There has been much evidence suggesting that police demonstrate a level of bias when dealing with people of color, and that has led to some distrust between communities and law enforcement. A 2020 study from New York University, for example, shows that black drivers were about 20 percent more likely to be stopped than white drivers relative to their share of the residential population.
But little if any research examining police profiling in traffic stops – known as the “denominator problem” – has calculated in other factors such as driving behavior and the level of enforcement in an area.
University of Utah civil and environmental engineering associate professor Xiaoyue Cathy Liu (pictured) is part of a team that has received a $430,000 grant from the National Science Foundation to analyze more data and examine if racial profiling exists among police who pull over motorists. Other researchers involved include Arizona State University School of Criminology and Criminal Justice associate professor Danielle Wallace and University of California, Riverside, public policy associate professor Ran Wei.
Liu, a transportation engineer, will examine publicly available transportation data from the National Household Travel Survey (NHTS), Census Transportation Planning Products (CTPP) and other sources from several California cities such as Los Angeles, San Francisco and San Diego, as well as Madison, Wis. This information will include traffic stops, accidents, trips to and from work, and household census data involving race, ethnicity and income. The researchers will also look at historical data of people who are pulled over on particular roads, broken down by race.
The team will develop mathematical modeling that analyzes the data to determine how often people are pulled over by police in a certain area based on race-specific driving pattern but also factors in the level of drivers’ unlawful behavior and how much police officers patrol a certain area. Liu hopes this will provide better empirical evidence on whether there is racial profiling among police officers when they make traffic stops.
“Very few people have attempted this problem. It actually is quite challenging to estimate the number of people traveling on a road by race,” Liu said. “Also, few people have considered other factors like police enforcement level and unlawful behavior when trying to identify police racial profiling in traffic stops.”
When the two-year study in complete, Liu said she and her colleagues plan to share the models and data publicly so other researchers and government officials can use them for their own studies.
“We’re hoping it can help police departments nationally fight against racial profiling,” Liu said. “And we plan to hold workshops and panel discussions to share the results.”