Motor Vehicle Theft: Crime and Spatial Analysis in a Non-Urban Region
Author(s): Deborah Lamm Weisel ; William R. Smith ; G.
David Garson ; Alexi Pavlichev ; Julie Wartell
August 2006
Motor vehicle theft in non-urban areas does not reveal the well-recognized hot spots often associated with crime in urban areas. These findings resulted from a study of 2003 vehicle thefts in a four-county region of western North Carolina comprised primarily of small towns and unincorporated areas.
Motor vehicle theft in non-urban areas does not reveal the well-recognized hot spots often associated with crime in urban areas. These findings resulted from a study of 2003 vehicle thefts in a four-county region of western North Carolina comprised primarily of small towns and unincorporated areas.
While the study suggested that point maps have limited value for areas with low volume and geographically-dispersed crime, the steps necessary to create regional maps – including collecting and validating crime locations with Global Positioning System (GPS) coordinates – created a reliable dataset that permitted more in-depth analysis. This in-depth analysis was inherently more valuable and identified distinctive crime patterns in the region. These patterns included:
• Vehicle thefts were widely dispersed – 95% of census block groups (235 of 248) in the region had at least one of 633 thefts during the year.
• The risk of vehicle theft was significantly higher in areas with higher concentrations of rental housing and in areas with manufacturing or industrial land use.
• In contrast to vehicle theft in urban areas, business premises were common theft locations in the region; “risky facilities” such as car dealerships and repair shops were prominent among these theft locations.
• An unusually high number of vehicles other than cars and trucks were stolen. These “hot products” included ATVs and mopeds.
In contrast to point maps, these findings about the nature of stolen vehicles pointed law enforcement towards highly specific crime prevention strategies. It is unlikely that such patterns – and the suggested responses – would have emerged without aggregate regional data about vehicle theft.
While the study revealed that address data were initially weak for spatial analysis, there are relatively simple ways to improve data quality; the usefulness of findings suggest efforts to improve data quality would yield important benefits in terms of crime prevention.
Research Approach It is well-established that both the volume of crime and crime rates are much lower in rural areas. Little is known, however, about the nature of crime in rural areas or its geographic concentration. The dearth is particularly true for vehicle theft; while the crime is oft-studied in urban locales, there is no research on vehicle theft in non-urban areas. Not only is little known about where vehicle theft occurs in non-urban areas – a phenomenon that could be redressed with point maps, but virtually nothing is known about the descriptive nature of vehicle thefts – such as the prevalence of joyriding or the types of vehicles stolen.
To assess the accuracy and usefulness of crime and spatial analysis for vehicle theft, incident reports for 2003 were collected from 11 law enforcement agencies – four sheriffs’ departments and seven municipal police agencies that serve the Western Piedmont region of North Carolina – an SMSA of nearly a half-million population. The agencies had been experiencing problems with an increasing number of stolen vehicles, while recoveries were declining. Regional data were necessary to ensure a sufficient volume for statistical analysis.
The Western Piedmont is an area known for furniture manufacturing and is comprised primarily of small towns. Hickory, the largest municipality, has a population of 37,000; the town is sited at the junction of the four counties and centered along a major East-West Interstate Highway (See Map 1). The jurisdictions in this area have a common economic base and a strong history of cooperation.
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