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    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Science

    Background:

    • Tabular data visualizations are prevalent tools for data analysis.
    • Understanding the performance of basic visualizations across diverse tasks is crucial for enhancing their impact.
    • Limited research exists on how different visualization types perform for various data analysis tasks.

    Purpose of the Study:

    • To evaluate the effectiveness of five common 2D visualization types (Table, Line Chart, Bar Chart, Scatterplot, Pie Chart).
    • To assess their performance across ten distinct data analysis tasks using two datasets.
    • To provide task-specific recommendations for visualization selection.

    Main Methods:

    • Conducted a crowdsourced experiment involving five small-scale (5-34 data points) 2D visualization types.
    • Evaluated effectiveness across ten common data analysis tasks using two distinct datasets.
    • Trained a decision tree model on collected experimental data to create a visualization recommender system.

    Main Results:

    • Significant variation in the effectiveness of visualization types was observed across different data analysis tasks.
    • Task-dependent effectiveness suggests that visualization design should consider specific contextual factors.
    • Developed a decision tree-based recommender system to guide visualization choices based on experimental data.

    Conclusions:

    • The choice of visualization significantly impacts effectiveness depending on the data analysis task.
    • Context-specific effectiveness is a key consideration for designing better data visualizations.
    • Experimental user data can be engineered into practical systems, such as visualization recommenders.