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Photorealistic Learned Landscapes for Augmented Reality
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Situated Brushing and Linking in Virtual and Augmented Reality.

Carlos Quijano-Chavez, Benjamin Lee, Nina Doerr

    IEEE Transactions on Visualization and Computer Graphics
    |April 7, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study explores situated analytics, using augmented reality (AR) for data visualization in physical spaces. AR outperformed virtual reality in speed and accuracy for situated brushing and linking tasks.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Augmented Reality

    Background:

    • Brushing and linking is a common technique in visual analysis for connecting multiple data views.
    • Its application in situated analytics, which links data to real-world objects, is less explored.
    • Prior research assessed situated brushing and linking in a virtual supermarket simulation.

    Purpose of the Study:

    • To replicate and extend previous work on situated brushing and linking.
    • To investigate its use in a physical space using augmented reality (AR).
    • To improve highlighting techniques for situated analytics.

    Main Methods:

    • Comparison of augmented reality (AR) with virtual reality (VR) using a video see-through display.
    • Study conducted in an actual physical space, extending previous virtual simulations.
    • Evaluation of brushing and linking effectiveness in situated analytics.

    Main Results:

    • Augmented reality (AR) demonstrated better performance in terms of time and accuracy compared to virtual reality (VR).
    • The effectiveness of situated brushing and linking techniques varied depending on the specific conditions.
    • Real-world stimuli significantly influence the effectiveness of situated analytics.

    Conclusions:

    • Augmented reality is a viable and effective tool for situated analytics in physical environments.
    • Highlighting techniques can be improved for better user performance in situated data analysis.
    • Understanding the role of real-world context is crucial for advancing situated analytics.