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    This study introduces a new machine learning method to predict urban deviance, encompassing both crimes and behaviors. The approach uses street view images and incident data to identify safety risks in cities.

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    Area of Science:

    • Urban planning and development
    • Computer science and artificial intelligence
    • Criminology and sociology

    Background:

    • Urban safety is crucial for citizen well-being and sustainable city development.
    • Existing machine learning methods for urban safety have limitations due to small datasets and narrow crime focus.
    • Predicting broader 'deviance' offers a more comprehensive urban safety assessment.

    Purpose of the Study:

    • To develop a novel machine learning method for predicting urban deviance, including formal crimes and informal behaviors.
    • To leverage large-scale geo-tagged incident data and Google Street View imagery.
    • To identify spatio-temporal visual attributes associated with urban deviance.

    Main Methods:

    • Collected a large-scale dataset of incident reports and sequential Google Street View images from seven metropolitan cities.
    • Designed a convolutional neural network (CNN) to learn visual features from street imagery.
    • Analyzed the importance of visual attributes for deviance identification and severity estimation.

    Main Results:

    • The proposed framework reliably recognizes real-world urban deviance across different cities.
    • Identified key visual attributes contributing to deviance prediction and severity assessment.
    • Demonstrated the effectiveness of spatio-temporal visual learning for urban safety analysis.

    Conclusions:

    • The novel method provides a more comprehensive approach to understanding and predicting urban safety.
    • Visual attributes from street imagery are significant indicators of urban deviance.
    • This research offers valuable insights for urban planning and safety management.