Abstract
This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like “basic” vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation that take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition.
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Notes
- 1.
Historically, much of this work focused on graphic displays, which convey information using the geometric and radiometric properties of an image (as opposed to simple text alone). Meanwhile, more modern work focuses on visual displays, which rely on the extensive use of visual intelligence for their interpretation. For purposes here, graphic displays and visual displays are considered much the same, with the former term emphasizing the means, and the latter the ends.
- 2.
A single framework for all aspects of visualization (e.g., usability) is problematic, owing to the heterogeneous nature of the components (e.g., perceptual vs. motor mechanisms) and the possible lack of specificity in the tasks for which it might be used [32]. Discussion here focuses on a more restricted set of issues, viz., the extent to which visualization can enable a human to perceive some well-defined structure in a dataset. This abstracts away from details of particular tasks, and so increases the chances of a systematic framework for at least some parts of visualization. Vision science uses a similar approach, focusing on well-defined functions rather than on ways that vision might help carry out some poorly-defined task [21].
- 3.
In some cases (e.g., cartography), the “machine” component may be distributed over a several different devices—and perhaps even the occasional human—with only the result contained in the display (e.g., a map). From a functional point of view, such a configuration is still considered a single visualization system. The performance of such “off-line” systems differs from “on-line” systems only in one respect: because the time scales of the two components are quite different, the effectiveness of an off-line display can usually be assessed in terms of the speed, accuracy, and effort exerted by the human component alone. (Depending on the situation, however, it may be necessary to take into account such things as the cost of producing the display.)
- 4.
Although inaccuracies are usually caused by mechanisms in the human viewer, they can also be due to the machine component—e.g., insufficient sampling, or a bias in an algorithm. According to the extended-vision thesis, the source is irrelevant: the measure of interest is based on the performance of the entire system. For the most part, discussion here focuses on the human viewer, since this is typically the largest source of inaccuracy. But if need be, the accuracy of the machine component could also be evaluated. Similar considerations apply to other measures of performance.
- 5.
This assumes that the viewer can interpret the visual (or possibly other) representations used as a correlation. Developing such a “return route” to enable the inverse mapping may be an important part of training. Note that some of the performance limitations could occur at this stage, rather than the formation of the visual result proper.
- 6.
Other issues exist, such as those concerned with visualization as a technology [32]. However, these are not directly relevant to concerns about the nature of a scientific framework, and so are not discussed here.
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Acknowledgements
Many thanks to Stephen Few, Dave Kasik, Minjung Kim, Tamara Munzner, Vicki Lemieux, Michael Sedlmair, Ben Shneiderman, and Jack van Wijk for their comments on earlier versions of this paper. Thanks also to Tamara Munzner for feedback on the idea of the operations inventory, and to Jack van Wijk for interesting discussions about the limits of a possible science of visualization. Support for this work and several of the studies mentioned was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), and The Boeing Company.
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Rensink, R.A. (2014). On the Prospects for a Science of Visualization. In: Huang, W. (eds) Handbook of Human Centric Visualization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7485-2_6
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