SMART Analysis of AAPD Traffic Stop Data,

2017-2019

The Southeast Michigan Criminal Justice Policy Research Project (SMART) at Eastern Michigan University conducted an analysis to identify potential evidence of racial disparities in traffic stops collected by Ann Arbor Police Department between January 1st 2017 and December 31st 2019.
A further aim of the report was to serve as a useful resource and guide for how communities, policy and law makers, and Civilian Oversight Boards, may make use of such traffic stop data in order to guide their work and priorities.
The resulting analysis represents the most comprehensive and nuanced analysis of traffic stop disparities in the history of AAPD and the city of Ann Arbor.

In order to conduct this analysis, SMART examined several frequencies–the overall Frequency of Stops, the frequency of specific recorded Reasons for Contact, and the frequency of Searches.
SMART then cross-tabulated those frequencies by Race and Gender in order to conduct a Benchmark Analysis designed to identify disparities.
This methodological design offer an important new level of nuance to our understanding of the distribution of disparities in Ann Arbor traffic stops

Our analysis identified significant disparities across every dimension examined, with non-white motorists being Stopped and Searched more frequently and White motorists being Stopped and Searched less frequently than would be expected in every instance.
These disparities were not uniform across racial categories nor across various Reasons for Contact.
The largest disparities identified in this analysis involve Multi-Racial and African-American male drivers for stops initiated for Equipment Violations (which occurred 2.41x more likely than would be expected) as well as for Searches after the initial stop (which occurred between 5.4x to 3.65x more often than would be expected).

SMART recommends that police administrators, elected officials and oversight practitioners use this analysis to inform their priorities, taking into account especially the Reasons for Contact and post-contact Outcomes which exhibit the largest disparities.
SMART also offers specific recommendations for more consistent and robust data collection and publication practices, especially pertaining to post-stop outcomes, which would enable more nuance along this dimension in future analyses.