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    This study introduces TreeEvo, a tool for analyzing multi-generational family tree structures and their relation to ancestral traits. It enables hypothesis testing for complex family structures across generations.

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    Area of Science:

    • Computational Social Science
    • Genetics
    • Data Visualization

    Background:

    • Understanding family tree structural heterogeneity across generations is crucial for multi-generational transmission research.
    • Previous research has been limited to parent-child relationships due to a lack of appropriate tools for complex, multi-generational analysis.

    Purpose of the Study:

    • To develop and evaluate TreeEvo, a novel system for generating and testing empirical hypotheses in multi-generational family research.
    • To address the limitations in analyzing complex family tree structures over multiple generations.

    Main Methods:

    • Developed TreeEvo, a system integrating Sankey diagrams with a pixel-based encoding technique for visualizing complex family trees.
    • Implemented semantic zooming for detailed information access and embedded a Multinomial Logit Model (MLM) for statistical analysis.
    • Conducted an iterative design study with social scientists and historians.

    Main Results:

    • TreeEvo effectively summarizes and organizes family trees by structural features, enabling dynamic analysis.
    • The system compactly encodes complex tree structures and provides space-efficient visualization with semantic zooming.
    • Statistical associations between family tree structure and ancestral traits were examined using MLM.

    Conclusions:

    • TreeEvo is an effective and useful tool for multi-generational family research, facilitating hypothesis generation and testing.
    • The system enhances the study of structural heterogeneity in family trees across generations.
    • Demonstrated utility through a case study with a large real-world dataset of 54,128 family trees.