<p>This study examines the concentration and temporal stability of crime at street segments in Cambridge, Massachusetts, a small but densely populated city in the northeastern United States. Ten years of police-reported crime data (2011–2020) are used to analyze longitudinal crime patterns across 2323 street segments using group-based trajectory modeling with a complementary <i>k</i>-means longitudinal clustering analysis. Consistent with prior research, crime is highly concentrated at micro-places; however, the results reveal an extreme pattern of concentration. Fewer than 1% of street segments account for approximately 25% of all reported crimes, while just over 6% of segments generate half of all incidents. These concentrations are remarkably stable over time, with most street segments exhibiting persistently low crime and a small number remaining chronically high-crime across the decade. These findings highlight the practical actionability of crime prevention at a very small number of locations. The stability and scale of concentration observed in Cambridge suggest that sustained interventions targeting a limited set of street segments may yield substantial crime reductions, even in compact urban settings. Instead of episodic enforcement, these results emphasize the feasibility of longer-term, problem-oriented, and regulatory approaches that engage both police and non-police actors responsible for managing high-risk places. Taken together, this study contributes to the crime-and-place literature by demonstrating how extreme micro-level concentration and long-term stability can inform more focused and durable place-based prevention strategies.</p>

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Street segment crime trajectories and extreme crime concentration: Evidence from Cambridge, Massachusetts

  • Stephen D. Douglas

摘要

This study examines the concentration and temporal stability of crime at street segments in Cambridge, Massachusetts, a small but densely populated city in the northeastern United States. Ten years of police-reported crime data (2011–2020) are used to analyze longitudinal crime patterns across 2323 street segments using group-based trajectory modeling with a complementary k-means longitudinal clustering analysis. Consistent with prior research, crime is highly concentrated at micro-places; however, the results reveal an extreme pattern of concentration. Fewer than 1% of street segments account for approximately 25% of all reported crimes, while just over 6% of segments generate half of all incidents. These concentrations are remarkably stable over time, with most street segments exhibiting persistently low crime and a small number remaining chronically high-crime across the decade. These findings highlight the practical actionability of crime prevention at a very small number of locations. The stability and scale of concentration observed in Cambridge suggest that sustained interventions targeting a limited set of street segments may yield substantial crime reductions, even in compact urban settings. Instead of episodic enforcement, these results emphasize the feasibility of longer-term, problem-oriented, and regulatory approaches that engage both police and non-police actors responsible for managing high-risk places. Taken together, this study contributes to the crime-and-place literature by demonstrating how extreme micro-level concentration and long-term stability can inform more focused and durable place-based prevention strategies.