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Visualization in Motion: A Research Agenda and Two Evaluations.

Lijie Yao, Anastasia Bezerianos, Romain Vuillemot

    IEEE Transactions on Visualization and Computer Graphics
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    Data visualization in motion can be challenging, but viewers can still accurately read information from moving charts like bar and donut charts, even with increased speed or complex trajectories.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Data Science

    Background:

    • Visual data representations are increasingly used in dynamic contexts with viewer-visualization motion.
    • Applications include sports analytics, video games, and wearable devices, presenting unique design challenges.
    • Understanding viewer data-reading accuracy in motion is crucial for effective design.

    Purpose of the Study:

    • To establish a research agenda for visualizations in motion.
    • To experimentally investigate the impact of motion characteristics (speed, trajectory) on data readability.
    • To compare the performance of moving bar and donut charts.

    Main Methods:

    • Defined "visualizations in motion" and outlined research opportunities and challenges.
    • Conducted experiments with stationary viewers reading data from moving bar and donut charts.
    • Varied motion speed and trajectory complexity to assess impact on data reading accuracy.

    Main Results:

    • Increased motion speed and trajectory complexity negatively impacted data reading accuracy.
    • Bar charts were more affected by motion characteristics than donut charts.
    • Despite negative impacts, both chart types maintained relatively high data reading accuracy.

    Conclusions:

    • Motion characteristics significantly influence data readability in moving visualizations.
    • Designers must consider speed and trajectory when creating visualizations for dynamic environments.
    • Further research is needed to optimize visualizations in motion for various applications.