Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations

Journal Article · Tue Aug 29 00:00:00 EDT 2017 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/tvcg.2017.2743939· OSTI ID:1377597

Matzen, Laura E. ^[1]; Haass, Michael J. ^[1]; Divis, Kristin M. ^[1]; Wang, Zhiyuan ^[2]; Wilson, Andrew T. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. In conclusion, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1377597
• Report Number(s): SAND-2017-8681J; 656226
• Journal Information: IEEE Transactions on Visualization and Computer Graphics, Vol. 24, Issue 1; ISSN 1077-2626
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Visual saliency
evaluation
eye tracking