Statistical Traffic Analysis Report
This tool was created by
to help examine traffic stop records for evidence of racial disproportionality and is based on the method developed by Grogger & Ridgeway (2006).
In order to use this tool, you need to have an Excel or CSV file
of the traffic stop records for a city on which you want to run
analysis. This data will need to have the date and time of each
stop and the race of the driver that was stopped. A year or more
of data is recommended.
When you are ready to begin, click the button below. The
process will take
approximately 10 minutes.
To study the racial distribution of traffic stops, the tool uses
the Veil of Darkness (VOD) approach, which is based on the logic
that police officers are less capable of determining the race of
a motorist after dark than they are during daylight. Using this
method, the existence of racial disproportionality in traffic
stops is assessed by comparing the race distribution of stops
made during daylight to the race distribution of stops made
after dark, after adjusting for other factors in a regression
model. The analysis is limited to stops that occur during the
evening intertwilight period (roughly between 5:00 PM and 9:00
PM, depending on location) in order to reduce the variation in
travel patterns that are conditional on time of day.
The VOD method was developed and first employed by Jeffery
Grogger and Greg Ridgeway in an analysis of traffic stops in
Oakland, California, and Cincinnati, Ohio. The method has also
been used in studies focusing on the nature of traffic stops in
Minneapolis, Minnesota, Syracuse, New York, and more recently in
San Diego, California, and the state of Connecticut.
Our tool incorporates one enhancement from these previous
studies. That is, our model accounts for within-officer
correlation that is likely to occur (when this information is
available in the data). By doing so, we recognize that officers
may have inherent differences in the percentage of a racial or
ethnic subset of the population that they are likely to
encounter. These differences may be caused by factors such as
geographic deployment or unit assignment.
How to interpret results
One benefit of the VOD approach is its statistical
disproportionality present in a community using a reliable data
source. If the reported risk ratio (i.e., risk
of being in a
traffic stop reference group during light vs. dark periods) is
acceptably close to 1.0, or in other words if the percentages
reference group traffic stops during light vs. dark
are acceptably close to one another, it suggests that daylight
not meaningfully associated with the race or ethnicity of the
who was stopped. Alternatively, if the risk ratio is
greater than 1.0, it suggests possible racial
(i.e., that reference group motorists, a racial or ethnic subset
the population of interest, are more likely to be among those
stopped during times when visibility is higher compared to times
when visibility is lower).
We provide the level of statistical
significance for the effect of daylight vs.
intertwilight as well. Significance levels between 0 and 0.05
by risk ratios greater than 1.0) are more indicative of
a potential problem with
disproportionality, and significance levels between 0.05 and 1.0
indicative of a potential problem. However, given the large
available for traffic stop data, interpretation of results
should be more
focused on the size of the difference in the percentages (or,
This tool provides a quantified description of traffic stop
disparities in a community. The results can be useful in a
of ways: to determine whether there is meaningful evidence of
racial disproportionality in traffic stops for a given reference
group, to compare results across various subgroups of stops
across gender, across police units) to pinpoint subgroups that
more attention than others, and to compare results before vs.
an intervention is implemented.
This method identifies patterns of disproportionate contact with
enforcement in regard to traffic stops for selected reference
in a defined time range. This tool does not
identify agencies and/or officers that are engaging in the
of racial bias. Finally, this method does not analyze or provide
context for the reason or cause for a traffic stop or set of
stops to be conducted by a law enforcement officer.
This tool does not provide a customized analysis for each
and results should be considered preliminary. Further analyses
still be warranted to better understand the results and verify
the preliminary analysis has been conducted in a proper and
1. Grogger & Ridgeway (2006). Testing for Racial Profiling in
Traffic Stops From Behind a Veil of Darkness. Journal of the
American Statistical Association, 101(475), 878-887.