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    This study introduces an immersive lens visualization to effectively display urban building data over time, overcoming clutter and occlusion issues in 3D environments. The novel approach enhances accuracy and efficiency in analyzing spatiotemporal urban patterns.

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    Area of Science:

    • Computer Graphics and Visualization
    • Urban Analytics
    • Geographic Information Systems (GIS)

    Background:

    • Visualizing temporal data on urban buildings (e.g., shadows, solar potential) is crucial for understanding dynamic urban phenomena.
    • Dense 3D urban environments present challenges like occlusion and visual clutter for time-varying building data.

    Purpose of the Study:

    • To introduce an immersive lens visualization technique that addresses occlusion and clutter in 3D urban environments.
    • To enable effective analysis of spatiotemporal urban patterns through improved data visualization.

    Main Methods:

    • Developed an immersive lens visualization integrating a view-dependent cutaway de-occlusion technique.
    • Employed a conformal mapping algorithm to create a layered temporal display for building facades.
    • Designed view-responsive cutaways inspired by architectural illustrations to maintain spatial orientation.

    Main Results:

    • The lens visualization effectively embeds temporal displays on building facades, enabling occlusion-free access to data.
    • User studies demonstrated that the lens approach improves task accuracy and completion time compared to conventional methods.
    • The technique reduces navigation effort and increases user confidence in interpreting spatiotemporal urban data.

    Conclusions:

    • The proposed immersive lens visualization is a valuable tool for analyzing dynamic urban data in complex 3D environments.
    • The findings provide design recommendations for future research in spatially-embedded lenses for 3D visualization and urban analytics.