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Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

Author

Listed:
  • Md Amiruzzaman

    (Kent State University)

  • Andrew Curtis

    (Case Western Reserve University)

  • Ye Zhao

    (Kent State University)

  • Suphanut Jamonnak

    (Kent State University)

  • Xinyue Ye

    (Texas A & M University)

Abstract

The complex interrelationship between the built environment and social problems is often described but frequently lacks the data and analytical framework to explore the potential of such a relationship in different applications. We address this gap using a machine learning (ML) approach to study whether street-level built environment visuals can be used to classify locations with high-crime and lower-crime activities. For training the ML model, spatialized expert narratives are used to label different locations. Semantic categories (e.g., road, sky, greenery, etc.) are extracted from Google Street View (GSV) images of those locations through a deep learning image segmentation algorithm. From these, local visual representatives are generated and used to train the classification model. The model is applied to two cities in the U.S. to predict the locations as being linked to high crime. Results show our model can predict high- and lower-crime areas with high accuracies (above 98% and 95% in first and second test cities, accordingly).

Suggested Citation

  • Md Amiruzzaman & Andrew Curtis & Ye Zhao & Suphanut Jamonnak & Xinyue Ye, 2021. "Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach," Journal of Computational Social Science, Springer, vol. 4(2), pages 813-837, November.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:2:d:10.1007_s42001-021-00107-x
    DOI: 10.1007/s42001-021-00107-x
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    References listed on IDEAS

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    1. Sarah E Wiehe & Mei-Po Kwan & Jeff Wilson & J Dennis Fortenberry, 2013. "Adolescent Health-Risk Behavior and Community Disorder," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
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    6. Andrew Curtis & Jacqueline W. Curtis & Lauren C. Porter & Eric Jefferis & Eric Shook, 2016. "Context and Spatial Nuance Inside a Neighborhood's Drug Hotspot: Implications for the Crime–Health Nexus," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(4), pages 819-836, July.
    7. Andrew Curtis & Chaz Felix & Susanne Mitchell & Jayakrishnan Ajayakumar & Peter R. Kerndt, 2018. "Contextualizing Overdoses in Los Angeles's Skid Row between 2014 and 2016 by Leveraging the Spatial Knowledge of the Marginalized as a Resource," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(6), pages 1521-1536, November.
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    Cited by:

    1. Cesar Guevara & Matilde Santos, 2022. "Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning," Mathematics, MDPI, vol. 10(22), pages 1-27, November.
    2. Md Amiruzzaman & Ye Zhao & Stefanie Amiruzzaman & Aryn C. Karpinski & Tsung Heng Wu, 2023. "An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables," Journal of Computational Social Science, Springer, vol. 6(1), pages 315-337, April.
    3. Huafang Xie & Lin Liu & Han Yue, 2022. "Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
    4. Suphanut Jamonnak & Deepshikha Bhati & Md Amiruzzaman & Ye Zhao & Xinyue Ye & Andrew Curtis, 2022. "VisualCommunity: a platform for archiving and studying communities," Journal of Computational Social Science, Springer, vol. 5(2), pages 1257-1279, November.

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