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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R44ES031875&format=word)
| Principal Investigator: Goovaerts, Pierre E | |
|---|---|
| Institute Receiving Award | Biomedware |
| Location | Ann Arbor, MI |
| Grant Number | R44ES031875 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 15 May 2020 to 31 Jul 2024 |
| DESCRIPTION (provided by applicant): | 7. Project Summary/Abstract A key component in any investigation of association and/or cause-effect relationships between the environment and health outcomes is the availability of accurate models of exposure. Because the cost of collecting field data is often prohibitive, it is critical to incorporate any source of secondary information available to supplement sparse datasets. Secondary data can take many forms (e.g., continuous or categorical measurement scale), display various sampling densities (e.g., data available everywhere or at specific locations), and be recorded over different spatial supports (e.g., point observations, census tracts, rasters). Surprisingly, there is currently no commercial software for the geostatistical treatment of multivariate space- time data, including the merging of data layers measured on different spatial supports. This SBIR project is developing the first commercial software to offer tools for geostatistical multivariate space- time (ST) interpolation and modeling of uncertainty. The research product will be a stand-alone desktop ST tool, building on the legacy core software developed by BioMedware, an Esri partner. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, geochemistry or soil science, broadening significantly the commercial market for the end product. This project will accomplish three aims: Review of the main spatial coregionalization models available in the geostatistical literature (i.e., traditional vs extended, intrinsic) and the comparison of their performances (i.e., prediction accuracy) and user- friendliness (i.e., ease of inference) for multivariate spatial interpolation. This will be followed by an extension to the space-time framework. Develop a fully functional and tested multivariate ST interpolation, simulation and visualization module ready for commercial distribution. Conduct a formal usability study to evaluate the design of the prototype based on usability protocols developed by the NIH involving (i) expert evaluation by the firm Tec-Ed and (ii) usability testing by representative users. These technologic, scientific and commercial innovations will enhance our ability to model geostatistically multivariate space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiology. |
| Science Code(s)/Area of Science(s) |
Primary: 81 - Statistics/Statistical Methods/Development Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | No publications associated with this grant |
| Program Officer | Daniel Shaughnessy |