Illinois Traffic Stop Data Analytical Strategy

1. Illinois Traffic Stop DataAnalytical Strategy

2. Illinois Traffic Stop Statistics Study • Legislative History • Some problem areas • Schedule • Data Elements vs. Analytical Strategy • Troublesome language • Statistical aberrations • “False Stops” • Race vs. Ethnicity • Search Data

3. Race and Analysis • Using race in this context can be problematic. • The state law lists five “races” • Caucasian • African American • Native American / Alaskan • Hispanic • Asian / Pacific Islander • Unfortunately these categories do not conform very well to census categories.

4. Census categories • Census 2000 lists the following categories of race: • White • Black or African American • American Indian and Alaska Native • Asian • Hawaiian or Pacific Islander • Some other race • Two or more races (up to six) • Note that the census bureau does not consider “Hispanic” a race!

5. Hispanics as a race • The census, however, in a separate question, asks all respondents if they are of “Hispanic origin”. • About 12.5% of the participants identified themselves as Hispanic. • All of those individuals also listed a race. Some said they were of more than one race—oft times indicating that the second “race” was Mexican or Cuban. • 90% of people who said they were Hispanic listed their race as white!

6. Reconciliation • In order to match the state law we have done the following: • Anyone that listed themselves as Hispanic was categorized as Hispanic (PERF procedures). • Any person that reported that they were of two or more races (less than 2% of the total) was excluded except for those that indicated that they were Hispanic. They were counted as Hispanic. • Any person that listed their race as “some other” is excluded but 97% of them reported that they were Hispanic, and were thus included. • We merged Native Hawaiian/Pacific Islander with Asian

7. Analytical Strategy • Big Question One : To what extent, if any, does a driver’s race influence an officer’s decision to stop a vehicle for a traffic violation. • Big Question Two: To what extent, if any, does race influence what happens after the stop. • Does race influence disposition? • Does race influence the decision to search the vehicle and or the driver?

8. Race and the Stop Decision • Two basic approaches: • Compare the proportion of stops of minority drivers with the minority population of drivers “at risk” for being stopped. • This, of course raises the question of what is the population “at risk”? What is the benchmark? • Examine the reason for stop by race. If race does not influence the decision to stop then reasons should look alike across races. • First let’s look at methods that do not rely on a benchmark.

9. Traffic Stops St. Louis

10. Reason for Stops in Columbia MO

11. Post Stop Activities • Two other analyses will examine what happens after the stop. Both do not need a benchmark! • Dispositions • Searches

12. Dispositions (Joplin Missouri)

13. Searches St. Louis

14. Benchmarks • We have seen three analyses (reason, disposition, search) that do not require a benchmark. These approaches examine the “universe” of stops and look at differences across the groups. • This brings us back to examining the relative frequency of stops by race. • Let’s say that in community X, 25% of all stops are for Hispanics. That does not provide much information unless we have some basis for comparison. Benchmarks provide that basis.

15. What is a Benchmark? • Benchmarks can allow us to understand whether any group is being stopped disproportionately. • It tells us who is using the roadways. • Helps us understand the “true” demographics of a community.

16. Not all benchmarks are equal • Strong Benchmarks • Provide meaningful information • Allow for high quality analysis • Can indicate the existence of a problem • Can indicate the degree, nature, and specifics of a problem. • Weak Benchmarks • Disparities can be masked • Recommendations for future action are ineffective

17. Choosing a benchmark • Factors • Level of measurement precision desired • Availability of financial and personnel resources • Availability of data • Internal Benchmarks • Compares data within department (officers, squads, shifts) • External Benchmarks • Jurisdictional level analysis • No single benchmark is universally acceptable

18. Types of External Benchmarks • Unadjusted Census Data • Adjusted Census Data • Observational (Traffic Survey) • Push/Pull • Traffic Accident Status

19. Why do we Adjust Census Data? • Unadjusted Census data benchmarks are generally inaccurate: • can indicate a disparity where none exists • can “mask” (or hide) disparities • cannot rule out alternative hypotheses • have high “miss” rates for minorities • Census data and stop data measure different populations: • Census data: the residents of a jurisdiction; stop data: both resident and non-residents • Census data: static populations; stop data: transient population • Comparisons • to make a valid comparison, the two items compared must be the same • “matching the numerator to the denominator”

20. Census Adjusted Benchmarks • Age • residents under the age of 15 are generally not at risk of being stopped • Location of the stop • greater police presence in an area causes greater risk of being stopped • different locations may have different driver demographics (near highways, in college towns, etc) • racial/ethnic groups tend to live in “clusters” • Driving quantity of a racial/ethnic group • those without access to vehicles have lesser risk of being stopped • driving by racial/ethnic groups vary across days of the week, time of day, seasons • Driving quality of a racial/ethnic group • Influx of nonresident drivers • journey-to-work data • attractions inherent in a jurisdiction (shopping, entertainment, universities) • major highways

21. Observational studies • Involves “stationary” and “rolling” surveys • stationary: surveyors on street corners record the perceived race/ethnicity of a driver • rolling: surveyors in cars record the perceived race/ethnicity of a driver • Based on the data gathered from the surveys, a demographic driver profile is created • Stop/search data compared to observed data • More this afternoon.

22. Push /Pull • Based on the idea that: • nonresidents enter a jurisdiction to shop, vacation, travel through the region, access entertainment, etc • residents leave/enter a jurisdiction to work • Push values determined by considering (by race): • vehicle ownership • proportion of people who drive 10 or miles to work • driving time between jurisdictions • Pull (“draw”) values determined for each jurisdiction based on: • percent state employment • percent of state retail trade • percent of state food and accommodation sales • percent of state average daily road volume • The 2 values (for push and pull) were combined to determine the demographic profile of a driver in a given jurisdiction

23. Traffic Accident Data • Motorists “not-at-fault” in two vehicle traffic accidents provide a representative sample of the roadway demographics of a particular jurisdiction • Not yet fully validated in a racial profiling data analysis setting • May be difficult to implement as some jurisdictions do not record the race/ethnicity of motorists involved in accidents

24. Our Benchmarking Plan • NUCPS will use Adjusted Census Benchmarking: • Includes persons 15 years or older. • A community’s benchmark consists of two levels: • Community level • County level • Theory is that the adjusted county driving population more accurately represents the driving population for the majority of communities. • For example, The City of Lake Forest has an adjusted minority population of 7.2 % but Lake County has an adjusted minority population of 23.5%. Both of these will be used.

25. Special Cases • Cook County • We have constructed benchmarks for the six judicial districts. These more accurately reflect transportation patterns. • For communities that are in more than one county or that border another county we can use both. • County Sheriffs will use the county • ISP can use state, county, district etc. • Special Departments (university, park police) will use the closest area.

26. What will our report look like? • One statewide analysis and an analysis of every participating agency. • No individual officer level analysis • Modeled largely on other statewide systems, particularly Missouri. http://www.ago.state.mo.us/racialprofiling/racialprofiling.htm • Period for review and agency comment.

27. Summary of analysis • Three non-benchmarked items: reason, disposition, search by individual race, and by white/minority. • Focus on consent searches • Adjusted census benchmark based on community, county or judicial district (Cook County). • Comparison of stops by individual race, and by white/minority. • Our report will indicate where disparities exist. We will not claim that these disparities indicate the presence of racial profiling. • Your task will be to understand and explain the data to stakeholders.

28. Next Steps • Distribution of benchmark data • Questions contact Aviva Grumet-Morris (847) 491-2108 • County and City level benchmarks • Minority population by race • Communities in each Cook County Judicial District • Outreach activities • IACP August • Fairview Heights July 20 MTU 14 • Carbondale Sept. 13 • Data systems design and procurement • Preliminary Analysis