L2.10: Bridging the Gap Between Data, Information, and Insights

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Models, both mental and otherwise, play a critical role in bridging the gap between data, information, and insights. They are an idealized representation of how to use data to solve problems. Recalling the table from Lesson 1, let's further explore this relationship between data, models, and insights.

Table 2.2: Data Certainty versus Model Certainty
  Model Certainty:
Low
Model Certainty:
High
Data Certainty:
High
Model Building Puzzle Solving
Data Certainty:
Low
Mystery Solving Data Foraging

Models represent our understanding of how the world works. We construct models for:

  • simplification and organization
  • communication with oneself or others
  • prediction
  • manipulation

Models also give us the ability to overcome incomplete data by mentally filling in gaps, making an intuitive leap with only the sparsest of data. This is a sophisticated form of geospatial reasoning. Expertise in geospatial reasoning increases with experience because as we learn or experience additional models, our mind expands to accommodate them.

You can "preload" your mental models with typical understandings of place; these include conceptual models of how a place is organized and works. For example, why individuals use a store at a particular location is related to the number of people in the surrounding community. Theories of spatial organization can be a shortcut in our attempts to model an unfamiliar pattern. A few important geographic models are:

  • Gravity Model: The model states that the potential use of a service at a particular location is directly related to the number of people in a location and inversely related to the distance people must travel to reach the service. This explains why stores are clustered in malls.
  • Christaller's Central Place Theory: Christaller's theory explains the distribution of services, based on the fact that settlements serve as market area centers for services. Larger settlements, e.g., large cities, are fewer and farther apart than smaller settlements, e.g., small towns, and provide services for a larger number of people who are willing to travel farther.
  • Weber Model of Industrial Location: Weber invented a least cost theory of industrial location, which explains the pattern of the industry at a macro-scale. It is based on the notion that firms seek a site of minimum transport and labor cost. It looks at the factors of the cost of transporting goods, cost of labor, and capital.
  • Von Thunen's Agricultural Model: Based on a center market area, the Von Thunen model uses rings based on how long the good will last before becoming unusable, the product weight, and transportation networks around a central market to depict the best location for an agricultural activity. For example, farmers nearest to a city produced milk since it is perishable and commands a higher price.
  • Core Periphery Model: A model that describes how economic, political, and/or cultural power is spatially distributed between dominant core regions, and more marginal or dependent semi-peripheral and peripheral regions.

Mental models are a normal, everyday human activity essential for geographic problem solving. To make the point, you will predict part of a map by literally "using the data you see to predict the data beyond what is given."

Try This!

Predict the missing part of a map by using the data you see and models you assume to predict the data beyond what is given. Using a pen and paper, sketch the missing half of the map using the symbols for roads and mountains. Remember that you will have to go beyond the information that is given. As you complete this exercise, think about the following:

  • What assumptions (models) are you applying about the area that is depicted in the map?
  • What strategies are you using for completing the map?
  • How sure are you of the answers that you have produced?
complete_the_map2a.jpg
Figure 2.11: Complete the bottom half of the map
Source: Bacastow

You can compare your results with this solution map. This is a link to Coursera.

A word of caution is in order. While models provide a useful way of understanding the world around us, blind adherence to a model can be disastrous. When we close our mind to disconfirming evidence and the possibility of alternative outcomes, we fail to see the weaknesses of our model and we will fail. History is replete with examples of people adhering stubbornly to an outdated paradigm (model) despite overwhelming evidence that a new way of thinking (a new model) is necessary.

Models, Frames, and Insights

Models and theories provide a basis for a structure that is called a frame. A frame helps us understand the world and may be influenced by a model or theory. For example, an individual's day-to-day interactions with family and associates is termed their "Pattern of Life." The family goes to work at a particular time using a particular route. The Central Place Theory may help to form a narrative that explains the Pattern of Life. Here, a theory suggests that our family may travel to larger settlements for services not available in smaller settlements. Models and theories may also direct our attention toward the information we seek.

In summary, we frame things to make sense of what we see in the real world and to fill in missing data. The frame may be influenced by an existing model or theory. A frame can take the following forms:

  • A narrative explaining the chronology of events and the causal relationships between them.
  • A map showing distances, directions, and connections of things such as landmarks, routes, and destinations.

Insights result from a narrative that accounts for what the data reveals within the frame; this human process of explanation is termed sensemaking. There are three primary outcomes for GEOINT sensemaking:

  1. To identify patterns
  2. To describe patterns
  3. To predict future patterns

As you can see, GEOINT data is a key part of a discourse to make sense of indefinite and ambiguous situations. Fitting the GEOINT data to the frame involves cognitive work to understand the relationships among data and sequence of events.