GEOG 885
Advanced Analytic Methods for the GEOINT Professional

Spatial Thinking & Geospatial Reasoning

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To paraphrase William Millwood (Moore, p. 3 ), creating geospatial analysis requires transformations resulting from an intellectual endeavor that sorts the significant from the insignificant, assessing them severally and jointly, and arriving at a conclusion by the exercise of reasoned judgment. This endeavor when dealing with geospatial problems is geospatial reasoning, or an operation in which present facts suggest other facts. Geospatial reasoning creates an objective connection between our present geospatial beliefs and the evidence for believing something else.

Spatial Thinking

Spatial thinking includes processes that support exploration and understanding. An expert spatial thinker visualizes relations, imagines transformations from one scale to another, mentally rotates an object to look at its other sides, creates a new viewing angle or perspective, and remembers images in places and spaces. Spatial thinking also allows us to externalize these operations by creating representations such as a map.

Spatial thinking begins with the ability to use space as a framework. An object can be specified relative to the observer, to the environment, to its own intrinsic structure, or to other objects in the environment. Each instance requires the adoption of specific spatial frames of reference or context. The process of interpretation begins with data which is generally context-free numbers, text, or symbols. Information is derived from data by implying some degree of selection, organization, and preparation for a purpose — in other words, the data is placed into a spatial context. For example, the elevation at a specific location is an example of data; however, the elevation only has meaning when placed in context of sea level. The spatial context is critical because it is the space the data is in that ultimately determines its interpretation. There are three spatial contexts within which we can make the data-to-information transition; these include life spaces, physical spaces, and intellectual spaces. In all cases, space provides an interpretive context that gives meaning to the data.

  • Life space is the four-dimensional space-time where spatial thinking is a means of coming to grips with the spatial relations between self 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 and the actions that we perform in space.
  • 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.
  • Intellectual space is in relationship to concepts and objects that are not in and of themselves necessarily spatial, but the nature of the space is defined by the particular problem. This is cognition with space and involves thinking with or through the medium of space in the abstract. An example might be the territorial dispute between two ethnic groups.

Learning to think spatially is to consider objects in terms of their context. This is to say, the object's location in life space, physical space, or intellectual 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 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, life space rather than intellectual space, it is likely the analysis will be flawed.
  • Recognize spatial schemes (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 a task at hand. This ability allows a geospatial analyst to identify patterns of significance on a map, such as an airfield.
  • 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.
  • Mental rotating an object and envisioning 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.

Golledge’s First-Order Primitives constitute a broad list of cognitive schemes for geospatial analysis (R. G. Golledge "Do People Understand Spatial Concepts: The case of First-Order Primitives", Theories and Models of Spatio-Temporal Reasoning in Geographic Space. Pisa: Springer-Verlag, 1992). The schemas are:

  • Location. This includes a descriptor with identity, magnitude, location, and time. An additional cognitive component might be familiarity. Occurrences are often called environmental cues, nodes, landmarks, or reference points.
  • Spatial distributions. Distributions have a pattern, a density, and an internal measure of spatial variance, heterogeneity or dispersion; occurrences in distributions also have characteristics such as proximity, similarity, order, and dominance.
  • Regions. Areas of space in which either single or multiple features occur with specified frequency (uniform regions) or over which a single feature dominates.
  • Hierarchies. Multiple levels or nested levels of phenomena including features.
  • Networks. Linked features having characteristics, connectivity, centrality, diameter, density, including physical links such as transportation systems, or non-visual.
  • Spatial associations. Associations include spatial autocorrelation, distance decay, and contiguities; examples include interaction frequencies or geographic and areal associations such as the coincidence of features within specific areas.
  • Surfaces. There are generalizations of discrete phenomena, including densities of occurrence, flows over space and through time (as in the spatial diffusion of information or phenomena).

Reasoning

The three well-known reasoning processes trace the development of analytic beliefs along different paths. Inductive reasoning reveals “that something is probably true,” deductive reasoning demonstrates “that something is necessarily true.” It is generally accepted within the intelligence community that both are limited: inductive reasoning leads to multiple, equally likely solutions and deductive reasoning is subject to deception. Therefore, a third aid to judgment, abductive reasoning, showing “that something is plausibly true,” is used to offset the limitations of the others. While analysts who employ all three guides to sound judgment stand to be the most persuasive, fallacious reasoning or mischaracterization of rules, cases, or results in any of the three can affect reasoning using the others.

  • Inductive reasoning, moving from the specific case to the general rule, suggests many possible outcomes, or the range of what might happen in the future. However, inductive reasoning lacks a means to distinguish among outcomes. An analyst has no way of knowing whether a solution is correct.
  • Deductive reasoning, on the other hand, moves from the general to the specific. Deductive reasoning becomes essential for predictions. Based on past perceptions, certain facts indicate specific outcomes. If, for example, troops are deployed to the border, communications are increased, and leadership is in defensive bunkers, then war is imminent. However, if leadership remains in the public eye, then these preparations indicate that an exercise is imminent.
  • Abductive reasoning reveals plausible outcomes. Abductive reasoning is the process of generating a best explanation for a set of observations. When actions defy accurate interpretation through existing paradigms, abductive reasoning generates novel means of explanation. In the case of predictions, an abductive process presents an “assessment of probabilities.” Although abduction provides no guarantee that the analyst has chosen the correct hypothesis, the probative force of the accompanying argument indicates that the most likely hypothesis is known and that actionable intelligence is being developed.

Geospatial Reasoning

It’s not too far of a stretch to say that people who are drawn to the discipline of geospatial intelligence have minds accustomed to assembling information into three-dimensional mental schemas. We construct schemas in our mind, rotate them, and view them from many angles. Furthermore, the experienced geospatial professional imagines spatial schemas influenced in the fourth dimension, time. We mentally replay time series of the schema. So easy is the geospatial professional’s ability to assemble multi-dimensional models that the expert does it with incomplete data. We mentally fill in gaps, making an intuitive leap toward a working schema with barely enough data to perceive even the most rudimentary spatial patterns. This is a sophisticated form of geospatial reasoning. Expertise increases with experience, because as we come across additional schemas, our mind continuously expands to accommodate them. This might be called spatial awareness. Being a visual-spatial learner, instead of feeling daunted by the abundance and complexity of data, we find pleasure in recognizing the patterns. Are we crazy? No, this is what is called a visual-spatial mind. Some also call these people right brain thinkers.

The concept of right brain and left brain thinking developed from the research of psychobiologist Roger W. Sperry. Sperry discovered that the human brain has two different ways of thinking. The right brain is visual and processes information in an intuitive and simultaneous way, looking first at the whole picture then the details. The left brain is verbal and processes information in an analytical and sequential way, looking first at the pieces then putting them together to get the whole. Some individuals are more whole-brained and equally adept at both modes.

The qualities of the Visual-Spatial person are well documented but not well known (See the Visual-Spatial Resource homepage). Visual-spatial thinkers are individuals who think in pictures rather than in words. They have a different brain organization than sequential thinkers. They are whole-part thinkers who think in terms of the big picture first before they examine the details. They are non-sequential, which means that they do not think and learn in a step-by-step manner. They arrive at correct solutions without taking steps. They may have difficulty with easy tasks, but show a unique ability with difficult, complex tasks. They are systems thinkers who can orchestrate large amounts of information from different domains, but they often miss the details.

Sarah Andrews likens some contrasting thought processes to a cog railway. Data must be in a set sequence in order to process it through a workflow. In order to answer a given question, the thinker needs information fed to him in order. He will apply a standardized method towards arriving at a pragmatic answer, check his results, and move on to the next question. In order to move comfortably through this routine, he requires that a rigid set of rules be in place. This is compared with the geospatial analyst who grabs information in whatever order, and instead of crunching down a straight-line, formulaic route toward an answer, makes an intuitive, mental leap toward the simultaneous perception of a group of possible answers. The answers may overlap, but none are perfect. In response to this ambiguity, the geospatial analyst develops a risk assessment, chooses the best-working answer from this group, and proceeds to improve the estimate by gathering further data. Unlike the engineer, whose formulaic approach requires that the unquestioned authority of the formula exists in order to proceed, the geospatial intelligence professional questions all authority, be it in the form of a human or acquired data.

The Geospatial Spin

The two intelligence models presented in GEOG 885, structured analytic techniques and alternative competing hypotheses, are widely known and frequently used by intelligence professionals. They are, however, generic in nature. In 1970, American/Swiss Geographer and Cartographer Waldo Tobler made the following observation while studying urban development in Detroit, MI:

"Everything is related to everything else,
but near things are more related than distant things."

 

Think about this for a moment. A hunter gathers food for his/her family closer to home than farther away. There are a greater number of farms closer to the market town. A small snack food company maximizes the distribution of their products closer to the processing facility, with distribution decreasing further away from the source. IED laden terrorists place their lethal products closer to home base than further away. Thieves tend to steal closer to home base than further away. Earthquakes tend to inflict the most damage near the epicenter. Planets closer to the sun are hotter than those further away. The list, and applications, is endless.

Since then, Geographers have recognized the profoundness of Tobler’s observation, and commonly refer it to it as the “First Law of Geography." Now it’s not really a law, but is widely accepted as a general model to explain/understand geospatial patterns associated with natural and man-induced phenomena. And so, in GEOG 885, we’ll use Dr. Tobler’s law to help put a geospatial spin on the two general intelligence models while analyzing our two case studies.