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Race and Prosecutions
2018 Update
A Report of the Santa Clara County
District Attorney’s Office
Jeff Rosen, District Attorney
1 | P a g e
Introduction
Racial disproportionality in our criminal justice system remains a stark and vexing problem.
While we do not have a solution for it yet, we are determined to continue to study the issue in
hopes to understand it better. One day, we hope to see a system that is as humanly fair and free
of bias as possible.
To continue the process of getting there, the Santa Clara County District Attorney’s office has
greatly expanded the collected data for our Race and Prosecutions Report for 2018, the third such
collation. In this year’s report, the District Attorney’s Office continues to examine race in our
criminal justice system, and looks at other data that may be connected to involvement in the
criminal justice system: income inequality, juvenile safety, education and early life inequality.
Newly-collated for this report we report income, unemployment and poverty stats on the five zip
codes where the greatest number of defendants come from. We have included data on health
from those zip codes, data on housing over crowdedness from those zip codes, and on the
perceptions of safety by the residents in those zip codes.
We have also expanded the race-based data we collect on youth crime, victimhood and even
perception of safety in our community and schools.
Previously we had reported drop-out rates by race in our County. Now the youth data is greatly
expanded to also include perceptions of safety at school by racial groups in our County, physical
fighting at school by race, self-identification as gang-affiliated by race in our youth.
Our ongoing effort to study racial disproportion began in 2016, with the Santa Clara County
DA’s Office first comprehensive report on Race and Prosecution Data. The report sought to
determine whether racial disproportionality exists in the County’s defendant population, and to
explore the racial disparities that did emerge. The 2016 report showed that racial
disproportionality between the percent of defendants from different racial and ethnic groups
compared to Census data about our county’s racial composition exists across all crime types, and
in cases investigated by the police in all different ways.
In 2017 the DA’s Office partnered with the non-profit research organization BetaGov to study
internal office procedures, in an effort to identify whether decisions made within the DA’s office
led to further disproportionality. This random control trial study did not find any
disproportionality or bias in the decisions made by deputy district attorneys at the charging stage
of a case.
Our goal is to build each year on our understanding of racial disproportionality, eventually creating
a long-term and layered database of statistics that we can use to tackle and diminish it.
2 | P a g e
Demographics of Santa Clara County
Santa Clara County is the largest county in Northern California, with nearly 2 million residents.
The 15 cities in the county are diverse and have widely different racial compositions. Santa Clara
County overall has the highest median income of any California county, and ranks 4th in the state
for employment. The county’s wealth is not evenly distributed among and between cities or
races, as seen in the charts below:
1
2
1 US Census, 2016 American Community Survey
2 US Census, 2017 American Community Survey 5-Year Estimate
White/Caucasian33%
Black/Af.Amer.2%
Asian/P.Isl.35%
Hispanic/Latino26%
2+ Races3%
Other1%
Santa Clara County: Race/Ethnicity 2016
118,236
71,493 69,052
128,927
63,576
White/Caucasian Black/AfricanAmerican
Hispanic/Latino Asian/Pac.Islander Other
Median Household Income: 2017
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A side-by-side comparison of the racial composition in the County’s largest cities shows how
each city has a slightly different racial and ethnic breakdown from the County average. San
Jose’s demographics are the most like Santa Clara County as a whole, but Gilroy, Milpitas, and
Palo Alto each have a single race with 50% or more of the population.
27.30%
32.90%
33.50%
2.80% 3.00%
San Jose: 2016
32.90%
44%
15.60%
3.40% 3.60%
Santa Clara: 2016
57.70%27.80%
7.10%
4.70% 2.20%
Palo Alto: 2016
30.80%
5.70%
59.90%
1.60%
Gilroy: 2016
13.60%
65.10%
16%
2.80% 2.50%
Milpitas: 2016
44.80%
27%
21.40%
3.80% 2.30%
Mountain View: 2016
4 | P a g e
Zip Code Analysis
In both the adult and juvenile populations, there are 5 primary zip codes that represent the lions’
share of the criminally charged. In 2017, the adult and juvenile population’s top five zip codes of
residence for individuals charged with a crime were: 95020 (Gilroy), 95112 (Downtown San
Jose), 95111 (Southeast San Jose), 95122 (East San Jose) and 95127 (East San Jose/Foothills).
The Santa Clara County Public Health Department compiles robust data about education,
employment, health and safety for each of the zip codes in the County. Looking more closely at
our defendant zip codes, we find that defendants most commonly reside in poorer, more
dangerous, and less healthy neighborhoods. One particularly important statistic involves the
share of the population who feel that crime is a major problem. In the County overall, 42% of
residents feel that crime is a problem, whereas that number is nearly double that, 81%, in East
San Jose. In Downtown and East San Jose, the median household income is far below the County
average, with twice the number of residents living below the poverty line. Perhaps
unsurprisingly, Santa Clara County ranks among the highest in the nation for income inequality.3
3 https://www.mercurynews.com/2018/02/15/income-inequality-in-the-bay-area-is-among-nations-highest/
1,574
1,932
1,442 1,418 1,369
95020 95112 95111 95122 95127
Charged Defendants by Zip Code (2017)
5 | P a g e
In examining data from the Santa Clara County Department of Public Health’s surveys, and data
from the United States Census Bureau, we can see some of the differences in these regions:4
County Overall 95020
(Gilroy)
95112 (Downtown
San Jose)
95111 (Southeast San Jose)
95122 (East San
Jose)
95127 (East
Foothills)
Median household income
106,761 90,144 60,569 66,549 66,606 84,239
Unemployment Rate 5.7% 6.0% 7.7% 7.5% 8.0% 6.8%
Families w/ children below Poverty Line
7.4% 13.1% 14.0% 17.7% 17.9% 10.8%
Children below Poverty Line
9.7% 18.4% 15.9% 22.4% 20.2% 15.0%
Adults with fair or poor self-rated health
19% 85% 74% 69% 62% 72%
Adults reporting neighborhood crime is somewhat or a major problem
42% 63% 66% 75% 81% 63%
What Can Be Done About County Inequality and Specific Zones of Crime?
The District Attorney’s Office uses this zip code data to set policy in addressing the unique needs
in these parts of the County. The Community Prosecution Unit is specifically stationed in
Gilroy, Downtown San Jose and East San Jose, working to understand the needs of those
neighborhoods and to set crime prevention policies in those communities. Prevention efforts are
an important part of the District Attorney’s role in stemming the flow of new criminal behavior.
The efforts of the Community Prosecution Unit have been instrumental in addressing the unique
needs of these neighborhoods.
Criminal Prosecution:
Prosecutions begin when investigating agencies submit criminal cases to the District Attorney’s
office for review. These case submissions often include police reports, documents, recordings,
photos and other materials. Prosecutors review those materials to determine whether to file
criminal charges. That means that the suspects who are being considered for the potential filing
of criminal charges are those who the police agencies arrest or investigate.
The District Attorney’s Office reviews submitted case documents to determine:
• whether a crime has been committed,
• whether we know who committed the crime,
• whether we can prove the case in court beyond a reasonable doubt, and,
• whether prosecuting the case is the right thing to do
One of the most important decisions prosecutors make is whether to charge someone with a
crime. So we examined whether there was a difference in the rate at which charges were filed
4 US Census Bureau, American Community Survey 2014-2017, and Santa Clara County Dept. of Public Health Zip Code Profiles (2016), available at: https://www.sccgov.org/sites/phd/hi/hd/Pages/zipcodes.aspx
6 | P a g e
against a suspect based? on race/ethnicity. As seen below, the percentage of cases charged was
nearly constant across all races and ethnicities.
% Felonies
Charged
% Misdemeanors
Charged
White 83.8 86.3
Black/ African Amer. 82.6 86.9
Hispanic 86.7 87.5
Asian/ Pacific Islander 82.5 79.8
Other 80.3 80.4
Unknown 73.5 76.6
Race of Prosecuted Defendants:
Clear racial disparities appear in the relative percentages of prosecuted defendants as compared
to their representation in the community. To see this troubling pattern, we examined the
percentages of our total prosecutions for adult felonies and misdemeanors against people of
different racial or ethnic groups. As discussed here, race and ethnicity are based on the
defendant’s self-identification at booking or arrest. “Unknown” does not mean that a person
does not know their racial or ethnic identification, but rather that that information was not
entered into the electronic database to tabulate these totals.
When compared to the racial make-up of our County, we prosecute a higher percentage of
Hispanic/Latino and Black/ African-American defendants compared to their representation in our
community. We prosecute a lower percentage of Asian/ Pacific Islander defendants compared to
their representation in the community. White/ Caucasian defendants are prosecuted in a
percentage that is closest to their makeup of our County.
21.5
12.7
44.9
8.3
12.5
25.2
10.8
46.1
7.510.4
33
2
26
35
4
0
5
10
15
20
25
30
35
40
45
50
White/Caucasian Black/AfricanAmerican
Hispanic/Latino Asian/Pacific Islander Other
% o
f Fi
led
Cas
es
Felony & Misdemeanor Defendants: 2017
Felony Misdemeanor Population
7 | P a g e
.
Youth Indicators of Racial Disparity:
Long before racial inequity presents in the adult criminal population, there are complex social
factors that seem to contribute to early disparities in education, safety, wealth and health among
minority communities in Santa Clara County. The Crime Strategies Unit and the Community
Prosecution Unit at the DA’s office have worked hard to identify and address the root causes of
the racial disparity we see among the young people within the criminal justice system. The DA’s
Community Prosecutors work on Juvenile-based programming and intervention, with the aim of
decreasing recidivism and preventing the county’s youth from crime, arrest, convictions and
incarceration.
To understand some of the root causes of racial disparity, we looked at existing data on some of
these factors. One major source was The California Healthy Kids Survey (CHKS), which is a
statewide survey of student sentiment.
This report also looked at studies published by the Santa Clara County Juvenile Justice
Commission along with internal D.A. Office data to determine whether early inequities existed
in the data.
White/Caucasian25%
Black/African American
11%
Hispanic/Latino46%
Asian/Pacific Islander
7%
Other3%
Unknown8%
Misdemeanor Defendants: 2017
White/Caucasian
21%
Black/African American
13%
Hispanic/Latino45%
Asian/Pacific Islander
8%
Other2%
Unknown11%
Felony Defendants: 2017
8 | P a g e
Perceptions of School Safety, by Race/Ethnicity:
5
As seen in this table, youth in Santa Clara County report feeling mostly safe in school. However,
the youth who reported feeling the most unsafe were Hispanic/Latino (5.4% were unsafe/v.
unsafe), Pacific Islander (9.9% felt unsafe/v. unsafe) and African American/Black (9% felt
unsafe/v. unsafe). Some of that perception may be related to the other indicators of school safety
seen in the following charts: Fighting at school and Self-Reported gang membership, which exist
in higher percentages for African American and Hispanic/Latino youth.
Physical Fighting at School, by Race/Ethnicity 6
5 WestEd, California Healthy Kids Survey. California Department of Education (Jul. 2017), at www.kidsdata.org 6 WestEd, California Healthy Kids Survey. California Department of Education (Jul. 2017), at www.kidsdata.org
9 | P a g e
Self-Report Gang Membership, by Race/Ethnicity 7
Education
High school dropout rates are much higher for African-American/Black and Hispanic/Latino students in
Santa Clara County compared to White/Caucasian or Asian/Pacific Islander students. In California, the
average percentage of students not completing High School is 10.7%.
Percent of Students Not Completing High School, by Race/Ethnicity: 20168
Hispanic/Latino 20.8%
African-American/Black 14.9%
Filipino 4.6%
White 4.5%
Asian-American 3.1%
2+ Races 6.2%
Juvenile Prosecutions:
A major focus of the Crime Strategies Unit in 2018 has been to better understand why some
juvenile crime in Santa Clara County seems to be increasing. The Crime Strategies Unit looked
at the racial/ethnic composition of juveniles charged with felony offenses, and found disparity
that was more pronounced than in adult court. The charts below depict the racial composition of
charged minors in juvenile court:
7 WestEd, California Healthy Kids Survey. California Department of Education (Jul. 2017), at www.kidsdata.org 8 California Dept. of Education, California Longitudinal Pupil Achievement Data System (CALPADS) (May 2016), at
www.kidsdata.org
10 | P a g e
Clearly, racial disparity exists in every charged category of filed juvenile cases. This is a focus of
the Juvenile Justice Commission, the Probation Department, the Juvenile Court and the District
Attorney’s Office. Much work has been done to discuss and address these issues in schools and
in the juvenile justice system, with much more to be done.
Victim Services
Between 2017 and 2018, the D.A. Office’s Victim Services Unit served nearly 10,000 victims of
crime, rendering an array of services from counseling to restitution. This unit, comprised of
victim advocates and support personnel, resides at the D.A.’s office and provides a critical
support network to those who have been impacted by crime. In order to understand the needs of
the served victim population, VSU began collecting self-reported demographic information on
the victims served. White and Asian/Pacific Islander are the two populations who seem under-
represented in the victim services population, even though those populations are not necessarily
less-likely to be victims of crime. This suggests that outreach to these groups may yield greater
participation in the justice process and/or the receiving of valuable support services.
Asian/Pacific Islander
3%
Black/ African
American21%
Latino/ Hispanic
67%
White/ Caucasian
6%
Other3%
Juvenile Carjacking: 2017
Asian/P.Islander7%
Black/ African
American19%
Latino/ Hispanic
65%
White/ Caucasian
8%
Other1%
Juvenile Robbery: 2017
Asian/Pacific Islander
5%
Black/ African
American14%
Latino/ Hispanic
74%
White/ Caucasian
7%
Juvenile Res. Burglary: 2017Asian/Pacific
Islander4%
Black/ African
American13%
Latino/ Hispanic
75%
White/ Caucasian
7%
Other1%
Juvenile Auto Theft: 2017
11 | P a g e
Gun and Gang cases:
One area where the racial disparities are particularly pronounced is in cases where a gun was
used or where a criminal street gang enhancement was alleged, under Penal Code 186.22. As
seen, the Hispanic/Latino and Black/African-American defendants are largely over-represented
in both categories, relative to their share of the population.
White/ Caucasian17%
Black/ Af. American
4%
Asian/ P.Islander9%
Hispanic/Latino34%
Other/Unknown36%
Victims Served by VSU from 2017-2018
White/ Caucasian13%
Black/ African American
17%
Hispanic/ Latino47%
Asian/ P.Islander11%
Other/ Unknown12%
Charged Gun Enhancements: 2017
12 | P a g e
What is the DA’s Office Doing to Understand and Address Racial Disparity?
Upon releasing the first “Race and Prosecutions” report in 2016, the District Attorney’s office
began taking a more systematic approach to studying its own data. In October 2016, D.A. Rosen
created a Crime Strategies Unit, designed to find data-driven solutions to crime, and to study
internal and external prosecution data.
BetaGov Trial
The D.A.’s Office partnered with the nonprofit BetaGov to study whether human bias may play a
role in charging decisions. After consulting with statisticians and academics from BetaGov, the
D.A.’s Office developed a race-blind controlled trial of issuing and negotiating practices to see if
it revealed any implicit bias in prosecution practices. The trial involved removing racial
identifiers from prosecutors’ consideration when deciding whether to file criminal charges.
Racial identifiers (names, references to race) were redacted from the police reports, and three
categories of felony cases were included in the sample: Felony Assault (PC 245), Robbery (PC
211) and Vehicle Theft (VC 10851). Prosecutors were first asked to make a race-blind charging
decision, then were asked to make a race-blind settlement offer. During the study, half of the
reviewed cases were redacted (race-blind) and half were not redacted (control group) to allow the
study to compare the results. The study is ongoing, but in the initial review, the issuing rates on
felony cases seem to be statistically similar to the control group. The one exception was for
African-American/Black defendants where more felonies were issued in the race-blind than in
the control group.
Importantly, cases that required review of photos, surveillance video or police body-worn camera
video needed to be removed from the trial, as did cases where a decision to file criminal charges
needed to be done quickly. These restrictions limited the sample size and may have affected the
outcomes.
White/ Caucasian8%
Black/ African American
17%
Hispanic/ Latino69%
Asian/ P.Islander0%
Other/ Unknown6%
Charged Gang Enhancements: 2017
13 | P a g e
There were 74 cases in the initial study. Conducting this trial proved challenging. The process of
removing racial identifying information is time-consuming and sometimes impossible due to the
nature of the case. As the study progresses, a much more robust sample size will reveal whether
measurable human bias enters at the charging stage of a criminal case.
Prevention and Outreach
The Community Prosecution Unit at the DA’s Office continues to work with youth, communities
of color and justice System Partners to address social root causes of crime. Our community
prosecutors work in the zip codes most affected by crime and social inequality in order to
prevent crime through early advocacy and support. We continue to invest in this program as
preventing crime is as important as prosecuting crime in Santa Clara County. This team will
continue to partner with the Crime Strategies Unit to understand the data behind race and
prosecution.
Black/ African
American21%
White16%
Hispanic47%
Asian16%
Race-Blind: Issued FeloniesBlack/ African
American8%
White15%
Hispanic50%
Asian12%
Unknown15%
Control Group: Issued Felonies
RACE AND PROSECUTIONS
REPORT2013-2017
RACE OF SANTA CLARA COUNTY RESIDENTS
Source: US Census Bureau, 2015 American Community Survey
White/Caucasian
34%
Black/ African
American
2%
Hispanic/Latino
27%
Asian/ P.Islander
34%
Other
3%
COUNTRY OF ORIGIN OF IMMIGRANT POPULATIONSANTA CLARA COUNTY
37.1% OF SANTA CLARA COUNTY RESIDENTS WERE BORN IN ANOTHER COUNTRY (2010 STUDY BY USC)
Mexico
23.00%
Vietnam
14.00%
India
12.00%
Other
34.00%
Philippines
9.00%
China
8.00%
ORIGIN
MEDIAN HOUSEHOLD INCOME IN SANTA CLARA COUNTY
2017 American Community Survey, US CENSUS DATA
118,236
71,493 69,052
128,927
63,576
White/Caucasian Black/African American Hispanic/Latino Asian/Pac.Islander Other
Median Household Income: 2017
PERCENT OF STUDENTS DROPPING OUT OF HIGH SCHOOL BY RACE
2016 KIDSDATA.ORG
Percent of Students Not Completing High School, by
Race/Ethnicity: 2016
Hispanic/Latino 20.8%
African American/Black 14.9%
Filipino 4.6%
White 4.5%
Asian American 3.1%
2+ Races 6.2%
RACE OF DEFENDANTS IN FILED MISDEMEANORS 2013-2017
White/Caucasian
25%
Black/African
American
11%
Hispanic/Latino
46%
Asian/Pacific
Islander
7%
Other
3%
Unknown
8%
RACE BY PERCENTAGE: 2017
19,905
21,95122,551
23,848
29,462
0
5000
10000
15000
20000
25000
30000
35000
2013 2014 2015 2016 2017
Filed Misdemeanors 2013-2017
RACE OF SUSPECTS IN NON-FILED MISDEMEANORS FROM 2013-2017
3418
3949
3444
45344347
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2013 2014 2015 2016 2017
Non-Filed Misdemeanors 2013-2017
White/Caucasian
26%
Black/African
American
9%
Hispanic/Latino
41%
Asian/Pacific
Islander
12%
Other
3%
Unknown
9%
RACE BY PERCENTAGE: 2017
RACE OF DEFENDANTS IN FILED FELONIES 2013-2017
Note that the passage of Proposition 47 in November of 2014 reclassified five felonies as misdemeanors.
9,081 9,239
6,933
7,393
6,715
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
2013 2014 2015 2016 2017
Filed Felonies 2013-2017
White/Caucasian
21%
Black/African
American
13%
Hispanic/Latino
45%
Asian/Pacific
Islander
8%
Other
2%
Unknown
11%
RACE BY PERCENTAGE: 2017
RACE OF DEFENDANTS IN NON-FILED FELONIES 2013-2017
Note that the passage of Proposition 47 in November of 2014 reclassified five felonies as misdemeanors.
White/Caucasian
22%
Black/African
American
13%
Hispanic/Latino
42%
Asian/Pacific Islander
9%
Other
2%
Unknown
12%
RACE BY PERCENTAGE: 2017
764
1,029981
1,2131,173
0
200
400
600
800
1000
1200
1400
2013 2014 2015 2016 2017
Non-Filed Felonies: 2013-2017
21.5
12.7
44.9
8.3
12.5
25.2
10.8
46.1
7.5
10.4
33
2
26
35
4
0
5
10
15
20
25
30
35
40
45
50
White/Caucasian Black/African American Hispanic/Latino Asian/Pacific Islander Other
% o
f F
ile
d C
ase
s
Felony & Misdemeanor Defendants: 2017
Felony Misdemeanor Population
2017 Comparison of PercentageRace in Filed and Non-Filed Misdemeanors and Felonies
Race Filed
Misdemeanors (%)
Non-Filed
Misdemeanors (%)
Filed Felonies (%) Non-Filed Felonies
(%)
Black/African
American
11 9 13 13
Hispanic/Latino 46 41 45 42
White/Caucasian 25 26 21 22
Asian/Pacific
Islander
7 12 8 9
Unknown 8 9 11 12
Other 3 3 2 2
■ The following slides address different crime types that the District Attorney’s Office
prosecutes and examines the race of the defendants prosecuted across different
crime types from 2013 to 2017.
Assault With a Deadly Weapon or By Force Likely to Cause Great Bodily Injury Felonies;PC 245(a)
This information reflects the following: 245(a)(1), 245(a)(2), 245(a)(3) and 245(a)(4) PC
403
514
594566 563
0
100
200
300
400
500
600
700
2013 2014 2015 2016 2017
PC 245(a)
Total in 5 Years: 2,640
Hispanic/Latino
53.82%
Black/African American
11.55%
Other
0.89%
Unknown
4.26%
White/Caucasian
20.60%
Asian/Pacific Islander
8.88%
RACE BY PERCENTAGE
Drug Possession Misdemeanors; H&S 11377(possession of methamphetamine)
Note that after the passage of Proposition 47 in November of 2014, a violation of H&S 11377 could only be charged as a misdemeanor and not as a felony.
2573
3021
2099
2386
2530
0
500
1000
1500
2000
2500
3000
3500
2013 2014 2015 2016 2017
HS 11377
Total in 5 Years: 12,609
Hispanic/Latino
47.47%
Black/African American
9.57%
Others
1.38%
Unknown
6.72%
White/Caucasian
28.93%
Asian/Pacific Islander
5.93%
RACE BY PERCENTAGE
Resisting ArrestPC 148
863
962
1086 1094
1252
0
200
400
600
800
1000
1200
1400
2013 2014 2015 2016 2017
PC 148
Total in 5 Years: 5,258
Hispanic/Latino
49.32%
Black/African
American
15.08%
Other
1.68%
Unknown
4.63%
White/Caucasian
23.14%
Asian/Pacific Islander,
6.15%
RACE BY PERCENTAGE
This information reflects the following: 148(a)(1), 148.3(a), 148.4(a)(1), 148.4(a)(2), 148.5(a), and 148.9
DUI Misdemeanors; Police Contact by Vehicle StopVC 23152/23153
This information reflects the following: 23152(a), 23152(b), 23153(a), 23153/23566(a), 23152(c), 23152(e) and 23152(f)
Hispanic/Latino,
50.25%
Black/African American,
4.68%
Other,
3.66%
Unknown,
7.18%
White/Caucasian,
23.39%
Asian/Pacific Islander,
10.83%
RACE BY PERCENTAGE
5232
5613
5177
44104125
0
1000
2000
3000
4000
5000
6000
2013 2014 2015 2016 2017
VC 23152/23153
Total in 5 Years: 24, 557
DUI FeloniesVC 23152/23153
This information reflects the following: 23152/23550(a), 23152/23550.5(a)
151
83
95
8075
0
20
40
60
80
100
120
140
160
2013 2014 2015 2016 2017
VC 23152/23153
Total in 5 Years: 484
Hispanic/Latino
65.33%Black/African American
2.67%
Others
2.67%
Unknown
2.67%
White/Caucasian
16%
Asian/Pacific Islander, 5.33%
RACE BY PERCENTAGE
Commercial BurglariesPC 460(b)
853 841
374
466
339
0
100
200
300
400
500
600
700
800
900
2013 2014 2015 2016 2017
PC 460(b)
Total in 5 Years: 2,873
Hispanic/Latino
35.40%
Black/African American
19.17%Other
1.77%
Unknown
10.03%
White/Caucasian
27.14%
Asian/Pacific Islander
6.49%
RACE BY PERCENTAGE
Residential BurglariesPC 460(a)
345
324
358361
308
280
290
300
310
320
330
340
350
360
370
2013 2014 2015 2016 2017
PC 460(a)
Total in 5 Years: 1,696
Hispanic/Latino
50.65%
Black/African
American
10.39%
Other
2.27%
Unknown
10.06%
White/Caucasian
21.75%
Asian/Pacific Islander
4.87%
RACE BY PERCENTAGE
RobberyPC 211
This information reflects the following: 211-212.5(a), 211-232.5(b), 211-212.5(c) and 211-213(a)(1)(A)
283
305322
356370
0
50
100
150
200
250
300
350
400
2013 2014 2015 2016 2017
PC 211
Total in 5 Years: 1,636
Hispanic/Latino
43.24%
Black/African American
23.78%
Other
1.35%
Unknown
9.19%
White/Caucasian
14.59%
Asian/Pacific Islander
7.84%
RACE BY PERCENTAGE
Misdemeanor Domestic ViolencePC 243(e)
787
752
735
709
672
600
620
640
660
680
700
720
740
760
780
800
2013 2014 2015 2016 2017
PC 243(e)
White/Caucasian
22%
Black/African
American
7%
Hispanic/Latino
50%
Asian/Pacific Islander
9%
Other
3%
Unknown
9%
RACE BY PERCENTAGE
Corporal Injury on a SpousePC 273.5
This information reflects the following: Misd & Felony level of 273.5(a), 273.5(e)(1), 273.5(f)(1), 273.5(f)(2) and 273.5(e)(2)
White/Caucasian
19%
Black/African
American
12%
Hispanic/Latino
47%
Asian/Pacific
Islander
12%
Other
2%
Unknown
8%
RACE BY PERCENTAGE
669
724
775
636
835
0
100
200
300
400
500
600
700
800
900
2013 2014 2015 2016 2017
PC 273.5
Homicide & Attempted MurderPC 187
It should be noted that when two or more people are involved in a killing then this data reflects each of those charged, even though there may be only one homicide victim. This information
reflects the following: 187, 664(a) 187 and 664(e) 187 PC.
99
119
93
102 103
0
20
40
60
80
100
120
140
2013 2014 2015 2016 2017
PC 187 & Attempted 187White/Caucasian
9%
Black/African
American
9%
Hispanic/Latino
54%
Asian/Pacific
Islander
10%
Other
3%
Unknown
15%
RACE BY PERCENTAGE
HS 11550 MisdemeanorUnder the Influence of Controlled Substance[Police Contact Often Face To Face]
This information reflects the following: 11550(a)(1) and 11550(e)
White/Caucasian
36%
Black/African
American
7%
Hispanic/Latino
42%
Asian/Pacific
Islander
8%
Other
2%
Unknown
5%
RACE BY PERCENTAGE: 2017
1448
1706
1405
1106
998
0
200
400
600
800
1000
1200
1400
1600
1800
2013 2014 2015 2016 2017
HS 11550
White Collar Crime: 2016[Insurance Fraud, Workers Comp. Fraud, Elder Abuse]
This information reflects the following: PC 550, IC & LC violations, PC 368
White/Caucasian
20%
Black/African American
3%
Hispanic/Latino
16%
Asian/Pacific Islander
11%Other
2%
Unknown
48%
■ The following slide addresses the racial breakdown of victims of violent crime
assisted by the District Attorney’s Office Victim Services Unit in 2015. It should be
noted that there was a dramatic increase in victims served in the second half of
2015 after VSU moved in-house to the District Attorney’s Office.
2017-2018 Victims of Violent Crimes assisted by DAO-VSU(where victim stated race at intake interview)
White/ Caucasian
17%
Black/ Af. American
4%
Asian/ P.Islander
9%
Hispanic/Latino
34%
Other/Unknown
36%
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