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Research projects in the SSES Program have emphasized the development of statistical methodology and computational aspects of spatial, spatio-temporal, and environmental 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)
Sources to Biomarkers (STB)
Carbon Dioxide (CO2)
Web-based Tutorials:
Tutorial on Bayesian Statistics for Geophysicists
Tutorial on Hierarchical Bayesian Modeling for Exposure to Arsenic
Tutorial on Fixed Rank Kriging (FRK) of CO2 data
Freeware:
Covariance Matching Constrained Kriging (CMCK)
Fixed Rank Kriging (FRK)
Fixed Rank Filtering (FRF)
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 spatial data fusion.
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The research is
concerned with optimal spatial prediction for very-large-to-massive spatial
and spatio-temporal datasets. Computational challenges are met by using
spatial random effects (SRE) models and spatio-temporal random effects
(STRE) models. Methodologies termed Fixed Rank
Kriging (FRK) and Fixed Rank Filtering (FRF) have been developed and
are being extended to the problem of spatial data fusion.
Spatio-temporal datasets from satellite instruments are considered. The SRE model has also been adapted
to analyze multiple regional climate models (RCMs).
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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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