Abstract
Effectively communicating the uncertainty that is inherent in any kind of geographic information remains a challenge. This paper investigates the efficacy of animation as a visual variable to represent positional uncertainty in a web mapping context. More specifically, two different kinds of animation (a ‘bouncing’ and a ‘rubberband’ effect) have been compared to two static visual variables (symbol size and transparency), as well as different combinations of those variables in an online experiment with 163 participants. The participants’ task was to identify the most and least uncertain point objects in a series of web maps. The results indicate that the use of animation to represent uncertainty imposes a learning step on the participants, which is reflected in longer response times. However, once the participants got used to the animations, they were both more consistent and slightly faster in solving the tasks, especially when the animation was combined with a second visual variable. According to the test results, animation is also particularly well suited to represent positional uncertainty, as more participants interpreted the animated visualizations correctly, compared to the static visualizations using symbol size and transparency. Somewhat contradictory to those results, the participants showed a clear preference for those static visualizations.
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Notes
- 1.
See https://d3js.org.
- 2.
- 3.
The experiment is available online at http://carsten.io/uncertainty/. The corresponding source code and data produced is available at https://github.com/crstn/UncertD3/.
- 4.
Some of the participants had single pages open for several minutes; since it is more likely that they answered the phone or went to get a coffee instead of actually looking at the test page for such a long time, all values above 60 s were set to 60 s.
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Keßler, C., Lotstein, E. (2018). Animation as a Visual Indicator of Positional Uncertainty in Geographic Information. In: Mansourian, A., Pilesjö, P., Harrie, L., van Lammeren, R. (eds) Geospatial Technologies for All. AGILE 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-78208-9_19
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