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Proportional Aggregation in Hierarchical Data Visualization.

Antonia Schlieder, Jan Rummel, Filip Sadlo

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    Stalactite plots offer a new way to visualize hierarchical data using proportional aggregation. This method aids in comparing data attributes across hierarchy levels, proving effective in user studies.

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    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Analysis

    Background:

    • Comparing data attributes within and between hierarchical levels is challenging.
    • Existing implicit hierarchy visualization methods primarily use additive aggregation.
    • Proportional aggregation for hierarchical data visualization is underexplored.

    Purpose of the Study:

    • Introduce stalactite plots, a novel visualization technique for proportional aggregation in hierarchical data.
    • Enable visual comparison of data attributes across different hierarchy levels.
    • Evaluate the effectiveness and usability of stalactite plots.

    Main Methods:

    • Developed stalactite plots, a visualization technique encoding proportional aggregation.
    • Conducted empirical evaluations with user studies (N=148, N=50).
    • Compared stalactite plots against established hierarchical visualization methods.

    Main Results:

    • Stalactite plots, with explanation, are understood comparably to existing techniques.
    • Users demonstrated faster and more accurate performance with stalactite plots on large datasets.
    • The technique effectively supports visual value comparison in hierarchical data.

    Conclusions:

    • Stalactite plots provide a viable alternative for visualizing hierarchical data with proportional aggregation.
    • The method enhances data attribute comparison, particularly for large and complex datasets.
    • Further research can explore applications and optimizations for stalactite plots.