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Data-Driven Approaches to Crime and Traffic Safety (DDACTS): An Historical Overview

NCJ Number
246804
Date Published
Author(s)
Alexander Weiss, Ph.D.
Annotation
This is an overview of the history and development of enforcement theories underlying the operational model entitled, “Data-Driven Approaches to Crime, and Traffic Safety” (DDACTS).
Abstract
One of the key elements of the DDACTS model is linking the strategy and tactics of traffic enforcement to the prevention of non-traffic crime. Data confirms that the application of high-visibility traffic enforcement is a proven and effective countermeasure that addresses both crime and traffic crashes whether they occur simultaneously or independently in time and/or location. James Q. Wilson - in his 1968 study entitled, “Varieties of Police Behavior” notes that traffic law enforcement not only prevents accident fatalities and injuries, but also provides an opportunity to identify fugitives, stolen merchandise, illegal weapons, and stolen cars. He argues that the more vehicle stops an agency conducts, the more likely it is to identify persons wanted by the police. In confirmation of this argument, Wilson’s and Boland’s study of policing activities and crime in various cities determined that cities with the highest rate of traffic citations per officer (the measure of “patrol aggressiveness”) had the lowest rates of commercial robbery. By 1990, the number of similar studies and associated empirical evidence showed that traffic enforcement impacted crime by detecting offenders traveling to and from crimes and by deterring offenders from using their vehicles in the commission of their crimes. More recent studies have found that the rates of traffic crashes and crime tend to be linked at certain geographic areas. DDACTS integrates data on location-based traffic crashes and crime data as an effective and efficient method for deploying patrols and other resources. 2 tables and 58 references
Date Created: February 22, 2018