SBIR Phase I: Advanced Data Analytics for Public Safety

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to make citizens safer and government more efficient via the creation of easy-to-use, intelligent public planning software. The project's software and analytical development focuses on fire protection resource allocation - tools to recommend the placement of stations, personnel, and equipment that consider ongoing and anticipated community needs. The software allows a fire chief to assess existing response times, receive suggestions for optimal resource configuration, and evaluate status and configurations via a simple dashboard. The project innovation entails deep data analytics in a geospatial context at extreme speeds. Previously, the complexity and computational burden of such problems has required a trained engineering staff and complicated, proprietary software. The innovations proposed in this project can and will be expanded to consider other resource allocation systems - offering new tools to increase the efficiency and effectiveness of sustainable agricultural harvests, military strategies, smart-grid network load optimization, and the configuration of facilities within the context of the sharing economy. The market opportunity is significant. Over two billion dollars is spent annually to maintain fire departments within Los Angeles County alone, and there are over 38,000 fire departments and stations nationwide. This Small Business Innovation Research (SBIR) Phase I project combines numerous operations research algorithms into a unified geospatial analysis platform, focusing on public safety. The industry standard for geospatial analysis is map visualization with overlay-style comparisons. This Phase I project pursues an ambitious and powerful Operations Research approach - defining an objective function and solving it for optimality while meeting one or more constraints. The technique offers a mechanism to solve many geospatial problems involving the pursuit of a desired objective against the realities of cost, capacity, and scarcity, toward determining the optimal solution. Selection of the initial algorithms to be incorporated within the software will occur based on a needs assessment with user-clients; it is anticipated that point-based problems (e.g., Teitz and Bart p-median), linear issues (e.g., Djikstra shortest path), and multi-objective applications (e.g., Maximal Covering Shortest Path) will be foundational. Workflow commonalities will be distilled and integrated into a geospatial framework for communicating with existing operations research solvers. As computational burden necessitates, heuristics will be implemented to facilitate rapid processing. The technical results of this Phase I project are a customer needs assessment, a simple-to-use prototype focused on the fire protection vertical, and a market feasibility study.