Abstract
Commercial break and enters are of concern to Vancouver businesses. The proximity to rapid transit stations creates easy access to commercial buildings. This study investigates the impact of transit station proximity on crime rates, particularly focusing on the new Broadway Subway extension to the metro system. Grounded in environmental criminology theories, this research combines network analysis with machine learning and econometric methods to extract historical data and provide crime forecasts. This approach utilizes panel data to observe junctions over multiple years, then a double machine learning framework is applied to accurately measure the effect of transit station proximity on crime rates. These findings are used to forecast future crime occurrences in the neighbourhoods where the Broadway Subway extension will occur. This analysis also provides valuable insights for urban planning and public safety strategies.
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Acknowledgment
The authors would like to thank the Department of Mathematical Sciences of Trinity Western University for their generous support. The authors also want to express our gratitude to the Mitacs Globalink Research Internship Program for supporting this study. The authors would like to express their gratitude for the invaluable insights and feedback from their collaborators.
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Srairi, M.A., Nootebos, D., Park, A.J., Spicer, V., Brantingham, P.L. (2025). Measuring Crime with Double Machine Learning: The Impact of Vancouver’s Broadway Subway Extension. In: Arai, K. (eds) Advances in Information and Communication. FICC 2025. Lecture Notes in Networks and Systems, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-031-85363-0_7
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