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Proceedings Paper

Does interactive animation control improve exploratory data analysis of animated trend visualization?
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Paper Abstract

OBJECTIVE: Effectively analyzing trends of temporal data becomes a critical task when the amount of data is large. Motion techniques (animation) for scatterplots make it possible to represent lots of data in a single view and make it easy to identify trends and highlight changes. These techniques have recently become very popular and to an extent successful in describing data in presentations. However, compared to static methods of visualization, scatterplot animations may be hard to perceive when the motions are complex. METHODS: This paper studies the effectiveness of interactive scatterplot animation as a visualization technique for data analysis of large data. We compared interactive animations with non-interactive (passive) animations where participants had no control over the animation. Both conditions were evaluated for specific as well as general comprehension of the data. RESULTS: While interactive animation was more effective for specific information analysis, it led to many misunderstandings in the overall comprehension due to the fragmentation of the animation. In general, participants felt that interactivity gave them more confidence and found it more enjoyable and exciting for data exploration. CONCLUSION: Interactive animation of trend visualizations proved to be an effective technique for exploratory data analysis and significantly more accurate than animation alone. With these findings we aim at supporting the use of interactivity to effectively enhance data exploration in animated visualizations.

Paper Details

Date Published: 4 February 2013
PDF: 13 pages
Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540I (4 February 2013); doi: 10.1117/12.2001874
Show Author Affiliations
Felwa A. Abukhodair, Simon Fraser Univ. (Canada)
King Abdulaziz Univ. (Saudi Arabia)
Bernhard E. Riecke, Simon Fraser Univ. (Canada)
Halil I. Erhan, Simon Fraser Univ. (Canada)
Chris D. Shaw, Simon Fraser Univ. (Canada)

Published in SPIE Proceedings Vol. 8654:
Visualization and Data Analysis 2013
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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