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

Md Amiruzzaman (), Andrew Curtis, Ye Zhao, Suphanut Jamonnak and Xinyue Ye
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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

Journal of Computational Social Science, 2021, vol. 4, issue 2, No 16, 813-837

Abstract: 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).

Keywords: Geonarrative; Machine learning; Semantic segmentation; Street-view image analysis; Urban crime (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s42001-021-00107-x

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