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Design Exposition with Literate Visualization.

Jo Wood, Alexander Kachkaev, Jason Dykes

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    Summary
    This summary is machine-generated.

    We introduce literate visualization, a new method for designing and communicating data visualizations by integrating code with design explanations. This approach enhances visualization validity and benefits both academic research and practical application.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Traditional data visualization processes often lack integrated design documentation.
    • Donald Knuth's literate programming concept offers a foundation for combining code and explanation.
    • There is a need for structured approaches to document visualization design choices.

    Purpose of the Study:

    • To propose and develop a novel approach called literate visualization.
    • To integrate the creation of data visualization code with the exposition of design decisions.
    • To enhance the validity and communication of data visualization design.

    Main Methods:

    • Developed a model of design exposition with four visualization designer archetypes.
    • Defined key characteristics of literate visualization: integrated 'notebook' documents, low authoring cost, and structured design guidelines.
    • Proposed narrative schemas and branching narratives for design structuring and alternative design capture.
    • Described the open-source literate visualization environment, litvis, using Elm, Vega, and Vega-Lite.

    Main Results:

    • Demonstrated the approach through examples in visualization idioms, validation via visualization algebra, and feminist data visualization.
    • Showcased the litvis environment integrating live coding, rendered output, and textual narrative.
    • Highlighted the potential for improved validity through rich documentation of the design process.

    Conclusions:

    • Literate visualization offers a robust framework for transparent and reproducible visualization design.
    • The integration of code and design exposition can significantly improve the quality and understanding of visualizations.
    • This approach has the potential to advance both academic visualization research and professional visualization practice.