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CLEVis: A Semantic Driven Visual Analytics System for Community Level Events.

Chao Ma, Ye Zhao, Andrew Curtis

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    |February 21, 2020
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    Summary
    This summary is machine-generated.

    CLEVis is a visual analytics system designed for community-level event (CLE) datasets. It helps frontline users explore neighborhood events and gain insights at fine-grained geographical resolutions.

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    Area of Science:

    • Data Science
    • Geospatial Analysis
    • Human-Computer Interaction

    Background:

    • Community-level event (CLE) datasets offer rich semantic and geospatial-temporal information crucial for frontline users like police and social workers.
    • Existing methods often lack the ability to explore these events at fine-grained neighborhood resolutions.

    Purpose of the Study:

    • To introduce CLEVis, a visual analytics system for exploring CLE datasets.
    • To enable frontline users to discover insights within specific community regions of interest.

    Main Methods:

    • CLEVis integrates automatic algorithms with interactive visualizations to leverage semantic information.
    • The system was designed and developed in collaboration with community workers and social scientists.

    Main Results:

    • CLEVis facilitates exploration of community events at neighborhood scales.
    • The system effectively utilizes semantic information through integrated algorithms and visualizations.

    Conclusions:

    • CLEVis enhances the ability of frontline users to derive insights from community-level event data.
    • Collaborative design ensures practical utility for real-world applications.