All-source intelligence analysts need improved modeling, analytic tools, and data visualization in order to understand dense urban areas and enhance situational awareness more effectively. POLassist helps you understand location data in an urban areas to improve situational awareness and response allocation. The POLassist prototype was developed as part of "Hacking4Defense" at Columbia University in the City of New York, 2020.
Hacking4Defense problem statement:
"All-source intelligence analysts need improved modeling, analytic tools, and data visualization in order to understand dense urban areas and enhance situational awareness more effectively."
Use cases:
Emergency response management (gas leaks, storms, fires etc.) and security threat management (terrorism, bomb threat) & Monitoring of, e.g., population density and Covid-19 regulations or military mission planning
Users:
Emergency response units (efficient response allocation), disaster relief (efficient response allocation), military (operation planning and execution), private industry (visualization of open source and proprietary location data)
Development:
Agile Product Development based on SCRUM: Weekly Sprints (14 iterations), Development of artefacts every 2-3 weeks (MVPs), Product ownership (Bob Hesse / Pat Mahaney) & Rapid Prototyping: Frequent check-ins and discussion with stakeholders (17 interviews), key interview partner: Palantir Technologies, incremental improvement of MVPs
Employed Data:
Location data from Columbia University libraries (roughly 3 months), January to March 2020; API requests with geoJSON tagging (Python)
Prototype:
Unity-based UI and 3D visualization, geomapping and interactive timebar (at the bottom) via C# dictionary; 2D visualization via R-based Leaflet packages and HTML export
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