Abstract
The Manhattan subway system serves 39% of its commuters as an essential public transit option; however, its annual ridership dropped by 3.48% from 2015 to 2018. This study hypothesizes that ground-level urban-design quality relates to passengers’ perceived safety and actual crime rates, subsequently affecting metro ridership. Current literature lacks intensive investigations into how the intertwined physical features and subjective perceptions of micro-scale street environments around subway stations correlate with crime frequencies. It sets out to quantify the correlations between crime reports and urban design quality within the ¼-mile buffer zone of Manhattan subway entrances with the application of Street View Imagery (SVI) and the artificial intelligence of computer vision (CV) and machine learning (ML). Key findings are 1) subjectively and objectively measured urban design quality from SVIs improve explanations of crime. 2) higher perceived safety does not necessarily link with lower crime risks. 3) parks as a point of interest (POI) serve as a crime deterrent. This study has significant implications for urban design and transportation policies and provides references for other urban areas to facilitate safer public transit services and systems by enhancing built environments.
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Su, N., Qiu, W., Li, W., Luo, D. (2022). Quantifying Association Between Street-Level Urban Features and Crime Distribution Around Manhattan Subway Entrances. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_18
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