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    This study introduces hypergeometric figures for analyzing community variable relationships. These archetypal analyses reveal geographic patterns in suicide and mental disorder data.

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    Area of Science:

    • Community ecology
    • Biostatistics
    • Spatial analysis

    Background:

    • Understanding complex relationships between community variables is crucial.
    • Geographic patterns of social and health phenomena require robust analytical tools.

    Purpose of the Study:

    • To introduce and illustrate the utility of hypergeometric figures for community variable analysis.
    • To demonstrate the application of archetypal three- and four-dimensional analyses.
    • To explore geographic patterns of suicide and mental disorder using this methodology.

    Main Methods:

    • Development and application of hypergeometric figure analysis.
    • Archetypal three- and four-dimensional geometric modeling.
    • Analysis of ecological and epidemiological data with spatial components.

    Main Results:

    • Hypergeometric figures effectively visualize interrelationships among community variables.
    • Archetypal analyses successfully identified distinct patterns in suicide and mental disorder distributions.
    • The methodology demonstrated significant correlations between geographic location and studied variables.

    Conclusions:

    • Hypergeometric figures offer a novel and powerful approach for community variable analysis.
    • The technique provides valuable insights into the spatial epidemiology of mental health and suicide.
    • Further research can extend this method to other complex ecological and social systems.