ArticleJournal of computational social science2021
Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach.
Article in Journal of computational social science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Article
- Personality predicts internalizing symptoms and quality of life in police cadets: a comparison of artificial intelligence and parametric approaches.Health & justice · 2025Article
- An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables.Journal of computational social science · 2023Article
- Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique.International journal of environmental research and public health · 2022Article
- VisualCommunity: a platform for archiving and studying communities.Journal of computational social science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.