Generating Imagery for Forecasting Terror Threats
Abstract
Maps indicating threat levels based on key feature proximity and incorporating event location uncertainty are useful for planning countermeasures in the global war on terrorism. Intelligence analysts and military planners need predictions about likely terrorist targets to better plan the deployment of security forces and sensing equipment. The authors have addressed this need using Gaussian-based forecasting and uncertainty modeling. Their approach excels at indicating the highest threats expected for each point along a travel path and for a "global war on terrorism" mission. It also excels at identifying the greatest-likelihood collection areas that would be used to observe a target. Their methods are extensions of Donald Brown's work on geospatial analysis and asymmetric-threat forecasting in the urban environment. He showed how to extract distinct signatures from associations made between historical event information and contextual information sources such as geospatial and temporal political databases. The authors have augmented this method to include uncertainty estimates associated with historical events and geospatial information layers.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2006
- Accession Number
- ADA495794
Entities
People
- Greg Schmidt
- Jason Dalton
- Jason Goffeney
- Ruth Willis
Organizations
- United States Naval Research Laboratory