Name
Abstract | ||
|---|---|---|
A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error bars, and ambiguation. Our results reveal significant differences in error rates and completion time for these different visualization types and different tasks. We recommend using ambiguation - using a lighter color value to represent uncertain regions - or error bars for judging durations and temporal bounds, and gradient plots - using fading color or transparency - for judging probability values. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TVCG.2015.2467752 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
data visualisation,encoding,probability,accumulated probability plots,ambiguation,centered error bars,completion time,error rates,fading color,gradient plots,lighter color value,temporal dimension,temporal uncertainty,uncertainty visualization,violin plots,visual encodings,Uncertainty,temporal intervals,visualization | Computer vision,Data visualization,Visualization,Probability plot,Computer science,Fading,Violin plot,Artificial intelligence,Lightness,Statistics,Visual perception,Encoding (memory) | Journal |
Volume | Issue | ISSN |
22 | 1 | 1077-2626 |
Citations | PageRank | References |
19 | 0.71 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theresia Gschwandtner | 1 | 171 | 17.43 |
| Markus Bögl | 2 | 42 | 3.78 |
| Paolo Federico | 3 | 52 | 5.20 |
| Silvia Miksch | 4 | 2212 | 174.85 |