 Authors: Chris Barnum, Nick Manrique, Tracy Payne and Tiffany Miller  St. Ambrose University, Davenport, Iowa  This is a preliminary examination. Please do not quote or cite any information in this document without the consent of the lead author.

 Davenport Police Department (especially) ◦ Chief Frank Donchez ◦ Maj. Donald Schaeffer ◦ Lt. Scott Sievert ◦ Dan DeFauw  The Criminal Justice students at St. Ambrose University

 We used trained observers to determine the racial and gender breakdowns of drivers on the streets of Davenport.

 Over 50 trained observers watched traffic in 29 locations in Davenport  The observers were recurrently deployed from September 2010 – May  The observers worked 7 days a week and watched vehicles in intervals from 8:00 am – 2:00 am  These observers recorded information for more than 16,500 vehicles.

 In order to determine if the data generated by the observers were valid, we compared them with 2010 census information for Davenport provided by the US Census Bureau

 The Davenport Police use “reporting areas” to locate traffic stops. Each traffic stop is assigned to one of these reporting areas.

 Each of our observation zones subsumes a number of these “reporting areas.”

 Information for stops occurring between January 2010 and September 2011  A total of over 15, 000 traffic stops.

 For each stop officers record information about: (1) the driver including race, gender, age (2) the stop including the reason, date, time and location and (3) the outcome including citation, arrest, warning, search, field interview or vehicle exit.

The DPD began emphasizing compliance in January, 2011.

 Comparison of 2010 data to 2011 data.  Very Similar

 The initial traffic stop comparisons consist of data for total stops compared to census data and day observations

 The goal of this analysis is to determine whether White and Black drivers were treated differently once a stop occurred.

 Arrests  Citations  Consent Searches  Exiting the Vehicle

 Logistic Regression This method enables a researcher to predict the likelihood of a given event (such as an arrest being made) on the basis of several other factors (such as the officer or driver’s age, gender, race, or the area of town or time of day). This method also allows a researcher to isolate and assess the relative strength of each of the predictor variables used in the analysis. Only 2011 data is used in the following analyses.

 Net of the other independent variables included in the model, the odds are 1.28 times greater that a White driver will be issued a citation during a traffic stop than will be a Black driver.

 Net of the other independent variables included in the model, the odds are 1.75 times greater that a Black driver will be arrested during a traffic stop than will be a White driver.

 Net of the other independent variables included in the model, the odds are 2.21 times greater that a Black driver will be asked for consent to search during a traffic stop than will be a White driver.

 Net of the other independent variables included in the model, the odds are 1.67 times greater that a Black driver will be asked to step out of the vehicle during a traffic stop than will be a White driver.

 Next step is to analyze disparity at the officer level