The Learner's Guide to Geospatial Analysis

Spatial Thinking


To paraphrase William Millwood (Moore pdf, 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 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 in 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, and density. Networks may also include physical links such as transportation systems, or non-visual systems.
  • Spatial associations. Associations include spatial autocorrelation, distance decay, and contiguities. Examples of these associations include interaction frequencies or geographic and areal associations. For example, the coincidence of features within specific areas (i.e., squirrels are normally near trees) is a spatial association.
  • 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).