Prediction of Surfaces in the Presence of
Uncertainty". The research is
concerned with optimal spatial prediction for very large to massive
datasets. Computational challenges are met by using
spatial random effects (SRE) models. A methodology termed Fixed Rank
Kriging (FRK) has been developed and is being extended.
Spatial datasets evolving in time are also considered.
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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 (e.g., arsenic, lead,
cadmium, and chromium) human exposures by linking sources to
biomarkers using a multi-scale hierarchical Bayesian statistical
model.
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Shannon LaDeau, (Cary
Institute of Ecosystem Studies), and
Kate Calder (OSU) are developing statistical models to
characterize the spread of West Nile Virus (WNV) in avian
populations across North America.
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The SSES Program has developed software for implementing covariance matching
constrained kriging. Programs written in R are available for download
at this webpage.
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