This SBIR project will develop software for identifying and correcting spatial patterns in data for a wide range of alcohol-related phenomenon including alcohol consumption, problematic outcomes, and treatment modalities. Identifying and correcting statistical relationships in spatially configured data sets would be invaluable to alcohol-related research, the overall health community, and even to most social scientists (and biomedical researchers). Ecological models or models with locational components that provide unbiased estimates and increased predictive performance enhance the researcher' ability to identify new patterns within alcohol-related phenomenon. While spatial analysis has been widely researched and is a proven statistical technique, commercially available software with reasonable diagnostics and commonly used regression techniques does not yet exist. This phase I project addresses this need and will pursue three objectives: (1) Research and increase the capabilities of the current software package, (2) Design interfaces easily useable (friendly) for alcohol researchers, and (3) Improve the speed and efficiency of the core code. The proposed software development will provide powerful diagnostic and corrective tool in the analysis of mapped data describing relationships between space and alcohol- related phenomenon.
The need for identifying and adjusting for spatial autocorrelation in alcohol- related data sets is huge (see page 21) and so there is a large market for the proposed statistical software. The proposed package is expected to provide an easy to use, speedy, and comprehensive tool relative to current packages.
