Geointelligence – Empowering Geospatial with Intelligence

BY SANGEETA DEOGAWANKA


With major Geointelligence and Homeland Security Conferences around the corner (see list at the end of this article), there is an increased focus on the emerging discipline of Geospatial intelligence.

Geospatial intelligence has been an indispensable tool in military operations for a long time. What is less known is its evolution as an interdisciplinary approach for commanders, humanitarian responders and homeland security planners to visualize events in a three-dimensional context.

The evolution of Geointelligence
As world events witnessed a shift towards international terrorism with impacts on regional conflicts, there arose a demand for detailed knowledge of the area of conflict. The need for powerful visualization in real-time brought about a fundamental shift in the doctrine of war-fighting. Imagery intelligence thus became core to military operations, as much as strategic knowledge of the area, the culture, society and the environment. What emerged was the convergence of geospatial, imaging and intelligence for a geo-driven decision making. The National Geospatial-Intelligence Agency (NGA), responsible for “providing timely, relevant, and accurate imagery, geospatial information, and products to support national security” thus became the proponent of Geointelliegnce. As extreme climate patterns, natural disasters and disease incidence became important concerns, Geointelligence came into use for strategic planning and combat operations.

Today, Geointelligence has changed the way you respond to events, as an organization, first responder or department with security and military concerns. This has been well documented in the Bin Laden operations or the fight against spread of Ebola.

Geointelligence defined
Geointelligence is a discipline and an emerging profession that involves use of technology, critical information and analytical rigor for a decision advantage in domains of humanitarian response, business intelligence and strategic defense or security

The standardized definition from National Geospatial-Intelligence Agency (NGA) is as follows:

“the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on the Earth. GEOINT consists of imagery, imagery intelligence, and geospatial information.” Title 10 U.S. Code §467

As the word suggests, Geointelligence = Geo + intelligence, where ‘Geo’ refers to the Geography or space attribute (physical, locational and human) and ‘intelligence’ is the unique tradecraft applied to the discipline. Intelligence is described as “information that has been analyzed and refined so that it is useful to policymakers in making decisions – specifically, decisions about potential threats to our national security”.

What is unique to Geointelligence
GEOINT moves beyond the realms of traditional GIS to incorporate the unique process of a specialized skill, termed ‘tradecraft’.

Tradecraft is the unique cognitive process, that applies location insights and activity based intelligence for a decision advantage. It makes use of culture based knowledge and techniques, standardized tools, analytical skills and various technological methods to anticipate events or actions.

COMPONENTS OF GEOINTELLIGENCE

In other words, the ‘tradecraft’ component empowers the geographic visualization of the operating environment with other information, reasoning and analysis. It is a cognitive process that applies intelligence to spatial relationships and processes, to unlock the full potential of geospatial technology. Thus, Geospatial intelligence integrates access and collaboration of various areas of expertise like signal intelligence, human intelligence, imagery intelligence, and so on.

GEOINT – Benefits
Geospatial technology alone cannot answer questions that call for additional knowledge and sense-making of multiple geographic entities and their relationships. For instance, standalone maps and imagery of tsunami affected areas cannot support humanitarian response. What is needed is information in real-time, analysis of the affected areas with respect to the local society (density of population), environment (proximity to water body, low lying areas), sensitive locations (nuclear installations), satellite imagery (real-time picture of affected sites), community structures (schools, halls), healthcare facilities, on-site responder agencies, crowdsourced information, social media inputs and so on.

GLOBAL MAPPING OF EMERGENCY STOCKPILES[UNITED NATIONS OFFICE FOR THE COORDINATION OF HUMANITARIAN AFFAIRS –OCHA]

The following benefits of Geointelligence have made it a specialized discipline and a professional calling:

• Competitive advantage from insights (trade secrets of cosmetic product, decision about establishing new sea port, mission planning of a non-profit organization)
• Improved productivity of core assets (swine flu health centers, defense installations, production facilities of chemical plant)
• First responder support to humanitarian operations
• Creation of a common architecture for multiple agencies to share geospatial information for mission statements
• Ability to map human movements, across time, space and terrain for tactical planning
• A powerful tool for civic planning, humanitarian missions, industrial surveillance, precision war, conflict resolution, homeland security
• Logistics support for military operations, disaster response, civic emergencies, and so on.
• Provide insights to help avert dangers, counter conflicts, predict opportunities or adapt to shifting conditions.
• Intelligence analysis using data from other INTs (SIGINT, HUMINT, MASINT, IMINT,OSINT) for additional context to the problem under consideration.
• Use of advanced sensor technology and multiple types of geospatial data to help visualize events. For instance, intelligence applied to a map of terrorist hideouts, data mined from geo-tagged tweets, satellite image of the terrain, drone surveillance and GPS tracking of cell phone devices in use; makes possible real-time mapping and analysis of terrorist movements across space and time.

Applications

EBOLA OUTBREAK RESPONSE – REGIONAL CONFIRMED AND PROBABLE CASES 29.10.14 – GAR

• National Policymakers (National Security Council, Prime Minister’s Office of any government)
• Military (Navy, S. Special Operations Command)
• Defense (National Defense and the Canadian Armed Forces; DOD)
• Intelligence Agencies (CIA, NSA)
• Emergency response systems, First Responders (WHO’s Global Outbreak Alert and Response Network – GAR)
• Homeland Security (S. Department of Homeland Security, Australian National Security Service)
• Law Enforcement (Calgary Police Service, Coast Guard operations)
• Global Humanitarian Agencies (N.’s OCHA)
• Human Geography Mapping (WWHGD)

List of Geointelligence Conferences 2015-2016:
9th Annual National Homeland Security Conference (UASI 2015) – San Antonio, TX, United States, June 9-11, 2015

GeoIntelligence Asia 2015 – New Delhi., India, June11-12, 2015

The GEOINT 2015 Symposium (USGIF) – Washington, D.C., June 22-25, 2015

8th Annual Geospatial Defence & Intelligence (APAC 2015) – Singapore, September 2015

Homeland Security events – Upcoming events and conferences across the world

Defence Geospatial Intelligence Conference 2016 – January 18th – 20th, 2016, London, U.K.

Resources:
Clark, A. J., Democratizing Geospatial Data for Disaster Response, June 12 2014, [web]

Doty, John M., Geospatial Intelligence: An Emerging Discipline in National Intelligence with an Important Security Assistance Role, The DISAM Journal, Spring 2005 [web]

Geospatial Intelligence (GEOINT)Basic Doctrine, Publication 1-0 September 2006, National Geospatial Intelligence Agency [web]

Hannay, P., Baatard, G., GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering, Edith Cowan University Research Online, 2011 [web]

Special Acknowledgement:

Dr. Todd S. Bacastow, Professor of Practice for Geospatial Intelligence, The Pennsylvania State University MOOC

Source: GIS Lounge - Maps and GIS

Shared rain

This April has been the wettest April on record in the UK, while parts of the country are also in official drought – leading to headlines of the wettest drought on record.
The miserable weather was (is) a good opportunity to finally produce a high-resolution version of the map series that I created during my PhD research and which I presented at last year’s conference of the Society of Cartographers in Plymouth. The maps are not new, and each individual maps can be viewed and downloaded here, but if you are viewing this on a higher resolution display, you may enjoy the map series in all its detail:

Click here, to view the video on Vimeo!



As described in the above linked pages, the animation shows precipitation data in relation to the world’s population distribution based on a gridded population cartogram (population data used here comes from SEDAC’s GPWv3 database). The precipitation data used in the map series is based on long term records and interpolations published onWorldclim.org and as described by Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005 (Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978). The maps therefore gives more space to highlight how and where rainfall patterns directly affect the human populations on the planet, adding another analytical dimension to the data display.

While a gridded cartogram display adds further analytical value for a visual examination of the data from a different perspective, the raw data itself can also be used in less serious ways to analyse the interrelation of population and rain which was one of the outtakes of my first looks at the original data. Using the absolute rainfall and the total population, one can calculate the rainfall per person in an area, which is a fairly useless calculation that reduces the appearance of rain in the most populous areas and tells very little about how people perceive the long term weather conditions, as the following map extract shows for Europe:

(click for larger view)

The outcome of combining population and rainfall data is a map display that results in a nice visualisation that should be taken as nothing else than a conceptual map of geospatial data that demonstrates, how two different dimensions of data interrelate. It can be useful for an exploration of the data from different perspectives, but is certainly less useful as a geographic representation of how population and climate conditions are related (geodeterminism, anyone?). The main reason for showing this map here is the comparison of the effective display of the interrelations between the two data dimensions using a gridded population cartogram projection, which does a very similar combination of the data as the ‘rainfall density’ map, but results in a much more useful visualisation of the topic. While the gridded population cartogram allows to distinguish between the human and the physical space (which is represented in the grid cells), the quantitative information of the rainfall patterns is preserved and can be understood from a people’s perspective (and may be useful to explain how some of these patterns can indeed be explained in a determinist way: many of the driest regions on the planet match the least populated places, though the global settlement patterns follow a more complex set of variables that all contribute to where we live in what densities). Combining the two dimensions in a conventional map projection is hard to achieve, as this is limited to the extent of the physical space. A simple choropleth display of the two data values is not appropriate when calculating the two against each other as in the above map (and thus not allowing to know whether a high data value results from low population values of from high amounts of precipitation). If at all, then this is useful for an exploratory analysis of how the two values interact.

Apparently there are other solutions to display multi-dimensional data in conventional maps, but these often are more complex and harder to read. This may also be the case for a gridded population cartogram when it is seen for the first time, but once the basic concept of the map is understood, it can be used to read and understand any other geospatial data from the human perspective.
These are two ways of showing the same data – mapping rainfall as a shared pain (or blessing, as it is for most of the world’s population). What the two versions of mapping the data show is that the way how one analyses data and how one puts it on a map matters a lot. There is not a single good or bad way to put geospatial data on a map, but they way it is processed and visualised. Bad maps don’t have to be wrong, but may simply miss out on an adequate presentation of their underlying data.

One good start for an advanced insight into how geospatial data can be analysed sensibly can be found in the book Geospatial Analysis by Mike de Smith, Mike Goodchild and Paul Longley, which is also available as a freely accessible online edition:http://www.spatialanalysisonline.com/output/. Only a appropriate – and sensible – analysis of data is then suitable for the subsequent geovisualisation in form of a map (as covered in many good books, such as the upcoming Cartographer’s Toolkit that gives a little bit space for a gridded cartogram as well). Far too often the two worlds of analysis and visualisation remain disparate worlds, with both sides remaining quite ignorant of what the other side of the same coin has to offer…

The content on this page has been created by Benjamin D. Hennig. You are free use the material under Creative Commons conditions (CC BY-NC-ND 3.0); please contact me for further details. I also appreciate a message if you used my maps somewhere else. High resolution and customized maps are available on request.

Source: Views of the World