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A case study of bicycle theft on the Washington DC Metrorail system using a Routine Activities and Crime Pattern theory framework


This article employs Routine Activities and Crime Pattern theories to explore the factors that lead to increased risk of bike theft, focusing on Washington Metropolitan Transit Authority Metrorail (Metro) property. Utilizing the Metro bike census and other data, we use negative binomial regression analysis to model the relationship between bike thefts and various station- and neighborhood-level risk factors that can either create or close off opportunities for bike thefts. The findings indicate that bike thefts around Metro stations are positively influenced by the number of targets, as measured by the number of bikes per station, and the presence of likely offenders, as measured by the volume of auto-related larcenies. Stations that have greater guardianship, as measured by the number of nearby businesses, are less likely to experience bike theft. The implications of these findings for theory, methodological considerations, and crime prevention are discussed. We suggest that bike racks can be placed in locations with greater informal guardianship, and discuss ways our findings can inform traditional interventions such as bike locking campaigns.

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  1. Pucher and Bueler (2009) discuss recent increases in both cycling use and bike-and-ride public transportation infrastructure in the US and Canada. A different study by the same authors (2008) indicates that in other industrialized nations such as the Netherlands, Germany, and Denmark, urban policies to encourage cycling were implemented much earlier on, as early as the mid-1970s.

  2. As indicated to the authors through correspondence with Ronald A. Pavlik Jr., Chief of the Metro Transit Police Department.

  3. A 1997 survey by Moritz put the average cost of a commuter bike at $300 which comes to $435 in 2013 dollars, adjusting for inflation:

  4. Based on a range of 190,000 to 570,000 [three times the reported statistic per Integrated Cycle Systems (2006)] bikes stolen annually, and using average $435/stolen bike.

  5. During the study period of 2013, there were six lines and 86 stations.

  6. The Bike Census figures are based on one-day observations for each station; they are not annual counts.

  7. NETS is a dataset provided by Wall and Associates Incorporated. They convert Dunn and Bradstreet Corporation’s global commercial database into a longitudinal data series that provides annual information on job creation and destruction, sales growth performance, mobility patterns, and other information for establishments in the U.S. economy. For our purposes, we used the address information in the database to estimate the number of business establishments in rail station block groups.

  8. For instance, in 2013 on all DC rail stations, combined reported fare evasions and fare evasion warnings totaled to 4459, making it the most frequent crime among all Part I and Part II crimes. The next most frequent crime was robbery “snatches,” totaling to 547.

  9. Research has demonstrated the difficulty of predicting the time of a bike theft, given a range of time it was left unattended. Ashby and Bowers (2012) analyze 303 bike thefts occurring at railway stations in Great Britain in which the offense was captured on a CCTV security camera, and the real offense time could therefore be determined. Using the time range that was reported to the police for each incident (starting with the time the victim left the bike unattended, and ending when they returned to the location), the authors demonstrate that using the start time, mid-point, or end time of the range to predict the actual time of theft results in poor predictions.

  10. The Pearson correlation between these two variables, business establishments and hourly average ridership, was 0.54 indicating that these two variables have a moderate correlation, however not to the extent to suggest that multicollinearity might be a problem.


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Correspondence to Jeremy M. Levy.

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Levy, J.M., Irvin-Erickson, Y. & La Vigne, N. A case study of bicycle theft on the Washington DC Metrorail system using a Routine Activities and Crime Pattern theory framework. Secur J 31, 226–246 (2018).

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  • Bike theft
  • Routine activities
  • Guardianship
  • Transit