The authors offer a clarification early on in the paper which I found useful; “When inaccuracy is known objectively, it can be expressed as error; when it is not known, the term uncertainty applies “. This definition sounds like it pertains to measurement, but I don’t know how one would distinguish between error and uncertainty when it comes to visualization, another focus of this paper. I also believe it is important to further classify within “error”, the various sources of error whether they be human, machine, statistical, etc. to give a holistic impression of the (in)accuracy of attained results.
I would have liked to see a discussion of accuracy versus precision and how the concept of uncertainty would apply to the precision of points in a dataset, ie. the degree to which the points relate to each other regardless of how they capture an absolute (ideal) value.
I liked how the authors drew on multiple discipline to illustrate how the concept of uncertainty is pertinent to many fields, drawing on Tversky and Economical/ Psychological theory to illustrate that “humans are typically not adept at using statistical information in the process of making decisions.” (141) The arguments put forth about how to depict uncertainty visually were very nuanced, from whether this would change individual’s decision-making when consulting a map, and whether it would lead to better decisions or just reduce the reliability of the data presented.
Furthermore, it makes sense that the theories and frameworks of mapping uncertainty are more well developed when it comes to traditional GIS mapping and less so in the domain of geographic data visualizations. I found the Figure 2 to be useful in teasing out how the concept of uncertainty would apply to different facets of a given project.
The challenge of representing uncertainty for dynamic information (which I think it becoming more and more crucial for streaming and big data) is definitely a big one and I’m interested to see how this field develops.
-FutureSpock