Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Contributors

Rafael Veras, Christopher Collins

Abstract

We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion, and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., color, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.

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Awards

This research was given a honourable mention at CHI 2019.

Publications

  • R. Veras and C. Collins, “Saliency Deficit and Motion Outlier Detection in Animated Scatterplots,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019.
    [Bibtex] [PDF]
    @InProceedings{ver2019b,
    author = {Rafael Veras and Christopher Collins},
    title = {Saliency Deficit and Motion Outlier Detection in Animated Scatterplots},
    year = 2019,
    month = May,
    booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
    number = 541
    }

Acknowledgements

 

Research

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