Learning to Read Uncertainty

Foody separates the concept of uncertainty into two types: ambiguity, which is defined as “the problem of making a choice between two or more alternatives” and vagueness, which is defined as “making sharp or precise distinctions” (114). Personally, I think this distinction confusing without more concrete examples or further explanation between how other “terms” used for uncertainty is related to these two groups. For instance, is reliability an issue related to ambiguity? Since I have always thought of uncertainty in terms of a balance between accuracy and precision, am I right to assume accuracy falls under the ambiguity type of uncertainty and precision falls under the vagueness type of uncertainty? Also, it would have been helpful to explain the “sorites paradox” (114).

The article, however, did spark my interest in how naïve users perceive uncertainty especially regarding topics addressed by public policy. Like the author mentions, uncertainty is inherent in data. But even if uncertainty were conveyed through GIS, how would people interpret them? Does “reading” uncertainty require a steep learning curve? Does the representation used to convey uncertainty have an effect on the faith readers place in the conclusion of the study? The image above shows the different ways in which dots can be used to depict uncertainty (MacEachren et al., 2005). It would be very interesting to see what kind of biases we have toward different methods of representation. Perhaps our biases will lead us to believe uncertainty is less “significant” if it is shown by different shades of one color compared to two different colors. Empirical studies from human cognition and geovisualization will be valuable to answer these questions.


MacEachren et al. (2005). Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know. Cartography and Geographic Information Science, 32(3), 139-160.

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