While there is substantial evidence suggesting the presence of racial and ethnic bias in police treatment of people of color throughout the United States, there is little to no empirical evidence indicating that police profile people of color for police contact. Identifying whether and the extent to which the police are profiling people of color for traffic stops is notoriously difficult. This lack of empirical evidence undermines efforts to correctly diagnose and intervene in racially biased policing behavior. Further, in addition to the negative experience(s) that minority groups have with police, when people feel treated unjustly by the police, they lose their faith in law enforcement’s authority to fairly represent and administer the law. When this occurs, individuals are less likely to obey the law. Therefore, when the police target people of color for traffic stops, it feeds a cycle of distrust for both parties, potential criminality, and stereotypes that have plagued societal relations, policing and crime rates in the United States, for decades. In short, it is absolutely essential to accurately measure racial profiling by the police.

The purpose of this research is to synthesize and analyze a disparate suite of publicly available transportation data that includes the Census Transportation Planning Products (CTPP), accident data, as well as data from the National Household Travel Survey (NHTS) and its oversamples to develop a methodological framework for constructing race-specific driving patterns to address racial profiling of drivers by the police. The first objective of this project focuses on the design of innovative methods to integrate both geographic and transportation data with varying spatial and temporal resolutions for resolving the denominator problem and creating a generalizable framework for estimating racially motivated traffic stops. The second objective focuses on measuring and establishing the validity of the methods used to determine racial profiling by using open source policing data to evaluate the racial and ethnic distribution of individuals who would be stopped if the police were not racially biased while simultaneously accounting for police activity in a location. The third objective focuses on the design of an open-source distribution platform for the developed methods and redistributable data, and made available through well-documented Python and R front ends to facilitate the use, re-use and expansion of the methods and data used for this project. When combined, these measurements, methods, and statistical models will inform policymakers and law enforcement agencies about biased police practices and provide a blueprint for improving outcomes and reducing the cycle of distrust between the police and communities of color.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Social and Economic Sciences (SES)
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Reggie Sheehan
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Arizona State University
United States
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