Traffic Stop Practices of the Iowa City Police Department: January 1 – December 31, 2002 - Crews, Angela D. (2004)
This report summarizes the findings of a study conducted using data collected by the Iowa City Police Department between January 1 and December 31, 2002. These data resulted from 13,459 interactions between law enforcement officers and citizens during traffic-related contacts. Information was collected about the driver, the officer, and the stop event. Driver demographics included race, sex, age, residency, and vehicle registration. The only information collected about the officer was officer badge number. Finally, data collected about the stop event include the date, time of day, reason for stop, search, property seized, force, and outcome of the stop. Data analysis was conducted with the aid of SPSS-11.0 (Statistical Package for the Social Sciences). Analyses were conducted on two levels. First, descriptive analysis, using percentages, summarized stop patterns, stop characteristics, and driver demographics. Second, a program called “chisquare automatic interaction detector” or CHAID was used to evaluate the variables in terms of their relationships with one another (multivariate analysis). The greatest percentage of stops was made in the month of March (10.1%), with the fewest in June and December (6.9%). Interestingly, nearly 32% of stops occurred between midnight and 3am, with the third shift (11pm-7am) responsible for the greatest percentage (44.2%). Stopped drivers were mostly White (84%), male (63%), young (median age of 23), Iowa City residents (62%), with Iowa vehicle registrations (88%). Drivers were mainly stopped for moving violations (70%), were not searched (96%), and were released with a warning (58%). Descriptive statistics are included for a general view of the stop event and characteristics. Multivariate CHAID analyses were conducted to make inferences about the relationships among variables. CHAID segments the sample of traffic stops and reveals the interrelationship between the potential predictors and the events involved in the stop. The CHAID procedure generates a “decision tree” that identifies significant predictors of each decision in question. In effect, the procedure “crossreferences” each event with each potential predictor. Results from CHAID analyses resulted in four events (moving violation, equipment/registration violation, being warned, being arrested) with significant predictors. All four events were significantly related to the age of the driver, although different age groupings surfaced in different stop events. In addition, the sex of the driver (being male) appeared as a second order predictor in being arrested. Race of the driver never appeared as a predictor of any event. These data provide no empirical evidence that the ICPD is systematically engaging in discriminatory stop practices. Stops conducted by the Iowa City Police Department, as a whole, during the study period, do not involve the race of the driver as a significant factor related to events and outcomes. This does not mean, however, that no individual citizen ever experienced discrimination. It is always possible that individual officers may engage in racially biased practices, both in determining which drivers they will or will not stop and in determining what steps to take after the initial contact. To detect discriminatory practices at this level, however, requires constant vigilance by the community, by all the officers within the department, and by the departmental administration. Statistical analysis, while valuable, cannot substitute for community involvement and effective management. The full report notes some minor inconsistencies with the data, provides a discussion of the “baseline dilemma,” makes recommendations for the continued collection of data for future trend analysis, and suggests modifications of the data collection instrument to include more variables and to clarify some possible areas of confusion for officers who are collecting the data