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Stack zooming for multifocus interaction in skewed-aspect visual spaces.

Waqas Javed1, Niklas Elmqvist

  • 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. wjaved@purdue.edu

IEEE Transactions on Visualization and Computer Graphics
|June 8, 2013
PubMed
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Stack zooming enables multifocus interaction in large, high-aspect-ratio 2D visual spaces. This method uses hierarchical 1D strips to maintain context and distance awareness during data exploration.

Area of Science:

  • Information Visualization
  • Human-Computer Interaction
  • Data Analysis

Background:

  • Many 2D visual spaces, such as time-series data, multimedia, and text, exhibit a virtually one-dimensional nature with high aspect ratios.
  • Analysis of these large datasets often requires users to maintain multiple foci while preserving context and distance awareness.

Purpose of the Study:

  • To introduce 'stack zooming,' a novel multifocus interaction method for exploring large, high-aspect-ratio 2D visual spaces.
  • To enhance context and distance awareness during data exploration tasks.

Main Methods:

  • The stack zooming approach builds hierarchies of 1D strips, where each stack represents a higher zoom level.
  • Sibling strips within stacks represent branches in the exploration, facilitating multifocus interaction.

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  • Correlation graphics are employed to illustrate relationships between different zoom levels and branches, aiding context and distance perception.

    • Main Results:

    • Stack zooming effectively supports multifocus interaction in visually one-dimensional spaces.
    • The method provides crucial context and distance awareness for complex data exploration.
    • Zoom hierarchies serve as graphical histories and aids for communicating insights.

    Conclusions:

    • Stack zooming is a valuable technique for analyzing large, high-aspect-ratio visual data.
    • The method enhances user understanding by maintaining context and distance awareness across multiple foci.
    • Further extensions with annotations and integrated statistics can broaden its applicability.