Data without the understanding brought by geospatial thinking and reasoning are really just meaningless symbols. This is because GEOINT data are separated from the knowledge of the place, this is to say, the physical and human aspects of the location the data represent. GEOINT data are fragmented and incomplete. We use the crafts of geospatial thinking and reasoning to help make it whole. Reginald Golledge makes this point in his Thinking Spatially article in Directions Magazine:
Our knowledge about a geographic area is never perfect, but we still make effective decisions in that area because we use mental processes of perceptual closure (interpolation), or overlay (aggregation), or dissolve (disaggregation), and summarization. When we start to get overwhelmed with detail, we spatially classify (as by proximity) or cluster (as in "next to") so as to collapse lots of separate bits of information into meaningful "clumps" or "chunks." Sometimes, we make gross classifications ("all cats are gray in the night," or "all these trees are the same" when looking at a eucalyptus forest). We mentally cluster food stores, clothing stores, bars, beaches, and other phenomena into largely undefined generic classes and then give place-specific identifiers to single out particular members (e.g., "Albertson's is the supermarket that has fresh Maine lobsters;" "Google's is the beach with the bad undertow"). And we all realize that geographic data can be perceived at a variety of scales. We might use the same thought processes to reason about a colony of ants or bees as we do to think about people's activities in cities. We may use the same concepts when looking at our neighborhood as we would when studying San Francisco or Sydney (Australia). And, often, we use the vaguest of principles to guess about where things might be found (e.g., from trying to find a missing glove to searching for a bus stop in an unfamiliar area).
Thinking Spatially - Directions Magazine, January 12, 2003
The human craft of bringing meaning to the models and data is called techne. Techne is a term derived from Greek that means "craftsmanship, craft, or art." It might be termed GEOINT data techne. Much of it is implicit and ambiguous, and is acquired largely by experience. The geospatial analyst uses techne when handling the data and forming judgments about a place. GEOINT data techne includes:
- The judicious application of knowledge. This is the ability to think and act using knowledge, experience, understanding, common sense, and insight. This implies a possession of knowledge to use data in a given situation. This involves an understanding of people, things, events, situations, and the willingness as well as the ability to apply perception, judgment, and action. It often requires control of one's cognitive biases. In short, data wisdom is a disposition to make optimum judgments about the nature of things on the Earth to deliver the highest quality outcome.
- The application of know-how. Apprentices, for example, work with their mentors and learn craftsmanship not through language but by observation, imitation, and practice. The key to acquiring tacit knowledge about using data is experience. Without some form of shared experience, it is extremely difficult for people to share each other's thinking processes. This has been described as "know-how" as opposed to "know-what" (facts), "know-why" (science), or "know-who" (networking).