Geospatial Thinking Is Critical Thinking
The role of structured analytic techniques in geospatial intelligence
By Katherine Hibbs Pherson, Pherson Associates; Robert J. Farnsworth; Steven D. Fleming, Ph.D., University of Southern California; Michael Hauck, Ph.D.; and William Lu, University of Southern California
In a world of swirling crises, ballooning data sources, and clever machines, cries for better thinking skills are universal. According to one popular bumper sticker, lack of critical thinking skills in America is akin to “the nation’s second national deficit.” The geospatial intelligence (GEOINT) Community is particularly sensitive about the role critical thinking plays in generating insights regarding time and space that set GEOINT apart from other intelligence disciplines. The community is keen to incorporate this recognition into its professional training and education.
The human brain’s unique configuration to conceptualize, draw inferences from data, and anticipate is balanced by the dangers of being led astray by its biases and intuitive traps, e.g., being fooled by assuming all data are valid, or intimidated by the insecurity that automated algorithms might know more than people do. The basic tenets of critical thinking provide a strong foundation upon which to evaluate the geospatial elements of space and time. Similarly, structured analytic techniques (SATs), popularized within the U.S. Intelligence Community (IC), help people make their thinking more explicit. Computational thinking helps people understand how to better interact with machines. These physical links to the real world can bridge empirical and experiential observation to help analysts make sense of their environment, solve problems, optimize their use of automated tools, and generate insights and actions.
Critical Thinking Enables Geospatial Problem-Solving
Successful critical thinkers are judged by results that almost always involve deliberation and intuition, logic and creativity, and interpreting data and anticipating situations that go beyond available data. Longtime CIA methodologist Jack Davis recognized this in defining critical thinking as the “adaptation of the processes of scientific inquiry” to your environment and its special circumstances. The key components of critical thinking include:
- Asking the right questions.
- Identifying your assumptions.
- Reaching out to sources of information beyond those readily available.
- Evaluating data for accuracy, relevance, and completeness.
- Assessing the data and forming hypotheses.
- Evaluating the hypotheses with an eye toward conflicting data.
- Drawing conclusions.
- Presenting your findings.
The study and understanding of critical thinking skills has come a long way since Aristotle defined the principles of formal logic some 2,300 years ago. The concepts of logic as deduction, based on evaluating data to determine the single possible answer, evolved as 13thcentury thinkers such as Roger Bacon added concepts of induction or inferences based on incomplete data, and broadened even further to help account for the complexities of our changing world. Charles Sanders Peirce identified abduction as the part of the scientific method that enables the generation of multiple hypotheses whose consequences can be derived by deduction and evaluated by induction. All of these modes of logical reasoning are important for geospatial professionals seeking to make sense of situations, whether replete with hard data or simply indicators of possible events or activity. Some argue that observations foreseeing future options — which by definition are lacking in data and are characterized by uncertainty — may be yet another stage in contemplating how the human brain processes the human environment.
The first three steps imply successful critical thinking depends upon having a solid framework (well-posed questions, identified assumptions, broad sources of information) on which to base the thinking. Cognitive psychologists — ranging from Frederic Bartlett in the 1920s to Gary Klein and Daniel Kahneman today — have written extensively about the explanatory structures humans naturally create to account for the data, beliefs, and experiences that are unique to each of us. Klein coined the term “sensemaking” to describe how humans form a frame or mental model based on few pieces of data and then adjust the frame as more data becomes available. Data that does not fit the frame may be ignored or discarded if it lies too far outside the frame or may even force fundamental changes to the frame if the data is compelling enough. Sensemaking is at the heart of telling good stories. After all, what makes a compelling story, if not the realization that the story helps us understand a topic in a new way?
Analysts often frame their stories with the journalistic questions that date back to Greek and Roman oratory traditions, namely, “Who?” “What?” ‘’When?” “Where?” “Why?” and “How?” “When” and “where” are explicit in the geospatial domain, but the domain also uniquely captures the “who” and the “what,” in a sense, tethering events and intentions to the physical world. Geospatial reasoning matches the patterns of observed data to the models, oftentimes maps or other geospatial displays, to answer questions of what is or might be happening. Reasoned explanations for “how” and “why” require additional context and data.
Structured Analytic Techniques Make Thinking Explicit
SATs externalize internal thought processes to make them clear and transparent enough to be shared, improved, and critiqued. They save time in the long run and inject rigor, facilitate imagination, and infuse accountability by providing the means to examine, question, and collaborate with others to overcome mind-sets, creatively anticipate the potential for disruptive change, and focus on information that helps distinguish one working hypothesis or developing scenario from another.
SATs fall into four families:
- Innovation techniques spur creative thinking. They enable us to generate new insights or discern all the aspects of the issue.
- Diagnostic techniques are used to understand what the information tells us. They help us best explain what has happened or is happening.
- Reframing techniques aid us in thinking about issues in a different way, challenge conventional wisdom, and mitigate groupthink.
- Strategic foresight techniques frame alternative ways for how a situation could evolve, identify key drivers, and assess the implications for each potential trajectory.
Richards Heuer began writing about techniques for externalizing thinking and considering multiple hypotheses in the 1960s, based on his efforts to apply the research of scholars such as Daniel Kahneman, Amos Tversky, and Robert Jervis to the real world of intelligence analysis and counterintelligence. In the past 15 years, these techniques have become broadly taught and used by intelligence communities across the globe. They are used increasingly within academic, business consulting, and industry settings. Their utility comes from providing analysts with guidance in how to “think through” or “unpack” difficult issues so they can assess the quality of sources and evidence, distinguish patterns and relationships, and justify the alternatives and judgments they make.
The role of SATs as aids in thinking — as opposed to predictive techniques — means they provide a bridge between the scientific method and the ambiguous realities of the intelligence world in which all facts are not, and may never, be known. To employ SATs well requires some knowledge and a little additional time. Nonetheless, making them a habit can save time in the end, prevent thoughtless errors, help integrate analysts into a collaborative process, and provide documentation for qualitative rigor applied to a wide range of problems.
As the GEOINT Community moves into increasing use of technology to help optimize use of large and varied datasets and think about situations in different ways, SATs are an important means by which we can bring the strengths of human cognition to intelligence problems. This is an important realization because postmortems of virtually every major intelligence failure in recent decades have identified engrained mind-sets as a key contributing issue.
Computational Thinking and Automated Tools
Computational thinking derives from the development of automated systems in the 1950s and reflects the process for the development of algorithms to express steps for solutions that could be carried out by machines. It gained strength as a concept with Jeanette Wing’s suggestion in 2006 that computational thinking should be a component of all education curricula.
Computational thinking is an expression of thinking that incorporates the critical thinking process and SATs to produce formulae for thinking — typically, a set of procedures that rigorously define a thought process. Without such discipline, it would not be possible to reduce the solution of meaningful problems to algorithms executable by a machine. For instance, the stages of forming the problem — expressing a solution that can be executed by a machine, executing it, and evaluating its effectiveness — is the critical thinking process applied to the specific domain of automation. The thinking skills of decomposition, pattern recognition, and identifying and testing multiple solutions are laid out in the step-by-step guidance in various SATs. This is not to say analysts should employ computational thinking as their exclusive — or even primary — approach to sensemaking and storytelling. Rather, analysts should develop computational thinking as a skill that enables them to frame problems in a way that allows them to define algorithms suitable for machine computation.
What is unique and particularly valuable to the evolving disciplines of geospatial thinking are the application of the specific types of logic used in creating computer programs and algorithms, including iteration, Boolean, and other logical operations, as well as the ordering of steps into algorithms. These skills bring a granularity and specificity to thinking processes that map well to manipulating the data details and measurements practiced in geospatial analysis.
These computational skills build another part of the bridge between the ambiguity of the real world and the carefully defined data fields and manipulations machines require to yield insights of value to intelligence consumers. Incorporating these skills more overtly in geospatial processes will help analysts work within both worlds by understanding the human thinking and machine thinking processes and leveraging the strengths of both in their observations and judgments.
Applying Thinking Regimens to Geospatial Analysis
Many continue to debate whether intelligence analysis is an art or a science. This discussion takes on additional significance in light of advances in availability and scale of data sources, new ways to process and manipulate them, and the extraordinary progress in machine learning. The field of geospatial information science (GISc) is going through a similar evolution as did computer science in the 1950s and 1960s. Critics claimed there could be no “computer science” because sciences are derived from natural phenomena and computers are simply man-made tools used to extract that information. Like many intelligence domains, GISc spans several traditional disciplines, principally geography, computer science, and mathematics. Geospatial principles are taught in several liberal arts curricula (for example, international relations and sociology) because of the ease with which geospatial visualizations can display data specific to those areas of study.
Geospatial science distinguishes between the skills required to produce accurate geospatial assessments and the use of information technology to display data without expert knowledge in the discipline. This is a critical point — today’s geospatial tools can be so powerful and intuitive, it can be easy to produce misleading visualizations that compellingly lead to false conclusions. Reliable geospatial product creation and analysis requires the individual to understand the common pitfalls in geographic data. Creating an accurate buffer map, for instance, requires awareness of the differences between a geographic coordinate system that displays the Earth as a curved surface and a projected coordinate system that displays it as a flat surface. For the foreseeable future, no matter how smart the machine, expert tools wielded by analysts who lack critical thinking skills will result in “nonsensemaking.”
Geospatial analysis, like other forms of intelligence analysis, requires practitioners to employ critical thinking processes to identify and assess significant data to arrive at a conclusion. Geospatial analysis is unique in that it does so through the exercise of spatial reasoning, which focuses on matching data to frames that involve the location, extent, distribution, pattern, association, interaction, or change of data within a geospatial sphere or “space.” The geospatial contexts within which data are interpreted and transformed into meaning include:
- Life space that enables thinking about the world in which we live and is exemplified by “patterns of life.”
- Physical space that enables thinking about geographic space and the ways in which the world works to understand and model natural phenomena such as earthquakes.
- Intellectual (or cognitive) space that involves abstract concepts that occur in space, such as a cultural model that describes the significance of a religious place.
In particular, geospatial analysts seek to gain a sense of the data through imagery and maps. The data are presented in an automated model processed within their mental model, which is an amalgamation of the data, beliefs, experience, and collaboration that makes up their expertise. In Klein’s data-frame concept of sensemaking, analysts are questioning the data and the frame, and consciously reframing to better explain relationships among the data. Diagnostic SATs such as an analysis of competing hypotheses, or reframing SATs such as remortem analysis and structured self-critique, can guide analysts through reconsidering their assumptions, seeking additional perspectives and data, considering other alternatives, and questioning how they might be wrong.
These models can be applied in all aspects of life with problem sets ranging from national security to retail supply chains. Models can be so simple to use that they require setting only a few parameters, while other models are incredibly complex, such as those that forecast the weather based on millions or billions of inputs. In criminal justice, for instance, risk terrain modeling can identify areas of high risk for criminal activities by gathering data on risk layers such as drug houses, crime hot spots, and population density, and then valuing the risk layers and mapping them. In epidemiology, disease vectors can be mapped for prevention programs by gathering data on desirable mosquito habitats, overlaying it on a geospatial format, and summing it to display areas most likely to expose humans to mosquito-borne disease.
The community needs exquisite collection, adept data handlers, automated assistance to manipulate and visualize, and the ability to put all this together into insights that lead to effective actions. The common thread needed to weave all these things together is rigorous thought. It takes thinking skills that make for rational thought, collaboration, and learning.
Critical thinking and SATs operationalize thinking processes and best practices while computational thinking helps analysts interact better with machine data, models, and applications to translate intent and capability into geospatial models that make sense of our environment and solve difficult problems. No matter how advanced technology becomes, humans still seek to convey our understanding in the most natural of ways — by telling stories about things that matter.
This article is part of USGIF’s 2018 State & Future of GEOINT Report. Download the PDF to view the report in its entirety and to read this article with citations.
