Zhang Tackles Misperceptions of Racial Bias in Policing

Racial bias

Yan Zhang presented a study on racial bias to an international crime analyst group on how data can be used to address key issues in policing.

Yan Zhang, an associate professor in the Department of Criminal Justice and Criminology, was invited to an international conference of crime analysts to present her latest research on racial bias in policing.

Zhang attended the International Association of Crime Analysts (IACA) Training Session in New Orleans, a group of professional crime analysts dedicated to improving their skills, making the best use of crime analysis, advocating for standards of performance and techniques, and networking with contacts across the world. Zhang presented on a panel sponsored by the National Institute of Justice that highlighted how data can help inform police practices, particularly involving the misperception of racial bias which can alienate some of the communities served.

Zhang’s research, “Community Racial Characteristics and Police Decision to Arrest in Traffic Stops” examined traffic stops that led to arrests at the Houston Police Department. She found that police decisions to arrest overwhelmingly are driven by neighborhood level factors, rather than the race of the individual arrested. Among the other issues tackled by the NIJ panel were a model that can be used to demonstrate how traffic stops are aimed at reducing crime rather than maximizing arrests of minorities and a new method to create an internal benchmark for identifying bias in traffic stops.

Race remains a central issue in American policing, including racial profiling in police decisions to stop, search, and arrest motorists. Most studies focus on the relationship between driver characters and police actions at an individual level. Zhang adopted a multi-level model to study the interaction between police and the public during traffic stops using community-level factors as well as geographic patterns.

The study was based on racial profiling data from the Houston Police Department in 2010. The department recorded nearly 500,000 traffic stops that year and one-third occurred on interstate highways. Nearly all of the remaining stops were geocoded (88 percent), and the study found more than one in five stops and arrests occurred in an areas containing only 1 percent of all city addresses. In those areas, Hispanics comprised 46 percent of the population, Black were 25 percent of the population and, Asians accounted for 6 percent of the population.

Of these 278,178 traffic stops in neighborhoods, 13 percent resulted in an arrest. Black drivers were the most likely to be arrested at 13.9 percent, followed by Hispanic drivers at 13.1 percent and White drivers at 12.8 percent. These individual level racial variations, however, were no longer significant after controlling for community level racial variations. In contrast, the concentration of Blacks, Hispanics or other minorities in communities is significantly associated with the chance of being arrested by police.

“These findings imply that police decisions to arrest in traffic stops were likely to be more aggressive and targeted in communities where there were high concentrations of Blacks, Hispanics, or other minorities,” said Zhang. “This kind of aggressive and targeted policing may largely involve “contextual” reactions that may occur without consciousness. . . these contextual racial biases may be tied to police organizational contexts where institutional discrimination may exist intentionally or unintentionally. ”

“Alternatively, officer decisions to arrest may be influenced by other intangible neighborhood characteristics. Regardless, residents in minority, especially Black concentrated communities, are likely to have impressions and perceptions that they are targeted and more likely to be stopped, searched, and arrested by police,” said Zhang.
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