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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Information Science

    Background:

    • Traditional scatterplots struggle with increasing data complexity and volume.
    • A wide array of modified scatterplot designs exist to address scalability challenges.
    • Selecting appropriate scatterplot designs for specific analysis goals is difficult for practitioners.

    Purpose of the Study:

    • To assist designers in making informed design choices for scatterplot visualizations.
    • To catalog scatterplot-specific analysis tasks and understand data characteristic influences.
    • To survey scatterplot-like designs and connect data, tasks, and design for effective visualization.

    Main Methods:

    • Literature survey to catalog scatterplot analysis tasks.
    • Analysis of how data characteristics influence design decisions.
    • Survey of scatterplot-like designs to understand design options.

    Main Results:

    • A framework connecting data characteristics, analysis tasks, and design choices.
    • Identification of challenges and open questions in scatterplot design.
    • Generation of example best practices for effective scatterplot design.

    Conclusions:

    • Effective scatterplot design requires careful consideration of data characteristics and analysis goals.
    • Bridging the gap between data, tasks, and design leads to better visualization practices.
    • This research provides a foundation for developing guidelines and improving scatterplot visualization effectiveness.