The ultimate objective of geospatial intelligence work is to produce knowledge. Good geospatial intelligence has separated the important from the less important and conceptualized a spatial order out of apparent chaos. Such analysis is not automatic, and the analysis is subject to many uniquely spatial fallacies, biases, and confusion between cause and effect, technical necessities, group-think, and individual analyst failings. Even the best geospatial analyst will sometimes run afoul of one of these pitfalls. The capable geospatial analyst knows what the pitfalls are and works for objective analysis and assessment. Geospatial analysts should be conscious of their reasoning processes. Quoting Richards Heuer [1] (p. 31), "they should think about how they make judgments and reach conclusions, not just about the judgments and conclusions themselves."
How is work accomplished? Academia and the geographic community almost exclusively teach the scientific method as a method to create knowledge. But, the truth be known, it seems the scientific method is seldom used in geospatial intelligence work. What method is used? I suggest that the intuitive method is the predominate method for producing geospatial intelligence. I call this the seat of the pants method which:
Is the solution to use the scientific method? Not necessarily. Some suggest the scientific method, which starts with a single hypothesis, is not appropriate for developing intelligence (Heuer, 2009). As Don L. Jewett stated, the problem with starting with a single hypothesis is that a bias can arise owing to an emotional attachment to the hypothesis and the temptation to misinterpret results that contradict the desired hypothesis (“What’s Wrong with Single Hypotheses,” The Scientist, Nov. 2005). However, other methodologies provide traceable and repeatable means to reach a conclusion.
We are not diminishing the importance of intuition and experience, rather we are proposing in this course a mixture of science and intuition as a means to produce good geospatial intelligence.
At the end of this lesson, you will be able to:
The Course Roadmap is intended to help you understand where we are in the overall learning process and to place our dual case study and project focus into context.
The image above shows a basic outline of assignments for all lessons in the course. Students are currently on Lesson 1.
Lesson 1 is one week in length. (See the Calendar in Canvas for specific due dates.) To finish this lesson, you must complete the activities listed below. You may find it useful to print this page out first so that you can follow along with the directions.
Step | Activity | Access/Directions |
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1 | Read the Lesson Overview and Checklist. | You are in the Lesson 01 online content now. Click on the Next Page to continue. |
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There are three different styles of reading that are referred to in the lessons:
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3 | Participate in the Graded Discussion. | Complete the DC Sniper Geospatial Thinking Exercise [6]. Post your analysis to the Lesson 1 Discussion Forum. To participate in the discussion, please go to the Lesson 1 Graded Discussion in Canvas. (That forum can be accessed at any time by going to the Canvas link on the menu bar and then selecting Lesson 1 Graded Discussion from the appropriate weekly module.) |
4 | Read Lesson Summary. | You are in the Lesson 1 online content now. |
If you have any questions now or at any point during this week, please feel free to post them to the GEOG 885 - General Discussion Forum. (That forum can be accessed at any time in Canvas by clicking on the Modules tab. The General Discussion forum is listed under the Orientation Section.)
Lowenthal defines intelligence in three ways:
To most, intelligence is information that is secret. This misses a fundamental point. Information is anything that can be known, regardless of how it is discovered. Intelligence is information that meets the stated or understood needs of policy makers, and has been collected, processed, and narrowed to meet those needs. Therefore, intelligence is a subset of the broader category of information. Intelligence and the entire process by which it is identified, obtained, and analyzed responds to the needs of policy makers. It can be said that all intelligence is information; not all information is intelligence.
Most people tend to think of intelligence in terms of military information. This is a component of intelligence, but political, economic, social, environmental, health, and cultural intelligence also provide important inputs to analysts. Policy makers and intelligence officials must also consider intelligence activities focused on threats to internal security, such as subversion, espionage, and terrorism.
Significantly, Intelligence is not about truth. It is more accurate to think of intelligence as proximate reality. Intelligence agencies face issues or questions and do their best to arrive at a firm understanding of what is going on. They can rarely be assured that even their best and most considered analysis is true. Their goals are intelligence products that are reliable, unbiased, and free from politicization.
De jure is a Latin term which means "by law" which is commonly contrasted to de facto which means "concerning the fact" or in practice but not necessarily ordained by law. The NIMA Act of 1996 establishing the National Imagery and Mapping Agency and the subsequent amended language in the 2003 Defense Authorization Act as codified in the U.S. Code, governs the mission of the National Geospatial-Intelligence Agency (NGA). The de jure definition of Geospatial Intelligence is found in U.S. Code Title 10, §467:
The term "geospatial intelligence" means the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.
The moniker GEOINT has become associated with geospatial Intelligence with a specific meaning and context. It has often been said that the 2003 renaming of NIMA to NGA recognized the emergence of geospatial information as an intelligence source in its own right, which is termed GEOINT. The term GEOINT connotes a source of intelligence like HUMINT, MASINT, COMINT, ELINT, SIGINT, IMINT. GEOINT is uniquely multi-source in that it integrates and enriches information collected by the other INTs into a spatiotemporal context.
The de jure definition drives us to focus on Geographic Information Systems and digital remote sensing, since these technologies, as a substantial component of workflows such as TPED (Tasking, Processing, Exploitation, and Dissemination), heavily leverage spatial data handling and image processing technologies to transform geospatial data. However, there is a growing recognition that GEOINT "must move from an emphasis on data and analysis to an emphasis on knowledge" (Priorities for GEOINT Research at the National Geospatial-Intelligence Agency, The National Academies Press, 2006, P. 9). Here, the use of the term knowledge means the confident understanding of a subject with the ability to use it for a specific purpose appropriately. This is to say, geospatial knowledge creation involves much more than automated data handling and is a complex cognitive process involving perception, learning, communication, association, and reasoning.
We would like to suggest the following as an emerging definition of Geospatial Intelligence, which might carry the moniker of GeoIntel, as a means to guide the preparation of the geospatial professional:
Geospatial Intelligence is actionable knowledge, a process, and a profession. It is the ability to describe, understand, and interpret so as to anticipate the human impact of an event or action within a spatiotemporal environment. It is also the ability to identify, collect, store, and manipulate data to create geospatial knowledge through critical thinking, geospatial reasoning, and analytical techniques. Finally, it is the ability to present knowledge in a way that is appropriate to the decision-making environment.
Central to this proposed definition is the notion that the best geospatial intelligence resource is an educated analyst. Intelligence is about nothing if not about "out-thinking" your opponent. For all the appropriate emphasis on technologies, methodologies, tools, and infrastructure, people are the most precious resource.
Intelligence analysis is using information about situations, characterizing the known, and, with appropriate statements of probability, predicting future situations. The descriptions of the future situations are drawn from what may only be available in the form of deliberately deceptive information; the analyst must correlate the similarities among deceptions and extract a common truth. A set of problem-solving approaches is essential for analysts. Because of the nature of the problem, the analyst must be tolerant of ambiguity, false information, and of partial information, far more than the experimental scientist. According to Richards Heuer [1], analysis involves incremental refinement.
The analytic process resides within the larger intelligence cycle. The intelligence cycle drives the day-to-day activities of the Intelligence Community. It starts with the needs of those who are often referred to within the Intelligence Community as intelligence "consumers" - that is, policymakers, military officials, and other decision makers who need intelligence information in conducting their duties and responsibilities. These needs - also referred to as intelligence requirements - are sorted and prioritized within the Intelligence Community, and are used to drive the collection activities of the members of the Intelligence Community that collect intelligence.
Once information has been collected, it is processed, initially evaluated, and reported to both consumers and so-called "all-source" intelligence analysts at agencies like the CIA, DIA, and the State Department's Bureau of Intelligence and Research. All-source analysts are responsible for performing a more thorough evaluation and assessment of the collected information by integrating the data obtained from a variety of collection agencies and sources - both classified and unclassified. This assessment leads to a finished intelligence report being disseminated to the consumer. The "feedback" part of the cycle assesses the degree to which the finished intelligence addresses the needs of the intelligence consumer and will determine if further collection and analysis is required. The cycle, as depicted in the figure below, is thus repeated until the intelligence requirements have been satisfied.
The cycle is a concept that describes the fundamental intelligence processing in a civilian or military intelligence agency or in law enforcement as a closed path consisting of repeating nodes. The stages of the intelligence cycle include the issuance of requirements by decision makers, collection, processing, analysis, and publication of intelligence. The circuit is completed when decision makers provide feedback and revised requirements. Any circular process is as weak as its weakest component. Another problem is stovepiping. In the traditional intelligence use of the term, stovepiping keeps the output of different collection systems separated from one another. This has several negative effects. First, it prevents one discipline from cross-checking another. Second, a newer usage of stovepiping is bypassing the regular analysis of raw intelligence, and sending only raw intelligence that supports a particular position to the highest national leadership.
The term "intelligence process" refers to the steps of the cycle. Intelligence, as practiced in the United States, is commonly thought of as having five steps. Mark Lowenthal (2006) added two phases for seven phases of the intelligence process as (1) requirements, (2) collection, (3) processing and exploitation, (4) analysis and production, (5) dissemination, (6) consumption, and (7) feedback.
Significantly, most discussions of the intelligence process end here with dissemination and the intelligence having reached the policy makers whose requirements first set everything in motion. However, Lowenthal bundles dissemination with consumption and adds feedback:
The following is a summary of parts of the RAND report Assessing the Tradecraft of Intelligence Analysis [2].
“Analysis” in the U.S. Intelligence Community has many meanings. The multiple components of the analysis cycle began with policymakers and military leaders, whose concerns would be turned into taskings for the major collectors. The take from those collectors is then processed at various levels, ultimately to be incorporated into all-source analysis, then disseminated back to policymakers and leaders. The cycle notionally distinguishes between intelligence sources and the analytic processes that are used to transform the raw data from these sources into intelligence products.
The intelligence cycle may be contrasted with the intelligence analytic cycle, which, according to the RAND report, typically includes three forms of analysis—technical processing analysis, single discipline analysis, and all-source analysis. However, the distinction between the first two types and all-source analysis is being blurred because of this use of tools, such as GIS, to integrate multiple intelligence sources.
Some suggest a continuum in the forms of analysis from collection system outputs at one end to analysis at the other. Along this continuum, there is a transition region where the data is used to support analysis. Past this transitional area, analysis splits into puzzle-solving and mystery-framing.
A puzzle tests the ingenuity of the solver and is “solved” with information. In a puzzle, one pieces together the puzzle pieces in a logical way in order to come up with the solution. In the past, a common intelligence puzzle was to piece together intentions based on capabilities. Puzzle-solving involves pulling together many sources of data and information and, using that evidence, identifying new patterns or trends and developing new knowledge. At the extreme of the puzzle-solving are complex puzzles. An example of a complex puzzle is the intentions of a secretive, heterogeneous, and fast adapting terrorist organization. Terrorist intentions are difficult to determine by looking at capabilities because terrorism is the tactic of those without great resources. In other words, for the terrorist threat, not only can intentions not be determined by looking at capabilities, but capabilities themselves have a strong mystery element to them. This brings us to the mystery-framing.
According to RAND, mystery-framing includes political and societal questions related to people, such as regional issues, national intent, or group intentions and plans. Here, understanding is much more a matter of subjective judgment, intrinsically less certain. The logic train is different for mysteries because no data can “solve” them definitively. They can only be framed, not solved, and thus the logic of argument and analysis is as important as the evidence, often more so. In the geospatial realm, information is always lacking because of accuracy, detail, or relevance. Therefore, many geospatial intelligence questions are verging on mysteries because the analyst can never provide definitive answers. Mysteries involving human perceptions about culture benefit from the insights of intelligence analysts who have learned through the experience of dealing with intelligence mysteries over a long period of time.
Within the geospatial community, the challenge is to move from processes that are driven purely by the data collected to ones driven by the problem to be solved. For geospatial, the move toward more problem-driven collection raises questions about different styles of analysis and the different requirements for analysts. For example, NGA’s concept of “geospatial intelligence” and its fielding of a geospatial framework provides a rich baseline from which to conduct analysis. Building and maintaining the framework is primarily “gathering,” which requires a highly efficient production process. By contrast, "hunting," or problem-centric analysis, requires empowering analysts in ways very different from the familiar production processes.
It should be no surprise that there are competing views of geospatial intelligence analysis. One school of thought is that intuition, experience, and subjective judgment predominate. Analysis here is an art, and non-quantitative methods predominate. Another school of thought is that quantitative data and analysis are most relevant. Analysis here is a science, and quantitative methods predominate. This controversy somewhat mirrors a long-standing debate in the intelligence community: if good analysis depends largely on subjective, intuitive judgment (an art) or systematic analytic methods (a science). Understanding this question is important to developing an effective approach to geospatial intelligence creation. To help understand these points of view, I will define the terms using the Merriam-Webster Collegiate Dictionary, tenth edition, as:
Interestingly, there are those that consider integrative geospatial data tools, such as those found in GIS, as primarily aids to intuition and experience-based analysis and not the application of quantitative analytic methods. This seems contrary to the technical capabilities GIS brings to the geospatial intelligence. It is sufficient to say that there is no bright dividing line between art and science, and a pure scientific approach to geospatial analysis is undesirable. The dissatisfaction with the push toward a science perspective in GIS has been seen as a step backwards by some. In their thinking, GIS’s models and analysis methods are not rich enough in geographical concepts and understanding.
Geospatial intelligence is geospatial analysis, and geospatial analysis, at its core, is geography. Geography is both the conscious use of creative imagination in the representations of the earth and the science of developing general truths about the earth. For something to be automatable, it must be modeled and the facts (inputs) quantified. Since a model is a simplified abstract view of the complex reality, the model represents a limited set of rules which allows an analyst to work out an answer if they have certain information. Quantifiability of the information is important because unquantifiable inputs cannot be tested, and thus unquantifiable results can neither be duplicated nor contradicted. However, we know that reliable models and data are not available for all analyses.
The table below illustrates the broad types of geospatial analyses. The lower right panel of the matrix identifies the ideal of analysis as a Scientific Process (upper right quadrant, "ideal of analysis") in which there is good knowledge of the data and models surrounding an output. In the model, analysts understand the problem that confronts them and can take into account the key factors that bear on the problem. The notion of fixed-in-advance standard procedures typically plays an important role in such geospatial analysis.
However, many of the analytic tasks in geospatial intelligence fall outside of the scientific quadrant. Consider the Puzzle Solving Process (lower left, "foraging for good data") quadrant in which there is agreement on models, but disagreement on data. The notion of "hunting" for the data to solve the problem plays an important role in such analysis.
Analysis as an Opinion Process (upper left quadrant, "strongly held beliefs") is the opposite. In this analytic environment, there is agreement on data, but disagreement on model. Analysis is characterized by analysts involved in a struggle for influence, and decisions emerge from that struggle. This kind of analysis necessitates bargaining, accommodation, and consensus, as well as controversy. The bottom line is that conclusions are most often the result of bargaining between diverse and strongly held beliefs.
Intelligence analysis as a Heuristic Process (lower left quadrant, "framed by experience") is the most contentious, with disagreement on data and models. Under these conditions, science and technology tools have significantly less direct relevance. Here, conclusions depend on parameters that change over the period the analysis is being made. As a consequence, the analytic process is experience-based . In the end, this is the framing of questions. They can only be framed, not solved, and thus the logic of argument and analysis is as important as the evidence.
Is geospatial intelligence an art or science? Analytic problems can fall into any of the four quadrants ---- you, the analyst, need to understand the problem solving environment and the nature of the problem solving process. The term “sensemaking” is used as a term to describe the analysis process. Sensemaking is defined is the deliberate effort to understand events using explanatory structure that defines entities by describing their relationship to other entities. Data elicit and help to construct the frame; the frame defines, connects, and filters the data.
A few words about the project. The purpose of the project is to provide a compelling deep dive into geospatial intelligence analysis using a structured methodology. We will split into teams. Each team will take on a particular analytic question associated with the same problem. Through the exploration of the analytic question, we hope that each team gets a comprehensive understanding of a particular problem and in the use of a structured approach in the development of geospatial intelligence.
The term project is to geospatially investigate the Jonathan Luna case.
Jonathan Luna was a Baltimore-based Assistant United States Attorney who was stabbed 36 times with his own penknife and found drowned in a creek in Pennsylvania. No suspects or motive for murder was determined. The federal authorities (FBI) lean towards calling it a (hypothesis 1) suicide but the local Lancaster County authorities, including two successive coroners, ruled it a (hypothesis 2) homicide. Your task is to (1) organize as a team to (2) assemble and (3) evaluate the geospatial evidence to determine if the evidence (including the geospatial aspects of the case) is consistent or inconsistent with the hypotheses. The following information is provided as a starting point for your geospatial analysis:
The project will culminate in an end of course PowerPoint briefing to your instructor; refer to the calendar for general briefing dates. Here is the hypothetical set up for the project. Your instructor has just been appointed as the commissioner of the Pennsylvania State Police. Upon review of "cold cases", the Luna case concerns him after being told that the geospatial aspects of the case were never considered. As such, he is hiring consultants (student teams) to perform a geospatial assessment. Each team will investigate the case and present a geospatial argument to either 1) reopen the case, or 2) let it be.
We will start on the Project in Lesson 02.
Case study-based learning encourages problem-solving skills; it affords a systematic way of looking at events, collecting data, analyzing information, and reporting the results. It is perhaps the ideal methodology for learning about geospatial analytics. Rather than following a rigid protocol, a scenario involves an in-depth, longitudinal examination. When done properly, a case study can create those moments that pull everything you learned into focus. When theory, practice, and experience all come to a decision that shapes a definitive course of action, it is no longer a question of what can be done, but of what should be done. Unfortunately, there are very few specific geospatial case studies available in the public domain.
This course will use the DC Sniper scenario as a case study examining the application of an analytic method to geographic problems. You will work independently with the DC Sniper case study in a step-by-step manner to learn how to use structured methods in geospatial analysis.This step-by-step learning will occur in parallel with another analysis project you will be working on as a team.
In October 2002, local, state, and federal authorities from the Washington, DC area joined in an unprecedented cooperative effort to capture the individuals charged with a series of shootings that paralyzed the National Capital Region. John Allen Muhammad and John Lee Malvo were apprehended following a 3-week shooting spree that brought together uniformed and investigative law enforcement personnel and communications resources from across the region. The extensive response and investigative effort required intelligence that was shared among hundreds of law enforcement officers from a variety of jurisdictions and levels of government.
Read and study the case study [8]. Complete the Exercise below.
Submissions Instructions: Complete the DC Sniper Geospatial Thinking Exercise. Post your analysis to the Lesson 1 Discussion Forum.
To participate in the discussion, please go to the Lesson 1 Graded Discussion forum in Canvas. (That forum can be accessed at any time in Canvas by clicking on the Modules tab. The Lesson 1 Graded Discussion forum is listed under the Orientation Section.)
Purpose: To identify the full range of spatial and geospatial forces, factors, and trends could have indirectly shaped the DC Sniper case.
General. We are using this technique to identify the critical geospatial factors that could have influenced the DC Sniper Case. Often analysts realize only too late that some additional information categories will be needed and then must go back and review all previous files and recode the data. With a modest amount of effort, “Outside-in Thinking” can reduce the risk of missing important variables early in the analytic process.
Most analysts spend their time concentrating on familiar factors and overlook many important geospatial aspects of a problem. That is, they think from the “inside”—namely, what they control—out to the broader world. Conversely, “thinking from the outside-in” begins by considering the external changes that might, over time, profoundly affect the analysts’ own field or issue. This technique encourages analysts to get away from their immediate analytic tasks (the so-called “inbox”) and think about their issues in a wider conceptual and contextual framework. By recasting the problem in much broader and fundamental terms, analysts are more likely to uncover additional factors, an important dynamic, or a relevant alternative hypothesis.
Using the provided DC Sniper Case Study:
In this lesson, you accomplished two goals of:
Before you move on to Lesson 2, double-check the Lesson 1 Checklist [9] to make sure you have completed all of the required activities for this lesson.
In our next lesson, we will examine organizational decision making. Decision making involves making a choice to alter some existing condition. It is choosing one course of action in preference to others. When the decision is being made by management on behalf of the organization, it is expending organizational or individual resources to implement organizational decision making. A decision is not a single, self-contained event; it is a complex process that extends over some period of time.
Links
[1] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/file/PsychofIntelNew.pdf
[2] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/geog885q//file/Lesson_01/Intel_Analysis_RAND_TR293.pdf
[3] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/geog885q//file/Lesson_01/3420_Methods.pdf
[4] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/geog885q//file/Lesson_01/AfghanIntel_Flynn_Jan2010_code507_voices.pdf
[5] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/geog885q//file/DC_Sniper/DC_Sniper_Case_8June09.pdf
[6] https://www.e-education.psu.edu/geog885/node/1914
[7] https://fas.org/irp/offdocs/wmd_report.pdf
[8] https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/DC_Sniper_Case_8June09.pdf
[9] https://www.e-education.psu.edu/geog885/l2_p2.html