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Race and Prosecutions

2018 Update

A Report of the Santa Clara County

District Attorney’s Office

Jeff Rosen, District Attorney

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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.

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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

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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)

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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

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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

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.

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%


13%

Hispanic/Latino45%


8%

Other2%

Unknown11%

Felony Defendants: 2017

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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

http://chks.wested.org/

http://www.kidsdata.org/



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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



http://www.cde.ca.gov/ds/sd/sd/filescohort.asp

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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.


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


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

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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

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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

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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 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 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%


9%

Other

2%

Unknown

12%


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%


11.55%

Other

0.89%

Unknown

4.26%

White/Caucasian

20.60%


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


Hispanic/Latino

47.47%


9.57%

Others

1.38%

Unknown

6.72%

White/Caucasian

28.93%


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


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)


Hispanic/Latino

35.40%


19.17%Other

1.77%

Unknown

10.03%

White/Caucasian

27.14%


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)


Hispanic/Latino

50.65%

Black/African

American

10.39%

Other

2.27%

Unknown

10.06%

White/Caucasian

21.75%


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


Hispanic/Latino

43.24%


23.78%

Other

1.35%

Unknown

9.19%

White/Caucasian

14.59%


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%


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%


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%


3%

Hispanic/Latino

16%


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%

race and prosecutions - sccgov.org€¦ · 1 us census, 2016 american community survey 2 us census,...

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