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This content will become publicly available on August 29, 2018

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

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 eyemore » 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.« less
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
Publication Date:
Report Number(s):
SAND-2017-8681J
Journal ID: ISSN 1077-2626; 656226
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Visualization and Computer Graphics
Additional Journal Information:
Journal Volume: 24; Journal Issue: 1; Journal ID: ISSN 1077-2626
Publisher:
IEEE
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Visual saliency; evaluation; eye tracking
OSTI Identifier:
1377597