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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Considerations for Visualizing Comparison.

Michael Gleicher

    IEEE Transactions on Visualization and Computer Graphics
    |September 4, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Designing effective data visualization requires addressing comparison challenges. This paper offers a four-step strategy to abstract comparison issues, aiding designers in creating better visualization tools for analysis.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Comparison is a fundamental yet challenging aspect of data visualization.
    • Existing visualization solutions often struggle to address the diverse needs of comparison tasks.
    • A systematic approach is needed to guide the design of effective comparison support.

    Purpose of the Study:

    • To provide a domain-independent strategy for abstracting and addressing comparison challenges in visualization design.
    • To offer designers a structured framework for considering general issues related to supporting comparison.
    • To aid in the development and evaluation of visualization tools that facilitate comparison.

    Main Methods:

    • The study presents a four-consideration framework to systematically analyze comparison problems.
    • Consideration 1: Identifying common elements of comparison (target items, user actions).
    • Consideration 2: Analyzing challenges arising from scale (item number, complexity, relationship complexity).
    • Consideration 3: Categorizing general strategies for addressing scaling challenges.
    • Consideration 4: Mapping strategies to visual design solutions.

    Main Results:

    • The framework provides a structured process for designers to approach comparison support.
    • It categorizes comparison issues and solutions in a manner applicable across different domains.
    • Case studies demonstrate the framework's utility in designing and evaluating visualization solutions.

    Conclusions:

    • The proposed four-consideration strategy offers a robust method for designing effective comparison support in visualization.
    • This systematic approach helps overcome common design difficulties associated with data comparison.
    • The framework empowers designers to create more insightful and user-friendly visualization tools for analytical tasks.