Geospatial data analysis for DHS programs

The Department of Homeland Security law enforcement faces the continual challenge of analyzing their custom data sources in a geospatial context. From a strategic perspective law enforcement has certain requirements to first broadly characterize a given situation using their custom data sources and then once it is summarily understood, to geospatially analyze their data in detail. For the past three years the generalized data-driven analysis and integration (GDDAI) project has operated through DHS Science and Technology on behalf of Immigration and Customs Enforcement (ICE) to develop and deploy hybrid data analysis methods that improve ICE analysts\´ ability to answer time-critical queries for their agents. GDDAI provides this hybrid environment through seamless customized interfaces built with commercial off the shelf (COTS) and government off the shelf (GOTS) tools. At its core, to help characterize user data, GDDAI relies upon multidimensional relational data cubes which are based on online analytical processing (OLAP) technologies that provide intuitive and graphical access to the massively complex set of summary views available in large relational (SQL) repositories. As an extension to OLAP we provide a proximity-based search engine, a rich map feature database which allows users to add their customized reports, and a geocoder that derives coordinates from addressable information in the user database. While the design of data cubes can be technically challenging, our primary contributions are in the ability to effectively "drill-through" from a summary analysis to a detailed analysis when a population of interest is identified. In addition to this data transformation capability, our geospatial analysis infrastructure can perform proximity searches on the detailed "drill-through" records. Iteratively users can conduct increasingly more fine-tuned geospatial searches by combining or excluding previous geospatial results with their current drill-through results.