illinois traffic stop data analytical strategy. illinois traffic stop statistics study legislative...
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Illinois Traffic Stop DataAnalytical Strategy
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
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.
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!
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!
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
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?
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.
Reason White 18527 Black 19226
Moving 15970 86%
11811 61.4%
Equipment 965 5.2%
1513 7.8%
License registration
1592 8.5%
5902 30.6%
Traffic Stops St. Louis
Reason for Stops in Columbia MO
White 11705 Black 3217
Moving 65% 49%
Equipment 20% 23%
License 15% 28%
Post Stop Activities
Two other analyses will examine what happens after the stop. Both do not need a benchmark!
– Dispositions– Searches
Dispositions (Joplin Missouri)
Outcome White 17820
Black821
Citation 10367 58%
443 54%
Warning 6773 38%
341 42%
No action 680 4%
37 4%
Searches St. Louis
White Black
Stops 18247 18409
Search (all types) 525 2.9% 1915 10%
Consent Searches
192 1% 609 3.3%
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.
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.
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
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
Types of External Benchmarks
Unadjusted Census Data Adjusted Census Data Observational (Traffic Survey) Push/Pull Traffic Accident Status
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”
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
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.
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
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
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.
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.
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.
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.
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