Geostatistical software for merging multivariate data with various spatial supports

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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 ST interpolation and modeling of uncertainty. The research product will be a stand-alone module into the desktop space-time visualization core 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 the main spatial coregionalization models available in the geostatistical literature (i.e., traditional vs extended, intrinsic) and compare their performances (i.e., prediction accuracy) and user-friendliness (i.e., ease of inference) for multivariate spatial interpolation through the cross-validation analysis of 4 datasets dealing with mapping of water lead levels, radon, meteorological and geochemical data. The comparison will include various cokriging types (i.e., one or several unbiasedness constraints) and other tools used by environmental epidemiologists, such as nearest monitors, inverse distance or purely spatial kriging.  Develop and test a prototype module that will guide non-expert users through the fitting of a linear model of coregionalization (LMC) and selection of an appropriate multivariate interpolation method (e.g., cokriging, kriging with an external drift, regression kriging), followed by the spatial interpolation based on BioMedware’s space-time visualization and analysis technology.  Conduct a usability study and identify additional methods and tools to consider in Phase II. 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.