MacEachren et al (2005) – Visualising uncertainty

MacEachren et al evaluate a broad set of efforts made to conceptualise and convey uncertainty in geospatial information. Many real world decisions are made on the basis of information which contains some degree of uncertainty, and to compound the matter, there are often multiple aspects of uncertainty that need to be factored into analysis. The balance between effectively conveying this complexity and overloading analysts with visual stimuli can support or detract from decision making, and constitutes a key persisting challenge explored in this paper.

A central discussion that I found interesting was that surrounding visual representations of uncertainty. Early researchers in the field strove to develop or unearth intuitive metaphors for visualision. Aids such as ‘fuzziness’ and colour intensity could act to convey varying degrees of uncertainty present in a dataset, almost as an additional variable. In the context of our other topic this week, we could ask who these metaphors are designed to assist, and how the choice of metaphor could influence potential interpretations (e.g. for visual constructs like fuzziness and transparency, do different individuals perceive the same gradient scale?).

The authors draw on judgement and decision making literatures to distinguish expert decision makers who adjust their beliefs according to statistical analysese of mathematically (or otherwise) defined uncertainties, from non-experts, who often misinterpret probabilities and rely on heuristics to make judgements. It might have been worth clarifying what was meant by experts in this instance (individuals knowledgeable about a field, or about probability and decision making?). The Tversky and Kahneman (1974) paper cited actually found that often experts (per their own definition) are similarly susceptible to probabilistic reasoning errors, so this polarity may be less distinct than suggested. Like some of the other papers in the geovisualisation literature, I found there was a degree of vagueness in who the visualisation was for (is it the ‘analysts’ mentioned in the introduction, or the lay-people cited in examples?).

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