Informe conjunto de 2019: Análisis de los datos del uso del perfil racial

THE OFFICE OF POLICE OVERSIGHT, THE EQUITY OFFICE,
AND THE OFFICE OF INNOVATION
2019 JOINT REPORT:
ANALYSIS OF APD
RACIAL PROFILING DATA
NOVEMBER 2020
TABLE OF
CONTENTS
Executive Summary
1
Introduction
3
Background
4
Terminology
4
Methodology
5
Data Sources
6
Data Analysis
8
Proportions of Stop by Race
8
Race Known Compared to Race Not Known in Motor
Vehicle Stops
9
Reason for Motor Vehicle Stops
10
Austin-Round Rock Metropolitan Area Population Compared
to City of Austin Demographics Analysis of Stops
12
Proportion of Searches, Hits, and High Compared to
Low-Discretion Searches, by Race
13
Proportion of Warnings or Field Observations by Race
16
Proportion of Arrests by Race
17
Proportion of Citations by Race
19
Geographic Disparity of Warnings, Field Observations,
and Arrests
21
Gender and Race
25
Motor Vehicle Stop Outcome Totals for the Four
Most Populous Races/Ethnicities
26
Outcome Percentages
27
Comparison of 2018 and 2019 Outcome Data
31
Discussion
33
Recommendations
35
Data Recommendations
37
Conclusion
40
Appendices
41
is
EXECUTIVE SUMMARY
This report by the Office of Police Oversight, Equity Office, and Office of Innovation examines Austin
Police Department (APD) motor vehicle stop data from 2015-2019 to understand how various racial/ethnic
groups in Austin experience motor vehicle stops. This report offers recommendations to address areas
where disproportionality exists and to improve data collection.
In summary:
Between 2015 and 2019, racial disparity persisted and, in many cases, grew worse.
Data from 2019 reveals that racial disparity in motor vehicle stops is still a pervasive problem,
with Black/African Americans being the most overrepresented of all racial/ethnic groups in Austin.
Black/African Americans made up approximately 8% of Austin's voting age population, but
experienced 14% of motor vehicle stops, 25% of stops resulting in searches, and 25% of
stops resulting in arrests.
In 2019, Black/African Americans and Hispanic/Latinos were overrepresented in motor vehicle stops
by 6% and 2%, respectively, while White/Caucasians were underrepresented by 6%, and Asians
were underrepresented by 3%.
The Black/African American driving population had two times more motor vehicle stops per
driving population than the White/Caucasian driving population.
From 2018 to 2019, the overrepresentation of Black/African Americans in motor vehicle stops
changed by 1%, from 7% to 6%. In this same period, the underrepresentation for
White/Caucasians in motor vehicle stops changed by 1%, from -7% to -6%. There was no
change for Latino/Hispanic or Asians in over or underrepresentation in motor vehicle stops.
Black/African Americans were the most overrepresented group across all categories except
citations, for which Hispanic/Latinos were the most overrepresented. On the other hand,
White/Caucasians were the most underrepresented across all categories, and Asians were slightly
underrepresented across all categories.
Hispanic/Latinos were overrepresented across all categories except warnings/field observations. In
this category, Hispanic/Latinos, White/Caucasians, and Asians were all underrepresented.
Once pulled over, Black/African Americans were three times more likely to be searched than
White/Caucasians and were the only racial/ethnic group to receive more high-discretion searches
than low-discretion searches.
Black/African Americans received 58% high-discretion searches versus 42% low-discretion
searches. This disparity has grown by an absolute percentage of 7.7% since 2018.
Black/African Americans were overrepresented in cases when their race was known by officers
before being stopped.
Black/African American drivers were 6% overrepresented when their race was not known
before a stop and were 10% overrepresented when their race was known before a stop.
In the Austin-Round Rock metro area, racial disparity in stops was the same, or worse, than in the
City of Austin.
In the Metro Area, Black/African Americans and Hispanic/Latinos were overrepresented by
7% and 5%, respectively. In other words, Black/African Americans, experienced 1% more
1
overrepresentation in the Metro Area compared to Austin, and Hispanic/Latinos experienced
3% more overrepresentation.
On the other hand, White/Caucasians and Asians were underrepresented by 12%
and 1%, respectively. In other words, White/Caucasians experienced 6% more
underrepresentation in the Metro Area compared to Austin, and Asians experienced 2% less
underrepresentation.
APD vehicle stop data from 2019 also revealed a geographic disparity in warnings, field
observations, and arrests.
Warnings and field observations were most concentrated on the west side of the city, while
arrests were most concentrated on the east side of the city.
A race and gender analysis also revealed disparity.
Among Black/African American drivers, Black/African American males represented 64% of
APD motor vehicle stops in 2019. Meanwhile, Black/African American females represented
36% of APD motor vehicle stops for the same period. The same was true for Hispanic/Latino
males and Hispanic/Latino females.
The gender gap further widens when focusing on motor vehicle stops that resulted in an
arrest. In 2019, there was a 60% difference between Hispanic/Latino men and women and a
56% difference between Black/African American men and women for motor vehicle stops
that resulted in arrests. In other words, both Black/African American and Hispanic/Latino
men were not only stopped more, but also arrested more after a motor vehicle stop
compared to women of the same race/ethnicity.
An analysis of outcome data (i.e., what happens after a stop), not taking proportionality into
account, also revealed racial disparities negatively impacting Black/African Americans and
Hispanic/Latinos.
Black/African Americans and Hispanic/Latinos received higher percentages of searches and
arrests, and Hispanic/Latinos received the highest percentage of citations. On the other
hand, White/Caucasians and Asians received higher percentages of warnings/field
observations.
Once stopped, Black/African Americans were three times more likely to be searched and
approximately three times more likely to be arrested than White/Caucasians.
2
INTRODUCTION
In January 2020, the Office of Police Oversight, Equity Office, and Office of Innovation released the
inaugural Joint Report: Analysis of APD Racial Profiling Data (Joint Report). The Joint Report analyzed
racial disparity in Austin Police Department (APD) motor vehicle stop data from 2015-2018. Additionally,
the report provided recommendations to address the disparities indicated by the analysis.
Texas Code of Criminal Procedure Article 2.134 requires APD to collect and report racial profiling data
annually. The inaugural Joint Report found that APD stopped Black/African Americans and
Hispanic/Latinos at disparate rates, and that these rates worsened from 2015-2018. Additionally, the
analysis found that APD stopped White/Caucasians less than their proportion of the population. Racial
disparities were also found when looking at the outcomes from those stops.
The City of Austin has committed to addressing racial and institutional inequities, improving safety
outcomes, and implementing policy and cultural changes to address the racial disparities in motor vehicle
stops and related citations, warnings, searches, and arrests.
First, the City's multifaceted strategy, Reimagining Public Safety, seeks to improve safety outcomes,
address racial and institutional inequities, and increase accountability and transparency in policing.
Additionally, in June 2020, Austin City Council passed Resolution 50, which formally adopted a goal of
zero racial disparity in policing. To track this goal, Council established the following policy goals for the
Safety Outcome of Strategic Direction 2023:
Zero racial disparity in motor vehicle stops by 2023;
Zero racial disparity in citations and arrests resulting from motor vehicle stops by 2023;
Zero use-of-force incidents per year by 2023; and
Zero deaths at the hands of Austin Police Department (APD) officers per year by 2023.
These goals demonstrate the City's commitment to increasing accountability and transparency within the
Austin Police Department, and they will be measured against the baseline disparities identified within the
January 2020 Joint Report: Analysis of APD Racial Profiling Data by the Office of Police Oversight, Equity
Office, and Office of Innovation and the findings in this report. The City will update the Strategic
Performance Dashboard with these performance metrics.
The purpose of this report is three-fold:
1. Measure disparities in the 2019 APD motor vehicle stop data, as well as trends in this data over the
past five years;
2. Understand how various racial/ethnic groups in Austin experience APD motor vehicle stops;
3. Offer recommendations to address areas of concern.
The data and recommendations contained in this report will be key to our efforts to reimagine public
safety, eliminate racial disparities, and achieve the goals of equitable policing and the fair administration
of justice.
In order to begin rebuilding trust with the communities that have been negatively impacted by inequitable
policing practices, the Austin Police Department must make every effort to address the racial and ethnic
disparities identified in this report and fulfill the accompanying recommendations
1 See Appendix 1 for a list of these recommendations and APD's response to each.
3
BACKGROUND
Terminology
Absolute Percentage: The difference in a number over two periods in time or between two population
types. For instance, if one observed a percent of 6% in 2018 and 7% in 2019, the absolute percentage
difference would be 1%.
American Community Survey (ACS): An ongoing survey conducted by the U.S. Census Bureau that
provides vital information on a yearly basis about the nation and its people. Every year, the U.S. Census
Bureau contacts over 3.5 million households (approximately 3%) across the country to participate in the
ACS, and population data is extrapolated based on the responses of a random sample of households
selected to fill out the survey.
Data Cleaning: The process of manipulating data from its originally published form into a format that
allows for additional analysis.
Disparate Impact: A situation in which an outcome or adverse effect falls disproportionately on a protected
group.
Disparity: "Unequal treatment or outcomes for different groups in the same circumstance or at the same
decision point."2
Disproportionality: The ratio between the percentage of persons in a particular group at a particular
decision point or experiencing an event (e.g. maltreatment, incarceration, traffic stop) compared to the
percentage of the same group in the overall population. This ratio could suggest underrepresentation,
proportional representation, or overrepresentation of a population experiencing a particular
phenomenon. 3
Metropolitan Statistical Area (MSA): An area that consists of one or more counties that contain a city of
50,000 or more inhabitants or contain a Census Bureau-defined urbanized area (UA) and have a total
population of at least 100,000. Counties containing the principal concentration of population-the largest
city and surrounding densely settled area-are components of an MSA.
Racial Parity: Racial parity exists when a particular racial/ethnic group experiences an outcome equal to
the percentage of their respective portion of the population. Racial parity is achieved when there is no
underrepresentation or overrepresentation between racial/ethnic groups.
Racial Profiling: The discriminatory practice by law enforcement officials of targeting individuals for
suspicion of crime based on the individual's race, ethnicity, religion or national origin. Criminal profiling,
generally, as practiced by police, is the reliance on a group of characteristics they believe to be associated
with crime. Examples of racial profiling are the use of race to determine which drivers to stop for minor
traffic violations (colloquially referred to as "driving while Black or Brown"), or the use of race to
determine which pedestrians to search for illegal contraband.4
Spread: The difference between the over or underrepresentation of two races/ethnicities'
disproportionality.
2 "Disproportionality and Disparities," Rowena Fong, Ruth G. McRoy and Alan Dettlaff; Encyclopedia of Social Work
3
"Disproportionality and Disparities," Rowena Fong, Ruth G. McRoy and Alan Dettlaff; Encyclopedia of Social Work
4 ACLU Racial Profiling Definition
4
Texas Commission on Law Enforcement (TCOLE): The state agency responsible for the licensure
of
peace
officers in the state of Texas.
Zero Disparity: A goal set by Austin City Council that seeks to achieve racial parity and equity. In essence,
motor vehicle stops of members of a particular racial/ethnic group should equal the percentage of their
respective portion of the population. Zero disparate impact means all groups would experience outcomes
at the same percentages.
Methodology
The goals of this analysis are as follows:
1.
Examine how often specific groups of people, identified by race/ethnicity, are stopped by APD
compared to their portion of the population; and
2. Explore whether disparate impact exists in the decisions following a traffic stop (e.g. search, arrest,
citation, or warning).
Relevant Population Base
While this report explores data related to motor vehicle stops, driving age is not a readily available
dataset. As a result, this report uses voting age data from the 2010 census as the relevant population
base. 5
Disproportionality and Racial Disparity
Racial disparity exists when a particular racial/ethnic group experiences an unequal outcome compared to
other groups despite being confronted with the same circumstances. When investigating racial disparity, it
is important to not only analyze outcome data, but also disproportionality data.
Disproportionality data provides insight as to how the outcomes experienced by a racial/ethnic group
compare to that group's share of the population, and whether the group is underrepresented,
overrepresented, or proportionally represented.
To determine underrepresentation and overrepresentation in traffic stops, one must calculate the
difference between a racial/ethnic group's percentage of the population and that group's percentage of
stops or outcomes.
A racial/ethnic group that experiences a percentage of vehicle stops that is less than its share of the
population is underrepresented. On the other hand, a racial/ethnic group that experiences a percentage of
vehicle stops that is more than its share of the population is overrepresented.
Calculations for Racial Disparity
Disparity is calculated in this report using two decimal points for motor vehicle stop percentages and two
decimal points for percent of population. When reported in charts and graphs, the disparity is rounded to
the nearest whole number percentage point (e.g. -2.51% is rounded up to -3%, or -2.49% is rounded down
to -2%). This report rounds to the nearest whole number percentage point when referring to percentages
in the text.
5This report uses the following population data as defined by the Census Bureau in the 2010 Decennial Census question P10:
Black/African American = Black or African American alone and Asian = Asian alone. Census Bureau 2010 Census question P11:
White/Caucasian = White alone, not Hispanic or Latino and Hispanic/Latino = Hispanic or Latino
5
Table 1: Model Calculation of Motor Vehicle Stop Proportionality
A
B
C
D
E
F
G
Motor Vehicle Stops
Population
Disparity
Motor Vehicle
Total Motor
Motor Vehicle
Difference
Demographic
Total
% of
Stops per
Vehicle
Stops % of
Population
Population
Population
(Population vs
Demographic
Stops (All)
Total
Stops)
-3% (-2.51%
5,517
139,445
3.96%
39,777
614,925
6.47%
rounded to closest
whole number)
KEY
Formula for Calculating Proportionality: A/B = C, D/E = F, and C-F = G.
G = Racial disparity
Use of General Population Data to Analyze Racial Disparity
The use of general population data to analyze racial disparity issues is widely accepted. For example, Title
VI of the Civil Rights Act prohibits discrimination based on race, color, and national origin in any program
or activity receiving federal financial assistance. In giving guidance to investigators, the Department of
Justice Civil Rights Division Title VI Legal Manual states, "In certain types of cases involving whole areas,
like cities, counties, or states, the investigating agency may use general population data where everyone
in that population may be affected."7
When analyzing disparate impact of the decision points after a traffic stop, we follow legally established
analytical frameworks affirming that the relevant population base for adverse disparate impact is the
subset of the population that is affected by the decision. 8 This disparate impact analysis avoids sample
bias. The number of outcomes per race/ethnicity is divided by the number of motor vehicle stops
experienced by that specific race/ethnicity to demonstrate what percentage of motor vehicle stops result in
a warning/field observation, citation, search, and arrest by race/ethnicity.
Data Sources
Traffic stop and race/ethnicity data: APD produces an annual report on racial profiling that includes the
number of vehicle stops it conducts, categorized by the race/ethnicity of the driver. This joint analysis
focuses on the four most populous races/ethnicities in Austin: White/Caucasians, Black/African Americans,
Hispanic/Latinos, and Asians.
Traffic stop outcome data: APD released data on 2019 vehicle stops categorized as warnings, field
observations, citations, and arrests. Information about motor vehicle stops is collected by police officers
on Texas Commission on Law Enforcement (TCOLE) forms and then added to the APD Reports web
6 Total motor vehicle stops includes all motor vehicle stops, not just those of the four racial/ethnic groups analyzed in this report. Total
population also uses the total population of people living the city of Austin, not just the population of the four racial/ethnic groups
analyzed in this report.
Disparity is reported at the whole number level. Motor vehicle stop percentages and percent of population uses two decimal points.
7
Section VII: Proving Discrimination - Disparate Impact, Pages 22-24, Title VI Legal Manual
8 Section VII: Proving Discrimination - Disparate Impact, Page 21, Title VI Legal Manual
9 Sample bias occurs when some members of a population are systematically more likely to be selected in a sample than others.
Sampling bias limits the generalizability of findings.
6
page. 10 The report team accessed this vehicle stop data through the City of Austin Open Data Portal. For
information related to reasons for arrests, the report team requested and received data from APD directly.
Population data: This report utilizes voting-age population data from the 2010 Census instead of the
American Community Survey (ACS) for several reasons. First, the Census Bureau says the 2010 Census is
99.99% accurate, while the margins of error for annual ACS population data for those 18 and older in
Austin are, on average, more than 30% for Black/African Americans and 9% for White/Caucasians per age
category. While the population of the 4 most populous races/ethnicities grew by 25% from the 2010
Census to the 2018 ACS data, the share of the population for each of the 4 races/ethnicities changed an
average of .5% in that time when comparing the 2010 Census data to the 2018 ACS data. The average
difference between using the 2018 ACS data and the 2010 Census data in 2019 total motor vehicle stop
disparities for the 4 most populous races/ethnicities in Austin is 1% per race/ethnicity.
10 Austin Police Department Reports webpage.
7
DATA ANALYSIS
Proportion of Stops by Race
Since 2015, racial disparity in motor vehicle stops has persisted. While some modest gains have been
made, they do not begin to close the gap between the most recent data and racial parity. To illuminate
how far conditions are from racial parity, one can analyze historical trends in the spread in stops among
different races.
In 2019, the spread between White/Caucasians and Black/African Americans in motor vehicle stops was
12%. In 2015, the spread between these two groups was 8%, with Black/African Americans
overrepresented by 5% and White/Caucasians underrepresented by 3%. While the gap between motor
vehicle stops for White/Caucasians and Black/African Americans improved by 2% from 2018 to 2019, this
does not represent significant progress toward racial parity.
Table 2: Proportionality by Race/Ethnicity of all Motor Vehicle Stops in 201912
# of APD
Motor Vehicle
City of Austin Over
City of Austin
Difference
Race
Motor Vehicle
Stops % of
18 Population
Over 18 % of
(Population
Stops
Total
(2010)
Population
vs Stops)
Asian
5,517
3.96%
39,777
6.47%
-3%
Black/African
19,520
14.00%
48,230
7.84%
6%
American
Hispanic/Latino
45,755
32.80%
188,318
30.62%
2%
White/Caucasian
65,704
47.10%
329,500
53.58%
-6%
From 2018 to 2019, the total number of motor vehicle stops increased by 54,064. In 2019, APD data
showed that White/Caucasians were stopped 65,704 times, representing 47% of all motor vehicle stops
of
that year. White/Caucasians represented 54% of Austin's population, meaning White/Caucasians were
underrepresented in motor vehicle stops by 6%. 13
In contrast, Black/African Americans were stopped 19,520 times and represented 8% of Austin's
population. In 2019, Black/African Americans were stopped at 14% and thus were overrepresented by 6%.
Hispanic/Latinos were overrepresented by 2% in 2019, and Asians were underrepresented by 3%.
11 "Spread" is the difference between over and under representation. See Terminology section for full definition. Any difference in
previous years' values between this and previous reports are due to adjustments in rounding.
12 The "Motor Vehicle Stops % of Total" column in this chart do not add up to 100% but rather 98% because this report compares
each racial/ethnic group to the total motor vehicle stops (139,445), including those of races/ethnic groups not included in the
report. The "City of Austin Over 18 % of Population" column in this chart similarly does not add up to 100% but rather 99%
because this report uses the total city of Austin over 18 population (614,925 in the 2010 Census), including people not in the four
races/ethnic groups of this report.
13 This report uses data for its calculations at the decimal level. The % of motor vehicle stops for White/Caucasian drivers is
47.10%, and the White/Caucasian share of the Austin population in the 2010 Census is 53.58%. When subtracting these numbers,
the difference/disparity is -6.48%, which rounds to -6%.
8
Chart 1: Proportionality of Race/Ethnicity of All Motor Vehicle Stops from 2015-2019
Asian
Black/African American
Hispanic/Latino
White/Caucasian
10%
7%
6%
6%
6%
5%
5%
2%
2%
1%
1%
0%
0%
-3%
-3%
-3%
-3%
-3%
-3%
-5%
-6%
-6%
-7%
-6%
-10%
2015
2016
2017
2018
2019
In short, data from both 2018 and 2019 consistently indicates that the largest disparity between stops and
proportion of the population within any racial/ethnic group continues to be amongst Black/African
American and White/Caucasian motorists: Black/African American motorists are overrepresented by 6%
and White/Caucasian motorists are underrepresented by 6%.
Race Known Compared to Race Not Known in Motor Vehicle Stops
APD collects data for each motor vehicle stop and indicates "whether the subject's race was known to the
officer before the stop.' "14 This data is reported by officers and is separated into Race Known versus Race
Not Known categories.
Between 2018 and 2019, there were increases in the proportion of motorists pulled over when their race
was known before the stop: a 2% increase in stops involving Hispanic/Latinos and a 1% increase in stops
involving Black/African Americans. Conversely, there was a 1% decrease in stops involving
White/Caucasians when their race was known before the stop.
14 As defined in the Austin Police Department's 2019 Racial Profiling Report Guide, published in the City of Austin's Open Data
Portal.
9
Further, the proportion of motorists pulled over when their race or ethnicity was not known before the
stop did not change for any race or ethnicity during the same timeframe. The data indicates that
Hispanic/Latino and Black/African American motorists were pulled over more often when their race was
known before the stop than when their race was not known before the stop.
Upon further examination of cases when race was known before the stop, Black/African Americans were
substantially overrepresented compared to other races. In 2019, Black/African Americans comprised 18%
of overall stops when race was known before the stop, a 10% overrepresentation compared to their share
of the voting-age population.
In contrast, White/Caucasian motorists comprised 50% of overall stops when race was known before the
stop. While this is a 3% increase for this group compared to when race was not known before the stop, it
is still a -4% underrepresentation when compared to their share of the voting-age population. Further,
Asians and Hispanic/Latinos were pulled over more when their race was not known before the stop
compared to when it was known.
In 2019, APD officers reported that race was known before the stop in 4% of motor vehicle stops overall, a
1% decrease from 2018.
Table 3: Proportion of Motorists Pulled Over if Race is Known before the Stop in 2019
Black/African
Hispanic/
White/
Race Known?
Asian
American
Latino
Caucasian
No - race or ethnicity was not
4%
14%
33%
47%
known before stop
5,403
18,289
43,747
62,615
Yes - race or ethnicity was
2%
18%
29%
50%
known before stop
95
970
1,557
2,678
When race was not known before the stop, Black/African American motorists comprised 14% of stops,
Hispanic/Latino motorists comprised 33% of stops, White/Caucasian motorists comprised 47% of stops,
and Asian motorists comprised 4% of stops. Black/African Americans were overrepresented in the Race
Not Known category by 6% and Hispanic/Latinos were overrepresented by 2% when compared to their
share of the voting-age population (see Table 2 for respective share of the voting-age population). In
contrast, White/Caucasians and Asians were underrepresented by 7% and 2%, respectively.
Reason for Motor Vehicle Stops
As stated previously, APD officers document motor vehicle stops on forms provided by the Texas
Commission on Law Enforcement (TCOLE). The form requires that officers enter a reason for the stop.
APD data reflects the following 4 reasons for vehicle stops:
moving traffic violation;
pre-existing knowledge (i.e. warrant);
vehicle traffic violation (equipment, inspection, or registration); and
violation of law other than traffic.
In
2019, moving traffic violations were the reason for three-quarters of all motor vehicle stops. Only 0.24%
of a total of 139,445 stops were due to pre-existing knowledge (i.e., warrant).
10
Table 4: Reason for Motor Vehicle Stops in 2019
Number
Proportion of
Reason for Stop
of Stops
Reason of Stops
Moving Traffic Violation
104,336
75%
Pre-existing knowledge (i.e.
332
0.2%
warrant)
Vehicle Traffic Violation
(Equipment, Inspection, or
14,795
11%
Registration)
Violation of law other than traffic
19,982
14%
Similarly, most stops that resulted in arrests were also due to moving traffic violations. In 2019, moving
traffic violations were the reason for 68% of stops that resulted in arrest. Meanwhile, only 3% of 2019
motor vehicle stops that resulted in arrests were due to pre-existing knowledge (i.e., warrant). 15
Table 5: Reason for Stop that Resulted in Arrest in 2019
Reason for
Number of
Percent of
Stop
Stops
Stops
Moving Traffic
5,305
68%
Violation
Pre-existing
knowledge (i.e.
246
3%
warrant)
Violation of law
other than
2,196
28%
traffic
Chart 2: Reason for Stop that Resulted in Arrest in 2019
Violation of law other than traffic
28%
Pre-existing knowledge (i.e. warrant)
3%
Moving Traffic Violation
68%
15 See the next section for more context on the role of warrants in arrests.
11
Across all categories of stops that resulted in arrest in 2019, Black/African Americans and Hispanic/Latinos
were disproportionately represented. The data demonstrates that moving traffic violations comprised
most of the reasons for motor vehicle stops and stops resulting in arrests.
Table 6: Reason for Stop that Resulted in Arrest by Race in 2019
Black/African
Hispanic/
White/
Reason for Stop
Asian
American
Latino
Caucasian
64
1,440
2,276
Moving Traffic Violation
1,492
1%
27%
43%
28%
Pre-existing knowledge (i.e.
1
88
120
37
warrant)
.4%
36%
49%
15%
22
399
968
799
Violation of law other than traffic
1%
27%
44%
36%
87
1,927
3364
2,328
Total
1%
25%
43%
30%
Austin-Round Rock Metropolitan Area Population Compared to City of Austin
Demographics Analysis of Stops
When compared against the broader Austin-Round Rock Metropolitan Statistical Area (MSA) voting-age
population, racial disproportionality in vehicle stops is largely consistent. 16 While there was a 1% decrease
in overrepresentation of Black/African Americans between 2018 and 2019, this group remained
overrepresented.
Table 7: Racial Disparities in Motor Vehicle Stops in the Austin Metropolitan Area in 2019
Austin Round Rock
Police Motor
Austin Round
Difference
Number of Motor
MSA Over 18
Race
Vehicle Stops %
Rock MSA Over
(population
Vehicle Stops
Population % of
of total
18 Population
vs stops)
population
Asian
5,517
3.96%
63,110
4.92%
-1%
Black/African
19,520
14.00%
91,439
7.14%
7%
American
Hispanic/Latino
45,755
32.81%
352,400
27.50%
5%
White/Caucasian
65,704
47.12%
756,128
59.00%
-12%
Comparing the Austin-Round Rock MSA racial composition to APD racial profiling data for motor vehicle
stops reveals further disproportionality. White/Caucasians were further underrepresented in motor vehicle
stops at -12% in the Austin-Round Rock MSA, versus a -6% underrepresentation solely within the City of
Austin.
Black/African Americans were 7% overrepresented when compared with the population in the Austin-
Round Rock MSA, versus 6% overrepresentation within the City of Austin. Hispanic/Latinos were
overrepresented by 5% when compared with the population in the Austin-Round Rock MSA. Conversely,
Asians were underrepresented by 1% when examining data from the Austin-Round Rock MSA.
16 Last year's report utilized the 2018 Census Bureau ACS data for population by race/ethnicity. This year's report uses the 2010
Decennial Census for consistency with the rest of the report, which uses the 2010 Decennial Census.
12
One suggestion for the overrepresentation in stops of Black/African Americans in Austin is a pattern of
Black/African Americans residing in surrounding communities and commuting to Austin for work and
entertainment. However, the data refutes this as the racial disparity is worse in the Austin-Round Rock
MSA.
Proportion of Searches, Hits, and High Compared to Low-Discretion Searches, by Race
Searches
Racial disparity in searches remained largely the same from 2018 to 2019, with the exception that
White/Caucasian motorists were further underrepresented by 1% and Black/African Americans were
further overrepresented by 1% in 2019.
Chart 3: Proportionality by Race/Ethnicity of Motor Vehicle Stops Resulting in a Search from 2015-201917
-
Asian
Black/African American
Hispanic/Latino
White/Caucasian
30%
20%
17%
-18%
16%
1.7%
18%
13%
13%
12%
12%
10%
1:1%
0%
-5%
-5%
-5%
-5%
-5%
-10%
-20%
-2-1-2
-23%
=24%
-24%
-25%
-30%
2015
2016
2017
2018
2019
17 2015 data is from the 2015 OPM report and 2015 APD Racial Profiling report. The 2016 APD Racial Profiling Report provides
overall motor vehicle stop and search numbers for 2015 which are different from the other 2015 reports. According to the 2016
APD report "although the state requires the reporting of motor vehicle stops that result in a citation or arrest, we [APD] have
modified this year's report to include all motor vehicle stops." This report uses the originally reported 2015 numbers when
possible in order to maintain continuity with previous OPM report figures. The ratios remain very similar. There are slight
discrepancies between data on the City of Austin Open Data Portal and official APD Racial Profiling report data. This report uses
the APD figures when possible, however in the case of 2019 data, this report uses data from the City of Austin Open Data Portal
because the 2019 APD Racial Profiling report does not separate various motor vehicle stop types by race.
13
In 2019, there were 9,454 searches initiated after APD motor vehicle stops. For the number of vehicle stops
that resulted in searches, Black/African Americans were overrepresented by 18% and Hispanic/Latinos by
13%. In contrast, Asians and White/Caucasians were underrepresented by 5% and 25%, respectively.
As with the overall stop data, the disproportionality in stops that resulted in searches highlights the
challenge of achieving racial parity. Racial parity is reached when there is no underrepresentation or
overrepresentation between racial/ethnic groups.
In 2019, the spread between searches involving Black/African Americans and White/Caucasians was 43%.
The spread between searches involving White/Caucasians and Hispanic/Latinos was 38%. In short, the
disproportionality of searches of White/Caucasians compared to Black/African American and
Hispanic/Latino populations underscores the gap between the existing circumstances and racial parity.
Hit Rates
According to the APD 2018 Racial Profiling Report, "productive searches or 'hits' are searches where
contraband is found (e.g., drugs or weapons). "18 The search "hit rate" describes the percentage of
searches that result in finding contraband.
Between 2018 and 2019, there was a 2% decrease in hit rate for White/Caucasians and a 3% decrease in hit
rate for Hispanic/Latinos. In contrast, there was a 1% increase in hit rate for Black/African Americans and
a
1% increase in hit rate for Asians during the same time period
Table 8: Search Hit Rates by Race/Ethnicity in 2019
Race/Ethnicity
Hits
Searches
Hit Rate
Asian
26
108
24%
Black/African American
772
2,397
32%
Hispanic/Latino
1,112
4,154
27%
White/Caucasian
681
2,723
25%
In 2019, White/Caucasians had a 25% search hit rate, or 681 hits among 2,723 searches, while
Black/African Americans had a 32% search hit rate, or 772 hits among 2,397 searches. There is a 7%
difference in hit rate between White/Caucasians and Black/African Americans. However, based on their
proportion of the population, White/Caucasians were underrepresented in motor vehicle searches by 25%
and Black/African Americans were overrepresented by 18% (see Chart 3). The difference in hit rate may
further be explained by the disproportionality of high and low-discretion searches.
High versus Low-Discretion Searches
Low-discretion searches are situations in which policy requires an officer to conduct a search, such as due
to an arrest or a vehicle being towed. High-discretion searches can only be conducted when there is
consent, probable cause, or contraband.19
In both 2018 and 2019, Black/African Americans were the only demographic more likely to receive a high-
discretion search than a low-discretion search. Black/African Americans were 7.7% more likely (absolute
percent increase) to have received a high-discretion search in 2019 than 2018. In the 2018 data, searches
18 APD 2018 Racial Profiling Report
19 The Science of Policing Equity: Measuring Fairness in the Austin Police Department While the search of a motor vehicle is
normally exempted from the search warrant requirement, police do need a basis for the search. The most common reasons cited
are consent, incident to arrest, probable cause, contraband in plain view, frisk for safety, and subject to towing; these are reported
here. Many factors contribute to the existence of probable cause, but the basic premise is that probable cause requires facts or
evidence that would lead a reasonable person to believe the vehicle contains contraband or evidence.
14
for Black/African Americans were almost equally high-discretion and low-discretion. In 2019, however,
APD officers used their discretion to search Black/African Americans 58% of the time.
Table 9: Discretion Classification for Search Types
Discretion Type
Search Type
Probable cause
Consent
High
Contraband/evidence in plain view
Frisk for safety
Incidental to arrest
Inventory of towed vehicle
Low
Arrest of person in vehicle
Towing of motor vehicle
APD data from 2019 shows that Black/African Americans receive proportionally more high-discretion
searches than other races/ethnicities. Since low-discretion searches are required by law and high-
discretion searches are not, data shows that Black/African Americans received disproportionally more
stops and optional searches than other races/ethnicities.
Table 10: Racial Disparities between High and Low-Discretion Searches in 2019
Black/African
Hispanic/
White/
Level of Search Discretion
Asian
American
Latino
Caucasian
23%
58%
44%
34%
High
19
1,124
1,458
755
77%
42%
56%
66%
Low
63
815
1,847
1,445
In 2019, 32% of all high-discretion searches resulted in hits, and high-discretion searches yielded more hits
than low-discretion searches. Black/African Americans received a larger total number of high-discretion
searches compared to White/Caucasians (1,124 versus 755) despite Black/African Americans having a
lower share of the total population.
15
Chart 4: Search Hit Rates for High-Discretion Searches by Race in 2019
35%
High Discretion Searches
30%
25%
Low Discretion Searches
20%
34%
32%
15%
30%
31%
10%
5%
0%
Asian
Black/African
Hispanic/Latino
White/Caucasian
American
Proportion of Warnings or Field Observations by Race
According to the APD General Orders, a field observation is "documentation of a subject stop when there
is not a corresponding incident report, supplement or citation for the stop." A warning issued by an
officer
is a statement that the motorist has committed some offense but is being spared the actual citation.
Officers use their discretion in deciding whether to issue a citation or warning.
While Black/African Americans continue to be overrepresented in motor vehicle stops resulting in
warnings or field observations, there has been a gradual shift toward racial parity since 2017.
20
20 Warnings and Field Observations are reported in the same City of Austin Open Data Portal dataset by APD and are grouped in
this analysis.
16
Chart 5: Proportionality by Race/Ethnicity of Motor Vehicle Stops
Resulting in a Warning or Field Observation from 2015-2019
-
Asian
Black/African American
Hispanic/Latino
White/Caucasian
10%
8%
8%
8%
7%
7%
6%
6%
4%
2%
0%
-1%
-1%
-1%
-2%
-2%
-2%
-2%
-2%
-3%
-3%
=3%
-3%
-4%
4%
-4%
-4%
4%
-6%
2015
2016
2017
2018
2019
White/Caucasians were underrepresented by 3% in 2015 and were underrepresented by 1% in 2019 when
compared to their share of the voting age population. Similarly, Hispanic/Latinos and Asians were also
consistently underrepresented between 2015-2019.
Proportion of Arrests by Race
Since 2015, racial disparities in arrests resulting from motor vehicle stops have persisted and worsened. In
2019, Black/African Americans were the most overrepresented racial/ethnic group in this category,
experiencing 17% overrepresentation in arrests resulting from motor vehicle stops. Hispanic/Latinos were
the second most overrepresented at 13%.
Chart 6: Proportionality by Race/Ethnicity of Motor Vehicle
17
Stops Resulting in Arrest from 2015-2019
Asian
Black/African American
Hispanic/Latino
White/Caucasian
30%
20%
18%
17%
17%
16%
16%
12%
13%
10%
10%
10%
7%
0%
-5%
-5%
-5%
6%
-5%
-10%
=16%
-20%
-19%
-22%
-23%
-24%
-30%
2015
2016
2017
2018
2019
Reason for Arrest
This data was not made available for the inaugural Joint Report and represents a new depth of
examination. According to the data, there are four scenarios in which people are arrested during motor
vehicle stops. The scenarios include the following:
Outstanding warrant: indicates the driver was found to have a pre-existing warrant for arrest.
Violation of city ordinance: indicates the driver was found to have violated a local ordinance, such
as playing amplified music from a vehicle.
Violation of penal code: indicates the driver was found to have committed a crime, such as
possession of controlled substance, driving while intoxicated, or other violations of criminal law.
Violation of traffic law: indicates the driver was found to have violated a traffic law, such as driving
with a suspended or invalid license.
Table 11: Reason for Arrest in 2019
Violation of
Violation of
Outstanding
Violation of
City
Penal Code
Warrant
Traffic Law
Ordinance
67%
27%
6%
0.1%
5,220
2,107
416
3
18
Chart 7: Reason for Arrest in 2019
Violation of City Ordinance
.1%
Violation of Traffic Law
6%
Outstanding Warrant
27%
Violation of Penal Code
67%
In 2019, 67% of all arrests were made based on a violation of penal code. The data shows 27% of arrests
from traffic stops were the result of an outstanding warrant. Six percent of drivers were arrested solely
based on violations of traffic laws.
Reason for Arrest by Race
Black/African Americans and Hispanic/Latinos were overrepresented across every category of reason for
arrest, while White/Caucasians and Asians were underrepresented.
Table 12: Reason for Arrest by Race in 2019
Violation of
Outstanding
Violation of
Violation of City
Race
Penal Code
Warrant
Traffic Law
Ordinance
1%
1%
0.2%
0%
Asian
70
16
1
0
Black/African
20%
35%
31%
25%
American
1,052
743
131
1
44%
44%
41%
50%
Hispanic/Latino
2,272
919
171
2
34%
20%
26%
25%
White/Caucasian
1,799
421
107
1
Proportion of Citations by Race
Racial disparities for Hispanic/Latino and Black/African American motorists worsened in terms of the
proportions of citations by race/ethnicity. While Hispanic/Latinos were proportionately represented in 2015
19
APD motor vehicle citations, by 2019, they were overrepresented by 9%. Additionally, Black/African
Americans remained consistently overrepresented by 5% in 2018 and 2019. Conversely, White/Caucasians
grew increasingly underrepresented from -2% in 2015 to -12% in 2019.
The trend over the last five years indicates a shift away from racial parity for every racial/ethnic group,
except Asians, who were consistently underrepresented. Implications of the racial disproportionality of
citations are explained in the Discussion section.
Chart 8: Proportionality by Race/Ethnicity of Motor Vehicle Stops Resulting in Citation from 2015-2019
-
Asian
Black/African American
Hispanic/Latino
White/Caucasian
15%
10%
9%
6%
5%
5%
5%
4%
4%
4%
3%
2%
0%
0%
-2%
-3%
-3%
-3%
3%
-3%
-5%
-6%
-6%
=8%
-10%
-12%
-15%
2015
2016
2017
2018
2019
20
Geographic Disparity of Warnings, Field Observations and Arrests
As shown in the map below, APD organizes the city into 10 sectors.
Map 1: APD Sectors
130
35
ADAM
RM 620
EDWARD
BAKER
IDA
CHARLIE
GEORGE
DAVID
HENRY
APT
FRANK
The geographic concentration of arrests and geographic concentration of warnings and field observations
remained largely unchanged between 2018 and 2019. The maps that follow display occurrences of
warnings, field observations, and arrests in 2019 by sector. The maps do not display occurrences of
citations as APD is currently unable to map citations. 21 Warnings and field observations occurred
at
a
21 See Data Recommendations section.
21
er rate on the west side of the city, with the highest numbers taking place in Adam Sector, followe
aker and David, respectively.
Map 2: Number of Warnings and Field Observations by Sector in 2019
130
35
ADAM
13,627
EDWARD
RM 620
7,352
BAKER
9,892
IDA
7,159
CHARLIE
6,996
GEORGE
3,601
DAVID
9,344
HENRY
8,774
APT
192
FRANK
7,715
Chart 9: Number of Warnings/Field Observations by Sector in 2019
ADAM
13,627
BAKER
9,892
DAVID
9,344
HENRY
8,774
FRANK
7,715
EDWARD
7,352
IDA
7,159
CHARLIE
6,996
GEORGE
3,601
88
572
0
5000
10000
15000
More arrests occurred on the east side of the city, with the highest numbers taking place in Charlie Sector,
followed by Edward and Henry, respectively.
23
e e oe by III
130
35
ADAM
626
EDWARD
RM 620
1,121
BAKER
698
IDA
869
CHARLIE
1,168
GEORGE
471
DAVID
879
HENRY
959
APT
FRANK
848
Chart 10: Number of Arrests by Sector in 2019
CHARLIE
1,168
EDWARD
1,121
HENRY
959
DAVID
879
IDA
869
FRANK
848
BAKER
698
ADAM
626
GEORGE
471
88
60
0
250
500
750
1000
1250
The geographic disparity in warnings and field observations, as well as for arrests, illuminates how far
existing conditions are from racial parity. 22 Additional visualizations of the data used in the analysis of
geographic disparities are publicly available on this Tableau dashboard, including visuals of motor vehicle
stops by race, time, zip code, and intersection.
Gender and Race
In 2019, regardless of race, male motorists in Austin were more likely to be pulled over or arrested than
female motorists. In terms of likelihood of being pulled over, the widest gap between the genders was
experienced by Black/African American and Hispanic/Latino motorists. Among Black/African American
drivers, Black/African American males represented 64% of APD motor vehicle stops in 2019. Meanwhile
Black/African American females represented 36% of APD motor vehicle stops for the same period. Among
Hispanic/Latino drivers, Hispanic/Latino males also represented 64% of APD motor vehicle stops in 2019.
In contrast, Hispanic/Latino females represented 36% of APD motor vehicle stops for the same period.
Table 13: Percentage of APD Motor Vehicle Stops by Race and Gender in 201923
Black/African
White/
Gender
Asian
Hispanic/Latino
American
Caucasian
Female
40%
36%
36%
41%
Male
60%
64%
64%
59%
Difference
20%
28%
29%
17%
The gender gap further widens when focusing on motor vehicle stops that resulted in an arrest. In 2019,
there was a 60% difference between Hispanic/Latino men and women and a 56% difference between
Black/African American men and women for motor vehicle stops that resulted in an arrest.
22 See Discussion section for further explanation.
23 The contrast between the difference totals for Black/African Americans (28%) and Hispanic/Latinos (29%) is due to rounding.
25
Table 14: Percentage of APD Motor Vehicle Stops that Resulted in Arrest by Race and Gender in 201924
Black/African
White/
Gender
Asian
Hispanic/Latino
American
Caucasian
Female
31%
22%
20%
33%
Male
69%
78%
80%
67%
Difference
38%
56%
60%
35%
The gender and race data highlights additional disparities in stops and arrests. The data indicates that
in
2019, among Black/African American motorists and Hispanic/Latino motorists, males in both groups were
not only the most stopped, but also most likely to be arrested after a motor vehicle stop.
Motor Vehicle Stop Outcome Totals for the Four Most Populous Races/Ethnicities
In 2019, APD conducted a total of 136,496 motor vehicle stops for the four most populous
races/ethnicities.
From 2015 to 2019, the total number of stops for the four most populous races/ethnicities decreased by
16,219. The overall number of citations for the four most populous races/ethnicities decreased by 55,217
and the overall number of arrests for the four most populous races/ethnicities decreased by 1,439.
However, warnings increased by 108%, or 40,437, for the four most populous races/ethnicities between
2015 and 2019.
In response to questions about increased stops, increased warnings, decreased citations, and decreased
arrests in 2019, APD responded that a few variables may have contributed to these changes. However,
APD reported that there was not a readily identifiable or specific cause, such as a policy change or formal
directive.
24
The calculated difference (35%) between White/Caucasian males and White/Caucasian females is due to rounding.
25 The four most populous races/ethnicities in this report (Asian, Black/African American, Hispanic/Latino, and White/Caucasian),
represents on average more than 97% of APD motor vehicle stops per year since 2015.
26
Chart 11: APD Motor Vehicle Stops by Outcome by Year from 2015-2019
Warnings and Field Observations
Citation
Arrests
160,000
37,501
140,000
33,832
39,572
77,938
120,000
106,069
50,108
100,000
91,563
85,102
80,000
60,000
57,977
50,852
40,000
20,000
9,145
9,097
12,111
11,128
0
7,706
2015
2016
2017
2018
2019
Despite the changes in total stops, citations, warnings, and arrests, racial disproportionality in these areas
has continued and has generally worsened since 2015. This is concerning, as it conveys a persistent
problem of racial bias in policing.
Outcome Percentages
Outcome percentages are calculated by the number of outcomes per race/ethnicity divided by the number
of motor vehicle stops of that race/ethnicity. The results of this calculation demonstrate what percentage
of motor vehicle stops result in a warning/field observation, citation, search, and arrest by race/ethnicity.
Table 15: Model Calculation of Post-Stop Outcome Percentages
A
B
C
Motor Vehicle Stops per
Total of Outcome Type per
Outcome Type
Percent Outcome
Demographic
Demographic
Field Observation/Warnings
5,517
3,555
64%
Citations
5,517
1,875
34%
Arrests
5,517
87
2%
KEY
Formula for calculating outcome percentages: B/A = C
C = Percent of an outcome per race/ethnicity and is compared to
other outcome percentages per race/ethnicity.
Comparing racial/ethnic groups in this way illustrates any differences in outcomes for each group without
factoring in citywide population proportionality. As a result, patterns and trends are more clearly highlighted.
Going forward, the Joint Report will use this method to analyze whether APD stop data reveals racial disparity
and, as the Joint Report matures, this analysis will become the principal measure to identify potential disparity.
27
Set side by side, comparing data across the four most populous races/ethnicities included in this report
highlights differences in the frequency of outcomes within each group. Motor vehicle stops with
White/Caucasians and Asians resulted in a higher percentage of warnings/field observations. Motor
vehicle stops with Black/African Americans and Hispanic/Latinos resulted in a higher percentage of
searches and arrests. Motor vehicle stops with Hispanic/Latinos resulted in the highest percentage of
citations.
Table 16: Percent of Stops Resulting in a Warning/Field Observation by Race/Ethnicity in 2019
Race
Motor Vehicle Stop Count
Warning/Field
Warning/Field Observations per
Observation Count
Number of Motor Vehicle Stops
Asian
5,517
3,555
64%
Black/African American
19,520
10,750
55%
Hispanic/Latino
45,755
22,039
48%
White/Caucasian
65,704
41,594
63%
In 2019, White/Caucasians and Asians had a higher percentage of motor vehicle stops that resulted in
warning/field observations at 63% and 64%, respectively. On the other hand, 55% of motor vehicle stops
with Black/African Americans and 48% of motor vehicle stops with Hispanic/Latinos resulted in a
warning/field observation.
Table 17: Percent of Stops Resulting in a Citation by Race/Ethnicity in 2019
Race
Motor Vehicle Stop Count
Citation Count
Citations per Number of Motor
Vehicle Stops
Asian
5,517
1,875
34%
Black/African American
19,520
6,843
35%
Hispanic/Latino
45,755
20,352
44%
White/Caucasian
65,704
21,782
33%
At 44%, Hispanic/Latinos had the highest percentage of motor vehicle stops that resulted in a citation.
White/Caucasians, Asians, and Black/African Americans had similar percentages of vehicle stops that
resulted in a citation at 33%, 34%, and 35%, respectively.
Table 18: Percent of Stops Resulting in an Arrest by Race/Ethnicity in 2019
Race
Motor Vehicle Stop Count
Arrest Count
Arrests per Number of Motor
Vehicle Stops
Asian
5,517
87
2%
Black/African American
19,520
1,927
10%
Hispanic/Latino
45,755
3,364
7%
White/Caucasian
65,704
2,328
4%
At 10%, Black/African Americans had the highest percentage of motor vehicle stops that resulted in an
arrest. At 7%, Hispanic/Latinos had the second highest percentage of vehicle stops that resulted in an
arrest. White/Caucasians and Asians had vehicle stops that resulted in an arrest at 4% and 2%,
respectively. Once stopped, Black/African Americans were approximately three times more likely to be
arrested than White/Caucasians.26
26 After being stopped, 9.9% of stops with Black/African Americans resulted in arrest. After being stopped, 3.5% of stops with
White/Caucasians resulted in arrest. Black/African Americans were arrested 2.8 times more than White/Caucasians. These
numbers appear differently in Table 17 because of rounding.
28
Table 19: Percent of Stops Resulting in a Search by Race/Ethnicity in 2019
Race
Motor Vehicle Stop Count
Search Count
Searches per Number of Motor
Vehicle Stops
Asian
5,517
108
2%
Black/African American
19,520
2,397
12%
Hispanic/Latino
45,755
4,154
9%
White/Caucasian
65,704
2,723
4%
At 12%, Black/African Americans experienced the highest percentage of motor vehicle stops that resulted
in a search. At 9%, Hispanic/Latinos experienced the second highest percentage of vehicle stops that
resulted in a search. White/Caucasians and Asians experienced vehicle stops that resulted in a search at a
rate of 4% and 2%, respectively. Once stopped, Black/African Americans were three times more likely
to
be
searched than White/Caucasians
Chart 12: Percentages of Outcomes of Motor Vehicle Stops Involving Asian Adult Population in 2019
Arrest
Outcomes
2%
Arrest
Citations
Warnings/Field Obervations
Citations
34%
Warnings/Field Obervations
64%
29
Chart 13: Percentages of Outcomes of Motor Vehicle Stops Involving
Black/African American Adult Population in 2019
Outcomes
Arrest
Arrest
10%
Citations
Warnings/Field Obervations
Warnings/Field Obervations
Citations
55%
35%
Chart 14: Percentages of Outcomes of Motor Vehicle Stops Involving Hispanic/Latino Adult Population in 2019
Arrest
Outcomes
Arrest
7%
Citations
Warnings/Field Obervations
Warnings/Field Obervations
48%
Citations
44%
30
Chart 15: Percentages of Outcomes of Motor Vehicle Stops Involving White/Caucasian Adult Population in 2019
Outcomes
Arrest
4%
Arrest
Citations
Warnings/Field Obervations
Citations
33%
Warnings/Field Obervations
63%
Comparison of 2018 and 2019 Outcome Data
By comparing data from 2018 to 2019, this analysis examined changes in rate of outcome by race. The
percentage of stops resulting in a warning/field observation increased for all races/ethnicities. In contrast,
the percentage of stops resulting in a citation, arrest, and search decreased for all races/ethnicities.
Table 20: Percent Change in Stops Resulting in a Warning/Field Observation by Race/Ethnicity from 2018-2019
2018 Warning/Field
2019 Warning/Field
Race
Observations per Number
Observations per
Difference (Absolute %
Number of Motor Vehicle
of Motor Vehicle Stops
change from 2018-2019)
Stops
Asian
48%
64%
16%
Black/African
42%
55%
13%
American
Hispanic/Latino
34%
48%
14%
White/Caucasian
47%
63%
16%
Between 2018 and 2019, the total number of warnings/field observations increased by 29,585, an absolute
percentage increase of 59%. 77 All racial/ethnic groups experienced an increased percentage of motor
vehicle stops that resulted in a warning/field observation. White/Caucasians and Asians experienced the
highest percentage increase at 16%. The percentage of motor vehicle stops with Black/African Americans
and Hispanic/Latinos that resulted in a warning/field observation increased by 13% and 14%, respectively.
27 See Appendix 3 for 2018 outcome totals and percentages. Absolute percentage is defined as the difference in a number over
two periods in time or between two population types. For instance, if one observed a percent of 6% in 2018 and 7% in 2019, the
absolute percentage difference would be 1%.
31
Table 21: Percent Change in Stops Resulting in a Citation by Race/Ethnicity from 2018-201928
2018 Citations per
2019 Citations per
Difference (Absolute %
Race
Number of Motor Vehicle
Number of Motor Vehicle
change from 2018-2019)
Stops
Stops
Asian
48%
34%
-14%
Black/African
42%
35%
-7%
American
Hispanic/Latino
54%
44%
-9%
White/Caucasian
47%
33%
-14%
Between 2018 and 2019, the total number of citations decreased by 5,972, an absolute percentage
decrease of 10%. All racial/ethnic groups experienced a decreased percentage of motor vehicle stops that
resulted in a citation. White/Caucasians and Asians experienced the largest decrease of 14% for both
groups. For Black/African Americans and Hispanic/Latinos, the percentage of motor vehicle stops that
resulted in a citation decreased by 7% and 9%, respectively.
Table 22: Percent Change in Stops Resulting in an Arrest by Race from 2018-2019
Race
2018 Arrests per Number
2019 Arrests per Number
Difference (Absolute %
of Motor Vehicle Stops
of Motor Vehicle Stops
change from 2018-2019)
Asian
2.4%
1.6%
-0.8%
Black/African
16%
10%
-6%
American
Hispanic/Latino
12%
7%
-5%
White/Caucasian
6%
4%
-2%
Between 2018 and 2019, the total number of arrests decreased by 3,381, an absolute percentage decrease
of 30%. All racial/ethnic groups experienced a decreased percentage of motor vehicle stops that resulted
in arrest. Black/African Americans and Hispanic/Latinos experienced the largest decrease, marked by
percentages that went down by 6% and 5%, respectively. For White/Caucasians, the percentage of motor
vehicle stops that resulted in an arrest decreased by 2%. For Asians, the number decreased by 0.8%.
Table 23: Percent Change in Stops Resulting in a Search by Race from 2018-2019
2018 Searches per
2019 Searches per
Difference (Absolute %
Race
Number of Motor Vehicle
Number of Motor Vehicle
change from 2018-2019)
Stops
Stops
Asian
3%
2%
-1%
Black/African
17%
12%
-5%
American
Hispanic/Latino
14%
9%
-5%
White/Caucasian
6%
4%
-2%
Between 2018 and 2019, the total number of stops resulting in a search decreased by 3,100, an absolute
percentage decrease of 25%. All racial/ethnic groups experienced a decreased percentage of motor
vehicle
stops that resulted in a search. For Black/African Americans and Hispanic/Latinos, the percentages
decreased by 5%, the largest decrease. For White/Caucasians and Asians, the percentage of motor vehicle
stops that resulted in a citation decreased by 2% and 1%, respectively.
28 For Hispanic/Latinos, the calculated difference in citations received in 2019 compared to 2018 (-9%) is due to rounding. The
number of 2018 citations per motor vehicle stops for Hispanic/Latinos was 53.7%, in 2019, that same number was 44.5% for a
difference of -9.3%.
32
DISCUSSION
Black/African Americans and Hispanic/Latinos are Repeatedly Overrepresented
An analysis of traffic stops alone shows disparities among race, geography, and gender. Once
disaggregated by race, the data shows disparity in the number of searches, use of discretion within
searches, warnings, citations, and arrests. Across all these areas, Black/African Americans and
Hispanic/Latinos were overrepresented, while White/Caucasians and Asians were underrepresented. An
analysis looking back across five years of data reveals that racial disparity has persisted and, in many
cases, is worsening.
In every year since 2015, Black/African Americans have been the most overrepresented in every category,
which includes motor vehicle stops, warnings, field observations, searches, citations, and arrests. In the
same time frame, White/Caucasians have been the most underrepresented in every category.
Racial Disparity in Searches
In 2018 and 2019, Black/African Americans were the only demographic to receive more high-discretion
searches than low-discretion searches. Compared to 2018, 2019 saw police conducting an even higher
proportion of high-discretion searches versus low-discretion searches with Black/African Americans.
Racial Disparity in Citations
The growing racial disparity in citations poses complex equity concerns. Research from the Brennan
Center for Justice at New York University School of Law examined the efficacy and equity of fines and
fees associated with the criminal justice system in ten states, including Texas. Findings from the research
reveal that citations are more frequently levied on low-income communities of color and are ultimately
more burdensome on less-affluent communities. 29 Further, the report states that fines and fees are an
inefficient source of government revenue. In 2017, Travis County spent almost $4.8 million in
administering misdemeanor and traffic courts, as well as $4.6 million jailing those who failed to pay fees
and fines. 30
Geographic Disparity
Geographic disparity was consistent from 2018 to 2019, with more arrests occurring on the east side of the
city, and more warnings on the west side of the city. Citation data was not usable for this geographic
analysis because, unlike warnings/field observations, it is collected in a write-in format, rather than
geolocation (see Data Recommendations section). Geographic disparity highlights issues around
gentrification and economic equity that often intersect with race.
5-Year Analysis Reveals Racial Disparity in Policing is a Persistent Problem
Over the last five years, the total number of warnings, field observations, citations, and arrests shifted.
Even with these changing numbers, racial disparity continued and generally worsened. This trend
indicates a persistent problem.
29 Menendez, Matthew, et al. The Steep Costs of Criminal Justice Fees and Fines: A Fiscal Analysis of Three States and Ten
Counties. Brennan Center for Justice at New York University School of Law, 21 Nov. 2019,
www.brennancenter.org/sites/default/files/2019-11/2019 10 Fees%26FinesFinal5.pdf
30 Menendez et al.
33
Outcome Percentages Show Disparate Impact on Black/African Americans and Hispanic/Latinos
In analyzing the decision to search, arrest, cite, or give a warning, the data showed that stops involving
Black/African Americans and Hispanic/Latinos resulted in higher percentages of searches and arrests.
Once stopped, Black/African Americans were three times more likely to be searched and were
approximately three times more likely to be arrested than White/Caucasians. The percentage of stops
involving Asians and White/Caucasians resulted in a higher percentage of warnings/field observations.
Stops involving Hispanic/Latinos resulted in the highest percentage of citations. Outcome percentages are
not equal among the four most populous races/ethnicities in Austin.
34
RECOMMENDATIONS
In order to address racially disproportionate impacts in policing and meet the City's equity goals, the
Office of Police Oversight, Equity Office, and Office of Innovation recommend that APD take the following
steps:
Acknowledge
1. Acknowledge that racial disparity has persisted and, in many cases, worsened over the last 5 years.
2. Acknowledge that the Department's efforts to address racial disparity have not worked, and that
APD is not on track to meet the vision of Fair Administration of Justice identified by City Council
and the community within Strategic Direction 2023.
Commit
3. Commit in writing to implementing the recommendations from the inaugural Joint Report: Analysis
of APD Racial Profiling Data in order to align with the goals of Resolution 50 and Strategic Direction
2023, Fair Administration of Justice.
4.
Commit in writing to the goal of zero racial disparity and to publicly sharing the Department's
efforts toward eliminating racial disparity, as well as the results of such efforts, whether successful
or not.
Such discussion should appear annually in the APD Racial Profiling Report.
Specific changes and their measured impacts must be included in APD's public reports.
Oversight of this process will be conducted by the Office of Police Oversight, the Equity
Office, and the Office of Innovation to ensure the analyses and reports are done properly
and with proportionality based on race/ethnicity and other demographic information.
Proportionality is essential to this discussion, as City Council used the inaugural Joint Report
as a baseline measurement of racial disparity in citations, field observations, warnings, and
arrests resulting from vehicle stops for Strategic Direction 2023 metrics. The Joint Report
utilizes proportional analysis to gauge racial disparity; for transparency and consistency,
APD should do the same.
5. Commit in writing to meeting and collaboratively engaging with the community to address racial
disparity in policing.
Engage with the Community
6. Work with the community to create a strategic plan to eliminate racial disparity in policing.
The plan should include benchmarks to measure the progress of Department changes within
APD General Orders, training, procedures, community interactions, reporting, and any other
related areas.
7. Meet and collaboratively engage with the community on a quarterly basis.
These meetings should center communities that are disparately impacted by APD in relation
to searches, arrests, and uses of force.
8. Work with the community to establish progress benchmarks and report back to City Council and
the City-Community Reimagine Public Safety Task Force.
Currently, APD does not have its own strategic plan to eliminate racial disparity in policing.
As stated in the inaugural Joint Report, any racial disparity more than zero is unacceptable.
APD needs benchmarks in order to both comply with City Council's Resolution 50 and track
progress toward the goal of zero disparity. APD should commit in writing to asking the
community for acceptable benchmarks and take responsibility for meeting them.
9. Meet the benchmarks, recommendations, and directives put forth by the community, City
Community Reimagine Public Safety Task Force, and City Council.
35
APD has not stated its responsibility and progress toward achieving goals outlined in
Resolution 50 and Strategic Direction 2023, Fair Administration of Justice.
In order to meet these goals, the Department should acknowledge its standing in achieving
these goals through quarterly meetings with the community.
Train and Intervene
10. Acknowledge the role of officer discretion in racial disparity and implement training and
intervention measures aimed at addressing it.
The inaugural Joint Report recommended an examination of the role that race plays in
police officer decisions and discretion. APD responded by saying "[a]dditional data and
analysis is necessary to determine how officer discretion, Departmental procedures, and
societal factors contribute to these disproportionalities."31
This Joint Report expands the analysis of searches, and the data reveals more about the
disproportionate use of police officer discretion in searches with Black/African Americans.
To be clear, officers' discretion to conduct searches yields a clear disparate impact on
Black/African American drivers. The Department should acknowledge the persistently
unequal outcomes that result from officer discretion and take both proactive and remedial
steps to correct this.
In addressing the role of officer discretion, APD should utilize recommendations from the
City-Community Reimagining Public Safety Task Force and the third-party contractor audit
of APD communications findings related to the cultural and educational factors contributing
to racial disparity.
Additionally, the Office of Police Oversight, Equity Office, and Office of Innovation renew all
recommendations set forth in the inaugural Joint Report on which APD has yet to taken action.32
31 See Appendix 1 for more information.
32 See Appendix 1 for more information.
36
DATA RECOMMENDATIONS
Data analysis is a key component in increasing transparency and accountability. The work of data analysis
is less inhibitive when the owners of data ensure that it is properly collected on the front end. Currently,
not all APD data is collected or reported uniformly. Additionally, there are unavailable data sets because
of
APD's method of collecting and/or reporting. In order to best track progress towards zero racial disparity in
policing, data must be accurately collected, publicly available, and usable for analysis. The following
recommendations were informed by the data cleaning process conducted by this report team.
Collect and Publish Additional Data
Arrest data cannot currently be attributed to one officer, which poses challenges in understanding
officer behavior on an individual level. This data should be collected. APD has access to APD officer
data that can be traced from year to year. Tracking officer-level data annually could serve to better
observe and understand behavioral trends amongst officers. Additionally, use of force and
response to resistance data associated with motor vehicle stops should be linked and shared.
Furthermore, in order to create a clear picture of policing across the city, ongoing officer activity
data should be collected, linked to initiation and outcomes, and shared. On August 21, 2020, the
City of Austin's Office of Innovation released the Calls for Service Trends: January 2017-June 2020
Dispatched vs. Officer Initiated. The APD data set used for this analysis detailed activities such as
directed patrol and checking area by county but was not linked to officer activity outcomes.
Additional information, such as how many officers were deployed doing these activities, individual
officer coded activities, associated geographic coordinates, demographic information of the citizens
involved in officer interactions, and linked outcomes can provide clarity surrounding the differences
among officers and geographic areas.
Engage in Uniform Data Collection and Entry
Citations are not geographically tracked in the same way as field observations, warnings, or arrests.
For citations, the geographic data collected is significantly incomplete, and thus APD omits this
data. Geographic data for citations should be collected in the same manner as field observations,
warnings, and arrests so that the data is complete and usable. Data containing the X and Y
coordinates and ZIP codes were collected for warnings and field observations and arrests, but not
for citations. A geographic analysis was not possible for citations because of the missing data.
Column names were also not uniform. For example, data regarding the county of arrest was under
the column titled "County_description" but for warnings the column was labeled "County_desc".
37
APD data is entered utilizing different labels. For example, here is how race is stored in the Arrests
table:
HISPANIC OR LATINO
WHITE
BLACK
ASIAN
MIDDLE EASTERN
UNKNOWN
AMERICAN INDIAN/ALASKAN NATIVE
HAWAIIAN/PACIFIC ISLANDER
Here is how race is stored in the Warnings and Field Observations table:
W
H
B
A
M
U
P
I
The lack of uniformity across different data topics makes it more difficult to analyze the data,
thereby undermining the utility of its collection. For the purposes of this report's analyses, data was
cleaned both manually and utilizing Python script. Uniform data entry is recommended.
Use Data Entry That Is Recognizable to Analytical Software
Geographic data was collected and shared on the City of Austin Open Data Portal as spherical
coordinates rather than standard latitude and longitude coordinates. Most analytical software
cannot use spherical coordinates. Longitude and latitude coordinates should be used going forward
because they are the most common geographic coordinate system.
38
Time is currently stored as a number and not as an actual timestamp. For example, the time 23:19
PM is stored as the number 2319 as seen here:
Table 24: APD Time Data Storage 2019
ID
Date
Time
LOCATION
4500
BLOCK
0 20191980202
7/17/2019
338.0
MANCHACA
RD
300 W BEN
1 20192081973 7/27/2019 2319.0
WHITE
BLVD WB
3000
BLOCK N IH
2
2019760381
3/17/2019
303.0
35 SVRD
NB
8100
3 20191940048 7/13/2019
26.0
GEORGIAN
DR
12000 N
4
2019892103
3/30/2019
2353.0
MOPAC
EXPY NB
To undertake time-based analysis, one would need to break down the numbers into the last two
digits for the minutes, and the first one or two digits for the hours, add a colon between them, and
then convert this to a timestamp. Data should be stored in timestamp format so it can be more
easily used.
Provide Retroactive Data-Cleaning for The Public
The purpose of data collection is to understand patterns and trends. This can most easily be
accomplished if the data is stored conveniently on the City of Austin Open Data Portal in a method
that can be analyzed. In the meantime, it should be APD's responsibility to make the data
presentable and usable for the public. Our data team has volunteered to contribute all data-
cleaning scripts to APD should the Department need to use them.
39
CONCLUSION
To begin rebuilding trust with the communities that have been negatively impacted by inequitable policing
practices, the Austin Police Department must make every effort to address the racial and ethnic disparities
identified in this report and fulfill the accompanying recommendations. The data contained in this report will be
crucial to our efforts to reimagine public safety, eliminate racial disparities, and achieve the goals of equitable
policing and the fair administration of justice. Zero disparity in motor vehicle stops is a goal that APD must
work collaboratively with other stakeholders to achieve, including City management and departments, City
Council, and the community. This is the path for APD to fulfill its mission and vision to protect and serve
all
the
communities in Austin effectively.
40
APPENDICES
Appendix 1
Summary of Recommendations in the
APD Response
January 2020 Joint Analysis and APD
Response: January 2020
Recommendations
Rec 1: Acknowledge that racial
The Austin Police Department consistently and unequivocally
disparity exists and is worsening.
acknowledges that racial disparities exist throughout aspects of our
city, including police enforcement actions. Accordingly, the
Department has readily taken many steps to address the disparities
within APD's purview over the past five years, as detailed in the
January 14, 2020 response. Racial disparities have persisted despite
these efforts, and the widening of certain gaps has raised additional
concerns that demand further attention and analysis.
Rec. 2: Acknowledge that the
The primary purpose of APD's annual racial profiling report is to
methodology previously used omitted
comply with state legislative mandates that require the reporting of
the context of proportionality and
specific data. Proportionality assessments are not compulsory.
therefore was an incomplete analysis.
However, recognizing the importance of such information, APD
This resulted in a perception that a
collaborated with the Center for Policing Equity to conduct a
trend of disparity did not exist.
comprehensive analysis of the racial disparities manifested in the
Department's enforcement actions. The report was the first to apply
the National Justice Database's independent analytic framework to
police data made available through President Obama's Police Data
Initiative, Measuring Fairness in the Austin Police Department. That
report is posted alongside the Department's racial profiling reports on
the City's website.
http://austintexas.gov/sites/default/files/files/Police/Austin_PDI_Report_
2016_Release.pdf
Rec 3: Acknowledge that race plays a
The Department acknowledges that the outcomes of many police
major role in who we stop, search,
activities result in racial disparities. Additional data and analysis is
and for whom we use discretion
necessary to determine how officer discretion, Departmental
favorably.
procedures, and societal factors contribute to these
disproportionalities.
Rec 4: To gain community trust,
The Department is committed to reducing racial disparities to zero,
proportional racial disparity in motor
particularly disparities that are the result of officer discretion.
vehicle stops, arrests, searches, field
observations, warnings, and citations
should be zero.
41
Rec 5: The official comprehensive
The Department will continue to release its state-mandated racial
analysis of racial profiling shall be
profiling report on an annual basis and welcomes the Office of Police
conducted and released by the City of
Oversight's independent analysis and insight, in the manner the City
Austin Office of Police Oversight,
Manager deems necessary and appropriate.
although state-mandated reporting
may continue under the purview of
the Chief.
Rec 6: In order to uphold data
In accordance with departmental procedures, Officers are required to
integrity, accuracy, and transparency,
document the race and ethnicity of the individuals they stop. The City
officers should verify the racial and
has contracted with Dr. Alex Del Carmen, an expert on racial profiling
ethnic identity with people they stop.
and discrimination, to regularly audit the Department's racial profiling
The verified data should be
data to ensure accuracy in data collection and reporting. The traffic
documented in officer reports and be
stop data, which includes race, is published in the racial profiling
published in the Racial Profiling data
datasets on the City's Open Data Portal.
sets on the City's Open Data Portal.
Rec 7: Analyze and report on the
Currently, the Department is not staffed or equipped to quantify and
operational inefficiencies and costs
analyze this data but would readily collaborate with the City Auditor's
that disproportionate racial disparities
office or another entity, at the direction of the City Manager.
create by the second quarter of the
fiscal year 2020 and provide to the
City Manager and Council.
Rec 8: Explore promising practices
The Department agrees the City should invest in sophisticated
from Oakland and Nashville that use a
oversight tools that are more adept at identifying, flagging, and
scoring mechanism for
tracking at-risk officers in order to facilitate timely and effective
disproportional behavior to identify
interventions.
at-risk officers and assign appropriate
interventions and use in the
determination of promotions.
Rec 9: Include implicit bias testing in
Based on the best available evidence from subject-matter experts on
the Austin Police Department hiring
bias, the Department operates with the understanding that every
process.
applicant will have implicit biases. Therefore, the Department
administers training to ensure all employees are aware of their biases,
promulgates explicit policies to set clear expectations that bias-based
actions are intolerable, and utilizes oversight mechanisms to identify
inappropriate behavior.
Rec 10: For current employees,
As stated above, the Department has mechanisms in place to identify
require implicit bias testing and flag
and rectify inappropriate behavior. Additionally, the Department is
high-scoring officers for appropriate
open to exploring proven, evidence-based testing methods that are
intervention.
capable of effectively supplementing current training, policies,
procedures, and audits.
42
Rec 11: Identify and implement bias-
The Department recommends the City contract with a suitable
countering policies, practices,
academic institution to conduct an independent, comprehensive, and
methods, processes, and standard
evidence-informed assessment of the Department's enforcement
operating procedures to mitigate bias.
practices, cultural norms and customs, training, accountability
procedures, and any resulting racial disparities. A similar partnership
between the City of Oakland and Stanford University yielded
promising results and provided a roadmap for creating community-
based strategies aimed at addressing the unique historical and cultural
challenges of a particular city: Data for Change & Strategies for
Change.
Rec 12: Include the comprehensive
The Department intends to incorporate the Racial History of Policing
Racial History of Policing curriculum
training in future cadet classes and is determining the best approach
in the cadet training academy and
and frequency for administering the training to existing officers.
adapt it into required training for
existing officers, at all ranks, annually.
Rec 13: Follow the guidelines for racial
The Department is committed to following the established guidelines
equity training established by the
for racial equity training and welcomes input from the Equity Office
Equity Office. The Equity Office and
and Office of Police Oversight.
Office of Police Oversight shall be
consulted for final selection of official
racial equity training for officers at all
ranks.
Rec 14: Develop a method to provide
The Department is eager to provide additional racial equity training for
racial equity training on an ongoing
all employees in an effective, feasible, and sustainable manner. The
basis (a minimum of 40 hours per
Department will consider this recommendation as part of the FY21
year) for all staff, sworn and civilian,
budget process.
in the department, annually, during
every year of service.
43
Appendix 2
Chart 16: Motor Vehicle Stops Per Year for the Four Most Populous Races/Ethnicities from 2015-2019
160,000
152,715
140,000
136,785
136,496
134,492
119,213
120,000
100,000
2015
2016
2017
2018
2019
Appendix 3
Table 25: Percent of Stops Resulting in a Warning/Field Observation by Race/Ethnicity in 2018
Warning/Field Observations
Warning/Field
Race
Motor Vehicle Stop Count
Observation Count
per Number of Motor
Vehicle Stops
Asian
4,387
2,116
48%
Black/African American
17,754
7,504
42%
Hispanic/Latino
39,946
13,664
34%
White/Caucasian
57,173
26,824
47%
Table 26: Percent of Stops Resulting in a Citation by Race/Ethnicity in 2018
Race
Motor Vehicle Stop Count
Citation Count
Citations per Number of
Motor Vehicle Stops
Asian
4,387
2,118
48%
Black/African American
17,754
7,446
42%
Hispanic/Latino
39,946
21,470
54%
White/Caucasian
57,173
26,943
47%
44
Table 27: Percent of Stops Resulting in an Arrest by Race/Ethnicity in 2018
Race
Motor Vehicle Stop Count
Arrest Count
Arrests per Number of
Motor Vehicle Stops
Asian
4,387
106
2%
Black/African American
17,754
2,804
16%
Hispanic/Latino
39,946
4,812
12%
White/Caucasian
57,173
3,406
6%
Table 28: Percent of Stops Resulting in a Search by Race/Ethnicity in 2018
Race
Search Count
Searches per Number of
Motor Vehicle Stop Count
Motor Vehicle Stops
Asian
4,387
150
3%
Black/African American
17,754
3,072
17%
Hispanic/Latino
39,946
5,514
14%
White/Caucasian
57,173
3,704
6%
45