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Lesson 4

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L4.02: Video Lecture 4.1

Please view my video lecture (7:58) concerning the GEOINT tradecraft.

L4.03: The GEOINT Tradecraft

So what is the GEOINT tradecraft when examined broadly beyond its implementation by any single nationality? Tradecraft is how GEOINT gets done; it includes the principles, tools, and standards of rigor for an analyst's thinking. An interesting early example of the GEOINT clandestine tradecraft is the artwork of Robert Baden-Powell [1], who served as a military intelligence officer in the British Army during the 1890s. Powell sketched enemy fortifications by pretending to be an entomologist making detailed sketches of butterflies and leaves that, on close scrutiny, were revealed to be maps of gun emplacements or trenches.

Photograph of Lt. Robert Baden Powell circs 1878
Figure 4.1: Young Lt. Robert Baden-Powell (13th Hussars) in stable dress, Circa 1878
Source: Wikimedia [2]

Current day GEOINT tradecraft is a merging of science and technology with the conventional notion of tradecraft. Historically, tradecraft is defined as how an intelligence officer makes clandestine contact with an agent to obtain information, how the information is passed (or processed), and the tactics for preventing detection. In a more modern sense, tradecraft is also defined as an organization's sources and methods. GEOINT sources may include information obtained clandestinely with humans (like Baden-Powell), with technical collection systems (satellites), and using modern-day open source information (tweets). Methods pertain to the techniques used by analysts. An organization's geospatial sources and methods may be closely guarded so as not to give opponents the opportunity to know the capabilities and interests of an intelligence gathering organization. Unique from other forms of intelligence tradecrafts, GEOINT’s tradecraft is based on geospatial reasoning. Here, geospatial reasoning is reflective, skeptical, and analytic; implying that the successful application of the tradecraft can never be rote, but must always involve the educated mind of the analyst in an active questioning and examination of assumptions, techniques, and data if it is to meet the rigorous standards of good intelligence work. In this course we use the following definition:

The GEOINT Tradecraft is unique and sometimes privileged organizational sources and methods for obtaining information of a place and making sense of the information to support the decision maker in understanding human activities and intentions. Methods comprise the technologic tools to organize geospatial data and the cognitive techniques used by the analyst to make sense of it when rendering judgments, insights, and forecasts.

As we said, the GEOINT tradecraft is how an organization carries out its work producing geospatial intelligence. This GEOINT organization might be a branch of government, a business, or a law enforcement agency. The implication is that tradecraft know-how is not unique to an intelligence community. In a commercial enterprise, manufacturing tradecraft would be the confidential business knowledge of how to manufacture a product. Frequently in business, this institutional knowledge is guarded from a competitor as a trade secret [3]. Critically, there is an interrelationship of humans and technologies that shapes the aspects of analysis, which is why GEOINT's tradecraft has been particularly influenced by the Geospatial Revolution. Geospatial intelligence's analytic craft has been shaped by Geographic Information Science (GIScience) and Geographic Technologies. The relationship might be illustrated as in Figure 4.2:

graphic showing the relationship between Geographic Technology, GIScience, and GEOINT Tradecraft.
Figure 4.2: The relationship between Geographic Technology, GIScience, and GEOINT Tradecraft.
Source: Bacastow

GEOINT tradecraft guides the collection of information about a place and the analysis in terms of human activities and intentions. Frequently unique from geographic analysis taught in academia, the GEOINT analyst may need to contend with deceptive information and operate in conditions of secrecy.

Geospatial Deception

Remotely sensed imagery, like the below DigitalGlobe image of Islamabad, Pakistan, is a central source of GEOINT data. 

Satellite Image of Islamabad, Pakistan
Figure 4.3: Satellite Image of Islamabad, Pakistan
Source: https://d396qusza40orc.cloudfront.net/geoint/images/screen_20060518154016_7ngaislamabad-20060518.jpg [4]

As you can imagine, there are circumstances where people want to deny successful analysis of such imagery. This is particularly true in a military context where we prevent enemies from performing reconnaissance by concealment, camouflage, and deception. However, it could be equally true for someone wanting to deny the ability to count the number of automobiles awaiting shipment in an auto factory's parking lot. A core skill of the GEOINT tradecraft is contending with deceptive information. Deception should not be mistaken for the error or misinformation you might deal with in a routine spatial analysis. Here deception is designed to gain an advantage. The idea of deceiving someone about geospatial features is not new and can occur on a grand scale. For example, after December 7, 1941, the Boeing aircraft factory was camouflaged as a village to hide it from Japanese aircraft attack. The plant had fake houses and trees over the factory.

Deception in the form of camouflage is common. In nature, protective coloration serves to protect some flora and fauna—either by making them difficult to see or by causing them to resemble something of little interest to predators. Deception in the form of camouflage allows an otherwise visible object to remain indiscernible from the surrounding environment. Avoiding being deceived requires the analyst to know the ways by which concealment or obscurity is attained. This know-how includes how the method is tailored to a particular observer and how the observer might make a false judgment about the camouflaged object.

Photograph of the Boeing Seattle Plant under Camouflage in WW II
Figure 4.4: Boeing Seattle plant under Camouflage as a village in WW II.
Source: https://d396qusza40orc.cloudfront.net/geoint/images/boeingaircoplantno2_81.jpg [5]

Geospatial Reasoning

Geospatial reasoning includes processes that support exploration, understanding, and sensemaking. Geospatial reasoning begins with the ability to use space as a context, or framework, to make sense of what we see. There are three spatial frames within which we can make the transition from what is observed to meaningful information; these are behavioral spaces, physical spaces, and cognitive spaces. In all cases, the definition of the space provides an interpretive context that gives meaning to the data.

  • Behavioral space is the four-dimensional space-time where spatial thinking is a means of coming to grips with the spatial relations between yourself and objects in the physical environment. This is cognition in space and involves thinking about the world in which we live. It is exemplified by navigation.
  • Physical space is also built on the four-dimensional world of space-time, but focuses on a scientific understanding of the nature, structure, and function of phenomena. This is cognition about space and involves thinking about the ways in which the world works. An example might be how an earthquake creates a tsunami.
  • Cognitive space is in relationship to concepts and objects that are not in and of themselves necessarily spatial. This is thinking with space. An example might be an invading army encroaching on national territory, a gang moving into a rival district, or a driver trying to steal a parking space.

Learning to think spatially is to consider objects in terms of their context. This is to say, the object's location in behavioral space, physical space, or cognitive space, to question why objects are located where they are, and to visualize relationships between and among these objects. The key skills of spatial thinking include having the ability to:

  • Understand the context. The significance of context was discussed above, but it is important to say that if the data upon which the decision is based are placed into the wrong spatial context, for example behavioral space rather than physical space, it is likely the analysis will be flawed since behavioral space is governed by the rules of culture (e.g., use the crosswalks when crossing a street) and physical space is governed by the rules of physics (e.g., I can't walk through a steel fence to cross the road at this point).
  • Recognize patterns and shapes. The successful spatial thinker needs to retain an image of the simple figure in mind and look for it by suppressing objects irrelevant to the task at hand. This ability allows a geospatial analyst to identify patterns of significance in a map, such as an airfield.
Four images of different airfields
Figure 4.5: A set of images of airfields.
Source: Bacastow
  • Recall previously observed objects. The ability to recall an array of objects that was previously seen is called object location memory.
  • Integrate observation-based learning. Synthesizing separately made observations into an integrated whole. The expert analyst moves through the data, gathering information from separately observed objects and views, and integrates this information into a coherent mental image of the area.
  • Mentally rotate an object and envision scenes from different viewpoints. The ability to imagine and coordinate views from different perspectives has been identified by Piaget and Inhelder (1967) as one of the major instances of projective spatial concepts. Mental rotation ability or perspective-taking ability could be relevant to those analysis tasks that involve envisioning what an object, such as a building, would look like if seen from another position.
Envisioning scenes from different viewpoints
Figure 4.6: Envisioning scenes from different viewpoints
Source: Google Earth.

L4.04: Video Lecture 4.2

Please view my second video lecture (10:58) concerning the GEOINT tradecraft.

L4.05: Sensemaking

Challenge Of Geospatial Analysis

Geospatial analysis can be very difficult to do well. The difficulty is cognitive and most frequently not related to an individual's ability to use the tools that Geographic Information Technologies (GIT) provide. GEOINT analysis has two different approaches—the single hypothesis approach and the multiple (or competing) hypotheses approach. The first is most popular in academia; the second is popular in intelligence analysis. Here's a brief comparison:

Table 4.1: Single vs. Multiple Hypothesis
Approaches Description
Single Hypothesis The natural human desire to reach an explanation can, and often does, lead the analyst to an interpretation based on a single hypothesis. Human nature is to trust the hypothesis, and the analyst is now blind to other possibilities. The early hypothesis becomes a tentative theory and then a ruling theory, and the analysis becomes focused on proving the ruling theory. The result is a blindness to evidence that disproves the ruling theory or supports an alternate explanation. Here, the results are left to the chance that the original tentative hypothesis was correct. A variation of this is to test a single working hypothesis for fact-finding that degenerates into a ruling theory.
Multiple Hypotheses The method of multiple hypotheses involves the development of several hypotheses that might explain the focus of the analysis. These hypotheses should be contradictory and compete so most will prove to be false. The development of multiple hypotheses prior to the analysis avoids the trap of the ruling hypothesis and thus makes it more likely that our analysis will lead to meaningful results. The major benefit is that the approach promotes greater thoroughness than an analysis directed toward one hypothesis. This leads to lines of inquiry that we might otherwise overlook, and thus to evidence and insights that might never have been encountered.

Biases

Good geospatial analysis requires you to monitor your mental progress, make changes, and adapt the ways you are thinking. This is self-reflection, self-responsibility, and self-management. Richards Heuer addresses this in his work, the Psychology of Intelligence Analysis [6]. [6] Heuer makes three important points relative to intelligence analysis:

  • Human minds are ill equipped ("poorly wired") to cope effectively with both inherent and induced uncertainty.
  • Increased knowledge of one's own inherent biases tends to be of little assistance to the analyst.
  • Tools and techniques that apply higher levels of critical thinking can substantially improve analysis of complex problems.

The core of his argument is that even though every analyst sees the same piece of information, it is interpreted differently due to a variety of factors. In essence, one's perceptions are molded by factors that are out of human control. These cognitive patterns, or mindsets, are potentially good and bad. On the positive side, they tend to simplify information for the sake of comprehension, but they also bias interpretation. The key risks of mindsets are that:

  • Analysts perceive what they expect to perceive;
  • Once formed, they are resistant to change;
  • New information is assimilated, sometimes erroneously, into existing mental models; and
  • Conflicting information is often dismissed or ignored.

He provides the following series of images to illustrate how poorly we are cognitively equipped to accurately interpret the world.

Question #1: What do you see in figure 4.7 below? 

Three triangles filled with the following phrases; Paris in the the spring, Once in a a lifetime, Bird in the the hand.
Figure 4.7: What do you see in this figure?
Source: Heuer, Psychology of Intelligence Analysis [6]
Answer for Question #1 [7]

Question #2: Look at the drawing of the man in the upper right of Figure 4.8. Are the drawings all of men?

Drawing of a man?
Figure 4.8: Impressions resist change.
Source: Heuer, Psychology of Intelligence Analysis [6]
Answer for Question #2 [8]

Question #3: What do you see in Figure 4.9—an old woman or a young woman? 

Drawing of an old Lady?
Figure 4.9: It is difficult to look at the same information from different perspectives.
Source: Heuer, Psychology of Intelligence Analysis [6]
Answer for Question #3 [9]

Now look to see if you can reorganize the drawing to form a different image of a young woman, if your original perception was of an old woman, or of the old woman if you first perceived the young one.

According to Heuer, and as the above figures illustrate, mental models, mindsets, or cognitive patterns are essentially the analogous images by which people perceive information. Even though every analyst sees the same piece of information, it is interpreted differently due to a variety of factors. In essence, one's perceptions are morphed by a variety of factors that are completely out of the control of the analyst. However, cognitive patterns are critical to allowing individuals to process what otherwise would be an incomprehensible volume of information. Yet, they can cause analysts to overlook, reject, or forget important incoming or missing information that is not in accordance with their assumptions and expectations. Ironically, the experienced analysts may be more susceptible to these mindset problems as a result of their expertise and past success in using time-tested mental models. Since people observe the same information with inherent and different biases, Richards Heuer believes an effective analysis method needs a few safeguards. The analysis method should:

  • Encourage products that clearly show their assumptions and chains of inferences; and
  • Emphasize procedures that expose alternative points of view.

Heuer advocates using Structured Analytic Techniques (SATs) as a means to overcome mindsets.

Structured Analytic Techniques (SAT)

Most people solve geospatial problems intuitively by trial and error. Structured analytic techniques (SAT) are a "box of tools" to help the analyst mitigate one's cognitive limitations and pitfalls. Structured thinking in general, and structured geospatial thinking specifically, is at variance with the way in which the human mind is in the habit of working. Structured analysis is an approach to intelligence analysis with the driving forces behind the use of these techniques being:

  • an increased understanding of cognitive limitations and pitfalls that make intelligence analysis difficult;
  • prominent intelligence failures that have prompted reexamination of how intelligence analysis is generated;
  • policies expecting interagency collaboration; and
  • a desire by policy makers who receive analysis that it be more transparent as to how conclusions were reached.

Taken alone, SATs do not constitute an analytic method for solving geospatial analytic problems. The most distinctive characteristic is that structured techniques help to decompose one's geospatial thinking in a manner that enables it to be reviewed, documented, and critiqued. "A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis [10]" (CIA, 2009) highlights a few of the key structured analytic techniques used in the private sector, academia, and the intelligence profession.

The US Intelligence Community began focusing on structured techniques because analytic failures led to the recognition that it had to do a better job overcoming cognitive limitations, analytic pitfalls, and addressing the problems associated with mindsets. Structured analytic techniques help the mind think more rigorously about an analytic problem. In the geospatial realm, they ensure that our key geospatial assumptions, biases, and cognitive patterns are not just assumed but are considered. The use of these techniques later helps to review the geospatial analysis and identify the cause of any error.

Moreover, structured techniques provide a variety of tools to help reach a conclusion. Even if intuitive and scientific approaches provide the same degree of accuracy; structured techniques have value in that they can be easily used to balance the art and science of GEOINT. Heuer categorized structured techniques by how they help analysts overcome human cognitive limitations. Heuer's grouping is as follows:

  • Decomposition and Visualization: The number of things most people can keep in working memory at one time is seven, plus or minus two. Complexity increases geometrically as the number of variables increases. In other words, it is very difficult to do error-free analysis only in our heads. The two basic tools for coping with complexity in the analysis are to: (1) break things down into their component parts so that we can deal with each part separately, and (2) put all the parts down on paper or a computer screen in some organized manner such as a list, matrix, map, or tree so that we and others can see how they interrelate as we work with them. Many common techniques serve this purpose.
  • Indicators, Signposts, Scenarios: The human mind tends to see what it expects to see and to overlook the unexpected. Change often happens so gradually that we do not see it, or we rationalize it as not being of fundamental importance until it is too obvious to ignore. Identification of indicators, signposts, and scenarios create an awareness that prepares the mind to recognize change.
  • Challenging Mindsets: A simple definition of a mindset is, “a set of expectations through which a human being sees the world.” Our mindset, or mental model of how things normally work in another country, enables us to make assumptions that fill in the gaps when needed evidence is missing or ambiguous. When this set of expectations turns out to be wrong, it often leads to intelligence failure. Techniques for challenging mindsets include re-framing the question in a way that helps break mental blocks, structured confrontation such as devil’s advocacy and structured self-critique such as what we call a key assumption check. In one sense, all structured techniques that are implemented in a small team or group process also serve to question your mindset. Team discussions help us identify and evaluate new evidence or arguments and expose us to diverse perspectives on the existing evidence or arguments.
  • Hypothesis Generation and Testing: “Satisficing” is the tendency to accept the first answer that comes to mind that is “good enough.” This is commonly followed by confirmation bias, which refers to looking at the evidence only from the perspective of whether or not it supports a preconceived answer. These are among the most common causes of intelligence failure. Good analysis requires identifying, considering, and weighing the evidence both for and against all the reasonably possible hypotheses, explanations, or outcomes. Analysis of Competing Hypotheses is one technique for doing this.
  • Group Process Techniques: Just as analytic techniques provide structure to our individual thought processes, they also provide structure to the interaction of analysts within a team or group. Most structured techniques are best used as a collaborative group process because a group is more effective than an individual in generating new ideas, and at least as effective in synthesizing divergent ideas. The structured process helps identify differences in perspective between team or group members, and this is good. The more divergent views available, the stronger the eventual synthesis of these views. The specific techniques listed under this category, such as brainstorming and Delphi, are designed as group processes and can only be implemented in a group.

Others have categorized techniques by their purpose: Diagnostic techniques are primarily aimed at making analytic arguments, assumptions, or intelligence gaps more transparent; Contrarian techniques explicitly challenge current thinking; and, Imaginative thinking techniques aim to develop alternative outcomes. In fact, many of the techniques will do some combination of these functions. These different categories of techniques notwithstanding, the analysts should select the technique that best accomplishes the specific task they set out for themselves. The techniques are not a guarantee of analytic precision or accuracy of judgments; they do improve the usefulness, sophistication, and credibility of intelligence assessments.

Sensemaking

The term “sensemaking” is used as a term to describe an analytic process or method. Sherman Kent, who has been described as "the father of intelligence analysis," is often acknowledged as first proposing an analytic method specifically for intelligence. The essence of Kent’s method was understanding the problem, data collection, hypotheses generation, data evaluation, more data collection, followed by hypotheses generation. Richards Heuer subsequently proposed an ordered eight-step model of “an ideal” analytic method, emphasizing early deliberate generation of hypotheses prior to information acquisition:

  • identifying possible hypotheses,
  • listing evidence for and against each hypothesis,
  • analyzing the evidence, 
  • refining hypotheses,
  • trying to disprove hypotheses,
  • analyzing the sensitivity of critical evidence,
  • reporting conclusions with the relative likelihood of all hypotheses, and
  • identifying milestones that indicate events are taking an unexpected course.

Heuer’s technique has become known as Analysis of Competing Hypothesis (ACH). The technique entails identifying possible hypotheses by brainstorming, listing evidence for and against each, analyzing the evidence and then refining hypotheses, trying to disprove hypotheses, analyzing the sensitivity of critical evidence, reporting conclusions with the relative likelihood of all hypotheses, and identifying milestones that indicate events are taking an unexpected course. The use of brainstorming is critical. The quality of the hypotheses is dependent on the existing knowledge and experience of the analysts since hypotheses generation occurs before additional information acquisition augments the existing knowledge of the problem.

While ACH is widely cited in the intelligence literature as a means for improving analysis, the primary advantage of ACH is a consistent approach for rejection or validation of many potential conclusions. According to David Moore [11], ACH makes it explicit that evidence may be consistent with more than one hypothesis. "Since the most likely hypothesis is deemed to be the one with the least evidence against it, honest consideration may reveal that an alternative explanation is as likely, or even more likely, than that which is favored. The synthesis of the evidence and the subsequent interpretations in light of multiple hypotheses is also more thorough than when no such formalized method is employed." The ACH matrix might look like:

Table 4.2: ACH Matrix
Evidence Hypothesis 1 Hypothesis .....
Evidence A    
Evidence B    
Evidence C    
........    

An excellent and simple explanation of the ACH approach is found in Structured Analysis of Competing Hypotheses: Improving a Tested Intelligence Methodology [12] by Kristan J. Wheaton and Diane E. McManis.

Analytic Stages

ACH and other problem solving approaches can be said to be applied in three stages. The stages and outputs are:

Table 4.3: Analytic Stages
Stage Description Result
Stage 1: Problem Initiation This stage develops the question. The question focuses on the nature of the geospatial and temporal patterns the analyst is seeking to identify and understand. Many new geospatial analysts struggle to translate the question into the context of spatial concepts. To overcome this, we stress the importance of understanding the general question. The question can be viewed as an active two-way discussion between the client requiring the information and the analyst supplying it. A question that seeks to:
  • Discover significant and previously unavailable geospatial information.
  • Describe a place.
  • Explain why an activity or transaction occurred at a place.
  • Judge the significance of a place.
  • Predict the location of geospatial activities.
Stage 2: Information Foraging The foraging actions are exploring for new information, narrowing the set of items that have been collected, and exploiting items in the narrowed set trade off against one another. Some analysts' work never departs from the foraging loop and simply consists of extracting information and repackaging it without much actual analysis. This stage recognizes that analysts tended to forage for data by beginning with a broad set of data and then proceeded to narrow that set down into successively smaller, higher-precision sets of data (Pirolli,1999). The data and information necessary for sensemaking.
Stage 3: Sensemaking This stage results in the development of a detailed analytic assessment. Sensemaking is the ability to make sense of an ambiguous situation; it is creating situational awareness and understanding in situations of high complexity or uncertainty in order to make decisions. It is "a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively" (Klein, G., Moon, B. and Hoffman, R.F. 2006). Making sense of sensemaking. IEEE Intelligent Systems, 21(4), 70-73.). An analytic result.

It is difficult for an analyst to appreciate how various SATs and geospatial tools fit together. The following table summarizes this relationship:

Table 4.4: How Various GEOINT Techniques and Tools Fit Together
Stage Possible SAT Example Geospatial Technology Operation
Stage 1: Problem Initiation
  • Decomposition and Visualization
  • Challenging Mindsets
  • Group Process Techniques
  • Geospatial data entry
  • Geospatial data conversion
  • Data validation
  • Geospatial data management
  • Attribute data management
  • Data visualization
Stage 2: Information Foraging
  • Brainstorming
  • Data visualization
  • Geospatial data processing/analysis
Stage 3: Sensemaking
  • Indicators, Signposts, Scenarios
  • Hypothesis Generation and Testing (Analysis of Competing Hypotheses)
  • Challenging Mindsets
  • Output of maps and reports

L4.06: Analytic Standards and Judgements

Professional Standards—A Core of the Tradecraft

The Analytic Standards of the Intelligence Community are the core of GEOINT's tradecraft. The following is based upon US Intelligence Community (IC) Directive Number 203, Analytic Standards, dated June 21, 2007 [13]. The standards articulate a commitment to meet the highest standards of integrity and rigorous analytic thinking, and serve as goals for analysts who strive for excellence in their analytic work. The analytic standards are characterized by:

  1. Objectivity: This standard requires that analysts perform their analytic and informational functions from an unbiased perspective. Analysis should be free of emotional content, give due regard to alternative perspectives and contrary reporting, and acknowledge developments that necessitate adjustments to analytic judgments.
  2. Independence of Political Considerations: Analysts should provide objective assessments informed by available information that are not distorted or altered with the intent of supporting or advocating a particular policy, political viewpoint, or audience.
  3. Timeliness: Analytic products that arrive too late weaken the utility and impact. Analysts will strive to deliver their products in time for them to be actionable by customers. An analyst has a responsibility to be aware of the schedules and requirements of consumers.
  4. Use of All Available Sources of Information: Analysis should be informed by all relevant information that is reasonably available. Where critical gaps exist, analysts should work to develop appropriate sources and access strategies.
  5. Individual Standards of the Analytic Tradecraft. An analytic result (see Table 4.5, below).
Table 4.5: Individual Analytic Standards
Individual Standard Description
Properly describes quality and reliability of underlying sources. Analytic products should accurately characterize the information in the underlying sources and explain which information proved key to analytic judgments and why. Consistent with classification of the product, factors significantly affecting the weighting that the analysis gives to available, relevant information, such as denial and deception, source access, source motivations and bias, age and continued currency of information, or other factors affecting the quality and potential reliability of the information, should be included in the product. When appropriate, analytic products may identify a prospective information strategy to improve the reporting base when significant gaps exist.
Properly caveats and expresses uncertainties or confidence in analytic judgments. Analytic products should indicate both the level of confidence in analytic judgments and explain the basis for ascribing it. Sources of uncertainty—including information gaps and significant contrary reporting—should be noted and linked logically and consistently to confidence levels in judgments. As appropriate, products also should identify indicators that would enhance or reduce confidence or prompt revision of existing judgments.
Properly distinguishes between underlying intelligence and analysts' assumptions and judgments. All assumptions are clearly stated and defined. Assumptions deal with identifying underlying causes and/or behavior of systems, people, organizations, states, or conditions. Assumptions comprise the foundational premises on which the information and logical argumentation build to reach analytic conclusions. Assumptions may also span information gaps that would otherwise inhibit the analysis from reaching defensible judgments. Judgments are defined as logical inferences from the available information or the results of explicit tests of hypotheses. They comprise the conclusions of the analysis. Analytic products should explicitly identify the critical assumptions on which the analysis is based and explain the implications for judgments if those assumptions are incorrect. As appropriate, analytic products should identify indicators that would signal whether assumptions or judgments are more or less likely to be correct.
Incorporates alternative analysis where appropriate. Where appropriate, analytic products should identify and explain the strengths and weaknesses of alternative hypotheses, viewpoints, or outcomes in light of both available information and information gaps. Analytic products should explain how alternatives are linked to key assumptions and/or assess the probability of each alternative. To the extent possible, analysis should incorporate insights from the application of structured analytic technique(s) appropriate to the topic being analyzed and include discussion of key indicators that, if detected, would help clarify which alternative hypothesis, viewpoint, or outcome is more likely or is becoming more likely.
Demonstrates relevance to the domain. Analytic products should provide information and insight on issues relevant to the products' intended consumers and/or provide useful context, warning, or opportunity analysis. The information and insight may be particularly difficult to obtain without extensive expertise. To meet this standard fully, analytic products should examine and explicitly address direct or near-term implications of the information and judgments for the intended audience and/or for appropriate security interests, and, when possible, also examine longer-term implications or identify potential indirect or second-order effects.
Uses logical argumentation. Analytic presentation should facilitate clear understanding of the information and reasoning underlying analytic judgments. Key points should be effectively supported by information or, for more speculative warning or "think pieces," by coherent reasoning. Language and syntax should convey meaning unambiguously. Products should be internally consistent and acknowledge significant supporting and contrary information affecting key judgments. Graphics and images should be readily understandable and should illustrate, support, or summarize key information or analytic judgments.
Exhibits consistency of analysis over time, or highlights changes and explains rationale. Analytic products should deliver a key message that is either consistent with previous production on the topic from the same analytic element or, if the key analytic message has changed, highlight the change and explain its rationale and implications.
Makes accurate judgments and assessments. Analytic elements should apply expertise and logic to make the most accurate judgments and assessments possible given the information available to the analytic element and known information gaps. Where products are estimative, the analysis should anticipate and correctly characterize the impact and significance of key factors affecting outcomes or situations. Accuracy is sometimes difficult to establish and can only be evaluated retrospectively if necessary information is collected and available.

Expressing Analytic Judgments

If you recall, intelligence is not truth. Intelligence is an approximation of truth with some level of confidence. It is necessary for decision makers to know how confident their analysts are in the results of an analysis; however, analysts are often reluctant to state the uncertainties surrounding their work because they may not:

  • Appreciate the value of that information to decision makers.
  • Trust the decision maker to understand any caveats as to their confidence in the work.
  • Expect an honest statement of uncertainty to be rewarded.
  • Know how to express the uncertainty.

An often cited example of the damage that vague statements can have is the US President's Daily Brief (PDB) from August 6, 2001. For example:

  • “Bin Laden since 1997 has wanted to conduct terrorist attacks in the US” (CIA, 2001, para. 1);
  • “Bin Laden implied...that his followers would ‘bring the fighting to America’” (CIA, 2001, para. 1);
  • Bin Laden’s “attacks against...US embassies...in 1998 demonstrate that he prepares operations years in advance and is not deterred by setbacks” (CIA, 2001, para. 6);
  • “FBI information...indicates patterns of suspicious activity in this country consistent with preparations for hijackings or other types of attacks” (CIA, 2001, para. 10);
  • “a call to [the US] Embassy in the UAE in May [said] that a group of Bin Laden supporters was in the US planning attacks with explosives” (CIA, 2001, para. 11).

The PDB briefing did not present the President with a clear estimate of Bin Laden’s likely activities in the coming months. Consider the different message to the decision maker if the above PDB were stated as, “Bin Laden implied...that his followers will probably ‘bring the fighting to America.’” U.S. Joint Publication 2-0, Doctrine for Intelligence Support to Joint Operations [14] suggests terms and expressions to communicate analytic judgments. The terms selected are based on three factors:

  • Number of key assumptions required,
  • the credibility and diversity of sourcing, and
  • the strength of argumentation.

Each factor should be assessed independently and then in concert with the other factors to determine the confidence level. Multiple levels are stated as Low, Moderate, and High. Phrases such as "we judge" or "we assess" are used to call attention to a product's key assessment. Supporting assessments may use likelihood terms or expressions to distinguish them from assumptions or reporting. Table 4.5 shows the guidelines for likeliness terms and the confidence levels with which they correspond.

Terms/Expressions:

  • Will, will not
  • Almost certainly, remote
  • Highly likely, highly unlikely
  • Expect, assert, affirm
Table 4.6: Guidelines for likeliness terms and the corresponding confidence levels (U.S. Joint Publication 2-0, Doctrine for Intelligence Support to Joint Operations)
Low Moderate High
  • Uncorroborated information from good or marginal sources
  • Many assumptions
  • Mostly weak logical inferences, minimal methods application
  • Glaring intelligence gaps exist
  • Partially corroborated information from good sources
  • Several assumptions
  • Mix of strong and weak inferences and methods
  • Minimal intelligence gaps
  • Well corroborated information from proven sources
  • Minimal assumptions
  • Strong logical inferences and methods
  • No or minor intelligence gaps exist
Terms/Expressions:

  • Possible
  • Could, may, might
  • Cannot judge, unclear
Terms/Expressions:

  • Likely, unlikely
  • Probable, improbable
  • Anticipate, appear
Terms/Expressions:

  • Will, will not
  • Almost certainly, remote
  • Highly likely, highly unlikely
  • Expect, assert, affirm

L4.07: Assignment

Scenario: For this assignment, you are a volunteer providing short-term GEOINT support to a Non-Governmental Organization (NGO).

Task: Using ACH (see L4.05 Sensemaking [15]), your task is to suggest the general location for a regional depot of critical emergency materials. Please note that I said general location—I do not intend for you to select the specific site for the facility. The depot is intended to provide relief support for possible regional disasters. Stocks in the depot's warehouse might include emergency food, various types of relief goods, mobile cooking facilities, rapid response equipment, medicines and medical kits.

Conditions: The location meets, as a minimum, criteria 1-4. You may wish to add an additional criterion:

  • Criterion 1: There is a significant probability of the occurrence of a disaster in the region.
  • Criterion 2: There will be significant demand for supplies based upon the site's proximity to a population in need.
  • Criterion 3: On a regional scale, the site minimizes transportation time to receive and distribute supplies.
  • Criterion 4: On a regional scale, the depot location has a minimal safety and security risk.
  • Criterion 5 (Optional): One additional criterion you determine as important.

Given: ArcGIS Online with access to the following data:

  • Three identified location names.
  • ArcGIS Online maps of natural hazards. (See Figure 4.10 below.)

You will perform the following:

Step 1: Using ArcGIS Online, explore the three identified locations listed below.

  • Site A is located in the vicinity of Lagos, Nigeria.
  • Site B is located in the vicinity of Dhaka, Bangladesh.
  • Site C is located in the vicinity of Honolulu, Hawaii, USA.

Step 2: Complete the below matrix (Table 4.7) using the identified locations from Step 1 and the geospatial information you can gather as evidence relative to the criteria. Using both the maps you have access to with ArcGIS Online and other relevant information you can find on the Internet, evaluate the criteria versus the locations. You will evaluate the criteria by moving across the rows, one piece of evidence at a time, and deciding whether the evidence is consistent (+), inconsistent (-), or not applicable (N/A) to the site being tested. This will help you determine if the broad range of evidence supports or refutes selecting the site.

ArcGIS Online map of natural disasters around the world. [16]
Figure 4.10: Natural Disasters around the world.
(Click on the map above to access the interactive ArcGIS online Natural Disasters webmap.)
Source: hArcGIS Online webmap [16]

Step 3: Now, working down the columns of the matrix in Table 4.7, review each site to draw tentative conclusions about the relative suitability of each one. Select the site most consistent with the evidence.

Step 4: State the selected location (Site A, B, or C) to the Lesson 4 Discussion Forum [17] and what you believe to be the nature of the event [18] (Geophysical, Meteorological, Hydrological, Climatological, or Biological) this site would have the greatest likelihood of supporting using the appropriate terms and expressions from Table 4.8.

Step 5: Comment on another student’s analysis in the discussion forum.

Table 4.7: Evidence Versus Hypotheses
  Site A:
Lagos, Nigeria
Site B:
Dhaka, Bangladesh
Site C:
Honolulu, Hawaii, USA.
Evidence Related to Criteria 1:
Probability of occurrence of an event.
     
Evidence Related to Criteria 2:
Expected demand for supplies determined by the proximity to a population.
     
Evidence Related to Criteria 3:
Minimal transportation time to get supplies to an event.
     
Evidence Related to Criteria 4:
Regional safety and security of the depot.
     
Other criteria and evidence you included in this analysis.      
  • State the selected site using the appropriate terms and expressions from Table 4.8, below.
  • Referring back to this lesson's discussion of ACH, describe how you applied the method.

Table 4.8: Confidence Levels
Low Moderate High
  • Uncorroborated information from good or marginal sources
  • Many assumptions
  • Mostly weak logical inferences, minimal methods application
  • Glaring intelligence gaps exist
  • Partially corroborated information from good sources
  • Several assumptions
  • Mix of strong and weak inferences and methods
  • Minimal intelligence gaps
  • Well corroborated information from proven sources
  • Minimal assumptions
  • Strong logical inferences and methods
  • No or minor intelligence gaps exist
Terms/Expressions:

  • Possible
  • Could, may, might
  • Cannot judge, unclear
Terms/Expressions:

  • Likely, unlikely
  • Probable, improbable
  • Anticipate, appear
Terms/Expressions:

  • Will, will not
  • Almost certainly, remote
  • Highly likely, highly unlikely
  • Expect, assert, affirm

Credits

The ArcGIS Online capabilities were developed by Joseph Kerski [19], Esri Education Manager.

L4.08: Discussion

Discussion: Locate an Emergency Depot

For this week's discussion I want to focus on the analysis of selecting the emergency depot location completed in L4.07. Here are a few things to do and discuss:

  • Post your selected site and a description of your process.
  • Find an analysis that you think does a great job. Post a comment describing what you liked.
  • Find an analysis that you think does a terrible job. Post a comment describing what you did not like.

Head over to the dedicated discussion forum here [20] to talk about these issues.

Discussion Grading

Participation in discussions is worth 10% of your overall grade in the class. To earn the full 10%, you need to make a total of 10 original posts or comments by the end of the class. If I were you, I'd shoot for posting twice each week. Read the Grading Policy [21] page for more details.

Forum link and grading link resolves to Coursera

L4.09: Summary

This lesson demonstrated the concepts of tradecraft and focused on the art of intelligence analysis aided by technology. Tradecraft was defined as the "knowledge work" of the GEOINT professional. It is the work that produces geospatial intelligence. Tradecraft consists of the principles, tools, and standards of rigor for an analyst's thinking. Unique from other forms of geographic analysis, the GEOINT tradecraft may be required to operate in secrecy and contend with deliberately deceptive information. This tradecraft is a merging of the conventional notion of an intelligence tradecraft with the advances in geographic information science and technologies (GIS&T). Geospatial sensemaking creates the objective connection between a geospatial problem representation and geospatial evidence. Here one set of activities—information foraging—focuses on finding information, while another set of activities—sensemaking—focuses on giving meaning to the information.

Note

Don't forget to complete the Lesson 4 Quiz!

L4.10: References

Central Intelligence Agency (2009), A tradecraft primer structured analytic techniques for improving intelligence analysis.Washington, D.C., Center for the Study of Intelligence.

Copeland, Nate. (2009), Booz Allen Hamilton, Personal Correspondence. 02 June 2009.

Heuer, R. (1999), Psychology of intelligence analysis. Washington, D.C.: Center for the Study of Intelligence, Central Intelligence Agency.

Moore, D. (2012), Sensemaking: A structure for an intelligence revolution (2nd ed.). Washington, DC: National Defense Intelligence College Press.

Urbánek, Jirí F., Jirí Barta, Josef Heretík, and Jaroslav Prucha (2010), Computerized aided camouflage. In Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics (AIC'10/BEBI'10), Nikos E. Mastorakis, Valeri Mladenov, and Zoran Bojkovic (Eds.). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 289-294.


Source URL: https://www.e-education.psu.edu/geointmooc/node/1982

Links
[1] http://publicdomainreview.org/2013/07/10/robert-baden-powells-entomological-intrigues/
[2] http://commons.wikimedia.org/wiki/File:Lt_Robert_Baden-Powell.jpg
[3] http://en.wikipedia.org/wiki/Trade_secret
[4] https://d396qusza40orc.cloudfront.net/geoint/images/screen_20060518154016_7ngaislamabad-20060518.jpg
[5] https://d396qusza40orc.cloudfront.net/geoint/images/boeingaircoplantno2_81.jpg
[6] https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/PsychofIntelNew.pdf
[7] https://www.e-education.psu.edu/emsc100s/sites/www.e-education.psu.edu.emsc100s/files/images_textversions/L4_Q1_answer.html
[8] https://www.e-education.psu.edu/emsc100s/sites/www.e-education.psu.edu.emsc100s/files/images_textversions/L4_Q2_answer.html
[9] https://www.e-education.psu.edu/emsc100s/sites/www.e-education.psu.edu.emsc100s/files/images_textversions/L4_Q3_answer.html
[10] https://www.e-education.psu.edu/drupal6/files/sgam/TradecraftPrimer-apr09.pdf
[11] http://www.ni-u.edu/ni_press/pdf/Sensemaking.pdf
[12] http://www.mcmanis-monsalve.com/files/publications/intelligence-methodology-1-07-chido.pdf
[13] http://www.dni.gov/files/documents/ICD/ICD%20203%20Analytic%20Standards%20pdf-unclassified.pdf
[14] http://www.dtic.mil/doctrine/new_pubs/jp2_0.pdf
[15] https://www.e-education.psu.edu/emsc100s/node/837
[16] http://education.maps.arcgis.com/home/webmap/viewer.html?webmap=dd0cc4f22841427384eed20145e3c201
[17] https://class.coursera.org/geoint-001/forum/list?forum_id=10012
[18] http://www.emdat.be/explanatory-notes
[19] http://www.josephkerski.com/
[20] https://class.coursera.org/geoint-001/forum
[21] https://class.coursera.org/geoint-001/wiki/grading