Graphical Perception for Immersive Analytics

• Main Authors: Whitlock, Matt, Smart, Stephen, Szafir, Danielle Albers
• Format: Conference Proceeding
• Language: English
• Subjects: Data visualization; Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality; Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality; Human-centered computing-Visualization-Empirical studies in visualization; Image color analysis; Navigation; Task analysis; Three-dimensional displays; Two dimensional displays; Visualization
• ISSN: 2642-5254
• DOI: 10.1109/VR46266.2020.1582298687237

Immersive Analytics (IA) uses immersive virtual and augmented reality displays for data visualization and visual analytics. Designers rely on studies of how accurately people interpret data in different visualizations to make effective visualization choices. However, these studies focus on data analysis in traditional desktop environments. We lack empirical grounding for how to best visualize data in immersive environments. This study explores how people interpret data visualizations across different display types by measuring how quickly and accurately people conduct three analysis tasks over five visual channels: color, size, height, orientation, and depth. We identify key quantitative differences in performance and user behavior, indicating that stereo viewing resolves some of the challenges of visualizations in 3D space. We also find that while AR displays encourage increased navigation, they decrease performance with color-based visualizations. Our results provide guidelines on how to tailor visualizations to different displays in order to better leverage the affordances of IA modalities.