GEOG 885
Advanced Analytic Methods for the GEOINT Professional

Structured Geospatial Analytic Method (SGAM)

Traditionally, analysts at all levels devote little attention to improving how they think. - Richards J. Heuer, Jr.

We often assume everyone has the ability to think in the geospatial domain. However, it has been long known that without specific prompting, people may be unaware of spatial patterns of an environment (Golledge, 1992). What does this mean? It means that:

  • creating accurate and meaningful geospatial intelligence is not typically an innate ability, and
  • geospatial analysis is an effortful cognitive act of imagining, identifying, and matching patterns.

Add to Golledge's observation the fact that all people observe the same information with inherent and different biases (see Heuer, Psychology of Intelligence Analysis) and it is clear that geospatial intelligence analysis needs a safeguard. The safeguard is a teachable process that forces the geospatial analyst to address their cognitive limitations.

Geospatial Intelligence, with possibly one exception, currently has no accepted analytic methodology to address these shortcomings. Those in the domain frequently use the word "tradecraft" as a catchall to say that the profession has a shared and documented analytic method. When one really takes a hard look at the "tradecraft" outside the realm of image interpretation, what we find is a collection of high level suggestions and tips. This is unfortunate, since geospatial analysis is an integral part of rendering geospatial intelligence.

The lack of a method for the geospatial analyst to reference is significant. It is a human tendency when confronted with a complex issue and no mental framework to organize thoughts, to unconsciously discount much of the relevant information. We mentally simplify the task and likely oversimplify the results. Further, judging intuitively or consciously, our judgments are subject to unconscious biases, blind spots, and limitations of working memory. When time permits and judgments are important, such as with National Security decisions or actions involving the major investments of money, we should break down the complex problem with an established and accepted method that makes the judgment more manageable and more rigorous. The method puts us in control and decreases the probability of error. Moreover, using an analytic method trains the analyst for time-critical situations. It helps develop the right intuitions, so we can make better high-speed judgments.

The Structured Geospatial Analytical Method (SGAM) is offered to solve the problems mentioned above. The method is organized into two major loops:

  1. a foraging loop aimed at seeking information, searching and filtering it, and reading and extracting information, and
  2. a sensemaking loop that involves iterative development of a mental model from the schema that best fits the evidence.

The foraging loop 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, before analyzing the information. The three foraging actions of exploring for new information, narrowing the set of items that has been collected, and exploiting items in the narrowed set tradeoff against one another under deadline or data overload constraints. It is important to note that much geospatial intelligence work never departs the foraging loop and simply consists of extracting information and repackaging it without much actual analysis. In a sense, this is the development of tactical intelligence. Tactical intelligence is the art and science of determining what the opposition is doing, or might do, to prevent the accomplishment of your mission. It is used to support immediate decision making related to operational planning and execution.

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. ). In a sense, this is the development of strategic intelligence. Strategic intelligence (STRATINT) is information that is required for forming policy and plans at the national and international level. The information needed for strategic intelligence comes from Open Source material.

The below figure represents the Structured Geospatial Analytic Process derived from and incorporating aspects of both Heuer's ACH and Pirolli and Card's sensemaking process. This is a generalized view of the geospatial analysis process that fits within the larger intelligence process. The rectangular boxes represent analytic activities. The arrows represent the flow from one activity to the next. The activities are arranged by degree of effort and degree of information structure. The overall analytic method has back loops. One set of activities focuses around finding information and another set of activities focuses on making sense of the information.

 Diagram shows the structured geospatial analytic process. Description in text.
Figure 1: Structured Geospatial Analytic Process
Source: T. Bacastow, D. Bellefiore, D. Bridges, S. Harter, The Pennsylvania State University, 2010.

The diagram summarizes how an analyst comes up with new information. The data flow shows the transformation of information as it flows from raw information to reportable results through the following steps:

  • Step 1: Question. Developing the question is a two-way interface between the client requiring information and the geospatial analyst supplying it. Critically, the question defines the broad nature of the spatial and temporal patterns the analyst is seeking to ultimately identify.
  • Step 2: Grounding and Team Building. Grounding is the raw evidence that reaches the analyst. Grounding is building a potential repertoire of prototypical geospatial and temporal patterns from which a number of hypothetical (possible alternative) patterns will be selected. Step 2 is where the analytic team is formed.
  • Step 3: Hypothesis Development. Hypotheses are the tentative representation of conclusions with supporting arguments. This step involves selecting all the reasonably possible geospatial and temporal patterns that might match the pattern envisioned during the development of your question.
  • Step 4: Evidence Development. Evidence refers to snippets extracted from items discovered in the grounding. Development of the evidence includes developing and applying Schemas, which are the representation or organized marshaling of the information so that it can be used more easily to draw conclusions. This includes developing a smaller subset, which Pirolli and Card call the "shoebox", of the data that is relevant for processing. Much of geospatial intelligence work never departs the foraging loop (Steps 1-4) and simply consists of extracting information and repackaging it without much actual analysis. In short, evidence is the development and accumulation of all facts to reject the hypothetical geospatial and temporal patterns determined in Step 3. GIS assists in the development and accumulation of the facts.
  • Step 5: Fusion. The multi modal (graphical and text) nature of geospatial intelligence data analysis, which is used to reduce the influence of unreliable sources, is essentially a fusion process. Fusion in this step uses the ACH process to combine graphical and textual data, to achieve inferences, which will be more efficient and potentially more accurate than if they were achieved by means of a single source. Simply put, the fusion process is the comparing of the evidence to each hypothetical geospatial and temporal pattern to determine consistency.
  • Step 6: Conclusions. The conclusion is a proposition about which hypothetical pattern(s) is (are) most consistent with the evidence and answers the question. Ultimately there is a presentation or other work product.

Basically, the data flow represents the converting of raw information into a form where expertise can apply, and then out to another form suited for communication. Information processing can be driven by bottom-up processes (from data to theory) or top-down (from theory to data). The bottom-up process is as described in steps 1 through 6. The top-down process is slightly different in that it follows the sequence of:

  1. Evaluate conclusion. Inquiries from clients or indicators from signposts may generate re-evaluations of the current conclusions developed by an analyst requiring the marshaling of additional evidence to support or disconfirm the analysis or the generation and testing of an alternative outcome.
  2. Deconstruct the synthesis. Reexamine the table of hypothesis and evidence beginning with the rankings.
  3. Examine the evidence. Re-examination of collected evidence or the searches for new evidence. Search for nuggets of information that may suggest new geospatial or temporal patterns that generate hypotheses about plausible relations among entities and events.
  4. Re-evaluate the hypotheses. Looking for new hypotheses may generate new searches, further data extraction, or a search for additional raw data.
  5. Question your grounding in the problem. New hypotheses may cause analysts to broaden their grounding in prototypical geospatial and temporal patterns.
  6. Question the question. Revalidate with the client the nature of the geospatial and temporal patterns the analyst is ultimately seeking to identify. Re-examine the process, use of tools, and quality.

The next several lessons will address the detailed aspects of each step within the process.