Place-based crime prevention: Using opportunity structures and environmental characteristics to estimate crime.

摘要

This project provides crime analysts with a model to identify auto theft opportunity and environmental characteristics that are related to repeat victimization of auto theft. First, several models were created to estimate the opportunity for auto theft in Lexington-Fayette, Kentucky. These models were then compared to two years of auto theft reports that were supplied by the police department. Several types of locations are found to create opportunity for auto theft, such as the locations of apartments, bars, and auto repair and auto parts shops. Other variables (location of parking structures, schools, and hotels) that would appear to be related to auto theft were not. An "Alternate Model" that combines several significant variables was formed.;The second aspect of the research involved the comparison of 75 randomly selected locations that experienced repeat auto theft victimization, to their matched counterparts that experienced single auto theft victimization, during the study period. Each crime location was visited and several variables were collected with regard to five factors: Watchers, Activity nodes, Location, Lighting, and Security (W.A.L.L.S.). Location and lighting are the most significant variables impacting repeat victimization. Watchers and security are significantly related to repeat victimization in residential areas but not commercial areas. Surprisingly, the presence of activity nodes is not significantly related to repeat victimization in either type of location.;When these two methodologies are combined, they provide crime analysts with a tool that can significantly estimate opportunity and identify environmental characteristics related to auto theft. Crime analysts must also share these models with city planners and architects so that they may use them to design the environment in order to prevent crime. Citizens may also benefit when local police departments share information as they are better equipped to recognize unsafe environmental features and aid the police in crime prevention. While no model will perfectly predict crime, the models suggested here create the necessary link between the abstract ideology of academics and the realistic needs of the police. When these models are applied in other cities, and adapted to fit other crimes, they will make crime prevention more effective.