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

Abstract

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.