Effectiveness of Animation in Trend Visualization

By George G. Robertson, R. Fernandez, Danyel Fisher, Bongshin Lee, John T. Stasko. IEEE Transactions on Visualization and Computer Graphics, 2008

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Food Science

View Paper: https://www.semanticscholar.org/paper/7a5f745b8f162fa5a2a25acafc4845b65d3f6410

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Design Considerations for Optimizing Storyline Visualizations (2012, background): “Studies have shown that although animations are engaging and attractive, they often fail to convey subtle trends in data [28].”
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations (2009, background): “Researchers have found that animating between time slices facilitates value change estimation better than static transitions between views [11], but that animation results in significantly lower accuracy in analytic tasks compared to small multiples of static charts [19].”
Visualizing the Hidden Activity of Artificial Neural Networks (2017, background): “We discard animation, since it is hard to track the motion of a large number of colored points over many frames [38].”
The Science of Visual Data Communication: What Works (2021, background): “Nevertheless, viewers frequently report that animated diagrams and data visualizations are more engaging and enjoyable than static versions, which may explain animation’s continued use (Abukhodair et al., 2013; Robertson et al., 2008; Tversky, Morrison, & Betrancourt, 2002).”
A Nested Model for Visualization Design and Validation (2009, background): “test the effectiveness of animation compared to both traces and small multiples for showing trends [36].”
Visual Multiplexing (2014, background): “Meanwhile, several empirical studies (e.g., Figure 3(a) from [RFF ∗ 08]) showed that animation does not necessarily improve users’ performance and may result in more errors.”
Graphical Perception of Multiple Time Series (2010, background): “We base this on findings that suggest that animation gives significantly lower accuracy for trend visualization compared to static charts [25].”
Visual Traffic Jam Analysis Based on Trajectory Data (2013, neutral): “However, these techniques have limitations [39].”