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Visual Highlighting for Situated Brushing and Linking

Nina Doerr, Benjamin Lee, Katarina Baricova, Dieter Schmalstieg, Michael Sedlmair

TL;DR

This paper investigates how highlighting can support brushing and linking between situated visualizations and physical referents in situated analytics (SitA). It presents a VR supermarket prototype that implements four highlighting techniques—Color, Outline, Link, and Arrow—and evaluates them in a within-subjects study across Inside-FOV and Outside-FOV shelf layouts with 20 participants performing single, multi, and statement tasks. The results show that Color and Link yield the best overall task performance and user experience, while Arrow consistently underperforms, with Outline offering a subtle alternative and potential for optimization. The study contributes an open-source VR prototype, empirical guidance on highlighting choices for SitA, and directions for hybrid or adaptive highlighting designs to balance saliency, clutter, and explicit referent connections in real-world AR contexts.

Abstract

Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.

Visual Highlighting for Situated Brushing and Linking

TL;DR

This paper investigates how highlighting can support brushing and linking between situated visualizations and physical referents in situated analytics (SitA). It presents a VR supermarket prototype that implements four highlighting techniques—Color, Outline, Link, and Arrow—and evaluates them in a within-subjects study across Inside-FOV and Outside-FOV shelf layouts with 20 participants performing single, multi, and statement tasks. The results show that Color and Link yield the best overall task performance and user experience, while Arrow consistently underperforms, with Outline offering a subtle alternative and potential for optimization. The study contributes an open-source VR prototype, empirical guidance on highlighting choices for SitA, and directions for hybrid or adaptive highlighting designs to balance saliency, clutter, and explicit referent connections in real-world AR contexts.

Abstract

Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
Paper Structure (37 sections, 6 figures)

This paper contains 37 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. Immersive and situated analytics
  4. Brushing and linking
  5. Attention guidance in augmented and virtual reality
  6. Experimental prototype for situated brushing and linking
  7. Scenario
  8. Highlighting techniques
  9. Brushing interactions
  10. User study
  11. Study conditions
  12. Tasks and data
  13. Experimental setup
  14. Measures
  15. Study design and procedure
  16. ...and 22 more sections

Figures (6)

  • Figure 1: Sequential frames of brushing on a situated scatterplot and identifying linked products (referents) during our user study, with the virtual tablet being held at head height for illustrative purposes. Frame 1: The user adjusts the data dimensions as desired. Frame 2: The user adjusts the filters to show only the data marks within the desired ranges. Frame 3: The user selects the data marks. Frame 4: The user finds the highlighted product(s) and confirms identification by pointing and selecting with controller.
  • Figure 2: Left: A top-down view of the virtual environment in which the study takes place. Right: A close-up view of the panel and the user interface prompting a user response.
  • Figure 3: Completion time measures using mean and 95% CIs.
  • Figure 4: Error measures for the single- and multi-selection task.
  • Figure 5: Mean and 95% CIs of the category ratings.
  • ...and 1 more figures