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    This study introduces a graph-based method to systematically analyze dashboard designs, revealing prevalent patterns. This approach aids in developing better authoring tools and leveraging artificial intelligence (AI) and machine learning (ML) for data visualization.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Software Engineering

    Background:

    • Dashboards are essential for data analysis and dissemination, yet a systematic understanding of their design patterns is lacking.
    • Existing methods for analyzing dashboard designs are limited in scope and quantification.
    • Machine learning (ML)-inspired approaches often focus on single visualizations or narrow design features.

    Purpose of the Study:

    • To develop a quantifiable and systematic approach for modeling dashboard content and composition.
    • To provide a descriptive overview of prevalent dashboard design patterns.
    • To support the development of advanced dashboard authoring tools and AI/ML applications.

    Main Methods:

    • A graph representation was developed to model dashboard designs, decomposing them into content blocks (nodes) and relationships (edges).
    • This graph representation was applied to a census of 25,620 dashboards from Tableau Public.
    • The approach was extended beyond prior work by capturing both content and composition.

    Main Results:

    • A comprehensive census of 25,620 dashboards was derived, detailing core building blocks and prevalent design patterns.
    • The study provides a descriptive overview of dashboard designs 'in the wild'.
    • The findings highlight common and less common dashboard design patterns.

    Conclusions:

    • The proposed graph representation offers a powerful method for systematically analyzing dashboard designs.
    • The derived census and methodology can guide the development of intuitive dashboard authoring tools.
    • This work facilitates making dashboards more accessible and enables advanced AI/ML techniques for data visualization.