Visualizing Clustering and Uncertainty Analysis of Multivariate Time-Series Data
Abstract not provided.
Publications
Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations
IEEE Transactions on Visualization and Computer Graphics
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. Finally, 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.
Wearable Technologies in Extreme Environments
Abstract not provided.
Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations
Abstract not provided.
Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data
Abstract not provided.
Human Comprehension of Data Visualizations
Abstract not provided.
Comparing Data Evaluation Task Effects on Data Driven Event Detection Models
Abstract not provided.
Effects of Task on Eye Movements During Comprehension of Abstract Data Visualizations
Abstract not provided.
Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data
Abstract not provided.
Moving from low level eye movement data to meaningful content in dynamic environments
Abstract not provided.
Rim-to-Rim Wearables At The Canyon for Health (R2R WATCH): Experimental Design and Methodology
Abstract not provided.
Visual Cognition: Human Comprehension of Data Visualizations
Abstract not provided.
Patterns of Attention: How Data Visualizations are Read
Abstract not provided.
Gaussian Mixture Models for Information Integration: Toward Gaze-Informed Information Foraging Models for Imagery Analysis
Abstract not provided.
Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data
Abstract not provided.
Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH): Lightening Talk
Abstract not provided.
Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH)-Poster
Abstract not provided.
Patterns of attention: How data visualizations are read
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Data visualizations are used to communicate information to people in a wide variety of contexts, but few tools are available to help visualization designers evaluate the effectiveness of their designs. Visual saliency maps that predict which regions of an image are likely to draw the viewer’s attention could be a useful evaluation tool, but existing models of visual saliency often make poor predictions for abstract data visualizations. These models do not take into account the importance of features like text in visualizations, which may lead to inaccurate saliency maps. In this paper we use data from two eye tracking experiments to investigate attention to text in data visualizations. The data sets were collected under two different task conditions: a memory task and a free viewing task. Across both tasks, the text elements in the visualizations consistently drew attention, especially during early stages of viewing. These findings highlight the need to incorporate additional features into saliency models that will be applied to visualizations.
Rim-to-Rim wearables at the canyon for health (R2R WATCH): Experimental design and methodology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
The Rim-to-Rim Wearables At The Canyon for Health (R2R WATCH) study examines metrics recordable on commercial off the shelf (COTS) devices that are most relevant and reliable for the earliest possible indication of a health or performance decline. This is accomplished through collaboration between Sandia National Laboratories (SNL) and The University of New Mexico (UNM) where the two organizations team up to collect physiological, cognitive, and biological markers from volunteer hikers who attempt the Rim-to-Rim (R2R) hike at the Grand Canyon. Three forms of data are collected as hikers travel from rim to rim: physiological data through wearable devices, cognitive data through a cognitive task taken every 3 hours, and blood samples obtained before and after completing the hike. Data is collected from both civilian and warfighter hikers. Once the data is obtained, it is analyzed to understand the effectiveness of each COTS device and the validity of the data collected. We also aim to identify which physiological and cognitive phenomena collected by wearable devices are the most relatable to overall health and task performance in extreme environments, and of these ascertain which markers provide the earliest yet reliable indication of health decline. Finally, we analyze the data for significant differences between civilians’ and warfighters’ markers and the relationship to performance. This is a study funded by the Defense Threat Reduction Agency (DTRA, Project CB10359) and the University of New Mexico (The main portion of the R2R WATCH study is funded by DTRA. UNM is currently funding all activities related to bloodwork. DTRA, Project CB10359; SAND2017-1872 C). This paper describes the experimental design and methodology for the first year of the R2R WATCH project.
Modeling Human Comprehension of Data Visualization
Abstract not provided.
Cognitive systems and sciences at SNL
Abstract not provided.
Modeling Attention: Human Comprehension of Data Visualizations
Abstract not provided.
Modeling Human Comprehension of Data Visualization
Abstract not provided.
Modeling human comprehension of data visualizations
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A critical challenge in data science is conveying the meaning of data to human decision makers. While working with visualizations, decision makers are engaged in a visual search for information to support their reasoning process. As sensors proliferate and high performance computing becomes increasingly accessible, the volume of data decision makers must contend with is growing continuously and driving the need for more efficient and effective data visualizations. Consequently, researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles to assess the effectiveness of data visualizations. In this paper, we compare the performance of three different saliency models across a common set of data visualizations. This comparison establishes a performance baseline for assessment of new data visualization saliency models.
Modeling Human Comprehension of Data Visualization
Abstract not provided.
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