In modern conflicts, adversaries hide in plain sight. Their intentions are often disguised in overwhelming volumes of data. Intelligence organizations are implementing Activity Based Intelligence (ABI) to uncloak these adversaries. ABI applies geographic thinking in new ways to help solve today’s complex intelligence problems.

At any given moment, every person, thing, place, or activity is connected to place and time. This spatiotemporal data is essential for intelligence. It is captured by sensors, transactions, and observations, which intelligence analysts can bring together in a Geographic Information System (GIS). GIS manages data that’s critical to discovering the unknown. GIS analytic tools transform data into intelligence, which drives action.

Today’s challenge is scaling out this workflow to the entire intelligence community, moving ABI practices from small, resource-rich teams to put them in the hands of every analyst. This will require organizations to harness the latest advances in technology and bring order to the growing volume of intelligence data.

The future of integrated intelligence (Video credit: Esri)

Space/Time Data Conditioning

Activity data comes from a variety of sources and requires specific conditioning before it is useful. Location is the only common value across the data sources and can be used to integrate disparate data sources. GIS allows analysts to define rules to integrate multiple data sources. Integrating data shows patterns where none previously existed.

A large volume of intelligence comes through manual intelligence reporting. These techniques might be Imagery Intelligence (IMINT) specialists watching drones or Human Intelligence (HUMINT) agents in the field. Structured observations become data points, which are integrated along with all the other sensor data, adding to the known intelligence data. After geo-referenceing, the data can be geo-enriched. This process connects observation data with foundation intelligence. Foundation intelligence provides context about the environment where these activities occur: cultural factors like religion, language, and ethnicity; the physical environment—urban or rural; and known locations like safe houses and meeting places. Connecting observations with known information gives critical context and helps find the suspicious activity within all the normal activity occurring around it.

Enabling a Spatiotemporal Data Environment

As the functional manager for geospatial, NGA, hosts the Intelligence Community GIS Portal (IC GIS Portal). While Sue Gordon was deputy director of NGA she stated “The IC GIS Portal, which is our first GEOINT service and provides easy access to NGA data, is now about two years old. Within that time, we’ve grown from zero users to almost 60,000 users worldwide.” This demonstrates the power of this data environment for supporting intelligence analysis, access to foundation GEOINT, and simple collaboration/sharing.

Access to this foundation intelligence and analyst reporting is critical to enabling ABI workflows. In addition, the GIS platform capabilities are expanding with cloud- based applications and services for real-time and big data analytics. GIS can connect to machine learning and artificial intelligence (AI) systems to assist in automated intelligence production and alerting. Imagery will be connected to these systems to enable on-the-fly analysis and production. As new data types are integrated, analysts will be able to spend less time on data conditioning and more time on analysis.

Enabling the Analyst

Ultimately, for ABI workflows to be successful, analysts need to be able to use their cognitive ability to make connections in the data. They need to leverage a powerful suite of analytic tools and visualization capabilities to make sense of data. They need to be able to create products which expose their analysis along with the underlying data and workflows.

ABI takes a discovery approach to building intelligence. Rather than merely providing updates on current intelligence, the ABI method calls for integrating all-source intelligence with other data to discover and monitor relevant information. With integrated data, analysts can discover a threat signature or indicator otherwise not discernable.

GIS provides visualization and analytic tools for working with intelligence data. Maps, the foundational of a GIS, can be used to understand complex patterns and visualize the spatial importance and relevance of data at a specific time. Geospatial analysis tools can be used to find statistically significant patterns in the data and to help predict future outcomes. Analysts use these visualizations to explore data and to develop assessments.

Implementing an Activity Based Intelligence Platform

ABI is emerging as a formal method of discovery intelligence. With ABI’s foundation in spatial thinking, GIS technology is a key enabler. An enterprise GIS creates a spatiotemporal data environment capable of connecting analysts with foundation intelligence data and applications for analysis and production. These tools have been implemented successfully in many organizations and have proved to scale to even the most complex agencies.