1 

Impact Evaluation of the AmeriCorps Urban Safety Program 

February 2018 

Prepared by:

Brian G. Moss, Ph.D.

External Evaluator
2 

Introduction

The AmeriCorps Urban Safety program (AMUS) was awarded funding from the Corporation for National 

and Community Services (CNCS) to implement a community-based intervention focused on reducing

crime throughout various areas throughout the city of Detroit. The AMUS program began in the 

Midtown region of the city in 2009 and was subsequently expanded to include more areas in 2013, 

2014, and 2016. Expansion was based on previous evidence of the AMUS program areas having 

significant reductions in the crime. 

This evaluation was formulated under the direction of the AMUS program director and the Director of 

the Center for Urban Studies, as well as with consultation with NORC. 

Program Background and Problem Definition

Wayne State University’s Center for Urban Studies (CUS), AMUS, and Community CompStat programs 

pursue permanent crime reductions through the use of real-time crime mapping analyses and 

collaborative problem-solving processes. Once AMUS was introduced into each target area, total annual 

Major Part I crimes decreased. 

Detroit accounts for approximately 7% of Michigan’s population but only 0.25% of the state’s 

geographic area. While both Detroit and Michigan have experienced decreases in annual violent and 

property crime totals since 2008, Detroit’s crime totals remain disproportionately high. According to the 

2014 Michigan State Police “Uniform Crime Data and Statistics”, Detroit comprised approximately 18% 

of Michigan’s total reported Part I property crimes (burglary, vehicle theft, larceny, and arson) and 

approximately 40% of the Part I violent crimes (homicide, robbery, sexual assault, and aggravated 

assault). 

In addition, the city has a substantial number of vacant and open houses, properties frequently 

associated with crime. The 2014 Motor City Parcel Mapping Survey indicated there are 27,485 structures 

throughout Detroit with an open point-of-entry (e.g., a broken or missing window or unsecured door). 

Of these structures, surveyors believe 93% are unoccupied. Recent research finds vacant structures to 

be the strongest predictor of assault risk when compared to nearly a dozen other risk indicators (Branas 

et al., 2012). This finding supports the “broken window theory” that further disorder is encouraged by 

outward signs of property abandonment. Once a property becomes vacant, the risk of violent crime 

within 250 feet is 15% higher than the rate of crime further away.

Sadly, public safety issues can adversely impact Detroit’s youth. Children who reside in low-income 

urban areas are significantly more like to be victims and witnesses of community violence. Within this 

context, children may be more prone to exhibit antisocial behavior, be socially withdrawn, abuse elicit 

substances, and have academic problems (Copeland-Linder, 2010). The Social Science Research Council

estimates that 17.4% of Detroit youth aged 16-24 are disconnected (neither working nor in school) from 

their community, which is the second highest percentage among the 25 largest metro areas in the 

country. This disconnect may be related to Detroit’s alarmingly low rate of high school graduates 

(77.6%) and the small percentage of residents (only 12.7%) earning a bachelor’s degree (American 

Community Survey). Furthermore, Wayne County recorded over 3,000 juvenile arrests in 2013--nearly 

twice as many as any other county in Michigan (Michigan Statewide Juvenile Arrest Analysis Report, 

2008-2013). If the future of these at-risk and disconnected youth did not seem uncertain enough, 
3 

homicide is the leading cause of death for children between the age of 1 and 18 in Detroit (Detroit 

News, 2010).

Most residents look to the Detroit Police Department (DPD) for assistance addressing the city’s crime 

troubles. However, the number of police officers currently patrolling Detroit has not been this low since 

the 1920s. In fact, it has been estimated that Detroit has lost nearly half its patrol officers between 2000 

and 2015 (Detroit News. July 9, 2015). Center researchers found that little public safety infrastructure 

exists for residents beyond DPD patrols. Center staff have held a series of informal discussions with DPD 

representatives and Detroit residents to elicit qualitative data concerning community involvement with 

community policing. The staff found that in a city of almost 143 square miles, there were only 12 

community policing organizations registered to work with the DPD (6 of which were established by 

AMUS in the past 3 years). Detroit and other large, urban cities need a cost-effective, replicable model 

to improve community safety. 

Past Research and Existing Evidence

Program Theory, Logic Model and Outcomes of Interest

The CompStat and AMUS model pairs community engagement to impact crime on the individual, 

neighborhood, and city levels. The model employed stems from one found in “The Co-implementation 

of CompStat and Community Policing” (Willis et al. 2010). These researchers used a mixed-mode, crosssectional analysis of 7 U.S. police departments that merged the reforms of community policing with 

CompStat analysis. More specifically, the researchers used surveys, secondary data analysis, fieldwork, 

focus groups, and interviews to structure an inductive study of crime reduction. Their results suggest

that 1) the police departments felt they could respond to a broader set of goals and engage in a wider 

variety of tasks by combining these reforms and 2) the two reforms had an “additive effect”—i.e., one 

reform compensated for the limitations of the other in helping departments to respond 

comprehensively to the diverse demands of their environments (p. 977).

The AMUS intervention model contains six main components directly connected to the Compstat 

process that emphasize: 

1) the identification of crime problems and trends; 

2) streamlining communication among researchers, community members, and police precincts in 

order to facilitate timely and efficient responses to crime trends. 

3) the use of CompStat to identify the few repeat and frequent offenders who are responsible for 

the large amounts of crime; 

4) identification of strategic actions to minimize the impact of high impact offenders. This includes 

identifying new and habitual high-impact offenders at CompStat meetings, while informing 

attendees of these individuals; 

5) encouraging community organizing so that residents are able to better police themselves and 

efficiently utilize resources to reduce crime; and

6) communicating directly with offenders to instill community messages against offending, provide 

notice of legal consequences to crime, and offer help. 

The applied theory of change framework links 68 MSY AmeriCorps member activities to meet each 

community’s need. The defined a theory of “layered deterrence” underpins every intervention in the 
4 

AMUS program. At every level, from the individual to the precinct, AMUS builds a network of deterrence 

meant to dissuade criminals from committing crime. At the individual level, the program educates 

residents about protective behaviors and provides devices and strategies to prevent home entry and 

slow down criminals. At the community level, block clubs and neighborhood patrols put “eyes on the 

street”, increasing the likelihood that criminal activity will be reported. Police-directed patrols put “cops 

on the dots”, i.e., deploys police in the locations where incidents have occurred. Also, corrections 

officers visit criminals at their homes to directly impart the message that their actions are being closely 

monitored. Overall, these layers serve as a “blanket of deterrence” and act as a network of screens that 

increase the cost and difficulty associated with criminal activity. 

Research questions evaluated: 

This evaluation assessed the AMUS program’s impact on key crime reduction outcomes by describing 

how the crime reports in AMUS target areas (police precincts) changed over the course of the fiscal year, 

as well as the associated cost savings related the level of crime reduction. In addition, an analysis of the 

impact the AMUS program on annual crime rates was evaluated. More specifically, the following impact 

and economic research questions were investigated:

Program Impact questions

1) Did the trend in targeted crime rates significantly decrease in AMUS target areas compared 

to non-AMUS areas?

2) Once implemented, do AMUS target areas experience significantly lower crime rates than 

non-AMUS areas? 

Economic impact questions

3) What are the monetary costs associated with crime in AMUS target areas? 

4) Given the reductions in crime, what is the total amount of imputed cost savings? 

Evaluation Components

Research Design and Rationale

Areas targeted to receive the intervention were selected during a conference between the State of 

Michigan and the City of Detroit Mayor’s office. In 2013, four police precincts were selected (precincts 4, 

7, 9 & 12), with an additional two precincts added in 2014 (precincts 5 & 6) and another two in 2016

(precincts 8 & 1). This evaluation’s focus is on the impact of the AMUS program within these areas. 

Since AMUS intervention areas were not randomly selected, the evaluation will use two different quasiexperimental designs to assess program impact (Shadish, Cook, and Campbell, 2002). First, an 

interrupted time series with comparison group design (ITS-CG) was utilized. Annual crime rates, before 

and after the point of intervention implementation were tracked and analyzed. The time interval 

between each crime measurement was equal for areas exposed to the AMUS program (treatment), as 

well as, non-targeted areas (comparison). At this time, there were only two police precincts that have 

never been exposed to the program (precincts 2 & 11). Therefore, to include as many data points as 

possible before and after program implementation, for a given year, comparison cases were derived 

from average crime rates for those precincts that had not received the AMUS program. More precisely, 

for years 2009 through 2012, no precinct included in this evaluation had received treatment. Therefore, 

the average number of crime reports were derived for these areas to serve as comparison cases for each 
5 

year. Once program implementation began for a precinct, it was removed from the average calculation 

of crime reports for the comparison groups. 

If, after exposure to the program, crime rate trends significantly decrease, there is supporting 

evidence for program effectiveness. Since non-random assignment was used, estimate of program 

effectiveness within treatment areas was susceptible to selection bias. Therefore, efforts were made to 

match comparison areas with similar characteristics to the AMUS target areas. Among the covariates 

available, baseline equivalency tests were conducted between comparison and treatment sites and can 

be found in Appendix B. Though several differences were noted between treatment conditions, a 

strength of the ITS-CG design is that the built-in differences-in-differences comparison of the outcome at 

the baseline, as well as the comparative change in trend between the treatment and comparison group, 

nets out differences more directly than other adjustment strategies (Juras, Comfort, & Bein, 2016). 

In the case where more recent AMUS areas (precincts 8 and 10) were targeted, post treatment 

measurements were limited to a single year, which prohibited the ability to assess the impact with the 

ITS-CG design. Furthermore, sample sizes for treatment and comparison areas will vary due to the 

staggered implementation of the program to police precincts. For example, two precincts were added in 

2016 and served as treatment groups (from 2016 forward) and comparison cases decreased over time 

as the AMUS program expanded. However, the use of average crime reports for non-participating AMUS 

areas somewhat mitigates this variation. For all inferential ITS-CG analyses, 2017 data were not included 

due to one month of data missing (December 2017).

The regression point displacement design (RPD) was also applied to AMUS data. The RPD design is 

particularly useful for community-based research when units are aggregated and treatment groups are 

limited (Trochim and Campbell, 2017). RPD design requires the use of pretest and posttest data points 

for comparison cases to generate a pre-post regression line. In our case, the pre-post regression line was 

generated from paired, annual crime data for the non-AMUS target areas during time period closest to 

when AMUS target areas began the program. Within the RPD design, once the comparison condition 

line has been established, the treatment case(s) can be analyzed to determine if there has been a 

significant vertical displacement from the fit line generated by the comparison cases. If treatment 

case(s) are significantly displaced below the comparison regression line there is evidence of a treatment 

effect (i.e. a significant reduction in crimes reported). As with the ITS-CG design, covariates were

incorporated into the analytic plan to adjust for potential selection issues. Available covariates included 

area demographic and neighborhood level variables (e.g., percent of families living in poverty, percent 

of citizens with less than a high school education, racial diversity, and rate of abandoned/boarded 

houses). Beyond typical law enforcement activities, there were no known intervention programs that 

may substantially compromise the evaluation’s impact assessment. 

Sampling Methods, Measurement Tools, and Data Collection

Available crime data spans from 2009 through the current program year (November 2017) for Detroit’s 

police precincts. These data were drawn from the Detroit Police Department’s (DPD) crime database 

and verified by CUS staff. In addition, CUS and AMUS staff provided qualitative, in-depth knowledge of 

program features that supported this evaluation. Data for the cost-effectiveness evaluation were also 

collected by local police and available to the CUS on a real-time basis. These data were aggregated 

across types of crime, police precinct, and annual time periods. 
6 

Analytic Approach

Program impact

To analyze quantitative data, Detroit Police data were used to identify crime report trends and other 

readily available demographic data. Specifically, crime report trend comparisons between treatment 

and comparison areas was also conducted. 

To assess the impact of the program on AMUS treatment areas, the ITS-CG analysis used the following 

segmented regression model: 

Y = β0 + β1T + β2Xt + β3TXt + β4level + β5trend + β6level(Xt) + β7trend(Xt)+ ei

where β0 represents the baseline level at T = 0 for the control group, β1 is interpreted as the change in 

outcome associated with a time unit increase (representing the underlying pre-intervention trend) for 

the control group, β2 is the baseline difference between the intervention precinct and control precincts, 

and β3 indicates difference in trend between the groups during the pre-treatment period, β4 is the 

change in crime level in the control group upon treatment implementation, β5 the trend change 

following the intervention for the control group, β6 is the difference in level change between the 

intervention and control group, and β7 represents the change in trend between the intervention and 

control group during the intervention period. Models were assessed for possible nonlinear relationships 

and it was found that the inclusion of higher order polynomial terms was not warranted. 

The basic RPD model is as follows:

Y = β0 + β1Xi + β2Zi + ei

where Y is the outcome variable (crimes reported), β0 is the intercept, Zi is the dichotomous assignment 

variable (1 = treatment, 0 = comparison), β1 is the pretest coefficient, β2 is the estimated treatment 

effect, and ei is the random error term. The main treatment effect is identified by a statistically 

significant p value in the β2 coefficient. In the case when the treatment unit had multiple years of 

intervention, additional binary assignment variables were added to the model for each extra year, 

analyzed separately, and added to the RPD figures 

Economic cost assessment

In addition to assessing program impact, the cost of crime was calculated and compared for AMUS 

target areas, before and after the program implementation. Descriptive analyses established a cost 

baseline for the program areas. Then, cost estimation entailed an accounting approach that attempted

to capture all costs associated with crime that individuals and society bear and place a dollar value on 

those costs. For example, robberies result in prevention expenditures (such as installing lighting or 

buying personal defense products), property loss to victims, expenditures on medical treatment for 

injuries, pain and suffering of victims and costs for investigating, adjudicating, and incarcerating 

offenders. This method of crime cost-benefit analysis was based on two studies: (1) McCollister, K.E., 

French, M.T. and Fang, H (2010), "The cost of crime to society: New crime- specific estimates for policy 

and program evaluation", Drug and Alcohol Dependence, 108, 98-109, and (2) Victim Costs and 

Consequences: A New Look" by T. Miller, M. Cohen & B. Wiersema, a NIJ Report (1996).
7 

Results

Description of Crime within AMUS Target Areas

Tables and Figures 1 through 8 provide 2009-2017 crime counts by DPD precinct and intervention 

condition. Data are presented in chronological order of AMUS implementation. Tables 1-4 include four 

precincts (4, 7, 9, & 12) that began receiving AMUS in 2013 and Tables 5-6 present data for precincts (5 

& 6) that began the intervention in 2014. Tables 7-8 provide data for two precincts (8 & 10) that started 

AMUS in 2016. Precinct 3 has received long-term exposure to the AMUS program. Since it was part of 

the initial AMUS pilot program in 2009 and has received treatment throughout the reporting period, 

precinct 3 was considered an outlier and omitted from this evaluation. Two precincts (2 & 11) never 

received any intervention. For all tables, years that are shaded indicate the precinct received AMUS, 

whereas unshaded years are years when the precinct did not receive AMUS. Also, 2017 data are 

provided but do not include an entire year’s worth of data. Therefore, focus was made on 2016 the 

primary end point. 

In Tables 1-4, annual counts of major crime categories, as well as yearly grand totals are given. When 

compared to the crime total for last pre-intervention year (2012), the four precincts that began AMUS in 

2013 had significant decreases in crime across all categories with few exceptions. There is a noticeable 

acceleration in the decline of crime rate reports after the first year. By 2016, crime in these precincts 

was reduced by approximately one-quarter. 

Those four precincts who began AMUS in 2014 and 2016 also exhibited rather consistent crime 

reductions from the pre-AMUS years. Though the time span is more limited with these precincts, there 

was a decrease in crime for all areas during the first program year. For those precincts that started in 

2014, crime continued to decline in the second and third year to about 13% by 2016.

Program Impact

1) Did the trend in targeted crime rates significantly decrease in AMUS target areas compared 

to non-AMUS areas?

Figure A displays overall trends of crime reports for each precinct by year of AMUS implementation. 

Panels A-D report the four precincts (4, 7, 9, & 12) that began the AMUS program in 2013, whereas 

panels E and F show data for the two precincts that began in 2014. 

Results of the segmented regression analyses found no difference in pre-intervention trends in crime 

reports between the comparison and intervention precincts for any of the six precincts. Precincts 7 and 

5 both had significantly lower, and precinct 9 had significantly higher, baseline levels of crime reports 

when compared to the comparison group. When compared to the comparison group regression line in 

the treatment period, none of the precincts exhibited significantly different levels of crime reports or a 

significant difference in trends. 

More detailed, crime specific, precinct-level trend data, over the study period, are provided in Figures 1-

8. 
8 

 

 

 

 

 

 

Figure A. Assessment of level and trend change in AMUS intervention areas after program 

implementation. 

2) Once implemented, do AMUS target areas experience significantly lower crime rates than 

non-AMUS areas? 

Assessment of program impact for each AMUS targeted precinct was conducted using the RPD

regression models outlined above. Data used for comparison areas were identified from the period 

leading up to implementation of the program. More explicitly, since precincts began receiving the AMUS 
9 

program in 2013, the 6 non-AMUS precincts were used for comparison cases. The relationship between 

2012 and 2013 crime rates for these 6 precincts served as the counterfactual comparison condition. 

Then, crime reports for the subsequent years of AMUS-targeted precincts were compared to these 

comparison cases. Analyses was grouped by year of initial implementation. 

Figures 9 through 11 provide a visual presentation of the immediate and latent impact of the AMUS 

program on targeted precincts. Within each figure, a regression line is fit to the data points generated by 

the comparisons cases and includes a 95% confidence interval (shaded gray) around the line. AMUS 

precinct crime rates that fall outside of the shaded area display a significant displacement from the 

regression line generated by the comparison cases. Within the figure legends, years that are highlighted 

yellow had crime reduction that was significantly significant at p<.05, whereas years highlighted green 

were significant at p<.10. Each analysis was conducted with and without the covariates noted above. 

There were no difference conclusions made about the absence or presence of a statistically significant 

displacement of treatment areas compared to the comparison areas. Therefore, only one set of results 

(those without covariates) are provided. 

Almost all precincts that started AMUS in 2013 experienced a delay in program impact on crime rates. 

Three of the four precincts (7, 9, & 12) reached and maintain a statistically significant reduction in crime

reports during the second year (2014). However, it took the remaining precinct (4) three years before 

exhibiting a significant reduction in crime. 

A similar pattern of delayed impact occurred for the two precincts that became involved with AMUS in 

2014. Here, one precinct (6) was marginally significant during the first year of implementation but both 

demonstrated significant reductions in year two (2015), which continued into year three (2016).

Interpretation of the two precincts that began AMUS in 2016 is confounded by the lack of follow-up 

years available. Neither precinct had significant decreases in crime in 2016. However, preliminary data 

for 2017 indicates it is likely both areas will experience a significant decline in crime, which is similar to 

the delayed impact revealed in the six other precincts.

Economic Impact

3) What are the monetary costs associated with crime in AMUS target areas?

Using figures provided by McCollister et al. (2010), estimates of the “cost per type of crime” are 

provided in Table A. These researchers’ estimates consider four dimensions of costs associated with 

crime (victim, criminal justice, crime career, and other intangible costs) that, when aggregated, reflect a 

monetary value. Omitted from the economic analyses were homicide and sexual assault counts due to 

either unstable cost estimators or missing data. 
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Table A. Estimated cost of crime by year. 

Crime

2009 

Cost per 

Crime 

2010 

Cost per 

Crime

2011 

Cost per 

Crime

2012 

Cost per 

Crime

2013 

Cost per 

Crime

2014 

Cost per 

Crime

2015 

Cost per 

Crime

2016 

Cost per 

Crime

2017 

Cost per 

Crime

Aggravated 

assault 106,639 108,338 111,810 114,124 115,795 116,702 117,759 120,318 122,106 

Arson 21,028 21,373 22,047 22,504 22,833 23,012 23,231 23,730 24,083 

Assault 13,950 14,179 14,627 14,929 15,148 15,267 15,412 15,743 15,977 

Burglary 6,439 6,545 6,751 6,891 6,992 7,047 7,114 7,267 7,375 

Damage to 

property 4,843 4,922 5,078 5,183 5,259 5,300 5,350 5,465 5,547 

Larceny 3,519 3,577 3,690 3,766 3,822 3,852 3,888 3,972 4,031 

Robbery 42,159 42,851 44,204 45,118 45,779 46,138 46,577 47,577 48,285 

Stolen 

vehicle 10,734 10,910 11,254 11,487 11,655 11,747 11,859 12,113 12,294 

Source: 

McCollister, K. E., French, M. T., & Fang, H. (2010). The cost of crime to society: New crime-specific estimates for policy 

and program evaluation. Drug and Alcohol Dependence, 108(1), 98-109. 

4) Given the reductions in crime, what is the total amount of imputed cost savings?

To calculate the economic impact, the difference between the frequency of crimes within each category 

between a given year and the preceding year determined if there was an increase or decrease in crime.

This difference was then multiplied by the corresponding cost by crime and year found within Table A. 

For each precinct that received AMUS, the baseline comparison the year immediately prior to program 

implementation. More specifically, the four precincts had a baseline of 2012 (4, 7, 9, 12), two precincts 

had a baseline of 2013 (5 & 6), and two had a baseline of 2015 (8 & 10). 

Table B displays the estimated savings after each precinct began the AMUS program. It is estimated that 

the crime reduced crime all eight precincts saved approximately 378 million dollars. Detailed results for 

each precinct by year and crime category can be found in Appendix B. 
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Table B. Estimated economic impact of AMUS by precinct

Initial AMUS Year Precinct Estimated Savings

2013 4 $ (72,953,663)

7 $ (45,068,617)

9 $ (52,390,511)

12 $ (79,944,632)

2014 5 $ (31,121,721)

6 $ (39,673,658)

2016 8 $ (35,977,010)

10 $ (21,104,354)

Grand total $ (378,234,167)

Summary Points

Crime Patterns

• Across major crime categories, crime reports within precincts served by AMUS have 

declined. 

• Crime reduction at the end of the first year AMUS implementation was smaller than in 

subsequent years, which suggests a phased-in impact period. 

• A consistent pattern emerged where reduction in subsequent crime rates were more than 

twice that of the implementation year. 

Program Impact

• In almost all areas AMUS served, there was a significant reduction in the frequency of crime

reported. 

• Trend analyses did not demonstrate significant level or trend differences between 

comparison and intervention precincts. 

• When compared to the period immediately preceding AMUS implementation, crime reports 

were significantly reduced and sustained after the first program year. 

Economic Impact

• All precincts participating in the AMUS program experienced a positive economic impact 

from reduced crime reports. 

• With the amount of crime reported reduced in AMUS areas, an estimated $378 million was 

saved by alleviating crime-related activities. 
12

References

Branas, C. C., Rubin, D., & Guo, W. (2012). Vacant properties and violence in neighborhoods. ISRN Public 

Health, 2012.

Copeland-Linder, N., Lambert, S. F., & Ialongo, N. S. (2010). Community violence, protective factors, and 

adolescent mental health: A profile analysis. Journal of Clinical Child & Adolescent Psychology, 39(2), 

176-186.

Hunter, G. (2015). Fewest cops are patrolling Detroit streets since 1920s. Detroit News. (July 5)

Juras, Comfort, & Bein, (2016). How study design influences statistical power in community-level 

evaluations. OAH Tier 1B Evaluation Technical Assistance Brief No. 3. Office of Adolescent Health. U.S. 

Department of Health & Human Services. 

McCollister, K. E., French, M. T., & Fang, H. (2010). The cost of crime to society: New crime-specific 

estimates for policy and program evaluation. Drug and Alcohol Dependence, 108(1), 98-109.

Miller, T., Cohen, M. & Wiersema, B. (1996). Victim Costs and Consequences: A New Look. U.S. 

Department of Justice, Office of Justice Programs, National Institute of Justice.

State of Michigan (2015). Michigan Statewide Juvenile Arrest Analysis Report, 2008-2013. Public Policy 

Associates, Incorporated.

Trochim, W.M.K., & Campbell, D. T. (1996). The regression point displacement design for evaluating 

community–based pilot programs and demonstration projects. Unpublished manuscript. Retrieved 

October 20, 2016, from http://www.socialresearchmethods.net/research/RPD/RPD.pdf

Willis, J. J., Mastrofski, S. D., & Kochel, T. R. (2010). The co-implementation of Compstat and community 

policing. Journal of Criminal Justice, 38(5), 969-980.
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Description of Crime with AMUS Target Areas

Table 1 Precinct 4

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE 

TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 889 163 1462 1886 1106 37 1286 538 1294 8661

2010 795 230 1512 1590 1172 24 1443 536 1034 8336

2011 821 129 1355 1180 1016 24 1340 472 996 7333

2012 728 98 1370 966 896 27 1134 484 876 6579

2013 666 79 1271 832 717 34 1227 332 767 5925

-8.5% -19.4% -7.2% -13.9% -20.0% 25.9% 8.2% -31.4% -12.4% -9.9%

2014 626 120 1256 662 700 16 1053 334 857 5624

-14.0% 22.4% -8.3% -31.5% -21.9% -40.7% -7.1% -31.0% -2.2% -14.5%

2015 584 123 1325 475 743 16 1068 266 521 5121

-19.8% 25.5% -3.3% -50.8% -17.1% -40.7% -5.8% -45.0% -40.5% -22.2%

2016 501 94 1275 483 669 29 938 265 371 4625

-31.2% -4.1% -6.9% -50.0% -25.3% 7.4% -17.3% -45.2% -57.6% -29.7%

2017 430 73 730 407 587 26 678 183 434 3548

-40.9% -25.5% -46.7% -57.9% -34.5% -3.7% -40.2% -62.2% -50.5% -46.1%

Grand 

Total 6040 1109 11556 8481 7606 233 10167 3410 7150 55752

Note: Shaded year indicates AMUS intervention provided. 
14

Figure 1. Crime trend lines for Precinct 4.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
15

Table 2 Precinct 7

Row 

Labels

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 780 68 1393 1158 1117 36 1934 473 1238 8197

2010 725 81 1416 999 1144 26 2004 422 1158 7975

2011 642 55 1278 768 964 26 1914 375 1038 7060

2012 570 45 1197 648 959 24 1825 361 1032 6661

2013 531 28 1098 637 793 21 1906 347 938 6299

-6.8% -37.8% -8.3% -1.7% -17.3% -12.5% 4.4% -3.9% -9.1% -5.4%

2014 527 39 1003 412 660 23 1312 250 757 4983

-7.5% -13.3% -16.2% -36.4% -31.2% -4.2% -28.1% -30.7% -26.6% -25.2%

2015 493 40 1008 364 716 25 1403 236 566 4851

-13.5% -11.1% -15.8% -43.8% -25.3% 4.2% -23.1% -34.6% -45.2% -27.2%

2016 502 71 1059 342 728 21 1414 199 588 4924

-11.9% 57.8% -11.5% -47.2% -24.1% -12.5% -22.5% -44.9% -43.0% -26.1%

2017 439 48 616 408 591 12 1127 189 525 3955

-23.0% 6.7% -48.5% -37.0% -38.4% -50.0% -38.2% -47.6% -49.1% -40.6%

Grand 

Total 5209 475 10068 5736 7672 214 14839 2852 7840 54905
16

Figure 2. Crime trend lines for Precinct 7.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
17

Table 3 Precinct 9

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 1472 149 2543 2552 1737 42 1848 892 2216 13451

2010 1444 201 2523 2542 1646 45 1571 708 1950 12630

2011 1248 175 2296 2382 1431 58 1347 589 1444 10970

2012 1263 128 2279 2222 1532 52 1494 601 1775 11346

2013 1218 119 2126 1865 1210 44 1454 504 1680 10220

-3.6% -7.0% -6.7% -16.1% -21.0% -15.4% -2.7% -16.1% -5.4% -9.9%

2014 1350 131 2265 1656 1212 46 1128 559 1223 9570

6.9% 2.3% -0.6% -25.5% -20.9% -11.5% -24.5% -7.0% -31.1% -15.7%

2015 1311 175 2065 1279 1078 45 1203 486 999 8641

3.8% 36.7% -9.4% -42.4% -29.6% -13.5% -19.5% -19.1% -43.7% -23.8%

2016 1407 194 1978 1292 1187 46 1204 411 882 8601

11.4% 51.6% -13.2% -41.9% -22.5% -11.5% -19.4% -31.6% -50.3% -24.2%

2017 1267 181 1245 1139 1008 24 986 318 806 6974

0.3% 41.4% -45.4% -48.7% -34.2% -53.8% -34.0% -47.1% -54.6% -38.5%

Grand 

Total 11980 1453 19320 16929 12041 402 12235 5068 12975 92403
18

Figure 3. Crime trend lines for Precinct 9.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
19

Table 4 Precinct 12

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 949 100 1852 2232 1396 27 2114 693 1383 10746

2010 960 97 1830 1942 1312 19 1988 655 1173 9976

2011 908 89 1746 1998 1449 34 1729 677 1140 9770

2012 972 56 1965 1819 1471 31 1778 707 1380 10179

2013 843 48 1860 1426 1217 25 1984 658 1406 9467

-13.3% -14.3% -5.3% -21.6% -17.3% -19.4% 11.6% -6.9% 1.9% -7.0%

2014 927 34 1828 1164 1124 36 1675 438 1304 8530

-4.6% -39.3% -7.0% -36.0% -23.6% 16.1% -5.8% -38.0% -5.5% -16.2%

2015 1031 60 1978 1104 1351 30 1946 379 1058 8937

6.1% 7.1% 0.7% -39.3% -8.2% -3.2% 9.4% -46.4% -23.3% -12.2%

2016 1000 101 1906 873 1221 34 1622 359 1229 8345

2.9% 80.4% -3.0% -52.0% -17.0% 9.7% -8.8% -49.2% -10.9% -18.0%

2017 754 66 1008 867 862 29 1262 251 979 6078

-22.4% 17.9% -48.7% -52.3% -41.4% -6.5% -29.0% -64.5% -29.1% -40.3%

Grand 

Total 8344 651 15973 13425 11403 265 16098 4817 11052 82028
20

Figure 4. Crime trend lines for Precinct 12.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
21

Table 5 Precinct 5

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 784 85 1453 1527 953 29 1446 477 1085 7839

2010 809 95 1405 1394 876 12 1335 340 1077 7343

2011 682 93 1256 1247 823 33 1141 307 771 6353

2012 723 58 1294 1245 961 37 1227 313 922 6780

2013 615 56 1132 1006 740 29 1371 323 903 6175

2014 716 51 1200 861 775 20 1077 316 712 5728

16.4% -8.9% 6.0% -14.4% 4.7% -31.0% -21.4% -2.2% -21.2% -7.2%

2015 700 71 1318 734 672 12 1097 240 575 5419

13.8% 26.8% 16.4% -27.0% -9.2% -58.6% -20.0% -25.7% -36.3% -12.2%

2016 684 81 1263 868 681 20 999 203 561 5360

11.2% 44.6% 11.6% -13.7% -8.0% -31.0% -27.1% -37.2% -37.9% -13.2%

2017 565 61 771 645 622 12 881 148 453 4158

-8.1% 8.9% -31.9% -35.9% -15.9% -58.6% -35.7% -54.2% -49.8% -32.7%

Grand 

Total 6278 651 11092 9527 7103 204 10574 2667 7059 55155
22

Figure 5. Crime trend lines for Precinct 5.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
23

Table 6 Precinct 6

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 1158 120 2185 2578 1435 47 1282 593 1322 10720

2010 1020 133 2080 2137 1359 34 1299 482 1105 9649

2011 951 92 1786 2243 1254 18 1139 394 1128 9005

2012 972 66 2046 1944 1279 35 1362 475 1196 9375

2013 903 73 2118 1453 1093 33 1403 466 1133 8675

2014 926 81 2235 1288 1081 39 1196 358 918 8122

2.5% 11.0% 5.5% -11.4% -1.1% 18.2% -14.8% -23.2% -19.0% -6.4%

2015 827 109 2280 1059 1015 38 1149 327 688 7492

-8.4% 49.3% 7.6% -27.1% -7.1% 15.2% -18.1% -29.8% -39.3% -13.6%

2016 920 121 2204 1063 1075 26 1101 260 706 7476

1.9% 65.8% 4.1% -26.8% -1.6% -21.2% -21.5% -44.2% -37.7% -13.8%

2017 895 79 1300 869 999 18 937 192 599 5888

-0.9% 8.2% -38.6% -40.2% -8.6% -45.5% -33.2% -58.8% -47.1% -32.1%

Grand 

Total 8572 874 18234 14634 10590 288 10868 3547 8795 76402
24

Figure 6. Crime trend lines for Precinct 6.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
25

Table 7 Precinct 8

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 1115 101 2084 2759 1655 30 2168 711 1730 12353

2010 1069 112 2160 2282 1552 31 2045 598 1466 11315

2011 1002 84 1922 2362 1355 28 1714 599 1527 10593

2012 949 58 1980 2111 1421 39 1829 652 1529 10568

2013 990 54 2025 1860 1385 37 2015 704 1524 10594

2014 1067 61 2143 1414 1258 30 1761 484 1429 9647

2015 1031 90 2160 1411 1250 30 1868 428 1121 9389

2016 890 92 2333 1174 1355 44 1615 290 1226 9019

-13.7% 2.2% 8.0% -16.8% 8.4% 46.7% -13.5% -32.2% 9.4% -3.9%

2017 950 93 1303 1273 1137 24 1399 257 1040 7476

-7.9% 3.3% -39.7% -9.8% -9.0% -20.0% -25.1% -40.0% -7.2% -20.4%

Grand 

Total 9063 745 18110 16646 12368 293 16414 4723 12592 90954
26

Figure 7. Crime trend lines for Precinct 8.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data 

since these specific crime rates do not include the month of December. 
27

Table 8 Precinct 10

Year

AGGRAVATED 

ASSAULT ARSON ASSAULT BURGLARY

DAMAGE TO 

PROPERTY HOMICIDE LARCENY ROBBERY

STOLEN 

VEHICLE

Grand 

Total

2009 1109 97 2029 1501 1205 47 1399 726 1061 9174

2010 981 80 1786 1454 1237 30 1317 619 889 8393

2011 909 95 1607 1413 1101 27 1266 561 818 7797

2012 905 53 1696 1030 1108 46 1373 525 1064 7800

2013 761 66 1688 1110 978 24 1214 527 830 7198

2014 786 62 1545 842 830 34 1017 413 741 6270

2015 657 71 1646 692 882 19 1133 294 621 6015

2016 693 69 1553 621 807 25 1050 248 575 5641

5.5% -2.8% -5.7% -10.3% -8.5% 31.6% -7.3% -15.6% -7.4% -6.2%

2017 629 80 1008 466 775 18 903 200 621 4700

-4.3% 12.7% -38.8% -32.7% -12.1% -5.3% -20.3% -32.0% 0.0% -21.9%

Grand 

Total 7430 673 14558 9129 8923 270 10672 4113 7220 62988
28

Figure 8. Crime trend lines for Precinct 10.

Notes: AMUS intervention period includes the time period within the shaded area. The reader should use caution when interpreting 2017 data since these 

specific crime rates do not include the month of December. 
29

Figure 9. Assessment of program impact for precincts provided AMUS beginning in 2013.

Note: The x-axis contains pretest crime totals, while the y-axis provides crime totals for a given year as indicated in the figure legend. Years highlighted yellow 

are statistically significant at p<.05. Years highlighted green are marginally significant with p<.10. 
30

Figure 10. Assessment of program impact for precincts provided AMUS beginning in 2014.

Note: The x-axis contains pretest crime totals, while the y-axis provides crime totals for a given year as indicated in the figure legend. Years highlighted yellow 

are statistically significant at p<.05. Years highlighted green are marginally significant with p<.10. 
31

Figure 11. Assessment of program impact for precincts provided AMUS beginning in 2016. 

Note: The x-axis contains pretest crime totals, while the y-axis provides crime totals for a given year as indicated in the figure legend. Years highlighted yellow 

are statistically significant at p<.05. Years highlighted green are marginally significant with p<.10. 

*Data for 2017 does not include crime counts for December. 
32

Appendix A: Comparison of treatment and control area characteristics

Precinct and year of AMUS implementation

2013 2014 2016

Variable 4 7 9 12 5 6 8 10 Control

Average 

number of 

families

14587 7362 15150 17878 11638 14439 20745 10618 13399

Percent in 

poverty

40.1* 38.1* 36.7 26.2* 39.3* 40.7* 28.9* 39.5* 35.9

Percent 

vacant 

homes

24.5* 24.1* 33.0 25.3* 32.3 29.6* 22.0* 46.7* 32.3

Percent 

less than 

high school 

education

44.3* 20.3 19.9* 14.2* 21.2 20.8 15.3* 24.2* 21.1

Percent 

African 

American

80.9* 86.9* 92.3* 92.8* 87.9 78.2* 92.3* 93.7* 88.3
33

Appendix B: Crime Costs by Precinct and Year
34

Precinct 4

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ (7,179,290) $ (4,668,080) $ (4,945,878) $ (9,986,424) $ (8,669,535) $ (35,449,207)

ARSON $ (433,827) $ 943,492 $ 69,693 $ (688,168) $ (505,742) $ (614,552)

ASSAULT $ (1,499,652) $ (229,005) $ 1,063,428 $ (787,159) $ (8,707,705) $ (10,160,093)

BURGLARY $ (936,928) $ (1,197,990) $ (1,330,318) $ 58,135 $ (560,498) $ (3,967,600)

DAMAGE TO PROPERTY $ (941,361) $ (90,100) $ 230,050 $ (404,434) $ (454,825) $ (1,660,670)

LARCENY $ 355,446 $ (670,248) $ 58,320 $ (516,346) $ (1,048,069) $ (1,820,897)

ROBBERY $ (6,958,408) $ 92,276 $ (3,167,236) $ (47,577) $ (3,959,398) $ (14,040,344)

STOLEN VEHICLE $ (1,270,395) $ 1,057,230 $ (3,984,624) $ (1,817,014) $ 774,503 $ (5,240,301)

Grand Total $ (72,953,663)

Precinct 7

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ (4,516,005) $ (466,808) $ (4,003,806) $ 1,082,865 $ (7,692,686) $ (15,596,440)

ARSON $ (388,161) $ 253,132 $ 23,231 $ 735,628 $ (553,908) $ 69,922 

ASSAULT $ (1,499,652) $ (1,450,365) $ 77,060 $ 802,902 $ (7,078,006) $ (9,148,061)

BURGLARY $ (76,912) $ (1,585,575) $ (341,472) $ (159,870) $ 486,748 $ (1,677,081)

DAMAGE TO PROPERTY $ (872,994) $ (704,900) $ 299,600 $ 65,584 $ (759,890) $ (1,972,600)

LARCENY $ 309,582 $ (2,288,088) $ 353,808 $ 43,691 $ (1,156,907) $ (2,737,914)

ROBBERY $ (640,906) $ (4,475,386) $ (652,078) $ (1,760,366) $ (482,853) $ (8,011,590)

STOLEN VEHICLE $ (1,095,570) $ (2,126,207) $ (2,265,069) $ 266,495 $ (774,503) $ (5,994,853)

Grand Total $ (45,068,617)
35

Precinct 9

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ (5,210,775) $ 15,404,664 $ (4,592,601) $ 11,550,562 $(17,094,858) $ 56,992 

ARSON $ (205,497) $ 276,144 $ 1,022,164 $ 450,869 $ (313,078) $ 1,230,601 

ASSAULT $ (2,317,644) $ 2,122,113 $ (3,082,400) $ (1,369,657) $(11,711,464) $ (16,359,051)

BURGLARY $ (2,496,144) $ (1,472,823) $ (2,681,978) $ 94,469 $ (1,128,371) $ (7,684,848)

DAMAGE TO PROPERTY $ (1,693,398) $ 10,600 $ (716,900) $ 595,720 $ (992,849) $ (2,796,827)

LARCENY $ (152,880) $ (1,255,752) $ 291,600 $ 3,972 $ (878,766) $ (1,991,826)

ROBBERY $ (4,440,563) $ 2,537,590 $ (3,400,121) $ (3,568,310) $ (4,490,537) $ (13,361,941)

STOLEN VEHICLE $ (1,107,225) $ (5,368,379) $ (2,656,416) $ (1,417,271) $ (934,320) $ (11,483,612)

Grand Total $ (52,390,511)

Precinct 12

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $(14,937,555) $ 9,802,968 $ 12,246,936 $ (3,729,869) $(30,038,108) $ (26,655,628)

ARSON $ (182,664) $ (322,168) $ 604,006 $ 972,927 $ (842,903) $ 229,198 

ASSAULT $ (1,590,540) $ (488,544) $ 2,311,800 $ (1,133,509) $(14,347,742) $ (15,248,534)

BURGLARY $ (2,747,856) $ (1,846,314) $ (426,840) $ (1,678,636) $ (44,250) $ (6,743,896)

DAMAGE TO PROPERTY $ (1,335,786) $ (492,900) $ 1,214,450 $ (710,492) $ (1,991,245) $ (3,315,973)

LARCENY $ 787,332 $ (1,190,268) $ 1,053,648 $ (1,286,893) $ (1,451,173) $ (2,087,354)

ROBBERY $ (2,243,171) $(10,150,360) $ (2,748,043) $ (951,549) $ (5,214,817) $ (21,307,941)

STOLEN VEHICLE $ 303,030 $ (1,198,194) $ (2,917,314) $ 2,071,396 $ (3,073,423) $ (4,814,504)

Grand Total $ (79,944,632)
36

Precinct 5

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ 11,786,902 $ (1,884,144) $ (1,925,094) $(14,530,630) $ (6,552,965)

ARSON $ (115,060) $ 464,620 $ 237,299 $ (481,659) $ 105,200 

ASSAULT $ 1,038,156 $ 1,818,616 $ (865,875) $ (7,860,901) $ (5,870,004)

BURGLARY $ (1,021,815) $ (903,478) $ 973,754 $ (1,644,620) $ (2,596,159)

DAMAGE TO PROPERTY $ 185,500 $ (551,050) $ 49,188 $ (327,252) $ (643,614)

LARCENY $ (1,132,488) $ 77,760 $ (389,246) $ (475,662) $ (1,919,636)

ROBBERY $ (322,966) $ (3,539,852) $ (1,760,366) $ (2,655,694) $ (8,278,878)

STOLEN VEHICLE $ (2,243,677) $ (1,624,683) $ (169,588) $ (1,327,719) $ (5,365,667)

Grand Total $ (31,121,721)

Precinct 6

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ 2,684,146 $(11,658,141) $ 11,189,607 $ (3,052,653) $ (837,041)

ARSON $ 184,096 $ 650,468 $ 284,759 $ (1,011,484) $ 107,840 

ASSAULT $ 1,786,239 $ 693,540 $ (1,196,482) $(14,443,606) $ (13,160,309)

BURGLARY $ (1,162,755) $ (1,629,106) $ 29,067 $ (1,430,745) $ (4,193,539)

DAMAGE TO PROPERTY $ (63,600) $ (353,100) $ 327,919 $ (421,545) $ (510,326)

LARCENY $ (797,364) $ (182,736) $ (190,651) $ (661,090) $ (1,831,841)

ROBBERY $ (4,982,904) $ (1,443,887) $ (3,187,690) $ (3,283,403) $ (12,897,885)

STOLEN VEHICLE $ (2,525,605) $ (2,727,570) $ 218,042 $ (1,315,425) $ (6,350,558)

Grand Total $ (39,673,658)
37

Precinct 8

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ (16,964,888) $ 7,326,368 $ (9,638,520)

ARSON $ 47,460 $ 24,083 $ 71,543 

ASSAULT $ 2,723,570 $(16,456,764) $ (13,733,194)

BURGLARY $ (1,722,237) $ 730,123 $ (992,114)

DAMAGE TO PROPERTY $ 573,859 $ (1,209,168) $ (635,309)

LARCENY $ (1,004,889) $ (870,704) $ (1,875,593)

ROBBERY $ (6,565,690) $ (1,593,416) $ (8,159,106)

STOLEN VEHICLE $ 1,271,910 $ (2,286,626) $ (1,014,716)

Grand Total $ (35,977,010)

Precinct 10

 Year 

Crime 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 

AGGRAVATED ASSAULT $ 4,331,461 $ (7,814,792) $ (3,483,332)

ARSON $ (47,460) $ 264,912 $ 217,452 

ASSAULT $ (1,464,116) $ (8,707,705) $ (10,171,821)

BURGLARY $ (515,944) $ (1,143,121) $ (1,659,066)

DAMAGE TO PROPERTY $ (409,899) $ (177,493) $ (587,392)

LARCENY $ (329,667) $ (592,562) $ (922,229)

ROBBERY $ (2,188,563) $ (2,317,697) $ (4,506,260)

STOLEN VEHICLE $ (557,218) $ 565,510 $ 8,292 

Grand Total $ (21,104,354)