Developmental Estimates of 

Subnational Crime Rates Based on the 

National Crime Victimization Survey

Robert E. Fay

Mamadou Diallo

Westat, 1600 Research Boulevard, Rockville, Maryland, 20850

https://www.westat.com

R&DP-2015:01

NCJ 249238 

December 17, 2015 

Bureau of Justice Statistics Research and Development Paper 

The authors acknowledge the support of the Bureau of Justice Statistics, Award #2008-BJ-CX-K067. 

The BJS Project Manager was Michael Planty, Victimization Unit Chief.

Research papers have been reviewed by the Bureau of Justice Statistics (BJS) to ensure the general 

accuracy of information presented and adherence to confidentiality and disclosure standards. This paper 

is released to inform interested parties of research and methodologies that will be implement to analyze 

and report findings from from the NCVS. Any opinions and conclusions expressed herein are those of the 

author(s) and do not necessarily represent the views of BJS and the U.S. Department of Justice. 
Developmental Estimates of Subnational Crime Rates 2

Abstract

Developmental Estimates of Subnational Crime Rates Based on the National Crime Victimization Survey 

presents rates of violent and property crime victimization for the 50 states and select metropolitan 

statistical areas, generated using small-area estimation (SAE) methods. The report describes the 

statistical modeling approach used to produce state-level estimates from the National Crime Victimization 

Survey data and auxiliary data sources. It compares SAE victimization rates for the 50 states from 

1999 to 2013 to FBI crime rates from the Uniform Crime Reporting Program. It shows trends in criminal 

victimization rates for each state from 1999 to 2013. State-level estimates of intimate partner violence 

are also presented.
Developmental Estimates of Subnational Crime Rates 3

Table of Contents

Section Page

Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.1 Small Area Estimation with the NCVS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Comparing the NCVS to the UCR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 NCVS (SAE) State Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1 NCVS (SAE) Estimates for Violent and Property Crime. . . . . . . . . . . . . . . . . . . . . . 11

2.2 Assessing the NCVS (SAE) Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Violent Crime by Type and by Relationship to Perpetrator. . . . . . . . . . . . . . . . . . . 25

2.4 Property Crime by Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.5 Availability of State Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3 Counties and Metropolitan Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4 Concluding Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Appendix A Treatment of Year 2006 and Series Crimes. . . . . . . . . . . . . . . . . . . . . . . . . . . . A1

Appendix B Trend Figures for the 50 States and Washington, D.C.. . . . . . . . . . . . . . . . . . . B1

Appendix C Trend Figures for Select Counties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C1

Appendix D Trend Figures for Select CBSAs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D1
Developmental Estimates of Subnational Crime Rates 4

Forward

The National Crime Victimization Survey (NCVS) is the only source of annual national data on 

a number of policy relevant subjects related to criminal victimization, including intimate partner 

violence, injury from victimization, weapon use, the cost of crime, reporting to police, and crime 

against vulnerable populations, such as the elderly and persons with disabilities. It is one of the 

two main sources of data on crime in the United States and the only source that provides detailed 

information on the level, nature and consequences of crime (The Nation’s Two Crime Measures). By 

capturing crimes not reported to police, known as the “dark figure of crime,” as well as those known 

to law enforcement, the NCVS serves as the primary, independent source of information on crime 

in the United States.

Though the NCVS was originally designed to provide national level estimates of criminal 

victimization, BJS has recognized an increasing need for victimization data at the state and local 

level. Three major reviews of the NCVS program (Penick and Owens, 1976; Biderman et al., 

1986; Groves and Cork, 2008) all point to the demand that local criminal justice administrators 

have for empirical information to shape policy. Subnational estimates are of value to both federal 

and nonfederal data users and stakeholders. Federal stakeholders that currently allocate funding or 

resources for crime victims and crime prevention based on official police crime estimates, could 

use the victimization estimates to understand how the allocation of funding would change when 

unreported crime is taken into account. Policy makers could use these estimates to examine local 

variations in crime both reported and unreported to police and make comparisons among states, 

and law enforcement officials could use the findings to begin to understanding differences in rates 

of crime and reporting to police across. The data can also be used in conjunction with official 

police statistics to begin to understand the correlation between the NCVS and official police reports 

of crime.

Research demonstrated that the NCVS could be enhanced to produce several types of subnational 

estimates. The small area estimates (SAE) presented in this current report by Fay and Diallo (2015) 

use statistical methods to generate model-based subnational estimates for all 50 states, DC and 

other large places. In addition to these model-based estimates, BJS is developing other approaches 

for generating subnational NCVS estimates. BJS is boosting the NCVS core sample in large states 

to obtain direct state-level estimates. Beginning in July of 2013, BJS initiated a pilot test boost of 

the NCVS sample in the 11 states with the largest population to assess the feasibility and cost of 

generating direct state-level estimates. Based on findings from the pilot test, BJS is boosting the 

NCVS core sample in 22 states and largest metropolitan areas. 

Next, BJS has started developing generic area typologies based on various geographic, social, 

economic, or demographic characteristics. These generic areas represent all places that are similar to 

each other based on the characteristics of interest. Initially these subnational geographic identifiers 

include region, population size, and urbanicity. Finally, BJS is testing the feasibility of fielding a 

low-cost mailed, self-administered companion survey in specific cities. The American Crime Survey 
Developmental Estimates of Subnational Crime Rates 5

research project is focused on developing and evaluating a cost effective supplement to the core 

NCVS that will help BJS and law enforcement better understand the counts and rates of crime at 

the local level. 

Collectively, the BJS will assemble and evaluate the various approaches based on estimate quality, 

relevance, level of geography, timeliness, burden, and cost to develop a routine approach to 

generating annual subnational estimates. The SAE estimates presented in this report will be labeled 

as developmental until the BJS implements the methodologies that will allow it to produce these 

estimates on an annual basis in conjunction with estimates from the other components of the 

subnational estimation program. 

Although the estimates in this report are labeled as “developmental” the methodology behind them 

has gone through rigorous review and verification. 
Developmental Estimates of Subnational Crime Rates 6

Introduction 1 

The Bureau of Justice Statistics’ (BJS) National Crime Victimization Survey (NCVS) has provided 

annual estimates of crime at the national level since 1993. BJS publishes the survey results in the 

Criminal Victimization series and in other topical reports, which are used by policymakers, the 

media, researchers, academics, and the general public. The lowest level of geography typically 

examined in these reports is four geographic regions: Northeast, Midwest, South, and West. 

While the NCVS can be used to examine a range of subgroup differences, BJS has not recently 

released information about crime at the level of states, counties, or metropolitan areas. One reason 

for not releasing geographically detailed estimates is sampling variability. The NCVS sample is 

designed to be nationally representative but is not necessarily representative for states, cities, or 

smaller geographic areas. Additionally, nearly all states lack the sample size needed to produce stable, 

reliable estimates on an annual basis. 

Another challenge in releasing estimates at the subnational level is the need to protect the 

confidentiality of individual respondents. The identity of NCVS respondents is protected under 

the confidentiality provisions of the Census Bureau’s Title 13 legislation. Detailed information 

about the characteristics of victims coupled with detailed geographic information could jeopardize 

this confidentiality.

Despite these challenges, in 2008 a panel of the National Research Council (NRC) recommended 

that BJS pursue a program to develop and disseminate small area estimation (SAE) estimates of 

major crime and victimization rates (Cork and Groves 2008, p. 8). In response, BJS awarded a series 

of grants to develop options and conduct research projects on the feasibility of implementing the 

NRC recommendation using SAE methods (Cantor et al., 2010). The SAE research identified a 

plausible methodological approach, produced preliminary estimates, and subsequently extended the 

methods to meet additional goals that BJS requested. 

This report presents findings from that research. It provides SAE estimates of state-level crime rates 

for the period 1999–2013 derived from the NCVS data. The estimates cover 13 overlapping 3-year 

averages, starting with 1999–2001 and ending with 2011–2013. The report also describes estimates 

for the largest counties and metropolitan areas for the period 1998–2012. These estimates also 

cover 13 overlapping 3-year averages, starting with 1998–2000 and ending with 2010–2012. (These 

estimates lag the state estimates by one year because the necessary UCR county estimates for 2013 

were unavailable in December 2014.)

The small area state estimates in this report are limited to categories of commonly occurring crimes, 

without linking them to any characteristics of the victims that could allow the possible identification 

of individual respondents. The complexity of the statistical methods used to produce the estimates 
Developmental Estimates of Subnational Crime Rates 7

Introduction 1

provides an additional layer of confidentiality protection. Technical details about the underlying 

statistical models are available in the report “Constructing and Disseminating Small Area Estimates 

for the National Crime Victimization Survey (NCVS): Continuation of Project 2008-BJ-CX-K067, 

Final Report.”

1.1 Small Area Estimation with the NCVS

SAE methods combine survey and auxiliary data through statistical models to produce estimates for 

areas where direct estimates from a survey are not very reliable. The NCVS SAE estimates, like other 

SAE estimates, are derived from three basic components:

1. A sample survey that measures the desired characteristics reliably at the national level.

2. Auxiliary information that provides geographically detailed data.

3. Models to relate the sample estimates to the auxiliary information.

The NCVS small area estimates are based on data from the survey and use statistics from the FBI’s 

Uniform Crime Reports (UCR) as auxiliary data. The UCR, which aggregates reports from law 

enforcement units, is an important source for crime rates at the state and local levels. The model 

attempts to predict where the NCVS estimated crime rates are high or low relative to other states 

from the UCR crime rates for particular types of crime. However, the NCVS SAE state estimates 

follow the same general trajectory of the national NCVS estimates over time, not the trajectory of 

the UCR national estimates.

The NCVS SAE models take advantage of the fact that the NCVS is a continuous survey that 

produces estimates of crime rates annually. Because true crime rates are fairly stable over time, the 

models can “borrow strength” from the NCVS direct estimates from other years. In other words, 

although some states can change relative to others over time, such change is typically slow. The true 

crime rate in one year, combined with the national trend, is a strong predictor of the following year’s 

rates. The SAE model identifies the extent to which individual NCVS state-level estimates can be 

averaged over time to improve the state-level predictions for individual years or for 3-year periods. 

The model does this by attempting to measure the relative stability in the geographic patterns of 

crime over time and then using this information to determine how much weight should be placed 

on past years. In predicting the true state-level crime rate for any one year, the model considers the 

available NCVS direct estimates of crime for the state for all of the years, but it gives more weight 

to recent years than distant ones. For example, the 2011–2013 SAE estimates for a state not only 

incorporate the available NCVS sample information from the survey in 2011, 2012, and 2013, but 

also use results for each of the years 1999–2010 to a lesser extent, with more weight placed on 2010 

than 1999.
Developmental Estimates of Subnational Crime Rates 8

Introduction 1

A number of SAE projects throughout the federal statistical system are similar to the NCVS SAE 

project, including the following:

• The Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program produces 

SAE estimates of poverty, including estimates for children ages 5–17 and median household 

income at the state and county levels. The SAIPE program also produces estimates of poverty 

for children ages 5–17 at the school district level. This program is based on survey data from 

the American Community Survey (ACS) and auxiliary data from anonymized Internal Revenue 

Service tax returns and food stamp participation in the Supplemental Nutrition Assistance 

Program (see https://www.census.gov/did/www/saipe/).

• The National Center for Health Statistics uses SAE methods to publish types of telephone 

usage (for example, landline vs. mobile phones) for states and selected substate areas (Blumberg 

et al., 2011). The estimates are based on survey data from the National Health Interview Survey. 

The auxiliary data are extracted from the ACS. 

• The Bureau of Labor Statistics’ Local Area Unemployment Statistics program issues SAE 

estimates of monthly employment and unemployment at the state and local labor market 

area levels (http://www.bls.gov/lau/home.htm) using survey data from the Current 

Population Survey combined with auxiliary data based on administrative sources, such as state 

employment agencies.

In each of these examples, the direct survey estimates are typically too unstable to be used at the 

small area level, even though each associated survey produces reliable national estimates. The three 

projects each yield plausible SAE estimates for their targeted small areas, but the projects are not 

intended to revise the national estimates directly available from the survey. Similarly, the NCVS small 

area estimates offer a statistical interpretation, based on NCVS data, of how crime is distributed 

across the states. They are not intended to improve or revise the national NCVS estimates of 

crime rates.

All of the NCVS (SAE) results presented in this report must be regarded as estimates rather than 

quantities known with certainty. For example, an estimate may show that a specific state has a crime 

rate higher than the national average during a specific period of time, but the interpretation of the 

observed difference must be tempered by the possibility that the difference may be mostly due to 

sampling variability rather than an underlying true difference. 

This report uses the acronym “NCVS (SAE)” to distinguish the small area estimates from direct 

estimates based on a tabulation of the NCVS survey data. The NCVS (SAE) estimates are shown 

in the form of 3-year averages. The 3-year NCVS (SAE) averages smooth out some of the random 

variation in the annual estimates and shift the emphasis from detecting short-term change to 

identifying longer term trends in crime.
Developmental Estimates of Subnational Crime Rates 9

Introduction 1

1.2 Comparing the NCVS to the UCR

The FBI’s UCR data have historically been the primary source for state- and substate-level crime 

statistics. The UCR estimates are based on crimes as reported by participating law enforcement 

agencies, although the FBI partially adjusts the state estimates for missing data from some 

jurisdictions. State-level UCR estimates are used in this report for purposes of comparison to the 

SAE estimates. 

Differences between national estimates from the NCVS and the UCR have been noted for decades, 

and a substantial body of research has attempted to account for these differences (e.g., Lynch 

and Addington, 2007). At the national level, the NCVS and UCR trends in crime were not always 

consistent during the period 1999–2013, and the NCVS shows a larger decline in violent crime rates 

across the period compared to the UCR (figure 1-1). Several differences between the NCVS and the 

UCR could account for the discrepancies, including the following: 

• The UCR is based on police reports, while the NCVS asks survey respondents about crimes 

regardless of whether they reported the crimes to police. 

• The UCR covers crimes committed against all persons and businesses, while the NCVS 

covers persons age 12 and older and excludes crimes against the homeless, institutionalized 

populations, and businesses. 

• The two collections differ in regard to the types of crimes included. In the UCR, violent crime 

includes murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault. 

In the NCVS, violent crime includes rape and sexual assault, robbery, aggravated assault, and 

simple assault. Simple assault, which is not included in the UCR, is the largest component of 

the NCVS estimate of total violent crime. 
Developmental Estimates of Subnational Crime Rates 10

Introduction 1

• In both the UCR and the NCVS, property crime is divided into burglary, motor vehicle theft, 

and larceny (theft) other than motor vehicle theft. However, the NCVS denominator for 

property crime rates is the number of U.S. households, while the UCR denominator is the 

estimated population. Methodological differences and changes over time affect both collections 

to some degree.

Figure 1-1 NCVS vs. UCR violent crime rates in the form of 3-year averages, 1993–2013, 

adjusted to exclude data for 2006. Rates are shown as number per 1,000. 

In addition to national differences between the NCVS and UCR crime rates, the relationship 

between the two collections could vary geographically. One reason for potential geographic 

differences is that crime statistics in the UCR are based on the location where the crime occurred, 

while NCVS data are based on the victim’s location of residence. Other variations in population 

composition and patterns of reporting to the police could also further impact subnational 

differences between the two collections.

This report compares NCVS (SAE) and UCR estimates as an aid to interpreting the SAE results. 

Because the two series measure different (although related) concepts, the comparisons are of clear 

interest, but they are not intended to argue that the new NCVS (SAE) estimates displace the utility 

of the UCR statistics.

Appendix A of this report details two methodological issues in preparing the estimates. One 

issue is a modification of the analysis for unusual NCVS results for 2006. The other issue notes 

methodological changes in NCVS estimation over this period. Because BJS now publishes consistent 

estimates for the entire period using the current methods, the NCVS (SAE) estimates do so as well.
Developmental Estimates of Subnational Crime Rates 11

NCVS (SAE) State Estimates 2

2.1 NCVS (SAE) Estimates for Violent and Property Crime

Although the NCVS and UCR estimates of violent crime for 2011–2013 share features in common 

(figure 2-1), there are many apparent differences between the geographic patterns of the two sets 

of state estimates. Washington and Oregon move from below the national average for violent crime 

in the UCR to above average in the NCVS (SAE). Florida shifts from above average in the UCR 

to below average in the NCVS (SAE). In general, the NCVS (SAE) estimates show comparatively 

lower crime rates in the Southeast than the UCR shows. A tier of states in the Northwest—Idaho, 

Montana, Wyoming, North Dakota, South Dakota, Minnesota, and some neighboring states—move 

from low violent crime rates in the UCR to rates closer to the national average in the NCVS (SAE). 

There are numerous other apparent differences between the NCVS (SAE) and the UCR.

There are also differences for estimates of property crime (figure 2-2). The UCR generally shows 

a distinct North/South divide, with higher property crime rates in southern states than in northern 

states. The UCR also shows that most states are relatively close to the national average for property 

crimes, compared to the wider differences shown for violent crime rates in the UCR. Compared 

to the UCR, the NCVS (SAE) estimates shift comparatively high property crime rates away from 

Southeastern states and generally toward the West, with California, Oregon, and Washington 

showing relatively high rates.

An examination of state average violent crime rates for the entire period 1999–2013 (figure 2-3) 

shows patterns similar to the estimated rates for 2011–2013. NCSV (SAE) estimates for 1999–2013 

were based on averaging estimates for five of the 3-year periods (1999–2001, 2002–2004, 2005–

2007, 2008–2010, and 2011–2013), thus including each year once. The NCVS (SAE) estimates for 

violent crime have a pattern similar to the estimated rates for 2011–2013, although the average 

national rate for 2011–2013 is lower than for 1999–2013. States that change color (figure 2-1 

vs. figure 2-3) when additional years are added generally moved to an adjacent color range. This 

agreement is a partial consequence of the NCVS (SAE) model, which looks for stability across time. 

The UCR violent crime estimates also appear geographically stable across time.
Developmental Estimates of Subnational Crime Rates 12

State Estimates 2

Figure 2-1 Comparison of NCVS (SAE) and UCR violent crime rates to the national averages, 

2011–2013
Developmental Estimates of Subnational Crime Rates 13

State Estimates 2

Figure 2-2 Comparison of NCVS (SAE) and UCR property crime rates to the national averages, 

2011–2013
Developmental Estimates of Subnational Crime Rates 14

State Estimates 2

Figure 2-3 Comparison of NCVS (SAE) and UCR violent crime rates to the national averages, 

1999–2013

Similar geographic stability is apparent in the comparison of property crime, both for the NCVS 

(SAE) and the UCR (figure 2-2 vs. figure 2-4). Consequently, much of the difference between the 

NCVS (SAE) estimates and the UCR in 2011–2013 for violent and property crimes is largely present 

during the entire 1999–2013 period.
Developmental Estimates of Subnational Crime Rates 15

State Estimates 2

Figure 2-4 Comparison of NCVS (SAE) and UCR property crime rates to the national averages, 

1999–2013

2.2 Assessing the NCVS (SAE) Estimates 

Figures 2-1 to 2-4 demonstrate that the NCVS (SAE) estimates exhibit a geographic distribution 

of crime that is substantially different from the UCR. This leads to the question of whether the 

NCVS (SAE) estimates reflect state-to-state variation in the NCVS data. To explore this issue, 15-

year averages of the NCVS crime rates were computed using the Census Bureau’s internal files for 

the 36 states with a population of 2 million or more in the 2010 census. Averaging 15 years of data 

provides reasonable precision for the NCVS direct estimates in large states, and it is far more precise 

than 3-year averages of the direct estimates.
Developmental Estimates of Subnational Crime Rates 16

State Estimates 2

The 15-year averages of the direct NCVS estimates were compared to an earlier set of NCVS (SAE) 

estimates of violent and property crimes developed for the period 1997–2011 (figures 2-5 and 2-6). 

States below the population threshold are shaded in gray. The NCVS (SAE) and UCR estimates 

differ slightly from other estimates shown in the report because data for 2006 were included in this 

analysis. Overall, there is close agreement between the two sets of 15-year estimates of violent crime 

and even closer agreement for property crime.

Figure 2-5 Comparison of NCVS (SAE) and NCVS direct violent crime rates to the national 

averages, 1997–2011
Developmental Estimates of Subnational Crime Rates 17

State Estimates 2

Figure 2-6 Comparison of NCVS (SAE) and NCVS direct property crime rates to the national 

averages, 1997–2011
Developmental Estimates of Subnational Crime Rates 18

State Estimates 2

For purposes of discussion, it is useful to consider a grouping of states sorted according to 

population. The grouping is illustrative only, and it is not part of the SAE model (table 2-1). 

1. Group S1, the 4 largest states: California, Texas, New York, and Florida (in decreasing size 

according to the 2010 Census population), with about 33% of the U.S. population.

2. Group S2, the next 7 largest states: Illinois, Pennsylvania, Ohio, Michigan, Georgia, North 

Carolina, and New Jersey, with about 24% of the population.

3. Group S3, the next 11 states (populations between 5 and 8.5 million): Virginia, Washington, 

Massachusetts, Indiana, Arizona, Tennessee, Missouri, Maryland, Wisconsin, Minnesota, 

and Colorado, with about 22% of the population. 

4. Group S4, the next 14 states (populations between 2 and 5 million): Alabama, South 

Carolina, Louisiana, Kentucky, Oregon, Oklahoma, Connecticut, Iowa, Mississippi, 

Arkansas, Kansas, Utah, Nevada, and New Mexico, with about 16% of the population.

5. Group S5, the next 7 states (populations between 1 and 2 million): West Virginia, 

Nebraska, Idaho, Hawaii, Maine, New Hampshire, and Rhode Island, with about 3.3% of 

the population.

6. Group S6, the next 7 states and the District of Columbia (populations less than 1 million): 

Montana, Delaware, South Dakota, Alaska, North Dakota, Vermont, Wyoming, and the 

District of Columbia, with about 1.9% of the population.

Because the allocation of the NCVS sample was approximately proportional to the population 

distribution throughout the period 1997–2013, the largest 11 states (groups S1 and S2) should have 

received more than half of the NCVS sample (figure 2-7 and table 2-1). At the other extreme, the 

smallest states divided a small share of the overall national sample (figure 2-7). Where there was 

more sample, the NCVS (SAE) 15-year violent crime estimates mirrored the corresponding direct 

estimates closely (figure 2-5 and table 2-2). For smaller sample sizes, the NCVS (SAE) estimates 

tended to pull the direct estimates of violent crime toward the mean, and the majority of states 

that changed color (figure 2-5) were in group S4, the states with population between 2 and 5 

million. This is consistent with the notion that in general, small area models tend to pull less certain 

estimates toward the mean.
Developmental Estimates of Subnational Crime Rates 19

State Estimates 2

Figure 2-7 Population shares for a grouping of states by size, 2010 Census results

Table 2-1 State Population Groups

Group 2010 Population Number of States

S1 More than 15 million 4

S2 8.5 to 15 million 7

S3 5 to 8.5 million 11

S4 2 to 5 million 14

S5 1 to 2 million 7

S6 Less than 1 million 8

For the period 1997–2011, there was very close agreement between the average NCVS (SAE) and 

NCVS rates for violent crime in the four largest states (California, Texas, New York, and Florida) 

(table 2-2). Most of the larger discrepancies between the average NCVS (SAE) and NCVS rates 

appear only in states in group S4.
Developmental Estimates of Subnational Crime Rates 20

State Estimates 2

Table 2-2 Total Violent Crime

State 2010 

Population

Group NCVS (SAE) 

Averagea 

1997–2011

NCVS 

Averageb

1997–2011

RMSE of SAE 

Average

SE of NCVS 

Average

Alabama 4,779,736 S4 28.5 26.5 4.3 6.8

Arizona 6,392,017 S3 41.3 41.6 3.2 3.7

Arkansas 2,915,918 S4 33.2 27.0 5.0 9.8

California 37,253,956 S1 31.4 31.0 1.5 1.5

Colorado 5,029,196 S3 46.7 45.7 4.1 5.7

Connecticut 3,574,097 S4 27.1 23.9 4.3 6.4

Florida 18,801,310 S1 32.5 30.3 2.4 2.6

Georgia 9,687,653 S2 22.8 18.5 3.2 4.0

Illinois 12,830,632 S2 33.8 33.6 2.6 2.9

Indiana 6,483,802 S3 33.8 36.6 3.8 5.3

Iowa 3,046,355 S4 33.9 39.7 5.0 9.5

Kansas 2,853,118 S4 32.8 31.8 4.9 8.6

Kentucky 4,339,367 S4 33.5 37.6 4.4 6.8

Louisiana 4,533,372 S4 34.4 30.4 4.4 6.9

Maryland 5,773,552 S3 39.6 39.9 3.1 3.6

Massachusetts 6,547,629 S3 31.6 32.8 3.4 4.2

Michigan 9,883,640 S2 35.2 35.1 3.4 4.2

Minnesota 5,303,925 S3 36.0 36.3 3.9 5.1

Mississippi 2,967,297 S4 31.3 19.9 4.9 9.3

Missouri 5,988,927 S3 32.9 37.3 3.8 5.0

Nevada 2,700,551 S4 46.6 50.6 4.5 6.4

New Jersey 8,791,894 S2 23.5 23.2 2.6 2.9

New Mexico 2,059,179 S4 38.0 36.5 5.2 9.3

New York 19,378,102 S1 28.5 28.0 2.1 2.2

North Carolina 9,535,483 S2 27.0 25.9 3.5 4.7

Ohio 11,536,504 S2 43.4 46.5 3.0 3.6

Oklahoma 3,751,351 S4 37.2 37.7 4.3 6.2

Oregon 3,831,074 S4 39.3 47.3 4.4 6.6

Pennsylvania 12,702,379 S2 33.3 31.2 3.0 3.6

South Carolina 4,625,364 S4 29.5 18.5 4.5 7.6

Tennessee 6,346,105 S3 37.7 35.7 3.9 5.4

Texas 25,145,561 S1 39.7 40.1 2.1 2.3

Utah 2,763,885 S4 40.5 48.4 4.8 8.1

Virginia 8,001,024 S3 27.4 24.9 3.2 3.8

Washington 6,724,540 S3 45.0 48.8 3.6 4.6

Wisconsin 5,686,986 S3 33.6 41.1 4.2 6.1

Note: Comparison of averaged small area estimates, NCVS (SAE), with averaged direct estimates from the NCVS of total 

violent crime for 1997–2011 for states with populations of 2 million or more in the 2010 Census.

a

SAE average for 1997–2011 is based on the preliminary estimates for 1997–1999, 2000–2002, 2003–2005, 2006–

2008, and 2009–2011.

b

Data for 2006 are included in the NCVS average.
Developmental Estimates of Subnational Crime Rates 21

State Estimates 2

The standard errors (SE, or square roots of the sampling variances) shown in the last column 

in table 2-2 measure the reliability of the 15-year averages of the direct NCVS estimates of the 

violent crime rates for 1997–2011. The standard errors are estimates of the precision of the sample 

estimates when considered over possible samples of the same size. The sampling variance depends 

on the expected size of the NCVS sample in the state and the sample design. Estimates with small 

standard errors are less affected by sampling variability than estimates with large standard errors.

Another measure of reliability is the root mean square error (RMSE) of the estimate under the SAE 

model. The RMSE is analogous to the standard error, but its interpretation rests on the assumptions 

made by the SAE model. The RMSEs measure the accuracy averaged over both the sampling and 

the distribution of true values under the model. 

In the four largest states—California, Texas, New York, and Florida—the RMSE of the NCVS 

(SAE) average and the SE of the NCVS are similar (table 2-2). These two reliability measures are 

aligned because the large sample sizes in these four states force the NCVS (SAE) average to agree 

closely with the NCVS average over the 15-year period. 

In general, in the states where the NCVS (SAE) average disagrees with the NCVS average, the 

difference is usually one standard error or less. For example, in Florida the NCVS (SAE) average is 

32.5 per 1,000 compared to 30.3 for the NCVS average, but the difference is less than the standard 

error of 2.6. In the other three states in group S1, the NCVS (SAE) and NCVS averages agree very 

closely. In group S2, the differences between the NCVS and NCVS (SAE) estimates are again within 

one standard error except for Georgia, which is just over one standard error. 

In group S2, the RMSEs of the NCVS (SAE) estimates are typically 10%–30% less than the SEs of 

the corresponding NCVS averages. In other words, the comparison suggests that the SAE model 

moderately improves the estimate over the direct 15-year NCVS average. To improve on this, the 

NCVS (SAE) estimates must usually differ somewhat from the direct estimates—if they consistently 

agreed exactly with the direct estimates, they would have the same measures of error.

In group S3, the RMSEs of the model estimates are 20%–40% less than the SEs of the direct 15-

year averages, suggesting that the model generally makes a larger improvement for group S3 than for 

group S2. In group S4, the RMSEs are 30%–50% less than the SEs. In general, as states decrease in 

population size, the RMSEs of the NCVS (SAE) averages tend to increase, but not as rapidly as the 

SEs of the direct NCVS averages.

Small states in groups S5 and S6 generally have even larger NCVS SEs than states in any of the 

other groups. Indeed, some of these states had no NCVS sample during part or all of the 15-year 

period. In the states with some NCVS data, the SAE estimates incorporate the direct NCVS state 

estimates but only to a limited extent, depending primarily on the modeled relationship with the 

auxiliary data instead. In states without any NCVS sample data, the SAE estimates depend entirely 

on the models. As shown previously, the 14 states and the District of Columbia in groups S5 and S6 

share a very small portion of the overall NCVS sample.
Developmental Estimates of Subnational Crime Rates 22

State Estimates 2

As noted in section 2.1, figures 2-1 and 2-3 show below-average NCVS (SAE) estimates for violent 

crime in the Southeast, compared to the UCR. There is considerable NCVS sample information 

available to support this general finding: Florida is in group S1; Georgia and North Carolina are 

in group S2; Virginia and Tennessee are in group S3; and Alabama, South Carolina, Kentucky, and 

Mississippi are in group S4. In table 2-2 all of these states have average direct estimates below their 

average NCVS (SAE) estimates, except for Kentucky. Thus, it is unlikely that the lower NCVS (SAE) 

estimates for violent crime in the Southeast will understate crime (as measured by NCVS definitions) 

for this group of states; in fact, they are more likely to be slight overstatements according to the 

observed NCVS 15-year averages.

Another region of interest is the Northwest, which includes Idaho, Montana, Wyoming, North 

Dakota, South Dakota, and Minnesota. Exception for Minnesota, these states had populations below 

2 million and were excluded from table 2-2. For these states, the average NCVS (SAE) violent crime 

rates are closer to the national average than are their corresponding UCR rates. There is very little 

direct evidence from the NCVS itself to confirm the apparent suggestion from the NCVS (SAE) 

estimates that the UCR is differentially understating crime in these states. A more likely explanation 

is that the SAE methods are pulling the NCVS (SAE) estimates toward the overall average because 

of the sparseness of the NCVS data in these states.

Findings for property crime (table 2-3) are similar to those for violent crime. Because the rates for 

property crime are higher than those for violent crime, the 15-year averages of the direct NCVS 

estimates for property crime have good precision in many states. In fact, most standard errors are 

small relative to the differences in property crime rates between states. The NCVS (SAE) averages 

differ from the direct averages by relatively small amounts. Given the relatively high precision levels 

of the direct estimates, the RMSEs for the NCVS (SAE) estimates are generally only slightly smaller 

than the standard errors of the NCVS averages, with reductions of generally less than 25%, even in 

states in group S4.
Developmental Estimates of Subnational Crime Rates 23

State Estimates 2

Table 2-3 Total Property Crime 

State

2010 

Population Group

NCVS (SAE) 

Averagea 

1997–2011

NCVS 

Averageb 

1997–2011

RMSE of SAE 

Average

SE of NCVS 

Average

Alabama 4,779,736 S4 158 147 16 20

Arizona 6,392,017 S3 261 268 10 10

Arkansas 2,915,918 S4 163 186 21 28

California 37,253,956 S1 201 202 4 4

Colorado 5,029,196 S3 204 198 14 16

Connecticut 3,574,097 S4 128 118 15 18

Florida 18,801,310 S1 155 153 7 7

Georgia 9,687,653 S2 138 133 10 11

Illinois 12,830,632 S2 172 170 8 8

Indiana 6,483,802 S3 170 173 13 15

Iowa 3,046,355 S4 173 175 21 28

Kansas 2,853,118 S4 174 170 19 24

Kentucky 4,339,367 S4 185 187 16 19

Louisiana 4,533,372 S4 184 183 16 20

Maryland 5,773,552 S3 187 188 9 10

Massachusetts 6,547,629 S3 147 144 11 12

Michigan 9,883,640 S2 182 179 11 12

Minnesota 5,303,925 S3 181 184 13 14

Mississippi 2,967,297 S4 133 127 20 27

Missouri 5,988,927 S3 164 162 13 14

Nevada 2,700,551 S4 237 245 15 18

New Jersey 8,791,894 S2 124 121 7 8

New Mexico 2,059,179 S4 185 187 20 26

New York 19,378,102 S1 119 119 6 6

North Carolina 9,535,483 S2 147 132 12 14

Ohio 11,536,504 S2 190 192 9 10

Oklahoma 3,751,351 S4 184 186 15 17

Oregon 3,831,074 S4 268 287 16 19

Pennsylvania 12,702,379 S2 139 135 9 10

South Carolina 4,625,364 S4 149 126 18 22

Tennessee 6,346,105 S3 166 167 13 15

Texas 25,145,561 S1 245 246 6 6

Utah 2,763,885 S4 231 244 19 23

Virginia 8,001,024 S3 135 133 10 11

Washington 6,724,540 S3 252 253 12 13

Wisconsin 5,686,986 S3 158 163 15 18

Note: Comparison of averaged small area estimates, NCVS (SAE), with averaged direct estimates from the NCVS of total 

property crime for 1997–2011 for states with populations of 2 million or more in the 2010 Census. 

a SAE average for 1997–2011 is based on the preliminary estimates for 1997–1999, 2000–2002, 2003–2005, 2006–

2008, and 2009–2011.

b Data for 2006 are included in the NCVS average.
Developmental Estimates of Subnational Crime Rates 24

State Estimates 2

For violent crime in group S1, the average RMSEs for the 15-year estimates are essentially identical 

to the average standard errors (figure 2-8). However, the advantage to the NCVS (SAE) estimates 

over the direct estimates grows with the progression from group S2 to group S4. The same pattern 

also holds for property crime, but in this case the RMSEs only become appreciably smaller than the 

SEs for group S4.

Figure 2-8 Comparison of SE for NCVS 15-year averages with RMSE of SAE estimates for 15- and 

3-year averages, by state population group
Developmental Estimates of Subnational Crime Rates 25

State Estimates 2

While the preceding discussion compared the measures of accuracy for the 15-year average SAE and 

direct estimates, the 3-year averages are of prime interest. The RMSEs for the 3-year SAE averages 

are larger than those for the 15-year averages, but not dramatically so (figure 2-8). However, because 

the NCVS direct estimates for 2011–2013 are based on about one-fifth of the sample observations 

for 1999–2013, the standard errors for the 3-year averages would be roughly twice as large as the 

standard errors for the 15-year averages. Thus, the NCVS (SAE) estimates for the 3-year averages 

are a substantial improvement in accuracy over the corresponding direct estimates, particularly for 

small states.

To summarize, the geographic distribution of the NCVS (SAE) estimates of violent crime for 

2011–2013 closely resembles the geographic distribution of the NCVS (SAE) rates for the 15-

year period 1999–2013. Similarly, the geographic distribution of the UCR violent crime rates for 

2011–2013 closely resembles that of the UCR rates for 1999–2013. In both time periods, the NCVS 

(SAE) estimates and the UCR differ from each other far more. At least in larger states, the NCVS 

(SAE) violent crime estimates largely mirror the result of averaging the direct estimates over a 

15-year period. Similarly, property crime shows even closer agreement. For states with populations 

below 2 million, however, and especially those with populations below 1 million, the NCVS (SAE) 

estimates are less accurate than for larger states because there are limited or no available NCVS data 

to inform them.

2.3 Violent Crime by Type and by Relationship to Perpetrator

In addition to total violent crime and total property crime, NCVS (SAE) estimates were obtained 

for major publication categories. As previously noted, the major components of NCVS violent 

crime are:

1. Rape and sexual assault. 

2. Aggravated assault. 

3. Simple assault.

4. Robbery.

Violent crime can also be disaggregated by victim relationship to the offender, including:

1. Intimates.

2. Other relatives.

3. Well-known/casual acquaintances.

4. Strangers.

It was necessary to collapse some of these categories because the statistical models do not support 

effective estimation of characteristics with a small number of occurrences. Because of the small 

number of reported cases of rape and sexual assault in the NCVS, this category was combined with 

aggravated assault for purposes of SAE estimation. To address an interest in violence by intimates 

and violence by strangers, these two classifications were retained as separate categories, with the 

remaining categories combined into an “other” relationship category.
Developmental Estimates of Subnational Crime Rates 26

State Estimates 2

For 2011–2013, there was little apparent agreement between the geographic distributions of the 

NCVS (SAE) and UCR rates for the combined variable of rape, sexual assault, and aggravated 

assault (figure 2-9). The model used both rape and robbery from the UCR, but aggravated assault 

from the UCR did not contribute appreciably to the prediction of combined assault in the NCVS.

Averaging over the 15-year period 1999–2013 appears to bring NCVS (SAE) state estimates closer 

to the national average than those seen for the 3-year period (figure 2-10). In comparison, the UCR 

estimates show about as many states at the extremes (less than 50% or more than 150% of the U.S. 

rate) for 1999–2013 as for 2011–2013.

The SAE model for robbery successfully incorporated UCR robbery as a predictor. The NCVS 

(SAE) and UCR estimates for robbery during 2011–2013 agree more closely than for most other 

geographic comparisons between the NCVS (SAE) and UCR estimates (figure 2-11). Similar 

geographic patterns were evident over the 15-year period 1999–2013 (figure 2-12).

While the NCVS collects and includes simple assault in its violent crime rate, there is no comparable 

component in the published UCR violent crime rate. Because simple assault is the largest single 

component of the NCVS crime rate, the geographic distribution of the NCVS (SAE) estimates of 

simple assault largely resembles that for total violent crime for 2011–2013 and 1999–2013 (figure 

2 13).

In addition to estimates of violent crime by type of crime, SAE estimates were produced by 

relationship of the victim to the perpetrator. Because the UCR does not provide information on the 

victim-offender relationship for nonfatal crimes, the SAE model incorporated statistical regression 

relationships based on the relative distribution of reported UCR crimes by type in addition to using 

direct estimates of violent crime by relationship from the NCVS. For example, robbery is more 

likely to be committed by strangers, so the small area models associate high UCR robbery rates with 

relatively high rates of crimes committed by strangers. Likewise, simple and aggravated assault are 

the most frequently reported forms of violence committed by intimate partners. 
Developmental Estimates of Subnational Crime Rates 27

State Estimates 2

Figure 2-9 Comparison of NCVS (SAE) and UCR rates for rape, sexual assault, and aggravated 

assault, 2011–2013
Developmental Estimates of Subnational Crime Rates 28

State Estimates 2

Figure 2-10 Comparison of NCVS (SAE) and UCR rates for rape, sexual assault, and aggravated 

assault, 1999–2013
Developmental Estimates of Subnational Crime Rates 29

State Estimates 2

Figure 2-11 Comparison of NCVS (SAE) and UCR rates for robbery, 2011–2013
Developmental Estimates of Subnational Crime Rates 30

State Estimates 2

Figure 2-12 Comparison of NCVS (SAE) and UCR rates for robbery, 1999–2013
Developmental Estimates of Subnational Crime Rates 31

State Estimates 2

Figure 2-13 NCVS (SAE) rates for simple assault, 2011–2013 and 1999–2013
Developmental Estimates of Subnational Crime Rates 32

State Estimates 2

The NCVS (SAE) estimates of the geographic distribution of intimate partner violence (figure 2-14) 

are in contrast with the NCVS (SAE) estimates of the geographic distribution of crimes committed 

by strangers (figure 2-15). Many of the relatively rural states had low to moderate levels of violent 

crime committed by strangers, while some of the more urban states had higher levels of crimes 

committed by strangers.

Figure 2-14 NCVS (SAE) rates for violence by intimate partners, 2011–2013 and 1999–2013
Developmental Estimates of Subnational Crime Rates 33

State Estimates 2

Figure 2-15 NCVS (SAE) rates for violence by strangers, 2011–2013 and 1999–2013
Developmental Estimates of Subnational Crime Rates 34

State Estimates 2

The residual category of other perpetrators includes the victim’s nonintimate friends, family, and 

acquaintances as well as instances where the victim was unsure of the relationship (figure 2-16).

Figure 2-16 NCVS (SAE) rates for violence by others, 2011–2013 and 1999–2013
Developmental Estimates of Subnational Crime Rates 35

State Estimates 2

2.4 Property Crime by Type

The three components of property crime are burglary, larceny (theft), and motor vehicle theft. The 

UCR indicates a general concentration of burglary in the South, but the NCVS (SAE) estimates 

distribute the incidence of burglary differently, even though UCR burglary is used as the auxiliary 

information in the small area model (figures 2-17 and 2-18). The NCVS (SAE) estimates show lower 

variability across the states, with all but two states within 70% to 130% of the national rate, while 

several states lie outside this range in the UCR.

Larceny (theft) is the largest component of property crime. The small area model uses UCR larceny 

as the auxiliary data. The comparison between the geographic patterns for the NCVS (SAE) and 

UCR estimates for larceny (figures 2-19 and 2-20) are similar to those seen previously for total 

property crime (figures 2-4 and 2-6). In the case of theft, the NCVS (SAE) estimates appear to show 

wider differences among the states than the UCR shows (figures 2-19 and 2-20).

Motor vehicle theft is the least common type of property crime measured by the NCVS. UCR 

motor vehicle theft serves as the auxiliary data for the small area model. A close agreement between 

the NCVS and UCR is expected here because the majority of NCVS victims of motor vehicle 

theft reported the crime to the police. For the most part, the geographic patterns are similar for the 

NCVS (SAE) and the UCR (figure 2-21). High rates in California, Nevada, and Washington are in 

line with UCR results. However, the NCVS (SAE) estimate for Texas is higher than in the UCR. 

This difference for Texas is also seen for the period 1999–2013 (figure 2-22).
Developmental Estimates of Subnational Crime Rates 36

State Estimates 2

Figure 2-17 Comparison of NCVS (SAE) and UCR rates for burglary, 2011–2013
Developmental Estimates of Subnational Crime Rates 37

State Estimates 2

Figure 2-18 Comparison of NCVS (SAE) and UCR rates for burglary, 1999–2013
Developmental Estimates of Subnational Crime Rates 38

State Estimates 2

Figure 2-19 Comparison of NCVS (SAE) and UCR rates for larceny, 2011–2013
Developmental Estimates of Subnational Crime Rates 39

State Estimates 2

Figure 2-20 Comparison of NCVS (SAE) and UCR rates for larceny, 1999–2013
Developmental Estimates of Subnational Crime Rates 40

State Estimates 2

Figure 2-21 Comparison of NCVS (SAE) and UCR rates for motor vehicle theft, 2011–2013
Developmental Estimates of Subnational Crime Rates 41

State Estimates 2

Figure 2-22 Comparison of NCVS (SAE) and UCR rates for motor vehicle theft, 1999–2013
Developmental Estimates of Subnational Crime Rates 42

State Estimates 2

2.5 Availability of State Estimates

For the most part, this report focuses on state estimates for the 2011–2013 or 2009–2013 average. 

However, estimates of rates and associated RMSEs were also produced for each 3-year period 

over the span 1999–2013. In addition, the American Community Survey was used to estimate 

the state populations in the NCVS universe, which included persons age 12 and over except for 

the institutionalized population. The estimates of the NCVS universe were then used to produce 

estimated counts of crimes for 3-year periods beginning with 2005–2007. Consequently, the project 

produced 3-year estimates of the following for each state:

1. Rates and RMSEs for violent crime by type of crime (toc) over time.

2. Rates and RMSEs for violent crime by victim-offender relationship (rel) over time.

3. Rates and RMSEs for property crime (prop) over time.

4. Violent and property crime counts (num) for the period 2005–2013.

The state estimates are available in two forms: (1) figures showing trend lines are available for all 

50 states and the District of Columbia (see appendix B) and (2) two Excel files provide all of the 

rates, RMSEs, and estimated counts, respectively, sorted by period and alphabetically by state (see 

appendices E and F). 
Developmental Estimates of Subnational Crime Rates 43

Counties and Metropolitan Areas 3

Using essentially the same SAE methods developed for states, 3-year estimates were produced for 

selected large counties and metropolitan areas. Estimation was limited to the 65 largest counties as 

determined by their 2010 Census populations. In 2010, each of these counties had a population of 

more than 800,000.

The metropolitan areas are specifically the Core Based Statistical Areas (CBSAs) used in publishing 

the 2010 Census. In general, CBSAs are composed of a set of counties. Although the Office of 

Management and Budget periodically updates the counties that make up the CBSAs, the NCVS 

(SAE) estimates use the 2010 Census definitions for the entire estimation period 1998–2012. Only 

the 51 CBSAs with populations of more than 1 million in 2010 were included. 

In place of UCR crime statistics for states obtained directly from the FBI, the county and 

metropolitan area UCR data were based on UCR estimates for counties released by the Interuniversity Consortium for Political and Social Research at the University of Michigan. Because 

these results appear later than the FBI’s state UCR statistics, the reference period for the available 

3-year estimates is 1998–2012 rather than 1999–2013. The five boroughs of New York City, each of 

which is a county, were combined into a single county for purposes of the NCVS (SAE) estimation 

because the UCR rates are based on citywide statistics. 

Table 3-1 provides the NCVS (SAE) estimates of the violent crime rates for 2010–2012 for the 

65 counties. Except for the offset by 1 year, the same full set of 3-year estimates of violent crime, 

by type of crime and relationship to the perpetrator, has been computed as for the state estimates. 

Similarly, 3-year estimates of property crime by type of crime are available.

Table 3-2 provides parallel results for the 51 CBSAs. In many cases, the modeled counties in table 

3-1 were included within the modeled CBSAs in table 3-2, but the county and CBSA models were 

fitted separately.
Developmental Estimates of Subnational Crime Rates 44

Counties and Metropolitan Areas 3

Table 3-1 NCVS (SAE) estimates of violent crime rates for counties, 2010–2012

State County

2010 Census 

Population FIPS Code

Violent crime 

rate, 2010–2012

Root mean 

square error

Arizona Maricopa County 3,817,117 04013 25.3 4.7

Arizona Pima County 980,263 04019 36.1 7.2

California Alameda County 1,510,271 06001 33.5 6.4

California Contra Costa 

County

1,049,025 06013 22.4 7.1

California Fresno County 930,450 06019 19.8 7.3

California Kern County 839,631 06029 20.3 7.5

California Los Angeles 

County

9,818,605 06037 20.4 3.2

California Orange County 3,010,232 06059 11.1 5.1

California Riverside County 2,189,641 06065 34.4 5.7

California Sacramento 

County

1,418,788 06067 44.1 6.5

California San Bernardino 

County

2,035,210 06071 20.3 5.8

California San Diego County 3,095,313 06073 13.6 5.1

California San Francisco 

County

805,235 06075 21.6 7.7

California Santa Clara 

County

1,781,642 06085 21.5 6.1

California Ventura County 823,318 06111 22.3 7.5

Connecticut Fairfield County 916,829 09001 20.2 7.4

Connecticut Hartford County 894,014 09003 15.9 7.4

Connecticut New Haven 

County

862,477 09009 21.6 7.4

Florida Broward County 1,748,066 12011 14.7 6.1

Florida Duval County 864,263 12031 21.9 7.5

Florida Hillsborough 

County

1,229,226 12057 23.4 6.8

Florida Miami-Dade 

County

2,496,435 12086 10.5 5.5

Florida Orange County 1,145,956 12095 10.5 6.9

Florida Palm Beach 

County

1,320,134 12099 9.3 6.7

Florida Pinellas County 916,542 12103 21.2 7.3

Georgia Fulton County 920,581 13121 22.7 7.3

Georgia Gwinnett County 805,321 13135 14.9 7.6

Hawaii Honolulu County 953,207 15003 39.4 7.3

Illinois Cook County 5,194,675 17031 20.3 4.2

Illinois DuPage County 916,924 17043 12.1 7.3

Indiana Marion County 903,393 18097 30.6 7.4

Maryland Baltimore Countya 805,029 24005 18.9 7.6

Maryland Montgomery 

County

971,777 24031 16.4 7.2
Developmental Estimates of Subnational Crime Rates 45

Counties and Metropolitan Areas 3

Maryland Prince George’s 

County

863,420 24033 17.4 7.4

Massachusetts Middlesex County 1,503,085 25017 22.0 6.4

Michigan Macomb County 840,978 26099 14.6 7.5

Michigan Oakland County 1,202,362 26125 12.5 6.8

Michigan Wayne County 1,820,584 26163 28.8 6.1

Minnesota Hennepin County 1,152,425 27053 23.9 7.0

Missouri St. Louis Countya 998,954 29189 20.0 7.2

Nevada Clark County 1,951,269 32003 21.2 5.9

New Jersey Bergen County 905,116 34003 9.8 7.4

New Jersey Middlesex County 809,858 34023 14.2 7.6

New York Erie County 919,040 36029 16.8 7.3

New York Nassau County 1,339,532 36059 21.1 6.6

New York New York City 8,175,133 36061b 14.6 3.5

New York Suffolk County 1,493,350 36103 11.9 6.4

New York Westchester 

County

949,113 36119 19.1 7.3

North Carolina Mecklenburg 

County

919,628 37119 15.1 7.3

North Carolina Wake County 900,993 37183 13.2 7.4

Ohio Cuyahoga County 1,280,122 39035 25.8 6.7

Ohio Franklin County 1,163,414 39049 22.2 6.9

Ohio Hamilton County 802,374 39061 28.6 7.6

Pennsylvania Allegheny County 1,223,348 42003 42.4 6.8

Pennsylvania Philadelphia 

County

1,526,006 42101 41.4 6.5

Tennessee Shelby County 927,644 47157 24.1 7.4

Texas Bexar County 1,714,773 48029 22.9 6.2

Texas Dallas County 2,368,139 48113 35.9 5.6

Texas Harris County 4,092,459 48201 18.6 4.6

Texas Tarrant County 1,809,034 48439 23.1 6.1

Texas Travis County 1,024,266 48453 24.3 7.1

Utah Salt Lake County 1,029,655 49035 29.7 7.2

Virginia Fairfax County 1,081,726 51059 16.4 7.0

Washington King County 1,931,249 53033 29.0 5.9

Wisconsin Milwaukee County 947,735 55079 29.7 7.3

a

Baltimore County, MD does not include the City of Baltimore, and St. Louis County, MO does not include the City of St. 

Louis.

b

Arbitrarily, the FIPS code for New York County (Manhattan), 36061, was used to designate New York City.
Developmental Estimates of Subnational Crime Rates 46

Counties and Metropolitan Areas 3

Table 3-2 NCVS (SAE) estimates of violent crime rates for metropolitan areas (CBSAs), 2010–

2012

CBSA Code CBSA Name

2010 Census 

Population

Violent crime rate, 

2010–2012

Root mean square 

error

12060 Atlanta-Sandy SpringsMarietta, GA5,268,860 19.3 4.4

12420 Austin-Round RockSan Marcos, TX1,716,289 20.9 6.3

12580 Baltimore-Towson, MD 2,710,489 30.8 5.5

13820 Birmingham-Hoover, 

AL

1,128,047 24.3 6.9

14460 Boston-CambridgeQuincy, MA-NH4,552,402 22.6 4.7

15380 Buffalo-Niagara Falls, 

NY

1,135,509 19.1 6.9

16740 Charlotte-GastoniaRock Hill, NC-SC1,758,038 18.9 6.2

16980 Chicago-JolietNaperville, IL-IN-WI9,461,105 18.5 3.6

17140 Cincinnati-Middletown, 

OH-KY-IN

2,130,151 23.6 5.9

17460 Cleveland-ElyriaMentor, OH2,077,240 22.0 6.0

18140 Columbus, OH 1,836,536 19.0 6.2

19100 Dallas-Fort WorthArlington, TX6,371,773 35.7 4.1

19740 Denver-AuroraBroomfield, CO2,543,482 40.2 5.7

19820 Detroit-WarrenLivonia, MI4,296,250 19.1 4.8

25540 Hartford-West 

Hartford-East 

Hartford, CT

1,212,381 22.8 6.8

26420 Houston-Sugar LandBaytown, TX5,946,800 20.5 4.3

26900 Indianapolis-Carmel, 

IN

1,756,241 25.8 6.3

27260 Jacksonville, FL 1,345,596 21.2 6.7

28140 Kansas City, MO-KS 2,035,334 33.9 6.0

29820 Las Vegas-Paradise, 

NV

1,951,269 21.6 6.1

31100 Los Angeles-Long 

Beach-Santa Ana, CA

12,828,837 19.0 3.2

31140 Louisville/Jefferson 

County, KY-IN

1,283,566 29.1 6.8

32820 Memphis, TN-MS-AR 1,316,100 23.7 6.8

33100 Miami-Fort 

Lauderdale-Pompano 

Beach, FL

5,564,635 9.7 4.4

33340 Milwaukee-WaukeshaWest Allis, WI1,555,908 21.4 6.5
Developmental Estimates of Subnational Crime Rates 47

Counties and Metropolitan Areas 3

33460 Minneapolis-St. PaulBloomington, MN-WI3,279,833 21.7 5.2

34980 Nashville-Davidson-

-Murfreesboro--

Franklin, TN

1,589,934 17.1 6.4

35380 New Orleans-MetairieKenner, LA1,167,764 21.4 6.9

35620 New York-Northern 

New Jersey-Long 

Island, NY-NJ-PA

18,897,109 13.4 2.7

36420 Oklahoma City, OK 1,252,987 30.7 6.8

36740 Orlando-KissimmeeSanford, FL2,134,411 11.4 5.9

37980 Philadelphia-CamdenWilmington, PA-NJDE-MD5,965,343 21.7 4.3

38060 Phoenix-MesaGlendale, AZ4,192,887 22.9 4.8

38300 Pittsburgh, PA 2,356,285 37.5 5.8

38900 Portland-VancouverHillsboro, OR-WA2,226,009 34.9 5.9

39300 Providence-New 

Bedford-Fall River, 

RI-MA

1,600,852 30.2 6.4

39580 Raleigh-Cary, NC 1,130,490 14.5 7.0

40060 Richmond, VA 1,258,251 18.0 6.8

40140 Riverside-San 

Bernardino-Ontario, 

CA

4,224,851 27.6 4.8

40380 Rochester, NY 1,054,323 23.3 7.1

40900 Sacramento--ArdenArcade--Roseville, CA2,149,127 42.8 5.9

41180 St. Louis, MO-IL 2,812,896 33.5 5.5

41620 Salt Lake City, UT 1,124,197 28.9 7.0

41700 San Antonio-New 

Braunfels, TX

2,142,508 20.1 5.9

41740 San Diego-CarlsbadSan Marcos, CA3,095,313 14.2 5.3

41860 San FranciscoOakland-Fremont, CA4,335,391 23.5 4.8

41940 San Jose-SunnyvaleSanta Clara, CA1,836,911 19.4 6.2

42660 Seattle-TacomaBellevue, WA3,439,809 36.2 5.1

45300 Tampa-St. PetersburgClearwater, FL2,783,243 22.9 5.5

47260 Virginia BeachNorfolk-Newport 

News, VA-NC

1,671,683 25.0 6.3

47900 Washington-ArlingtonAlexandria, DC-VAMD-WV5,582,170 17.4 4.4
Developmental Estimates of Subnational Crime Rates 48

Counties and Metropolitan Areas 3

Similar to the analyses for states, counties and CBSAs were categorized into groups determined by 

the size of their 2010 populations (table 3-3). Group 2 covers the same population range as state 

group S4 and group 3 corresponds to state group S5 (table 2-1). 

Table 3-3 Grouping of counties and CBSAs with NCVS (SAE) estimates

Group 2010 Population Number of Counties Number of CBSAs

1 More than 5 million 3 9

2 2 to 5 million 8 20

3 1 to 2 million 25 22

4 Less than 1 million 29 -

Total 65 51

Although the county and CBSA models for 2011–2013 were fitted separately, the average RMSEs by 

grouping are quite similar, suggesting that the relationship between the RMSE and population size is 

approximately the same for counties and CBSAs (figure 3-1). 

Figure 3-1 Comparison of RMSEs of NCVS (SAE) estimates for counties and CBSAs, 2011–2013
Developmental Estimates of Subnational Crime Rates 49

Counties and Metropolitan Areas 3

The RMSE averages for groups 2 and 3 (figure 3-1) are less than the corresponding RMSE averages 

for states (groups S4 and S5 in figure 2-8). Because the small area estimation was restricted to the 

largest counties and CBSAs, most of the areas were covered by self-representing samples in the 

NCVS design. Therefore, estimation for these counties and metropolitan areas is more precise than 

estimation for small states of the same population size; the reliability of the NCVS for small states 

is reduced because of the first-stage selection of primary sampling units. Some small states may not 

have been allocated any NCVS sample or may have had a sample in a relatively unrepresentative set 

of counties within the state.

County and CBSA level appendix tables, which are similar to the state appendix tables, include: 

1. Rates and RSMEs of violent crime by type of crime (toc) over time.

2. Rates and RSMEs of violent crime by relationship to the perpetrators (rel) over time.

3. Rates and RSMEs of property crime (prop) over time.

Similar to state estimates, county and CBSA estimates are available in two forms: (1) figures showing 

trend lines for the period 2000–2012 (see appendices C and D) and (2) two Excel files provide all of 

the rates and RMSEs (see appendices G and H). 

Note that Baltimore County does not include the City of Baltimore, and St. Louis County does not 

include the City of St. Louis.
Developmental Estimates of Subnational Crime Rates 50

Concluding Remarks 4

The first section cautioned that the NCVS (SAE) estimates are affected by sampling variability. The 

relatively large RMSEs associated with these estimates suggest that care is required when interpreting 

the estimates. Nonetheless, the NCVS (SAE) estimates offer insight into how crime, as measured 

by the NCVS, is distributed geographically. For states, the report displays results both for the most 

current period, 2011–2013, and for the longer 15-year period 1999–2013. Often the two results are 

similar for the same area; when the estimate for 2011–2013 is different, there is the possibility that 

the effect is transient or that it signals a trend that may persist for some time. 

In some small area applications, the model is able to use strong statistical relationships with the 

auxiliary data to produce credible estimates, even in the absence of any sample observations for 

some areas. In this application, the auxiliary data were moderately predictive, particularly for violent 

crime, and the estimates benefited more from the combination by the model of direct NCVS 

data over the 15-year period. Consequently, small states with a limited NCVS sample were at a 

disadvantage compared to larger states. Estimates of violent crime for states with populations of 1 

to 2 million are marginal, at best, and those for states with populations of less than 1 million offer 

little information.

For the four largest states, the NCVS (SAE) estimates improve the NCVS direct estimates by only 

modest amounts. Similarly, once the supplemental sample for the seven next largest states has 

accumulated data to publish direct estimates for 2014–2016, those estimates will be more accurate 

than any currently offered by the NCVS (SAE) for 2011–2013. In the meantime, the NCVS (SAE) 

estimates offer a preview of what may be expected when the supplemental data become available in 

these states. The NCVS (SAE) estimates may continue to offer the best available information in the 

states that are not supplemented.

The NCVS (SAE) county and CBSA estimates, even though they have somewhat large RMSEs, 

offer geographically detailed information that direct estimates from the NCVS may not provide for 

several years into the future.
Developmental Estimates of Subnational Crime Rates 51

References R

Cantor, D., Krentke, T., Stukel, D., and Rizzo, L. (2010). NCVS Task 4 Report: Summary of Options 

Relating to Local Area Estimation. Submitted by Westat to the Bureau of Justice Statistics, May 19, 

2010, http://www.bjs.gov/content/pub/pdf/westat_lae_5-19-10.pdf. 

Catalano, S. (2012). Intimate Partner Violence, 1993–2010. Washington, DC: U.S. Department of 

Justice, Bureau of Justice Statistics. NCJ 239203.

Cork, D.L and Groves, R.M (eds.) (2008). Surveying Victims: Options for Conducting the National 

Crime Victimization Survey. Washington, DC: National Academies Press.

Lauritsen, J.L., Owens, J.G, Planty, M., Rand, M.R., and Truman, J.L. (2012). Methods for Counting 

High-Frequency Repeat Victimizations in the National Crime Victimization Survey. Washington, 

DC: U.S. Department of Justice, Bureau of Justice Statistics. NCJ 237308.

Lynch, J.P. and Addington, L.A. (eds.) (2007). Understanding Crime Statistics: Revisiting the 

Divergence of the NCVS. New York, NY: Cambridge University Press.

Truman, J.L. and Langton, L. (2014). Criminal Victimization, 2013. Washington, DC: U.S. 

Department of Justice, Bureau of Justice Statistics. NCJ 247648. 
Appendix A

Developmental Estimates of Subnational Crime Rates A-1

Treatment of Year 2006 and Series Crimes 

As noted in the introduction, this appendix covers two features of the estimates. The first feature is 

a special treatment of NCVS estimates for 2006. With the change to a new sample design in 2006, 

the national NCVS estimates increased in 2006 compared to 2005, only to return to approximately 

the 2005 level in 2007. In 2006, many new interviewers were introduced as part of the overall 

redesign, providing a possible explanation for the anomaly. After 2006, publications in BJS’s 

Criminal Victimization series, including Criminal Victimization, 2013, call attention to the unusual 

2006 NCVS results. Displaying 3-year averages partially smooths the anomaly, but it still distorts the 

general crime trends (figure A-1 (a)).

A preliminary SAE model for 1997–2011 was developed using all NCVS data for those years. 

Estimates from this model appear in figures 2-5 and 2-6 as well as tables 2-2 and 2-3.

A revised model was developed that excluded the 2006 data. This approach replaced the 3-year 

average for 2004–2006 with the average of 2004 and 2005; replaced the average for 2005–2007 with 

the average of 2005 and 2007; and replaced the average for 2006–2008 with the average of 2007 

and 2008. Where NCVS (SAE) estimates are compared with UCR estimates, this redefinition of the 

3-year averages is applied to both the NCVS and the UCR. The 2006 data were similarly excluded 

from the 1999–2013 model that produced most of the state results in this report. Similarly, 2006 

was also excluded from the county and CBSA models. Figure A-1 compares national estimates of 

violent crime from the NCVS to the UCR, both for the original approach that included 2006 and 

the revised approach that omitted it. The effect of the 2006 NCVS data, producing a bump in the 

NCVS series, is evident in the upper graph (figure A-1 (a)). Both graphs show the substantial drop in 

the NCVS crime rate from 1999 to 2013, and a smaller relative drop in the UCR. 

The second feature is to note the treatment of series crimes. With the release of the 2011 NCVS 

estimates, BJS introduced a new method for weighting series crimes, that is, crimes where the 

respondent could not distinctly recall separate incidents of the same type of crime but could usually 

provide a specific number or an estimate of the number of times the crime occurred. The new 

method uses the reported number of times the crime occurred, capping the number at 10 and 

using 6 when the respondent could not provide a specific number (Lauritsen et al., 2012). The SAE 

estimates described here implement this approach to weighting series crimes for all years. The NCVS 

Victimization Analysis Tool (http://www.bjs.gov/index.cfm?ty=nvat) also uses this approach for all 

estimates it provides, both for years before and after 2011, as does the most recent BJS publications. 
Developmental Estimates of Subnational Crime Rates A-2

Figure A-1 NCVS vs. UCR violent crime rate, both (a) with and (b) without data for year 2006
Developmental Estimates of Subnational Crime Rates B-1

Appendix B

Trend Figures for 50 States and Washington, D.C.
Appendix

B

B‐2

Table of Contents

Alaska ............................................................................................................................................................4

Alabama ........................................................................................................................................................7

Arkansas......................................................................................................................................................10

Arizona ........................................................................................................................................................13

California.....................................................................................................................................................16

Colorado......................................................................................................................................................19

Connecticut.................................................................................................................................................22

Washington DC ...........................................................................................................................................25

Delaware .....................................................................................................................................................29

Florida .........................................................................................................................................................32

Georgia........................................................................................................................................................35

Hawaii .........................................................................................................................................................38

Iowa.............................................................................................................................................................41

Idaho ...........................................................................................................................................................44

Illinois..........................................................................................................................................................47

Indiana ........................................................................................................................................................50

Kansas .........................................................................................................................................................53

Kentucky......................................................................................................................................................56

Louisiana .....................................................................................................................................................59

Massachusetts ............................................................................................................................................62

Maryland.....................................................................................................................................................65

Maine ..........................................................................................................................................................68

Michigan......................................................................................................................................................71

Minnesota ...................................................................................................................................................75

Missouri ......................................................................................................................................................78

Mississippi...................................................................................................................................................81

Montana......................................................................................................................................................84

North Carolina.............................................................................................................................................87

North Dakota...............................................................................................................................................90
Appendix

B

B‐3

Nebraska .....................................................................................................................................................93

New Hampshire...........................................................................................................................................96

New Jersey ..................................................................................................................................................99

New Mexico ..............................................................................................................................................102

Nevada ......................................................................................................................................................105

New York...................................................................................................................................................108

Ohio...........................................................................................................................................................111

Oklahoma..................................................................................................................................................114

Oregon ......................................................................................................................................................117

Pennsylvania .............................................................................................................................................120

Rhode Island..............................................................................................................................................123

South Carolina...........................................................................................................................................126

South Dakota.............................................................................................................................................129

Tennessee .................................................................................................................................................132

Texas .........................................................................................................................................................135

Utah...........................................................................................................................................................138

Virginia ......................................................................................................................................................141

Vermont....................................................................................................................................................144

Washington...............................................................................................................................................147

Wisconsin..................................................................................................................................................149

West Virginia.............................................................................................................................................153

Wyoming...................................................................................................................................................156
Appendix

B

B‐4

Alaska  
Appendix

B

B‐5
Appendix

B

B‐6
Appendix

B

B‐7

Alabama
Appendix

B

B‐8
Appendix

B

B‐9
Appendix

B

B‐10

Arkansas
Appendix

B

B‐11
Appendix

B

B‐12
Appendix

B

B‐13

Arizona
Appendix

B

B‐14
Appendix

B

B‐15
Appendix

B

B‐16

California
Appendix

B

B‐17
Appendix

B

B‐18
Appendix

B

B‐19

Colorado
Appendix

B

B‐20
Appendix

B

B‐21
Appendix

B

B‐22

Connecticut
Appendix

B

B‐23
Appendix

B

B‐24
Appendix

B

B‐25

Washington DC
Appendix

B

B‐26
Appendix

B

B‐27
Appendix

B

B‐28
Appendix

B

B‐29

Delaware
Appendix

B

B‐30
Appendix

B

B‐31
Appendix

B

B‐32

Florida
Appendix

B

B‐33
Appendix

B

B‐34
Appendix

B

B‐35

Georgia
Appendix

B

B‐36
Appendix

B

B‐37
Appendix

B

B‐38

Hawaii
Appendix

B

B‐39
Appendix

B

B‐40
Appendix

B

B‐41

Iowa
Appendix

B

B‐42
Appendix

B

B‐43
Appendix

B

B‐44

Idaho
Appendix

B

B‐45
Appendix

B

B‐46
Appendix

B

B‐47

Illinois
Appendix

B

B‐48
Appendix

B

B‐49
Appendix

B

B‐50

Indiana
Appendix

B

B‐51
Appendix

B

B‐52
Appendix

B

B‐53

Kansas
Appendix

B

B‐54
Appendix

B

B‐55
Appendix

B

B‐56

Kentucky
Appendix

B

B‐57
Appendix

B

B‐58
Appendix

B

B‐59

Louisiana
Appendix

B

B‐60
Appendix

B

B‐61
Appendix

B

B‐62

Massachusetts
Appendix

B

B‐63
Appendix

B

B‐64
Appendix

B

B‐65

Maryland
Appendix

B

B‐66
Appendix

B

B‐67
Appendix

B

B‐68

Maine
Appendix

B

B‐69
Appendix

B

B‐70
Appendix

B

B‐71

Michigan
Appendix

B

B‐72
Appendix

B

B‐73
Appendix

B

B‐74
Appendix

B

B‐75

Minnesota
Appendix

B

B‐76
Appendix

B

B‐77
Appendix

B

B‐78

Missouri
Appendix

B

B‐79
Appendix

B

B‐80
Appendix

B

B‐81

Mississippi
Appendix

B

B‐82
Appendix

B

B‐83
Appendix

B

B‐84

Montana
Appendix

B

B‐85
Appendix

B

B‐86
Appendix

B

B‐87

North Carolina
Appendix

B

B‐88
Appendix

B

B‐89
Appendix

B

B‐90

North Dakota
Appendix

B

B‐91
Appendix

B

B‐92
Appendix

B

B‐93

Nebraska
Appendix

B

B‐94
Appendix

B

B‐95
Appendix

B

B‐96

New Hampshire
Appendix

B

B‐97
Appendix

B

B‐98
Appendix

B

B‐99

New Jersey
Appendix

B

B‐100
Appendix

B

B‐101
Appendix

B

B‐102

New Mexico
Appendix

B

B‐103
Appendix

B

B‐104
Appendix

B

B‐105

Nevada
Appendix

B

B‐106
Appendix

B

B‐107
Appendix

B

B‐108

New York
Appendix

B

B‐109
Appendix

B

B‐110
Appendix

B

B‐111

Ohio
Appendix

B

B‐112
Appendix

B

B‐113
Appendix

B

B‐114

Oklahoma
Appendix

B

B‐115
Appendix

B

B‐116
Appendix

B

B‐117

Oregon
Appendix

B

B‐118
Appendix

B

B‐119
Appendix

B

B‐120

Pennsylvania
Appendix

B

B‐121
Appendix

B

B‐122
Appendix

B

B‐123

Rhode Island
Appendix

B

B‐124
Appendix

B

B‐125
Appendix

B

B‐126

South Carolina
Appendix

B

B‐127
Appendix

B

B‐128
Appendix

B

B‐129

South Dakota
Appendix

B

B‐130
Appendix

B

B‐131
Appendix

B

B‐132

Tennessee
Appendix

B

B‐133
Appendix

B

B‐134
Appendix

B

B‐135

Texas
Appendix

B

B‐136
Appendix

B

B‐137
Appendix

B

B‐138

Utah
Appendix

B

B‐139
Appendix

B

B‐140
Appendix

B

B‐141

Virginia
Appendix

B

B‐142
Appendix

B

B‐143
Appendix

B

B‐144

Vermont
Appendix

B

B‐145
Appendix

B

B‐146
Appendix

B

B‐147

Washington
Appendix

B

B‐148
Appendix

B

B‐149

Wisconsin
Appendix

B

B‐150
Appendix

B

B‐151
Appendix

B

B‐152
Appendix

B

B‐153

West Virginia
Appendix

B

B‐154
Appendix

B

B‐155
Appendix

B

B‐156

Wyoming
Appendix

B

B‐157
Appendix

B

B‐158
Developmental Estimates of Subnational Crime Rates C-1

Appendix C

Trend Figures for Selected Counties
Appendix C

C-2 

Table of Contents

Maricopa County, AZ ....................................................................................................................................... 4

Pima County, AZ ............................................................................................................................................... 7

Alameda County, CA ....................................................................................................................................... 10

Contra Costa County, CA ............................................................................................................................... 13

Fresno County, CA .......................................................................................................................................... 16

Kern County, CA ............................................................................................................................................. 19

Los Angeles County, CA ................................................................................................................................ 22

Orange County, CA ......................................................................................................................................... 25

Riverside County, CA ...................................................................................................................................... 28

Sacramento County, CA ................................................................................................................................. 31

San Bernardino County, CA ........................................................................................................................... 34

San Diego County, CA .................................................................................................................................... 37

San Francisco County, CA.............................................................................................................................. 40

Santa Clara County, CA .................................................................................................................................. 43

Ventura County, CA ........................................................................................................................................ 46

Fairfield County, CT ........................................................................................................................................ 49

Hartford County, CT ....................................................................................................................................... 52

New Haven County, CT ................................................................................................................................. 55

Broward County, FL ........................................................................................................................................ 58

Duval County, FL ............................................................................................................................................ 61

Hillsborough County, FL ................................................................................................................................ 64

Miami-Dade County, FL ................................................................................................................................. 67

Orange County, FL .......................................................................................................................................... 70

Palm Beach County, FL .................................................................................................................................. 73

Pinellas County, FL ......................................................................................................................................... 76

Fulton County, GA .......................................................................................................................................... 79

Gwinnett County, GA ..................................................................................................................................... 82

Honolulu County, HI ...................................................................................................................................... 85

Cook County, IL .............................................................................................................................................. 88

DuPage County, IL .......................................................................................................................................... 91

Marion County, IN .......................................................................................................................................... 94

Baltimore County, MD ................................................................................................................................... 97

Montgomery County, MD ........................................................................................................................... 100

Prince George's County, MD ...................................................................................................................... 103
Appendix C

C-3 

Middlesex County, MA ................................................................................................................................ 106

Macomb County, MI .................................................................................................................................... 109

Oakland County, MI ..................................................................................................................................... 112

Wayne County, MI ........................................................................................................................................ 115

Hennepin County, MN ................................................................................................................................ 118

St. Louis County, MO .................................................................................................................................. 121

Clark County, NV ......................................................................................................................................... 124

Bergen County, NJ ........................................................................................................................................ 127

Middlesex, NJ ................................................................................................................................................ 130

Erie County, NY ........................................................................................................................................... 133

Nassau County, NY ...................................................................................................................................... 136

New York City, NY ...................................................................................................................................... 139

Suffolk County, NY ...................................................................................................................................... 142

Westchester County, NY ............................................................................................................................. 145

Mecklenburg County, NC ............................................................................................................................ 148

Wake County, NC ......................................................................................................................................... 151

Cuyahoga County, OH ................................................................................................................................. 154

Franklin County, OH ................................................................................................................................... 157

Hamilton County, OH ................................................................................................................................. 160

Allegheny County, PA .................................................................................................................................. 163

Philadelphia County, PA .............................................................................................................................. 166

Shelby County, TN ....................................................................................................................................... 169

Bexar County, TX ......................................................................................................................................... 172

Dallas County, TX ........................................................................................................................................ 175

Harris County, TX ........................................................................................................................................ 178

Tarrant, TX .................................................................................................................................................... 181

Travis County, TX ........................................................................................................................................ 184

Salt Lake County, UT ................................................................................................................................... 187

Fairfax County, VA ...................................................................................................................................... 190

King County, WA ......................................................................................................................................... 193

Milwaukee County, WI ................................................................................................................................ 196
Appendix C

C-4 

Maricopa County, AZ
Appendix C

C-5 
Appendix C

C-6 
Appendix C

C-7 

Pima County, AZ
Appendix C

C-8 
Appendix C

C-9 
Appendix C

C-10 

Alameda County, CA
Appendix C

C-11 
Appendix C

C-12 
Appendix C

C-13 

Contra Costa County, CA
Appendix C

C-14 
Appendix C

C-15 
Appendix C

C-16 

Fresno County, CA
Appendix C

C-17 
Appendix C

C-18 
Appendix C

C-19 

Kern County, CA
Appendix C

C-20 
Appendix C

C-21 
Appendix C

C-22 

Los Angeles County, CA
Appendix C

C-23 
Appendix C

C-24 
Appendix C

C-25 

Orange County, CA   
Appendix C

C-26 
Appendix C

C-27 
Appendix C

C-28 

Riverside County, CA
Appendix C

C-29 
Appendix C

C-30 
Appendix C

C-31 

Sacramento County, CA
Appendix C

C-32 
Appendix C

C-33 
Appendix C

C-34 

San Bernardino County, CA
Appendix C

C-35 
Appendix C

C-36 
Appendix C

C-37 

San Diego County, CA
Appendix C

C-38 
Appendix C

C-39 
Appendix C

C-40 

San Francisco County, CA  
Appendix C

C-41 
Appendix C

C-42 
Appendix C

C-43 

Santa Clara County, CA  
Appendix C

C-44 
Appendix C

C-45 
Appendix C

C-46 

Ventura County, CA
Appendix C

C-47 
Appendix C

C-48 
Appendix C

C-49 

Fairfield County, CT
Appendix C

C-50 
Appendix C

C-51 
Appendix C

C-52 

Hartford County, CT
Appendix C

C-53 
Appendix C

C-54 
Appendix C

C-55 

New Haven County, CT
Appendix C

C-56 
Appendix C

C-57 
Appendix C

C-58 

Broward County, FL
Appendix C

C-59 
Appendix C

C-60 
Appendix C

C-61 

Duval County, FL
Appendix C

C-62 
Appendix C

C-63 
Appendix C

C-64 

Hillsborough County, FL
Appendix C

C-65 
Appendix C

C-66 
Appendix C

C-67 

Miami‐Dade County, FL
Appendix C

C-68 
Appendix C

C-69 
Appendix C

C-70 

Orange County, FL
Appendix C

C-71 
Appendix C

C-72 
Appendix C

C-73 

Palm Beach County, FL
Appendix C

C-74 
Appendix C

C-75 
Appendix C

C-76 

Pinellas County, FL
Appendix C

C-77 
Appendix C

C-78 
Appendix C

C-79 

Fulton County, GA
Appendix C

C-80 
Appendix C

C-81 
Appendix C

C-82 

Gwinnett County, GA
Appendix C

C-83 
Appendix C

C-84 
Appendix C

C-85 

Honolulu County, HI
Appendix C

C-86 
Appendix C

C-87 
Appendix C

C-88 

Cook County, IL
Appendix C

C-89 
Appendix C

C-90 
Appendix C

C-91 

DuPage County, IL
Appendix C

C-92 
Appendix C

C-93 
Appendix C

C-94 

Marion County, IN   
Appendix C

C-95 
Appendix C

C-96 
Appendix C

C-97 

Baltimore County, MD
Appendix C

C-98 
Appendix C

C-99 
Appendix C

C-100 

Montgomery County, MD
Appendix C

C-101 
Appendix C

C-102 
Appendix C

C-103 

Prince George's County, MD
Appendix C

C-104 
Appendix C

C-105 
Appendix C

C-106 

Middlesex County, MA
Appendix C

C-107 
Appendix C

C-108 
Appendix C

C-109 

Macomb County, MI
Appendix C

C-110 
Appendix C

C-111 
Appendix C

C-112 

Oakland County, MI
Appendix C

C-113 
Appendix C

C-114 
Appendix C

C-115 

Wayne County, MI
Appendix C

C-116 
Appendix C

C-117 
Appendix C

C-118 

Hennepin County, MN
Appendix C

C-119 
Appendix C

C-120 
Appendix C

C-121 

St. Louis County, MO
Appendix C

C-122 
Appendix C

C-123 
Appendix C

C-124 

Clark County, NV
Appendix C

C-125 
Appendix C

C-126 
Appendix C

C-127 

Bergen County, NJ
Appendix C

C-128 
Appendix C

C-129 
Appendix C

C-130 

Middlesex, NJ
Appendix C

C-131 
Appendix C

C-132 
Appendix C

C-133 

Erie County, NY
Appendix C

C-134 
Appendix C

C-135 
Appendix C

C-136 

Nassau County, NY
Appendix C

C-137 
Appendix C

C-138 
Appendix C

C-139 

New York City, NY
Appendix C

C-140 
Appendix C

C-141 
Appendix C

C-142 

Suffolk County, NY
Appendix C

C-143 
Appendix C

C-144 
Appendix C

C-145 

Westchester County, NY
Appendix C

C-146 
Appendix C

C-147 
Appendix C

C-148 

Mecklenburg County, NC
Appendix C

C-149 
Appendix C

C-150 
Appendix C

C-151 

Wake County, NC
Appendix C

C-152 
Appendix C

C-153 
Appendix C

C-154 

Cuyahoga County, OH
Appendix C

C-155 
Appendix C

C-156 
Appendix C

C-157 

Franklin County, OH
Appendix C

C-158 
Appendix C

C-159 
Appendix C

C-160 

Hamilton County, OH
Appendix C

C-161 
Appendix C

C-162 
Appendix C

C-163 

Allegheny County, PA
Appendix C

C-164 
Appendix C

C-165 
Appendix C

C-166 

Philadelphia County, PA
Appendix C

C-167 
Appendix C

C-168 
Appendix C

C-169 

Shelby County, TN
Appendix C

C-170 
Appendix C

C-171 
Appendix C

C-172 

Bexar County, TX
Appendix C

C-173 
Appendix C

C-174 
Appendix C

C-175 

Dallas County, TX
Appendix C

C-176 
Appendix C

C-177 
Appendix C

C-178 

Harris County, TX
Appendix C

C-179 
Appendix C

C-180 
Appendix C

C-181 

Tarrant, TX
Appendix C

C-182 
Appendix C

C-183 
Appendix C

C-184 

Travis County, TX
Appendix C

C-185 
Appendix C

C-186 
Appendix C

C-187 

Salt Lake County, UT
Appendix C

C-188 
Appendix C

C-189 
Appendix C

C-190 

Fairfax County, VA
Appendix C

C-191 
Appendix C

C-192 
Appendix C

C-193 

King County, WA
Appendix C

C-194 
Appendix C

C-195 
Appendix C

C-196 

Milwaukee County, WI  
Appendix C

C-197 
Appendix C

C-198 
Developmental Estimates of Subnational Crime Rates D-1

Appendix D

Trend Figures for Selected CBSAs
Appendix

D

D‐2

Table of Content

Atlanta‐Sandy Springs‐Marietta, GA............................................................................................................. 4

Austin‐Round Rock‐San Marcos, TX.............................................................................................................. 7

Baltimore‐Towson, MD............................................................................................................................... 10

Birmingham‐Hoover, AL.............................................................................................................................. 13

Boston‐Cambridge‐Quincy, MA‐NH............................................................................................................ 16

Buffalo‐Niagara Falls, NY............................................................................................................................. 19

Charlotte‐Gastonia‐Rock Hill, NC‐SC........................................................................................................... 22

Chicago‐Joliet‐Naperville, IL‐IN‐WI ............................................................................................................. 25

Cincinnati‐Middletown, OH‐KY‐IN .............................................................................................................. 28

Cleveland‐Elyria‐Mentor, OH...................................................................................................................... 31

Columbus, OH .............................................................................................................................................34

Dallas‐Fort Worth‐Arlington, TX ................................................................................................................. 37

Denver‐Aurora‐Broomfield, CO .................................................................................................................. 40

Detroit‐Warren‐Livonia, MI ........................................................................................................................ 43

Hartford‐West Hartford‐East Hartford, CT ................................................................................................. 46

Houston‐Sugar Land‐Baytown, TX .............................................................................................................. 49

Indianapolis‐Carmel, IN............................................................................................................................... 52

Jacksonville, FL ............................................................................................................................................55

Kansas City, MO‐KS .....................................................................................................................................58

Las Vegas‐Paradise, NV............................................................................................................................... 61

Los Angeles‐Long Beach‐Santa Ana, CA...................................................................................................... 64

Louisville/Jefferson County, KY‐IN.............................................................................................................. 67

Memphis, TN‐MS‐AR...................................................................................................................................70

Miami‐Fort Lauderdale‐Pompano Beach, FL .............................................................................................. 73

Milwaukee‐Waukesha‐West Allis, WI......................................................................................................... 76

Minneapolis‐St. Paul‐Bloomington, MN‐WI ............................................................................................... 79

Nashville‐Davidson‐‐Murfreesboro‐‐Franklin, TN....................................................................................... 82

New Orleans‐Metairie‐Kenner, LA.............................................................................................................. 85

New York‐Northern New Jersey‐Long Island, NY‐NJ‐PA............................................................................. 88

Oklahoma City, OK ......................................................................................................................................91

Orlando‐Kissimmee‐Sanford, FL ................................................................................................................. 94
Appendix

D

D‐3

Philadelphia‐Camden‐Wilmington, PA‐NJ‐DE‐MD...................................................................................... 97

Phoenix‐Mesa‐Glendale, AZ...................................................................................................................... 100

Pittsburgh, PA ...........................................................................................................................................103

Portland‐Vancouver‐Hillsboro, OR‐WA..................................................................................................... 106

Providence‐New Bedford‐Fall River, RI‐MA.............................................................................................. 109

Raleigh‐Cary, NC........................................................................................................................................112

Richmond, VA............................................................................................................................................115

Riverside‐San Bernardino‐Ontario, CA...................................................................................................... 118

Rochester, NY............................................................................................................................................121

Sacramento‐‐Arden‐Arcade‐‐Roseville, CA............................................................................................... 124

St. Louis, MO‐IL .........................................................................................................................................127

Salt Lake City, UT.......................................................................................................................................130

San Antonio‐New Braunfels, TX................................................................................................................ 133

San Diego‐Carlsbad‐San Marcos, CA......................................................................................................... 136

San Francisco‐Oakland‐Fremont, CA ........................................................................................................ 139

San Jose‐Sunnyvale‐Santa Clara, CA ......................................................................................................... 142

Seattle‐Tacoma‐Bellevue, WA .................................................................................................................. 145

Tampa‐St. Petersburg‐Clearwater, FL ....................................................................................................... 148

Virginia Beach‐Norfolk‐Newport News, VA‐NC ........................................................................................ 151

Washington‐Arlington‐Alexandria, DC‐VA‐MD‐WV.................................................................................. 154
Appendix

D

D‐4

Atlanta‐Sandy Springs‐Marietta, GA
Appendix

D

D‐5
Appendix

D

D‐6
Appendix

D

D‐7

Austin‐Round Rock‐San Marcos, TX
Appendix

D

D‐8
Appendix

D

D‐9
Appendix

D

D‐10

Baltimore‐Towson, MD
Appendix

D

D‐11
Appendix

D

D‐12
Appendix

D

D‐13

Birmingham‐Hoover, AL
Appendix

D

D‐14
Appendix

D

D‐15
Appendix

D

D‐16

Boston‐Cambridge‐Quincy, MA‐NH
Appendix

D

D‐17
Appendix

D

D‐18
Appendix

D

D‐19

Buffalo‐Niagara Falls, NY
Appendix

D

D‐20
Appendix

D

D‐21
Appendix

D

D‐22

Charlotte‐Gastonia‐Rock Hill, NC‐SC
Appendix

D

D‐23
Appendix

D

D‐24
Appendix

D

D‐25

Chicago‐Joliet‐Naperville, IL‐IN‐WI
Appendix

D

D‐26
Appendix

D

D‐27
Appendix

D

D‐28

Cincinnati‐Middletown, OH‐KY‐IN
Appendix

D

D‐29
Appendix

D

D‐30
Appendix

D

D‐31

Cleveland‐Elyria‐Mentor, OH
Appendix

D

D‐32
Appendix

D

D‐33
Appendix

D

D‐34

Columbus, OH
Appendix

D

D‐35
Appendix

D

D‐36
Appendix

D

D‐37

Dallas‐Fort Worth‐Arlington, TX
Appendix

D

D‐38
Appendix

D

D‐39
Appendix

D

D‐40

Denver‐Aurora‐Broomfield, CO
Appendix

D

D‐41
Appendix

D

D‐42
Appendix

D

D‐43

Detroit‐Warren‐Livonia, MI
Appendix

D

D‐44
Appendix

D

D‐45
Appendix

D

D‐46

Hartford‐West Hartford‐East Hartford, CT
Appendix

D

D‐47
Appendix

D

D‐48
Appendix

D

D‐49

Houston‐Sugar Land‐Baytown, TX
Appendix

D

D‐50
Appendix

D

D‐51
Appendix

D

D‐52

Indianapolis‐Carmel, IN
Appendix

D

D‐53
Appendix

D

D‐54
Appendix

D

D‐55

Jacksonville, FL
Appendix

D

D‐56
Appendix

D

D‐57
Appendix

D

D‐58

Kansas City, MO‐KS
Appendix

D

D‐59
Appendix

D

D‐60
Appendix

D

D‐61

Las Vegas‐Paradise, NV
Appendix

D

D‐62
Appendix

D

D‐63
Appendix

D

D‐64

Los Angeles‐Long Beach‐Santa Ana, CA
Appendix

D

D‐65
Appendix

D

D‐66
Appendix

D

D‐67

Louisville/Jefferson County, KY‐IN
Appendix

D

D‐68
Appendix

D

D‐69
Appendix

D

D‐70

Memphis, TN‐MS‐AR
Appendix

D

D‐71
Appendix

D

D‐72
Appendix

D

D‐73

Miami‐Fort Lauderdale‐Pompano Beach, FL
Appendix

D

D‐74
Appendix

D

D‐75
Appendix

D

D‐76

Milwaukee‐Waukesha‐West Allis, WI
Appendix

D

D‐77
Appendix

D

D‐78
Appendix

D

D‐79

Minneapolis‐St. Paul‐Bloomington, MN‐WI
Appendix

D

D‐80
Appendix

D

D‐81
Appendix

D

D‐82

Nashville‐Davidson‐‐Murfreesboro‐‐Franklin, TN
Appendix

D

D‐83
Appendix

D

D‐84
Appendix

D

D‐85

New Orleans‐Metairie‐Kenner, LA
Appendix

D

D‐86
Appendix

D

D‐87
Appendix

D

D‐88

New York‐Northern New Jersey‐Long Island, NY‐NJ‐PA
Appendix

D

D‐89
Appendix

D

D‐90
Appendix

D

D‐91

Oklahoma City, OK
Appendix

D

D‐92
Appendix

D

D‐93
Appendix

D

D‐94

Orlando‐Kissimmee‐Sanford, FL
Appendix

D

D‐95
Appendix

D

D‐96
Appendix

D

D‐97

Philadelphia‐Camden‐Wilmington, PA‐NJ‐DE‐MD
Appendix

D

D‐98
Appendix

D

D‐99
Appendix

D

D‐100

Phoenix‐Mesa‐Glendale, AZ
Appendix

D

D‐101
Appendix

D

D‐102
Appendix

D

D‐103

Pittsburgh, PA   
Appendix

D

D‐104
Appendix

D

D‐105
Appendix

D

D‐106

Portland‐Vancouver‐Hillsboro, OR‐WA
Appendix

D

D‐107
Appendix

D

D‐108
Appendix

D

D‐109

Providence‐New Bedford‐Fall River, RI‐MA
Appendix

D

D‐110
Appendix

D

D‐111
Appendix

D

D‐112

Raleigh‐Cary, NC
Appendix

D

D‐113
Appendix

D

D‐114
Appendix

D

D‐115

Richmond, VA
Appendix

D

D‐116
Appendix

D

D‐117
Appendix

D

D‐118

Riverside‐San Bernardino‐Ontario, CA
Appendix

D

D‐119
Appendix

D

D‐120
Appendix

D

D‐121

Rochester, NY
Appendix

D

D‐122
Appendix

D

D‐123
Appendix

D

D‐124

Sacramento‐‐Arden‐Arcade‐‐Roseville, CA
Appendix

D

D‐125
Appendix

D

D‐126
Appendix

D

D‐127

St. Louis, MO‐IL
Appendix

D

D‐128

   
Appendix

D

D‐129
Appendix

D

D‐130

Salt Lake City, UT
Appendix

D

D‐131
Appendix

D

D‐132
Appendix

D

D‐133

San Antonio‐New Braunfels, TX
Appendix

D

D‐134
Appendix

D

D‐135
Appendix

D

D‐136

San Diego‐Carlsbad‐San Marcos, CA
Appendix

D

D‐137
Appendix

D

D‐138
Appendix

D

D‐139

San Francisco‐Oakland‐Fremont, CA
Appendix

D

D‐140
Appendix

D

D‐141
Appendix

D

D‐142

San Jose‐Sunnyvale‐Santa Clara, CA
Appendix

D

D‐143
Appendix

D

D‐144
Appendix

D

D‐145

Seattle‐Tacoma‐Bellevue, WA
Appendix

D

D‐146
Appendix

D

D‐147
Appendix

D

D‐148

Tampa‐St. Petersburg‐Clearwater, FL
Appendix

D

D‐149
Appendix

D

D‐150
Appendix

D

D‐151

Virginia Beach‐Norfolk‐Newport News, VA‐NC
Appendix

D

D‐152
Appendix

D

D‐153
Appendix

D

D‐154

Washington‐Arlington‐Alexandria, DC‐VA‐MD‐WV
Appendix

D

D‐155
Appendix

D

D‐156
NCJ249238

The Bureau of Justice Statistics of the U.S. Department of Justice is the principal 

federal agency responsible for measuring crime, criminal victimization, criminal 

offenders, victims of crime, correlates of crime, and the operation of criminal 

and civil justice systems at the federal, state, tribal, and local levels. BJS collects, 

analyzes, and disseminates reliable and valid statistics on crime and justice 

systems in the United States, supports improvements to state and local criminal 

justice information systems, and participates with national and international 

organizations to develop and recommend national standards for justice statistics. 

William J. Sabol is director.

Office of Justice Programs

Innovation • Partnerships • Safer Neighborhoods

www.ojp.usdoj.gov