Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

Exploration Strategies for Discovery of Interactivity in Visualizations

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality

Mobile Consumer Behavior in Fashion m-Retail: An Eye Tracking Study to Understand Gender Differences

© 2020 ACM. With exponential adoption of mobile devices, consumers increasingly use them for shopping. There is a need to understand the gender differences in mobile consumer behavior. This study used mobile eye tracking technology and mixed-method approach to analyze and compare how male and female mobile fashion consumers browse and shop on smartphones. Mobile eye tracking glasses recorded fashion consumers' shopping experiences using smartphones for browsing and shopping on the actual fashion retailer's website. 14 participants successfully completed this study, half of them were males and half females. Two different data analysis approaches were employed, namely a novel framework of the shopping journey, and semantic gaze mapping with 31 Areas of Interest (AOI) representing the elements of the shopping journey. The results showed that male and female users exhibited significantly different behavior patterns, which have implications for mobile website design and fashion m-retail. The shopping journey map framework proves useful for further application in market research

Survey of Surveys (SoS) ‐ Mapping The Landscape of Survey Papers in Information Visualization

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995.However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field.Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers,and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantumstep forward by surveying and classifying literature survey papers in order to help researchers understand the current landscapeof Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. Thispaper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops thefirst classification of classifications. The paper also enables researchers to identify both mature and less developed researchdirections as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in andoutside the field of Information Visualization and Visual Analytic

Alpscarf: Augmenting Scarf Plots for Exploring Temporal Gaze Patterns

Scarf plots visualize gaze transitions among areas of interest (AOIs) on timelines. Nevertheless, scarf plots are ineffective when there are many AOIs. To help analysts explore long temporal patterns, we present Alpscarf, an extension of scarf plots with mountains and valleys to visualize order-conformity and revisits. Alpscarfs are rendered in two complementary modes in aid of insight discovery. An R package of Alpscarf is available at github.com/chia-kaiyang/alpscarf