Older cars account for a majority of all stolen cars in the United States. This phenomenon has also been reported in other Western countries. Indices from Australia and the U.K. further reveal that the risk of theft increases as cars become older. This study examines mechanisms of theft of older cars through answering its main research question: “Why are older cars more stolen than their newer counterparts?” The question is addressed from the perspective of availability, security, location, and offender motive. This project utilizes Google Street View for two purposes: (1) to estimate the number of vehicles parked on the sampled streets in Newark; and (2) to measure land use and physical disorder at the street level. Vehicle security is measured by the presence of factory-installed electronic immobilizers. This study draws on the principle of triangulation, gathering an array of evidence from different analyses using data from different sources to investigate mechanisms of theft of older cars. Multilevel negative binominal regression is conducted for the street-level location analyses to examine the impact of physical disorder and land use on the counts of older cars parked and those stolen on the streets. Multilevel logistic regression analyses are performed to determine the effects of predictor variables on the likelihood of cars being stolen, recovered, and stripped of their parts. Results show that older cars are more stolen because there are more older cars available to steal. However, this pattern varies considerably across vehicles makes. Interaction terms indicate that Honda and Toyota become more likely to be stolen as they get older, while the opposite is true for Dodge and Ford. The vast majority of older cars lack electronic immobilizers which are found to reduce the likelihood of cars being stolen. Considering the magnitude of temporary thefts that are committed by opportunistic thieves, vehicle security is the most powerful determinant of theft of older cars. Physical disorder and certain types of land use have some impact on the likelihood of older cars being stolen, but their strength of predicting such an outcome is not close to that of security, vehicle age, and makes.
Subject (authority = RUETD)
Topic
Criminal Justice
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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