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    This study introduces Visual Analyzer for Urban Data (VAUD), a new visual analytics tool for exploring complex urban datasets. VAUD enables cross-domain correlation and information extraction from diverse data sources.

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    Area of Science:

    • Urban Informatics
    • Data Visualization
    • Computational Social Science

    Background:

    • Urban data is characterized by its massive volume, heterogeneity, and spatio-temporal nature.
    • Analyzing and visualizing such complex urban data presents significant challenges for researchers and analysts.

    Purpose of the Study:

    • To design and implement a novel visual analytics approach for urban data.
    • To support the integrated visualization, querying, and exploration of heterogeneous urban data sources.

    Main Methods:

    • Developed Visual Analyzer for Urban Data (VAUD), a visual analytics system.
    • Leveraged spatial-temporal and social inter-connectedness features for cross-domain correlation.
    • Enabled analysts to select, filter, and aggregate data across multiple sources.

    Main Results:

    • Demonstrated the capability to extract hidden information by integrating diverse urban data subsets.
    • Successfully applied VAUD to a real-world urban dataset encompassing cyber, physical, and social information.
    • Showcased the effectiveness of the approach on data from 14 million citizens over 22 days.

    Conclusions:

    • VAUD provides an effective solution for the visualization and analysis of complex urban data.
    • The approach facilitates deeper insights by integrating multi-domain urban information.
    • Cross-domain correlation via spatio-temporal and social links is crucial for urban data analysis.