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Vol. 4 | Spring 2013 Sanford Journal of Public Policy

109

The Lens of Law Enforcement: A GeospatialStatistical Program Evaluation of Denvers

HALO Camera Surveillance System

John Papazian

John Papazian is a denizen of Denver and a proud alumnus of the MPP program

at Duke University. He is currently studying statistics at the Institute for Advanced

Analytics in Raleigh. He is grateful for the assistance that he received from Professor

Philip Cook, Professor Elizabeth Frankenberg, and the staff of the Sanford Journal

while conducting this research.

Abstract

The Denver Police Department has recently implemented a new high-

tech surveillance program to prevent crime throughout the city. The High

Activity Location Observation (HALO) cameras can transmit video to

police headquarters in real time through an Internet-based wireless network.

The department has installed more than 100 HALO cameras at various high

crime areas in Denver as of 2012. This investigation attempts a program

evaluation of the surveillance system through a geospatial statistical

analysis of property crime. Although cameras have been installed across

the city, this investigation focuses on cameras installed in Police District#6, which encompasses the central business district. This investigation

establishes a statistically signicant relationship between the installation

of the HALO cameras and a reduction of thefts from motor vehicles in

the viewshed of the cameras in downtown Denver. The difference-in-

difference econometric approach suggests that the relationship is causal.

Other categories of crime also may have been reduced due to the HALO

cameras, but the statistical evidence is not strong enough to make a causal

claim. An alternative approach based upon kernel density estimation hot

spot maps is also explored. Policy recommendations are developed based

upon the empirical results of the program evaluation.


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Introduction

In 2009, the Denver Police Department unveiled a new video

surveillance program, the High Activity Location Observation (HALO)

system, to help prevent crime. These HALO cameras were an improvementover traditional closed-circuit television cameras because they incorporate

night vision with zoom and pivot capabilities that can read a license plate

from a block away and transmit video through a wireless network (Osher

2010). However, the cameras cost $20,000 per unit. As of 2012, the Police

Departmenthad installed more than 100 HALO cameras at various high

crime areas in Denver.

The American Civil Liberties Union has criticized these types ofcameras as ineffective in reducing crime (Biale 2008). However, no formal

statistical analysis of the Denver HALO cameras has yet been carried out

by the police department, the ACLU, or external researchers (Maher 2009).

This paper evaluates the surveillance system in downtown Denver through

a geospatial statistical analysis of property crime. The camera system was

also designed to help prevent violent crime such as robberies. However, the

data available on these types of crime is insufcient to draw conclusions,and therefore the focus is exclusively on property crime. The geographic

focus is on the central area of the city encompassed by Denver Police

District #6, where most of the HALO cameras were installed.

I employ a difference-in-differences econometric method to analyze

the data derived from a quasi-experiment. With this approach, I compare

property crime incidents in treated sites (areas within the HALO camera

viewsheds) to control sites (areas with similar characteristics but without

HALO cameras) both before and after the intervention. This investigation

establishes a statistically signicant relationship between the installation

of the HALO cameras and a reduction of thefts from motor vehicles in the

viewshed of the police cameras in downtown Denver.

Background

The city of Denver began experimenting with cameras in 2006 whenthe former Chief of Police, Gerald Whitman, assigned Lieutenant Ernie

Martinez to launcha pilot video surveillance project, which later grew into

the HALO crime prevention program (Maher 2009). The police department

wanted to keep up with technological advances pursued by larger cities

Papazian: The Lens of Law Enforcement


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such as Chicago. The departments Operations Manual states that the top

four principle objectives of the HALO program were (1) enhancing public

safety, (2) preventing and deterring crime, (3) reducing the fear of crime,

and (4) identifying criminal activity (Denver Police Department 2011).The program had immediate success; the rst installed camera helped

the police to capture gang members committing arson at the Holly Square

Shopping Center, a strip mall in northeast Denver. Following this initial

success, the Denver Police expanded the program. In 2008, the federal

government granted the Denver Police $1 million to install 50 additional

cameras to help with security while the city hosted the Democratic National

Convention. Those cameras remained in place after the conventionconcluded (Maher 2009).

By January 2010, Lieutenant Martinez had deployed the 81 wireless

cameras to high crime areas as part of Phase II of the HALO program. In

November 2010, the Colfax Business Improvement District helped nance

the purchase of additional cameras added as part of Phase III to help prevent

crime on Colfax Avenue, a thoroughfare notorious for prostitution and

organized crime (Martinez interview August 8, 2011). All HALO camerasare currently monitored and controlled from police headquarters by

uniformed ofcers. As of January 2012, the Denver Police Department has

released the exact locations of all cameras installed (HALO Street 2012).

Welsh and Farringtons (2009) meta-analysis suggests that cameras

installed in city centers led to small, non-signicant reductions in crime.

They compute an odds ratio as a comparable metric of relative effect size

across all studies included in their meta-analysis. However, their resultsmay not apply to the more powerful wireless Avrio cameras because their

study only analyzes the effects of traditional close circuit television (CCTV)

cameras. Additionally, their results may not apply to locations in the United

States. Most of their studies use data from metropolitan areas in the United

Kingdom, which were early adopters of surveillance technology. Finally,

many of the studies included in their meta-analysis lack a rigorous quasi-

experimental program evaluation and only analyze crime data before and

after the camera intervention without developing any type of control.

One recent study suggests that cameras may have helped prevent

crime in certain areas of Los Angeles. Cook and MacDonald (2010) analyze

crime data in Los Angeles before and after the formation of a number of


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Business Improvement Districts (BIDs). These BIDs raise money from

local businesses to pay local services such as private security guards,

trash collection, commerce promotion, and CCTV camera installation.

The Hollywood Entertainment BID has installed eight CCTV cameras atintersections in the district. Cook and MacDonald aggregate neighborhood

time series crime data, and then assignthe values to the corresponding

business improvement district. Next, they use the panel data to examine the

effects of BIDs through a difference-in-difference econometric model with

xed effects for neighborhood and year. Their results showa statistically

signicant effect of BIDs on crimes and arrests per year.

Applying Cook and MacDonalds approach, this investigationevaluates the original 44 HALO cameras installed in Denver Police District

#6 to determine whetherthe surveillance system helped to prevent property

crime in downtown Denver.

Data Preparation

The city of Denver provides geospatial les on their website for

depicting neighborhoods, police districts, streets, zoning restrictions, andcensus information. Many of the maps produced in this report are derived

from those geospatial les (Denver Maps 2012). The Denver Police

Department provides data on crime incidents from 2006 to the present

on their websiteas part ofthe National Incident Based Reporting System

(NIBRS). NIBRS lists each reported crime as a separate incident, recording

its type, location, date, and time when the incident was rst reported by

civilians or by ofcers (Crime Data 2012).For this investigation of the HALO cameras, I analyze Federal

Bureau of Investigation Uniform Crime Reporting (UCR) Part 1 property

crimes in central Denver. UCR Part 1 crimes are serious crimes that occur

regularly throughout the country and include burglary, larceny, theft from

motor vehicle, auto theft, and arson. These types of crimes are likely to be

reported to authorities. Chris Wyckoff, the Director of the Data Analysis

Unit for the Denver Police, reports that the online database provides useful

information for analyzing UCR Part 1 property crime rates in different

areas of Denver (Wyckoff interview August 16, 2011).

Crime incident data downloaded from the Denver Police Department

must be geocoded before it can be imported into a geographic information



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system. Geocoding involves assigning a specic longitude and latitude to

the address location of a criminal incident. The web application BatchGeo

was used to perform this task. As noted, the Denver Police Departments

website provides data on crime incidents since 2006, but the address dataare not properly cleaned for geocoding (Crime Data 2012). Therefore,

I cleaned the raw data before geocoding. Cleaning procedures included

spelling out common abbreviations, standardizing the entry format, and

removing miscellaneous information.

In order to determine consistency across the 250,000 criminal

incidents in the database, I programmed Visual Basicscripts to parse the

address information. Once I assigned a specic longitude and latitude toeach criminal incident, I imported the crime incident datainto the geographic

information system ArcGIS and wrote scripts in Python to perform the

actual spatial analysis.

Figure 1 displays a choropleth map of property crimes across all

neighborhoods throughout the entire cityof Denver. Figure 2 displays the

original 44 HALO cameras in central Denver with a push pin dot for

every incident of property crime in the time frame of 2006-2007. Althoughcameras were installed across the city, this investigation focuses only on

cameras installed in Police District #6, which encompasses the central

downtown area of the city.

Methodology

The differences-in-differences econometric approach compares

the treatment sites against control sites before and after the 2008 cameraexpansion. Under a randomized controlled policy experiment, sites would

be randomly assigned to receive treatment or not. The HALO intervention

did not use such a method. The camera sites were chosen primarily because

they were at intersections near high crime areas of the city. Moreover, it

is likely that some businesses lobbied to have cameras installed near

their stores. In this regard, the Denver Police surveillance program is a

quasi-experiment. Since control sites were not determined prior to the

intervention, the control sites must be determined in the present and then

applied retroactively.

I identied control sites through a Monte Carlo process, which

selects numbers at random similar to a roulette wheel at a casino. In the


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Figure 1: Cloropleth Map of Property Crimes across the Neighborhoods of Denver,

Colorado, 2006-2007

rst stage, the treatment sites are examined to determine what geographic

characteristics they have in common. Street corners with characteristicssimilar to street corners containing HALO cameras are labeled as candidate

control sites. These characteristics include similar zoning restrictions, low

housing utilization, and proximity to alcohol sales. In the second stage,

some of the candidate control sites are selected as control sites. I assigned



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Figure 2: Map of the Original 44 HALO Cameras (Treatment Sites)

and Locations of Property Crime Incidents, 2006-2007

every candidate site a random number between zero and one and then

selected the 44 sites with the highest numbers to serve as control sites. Astochastic process removes human bias and ensures that control sites are

scattered at random, similar to darts thrown on a board. Figure 3 illustrates

the candidate control sites that have similar geographic characteristics as

the original 44 HALO cameras sites in Police District #6. Figure 4 plots the


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44 control sites selected at random to match the original 44 HALO cameras

sites in order to balance the treatment and the control.

Control sites were selected based on three characteristics: (1) zoning

restrictions, (2) low housing utilization, and (3) proximity to alcohol sales.The zoning restrictions in central Denver are intricate with many different

ordinances regulating the development of land. According to the citys

geographic records, nearly all of the original 44 HALO camera sites are

located in the Downtown Zone. This is the area of the city where skyscrapers

are allowed to be built. Therefore, candidate control sites are selected only

from this zone. Furthermore, the original 44 HALO camera sites are all

located on city blocks that are relatively unpopulated. Most of the buildingsin this area are commercial rather than residential, and they have low

housing utilization. Therefore, the candidate control sites are restricted to

city blocks that house less than 400 people. Finally, the original 44 HALO

camera sites are located near bars, nightclubs, and stores selling alcohol.

Past scholarship has demonstrated a linkage between the accessibility of

alcohol and crime (Block and Block 1995). Therefore, the candidate control

sites are restricted to locations that are within a half mile of a business with

a liquor license in order to be similar to the HALO cameras sites.

Researchers frequently model crime through a Poisson distribution.

Discrete count data is bounded below by zero but not bounded above by

any integer. Count data does not follow a Normal distribution because of

the lower bound. The Denver crime data displays signs of over dispersion

because the variance is much larger than the mean. In fact, the mean is 14.7

criminal incidents per observation, while the variance is above 232. In other

words, there are some street corners with very high levels of crime, whilethere are other street corners with very low levels of crime. Therefore, I

model crime count as a negative binomial process rather than a Poisson

process. Ordinary Least Squares requires the residuals to be normally

distributed, which is not true in this case. Thus, I develop a generalized

linear model to test the impact of the HALO intervention using Maximum

Likelihood Estimation with Stata statistical software.

As noted, I use a differences-in-differences regression model to measurethe effect of the treatment. In this model, the unit of the observation, Y, is the

count of crime in each individual viewshed (whether real or hypothetical)

for a period of two years. A viewshed is dened as a circular area around a

site with a radius of 50 yards as seen in Figure 6. The HALO cameras are



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Figure 3: Map of the Candidate Control Sites to Match Original 44 HALO Cameras

visible from roughly half a block away, and therefore their maximum crime

deterrence is set to a distance of 50 yards. For the treatment group, the count

of crime in the real viewshed after a camera was installed is compared to thecount of crime in the same viewshed before any camera was installed. For

the control group, the count of crime in a hypothetical viewshed in the post-

period is compared to the count of crime in the same hypothetical viewshed

in the pre-period.


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Figure 4: Map of the 44 Control Sites to Match Original 44 HALO Camera Sites



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Figure 5 - Histogram of Crime Incidents across all Observations

To estimate a causal impact of HALO cameras on property crime in

downtown Denver, I use the following equations:

log(count of crime) = (logYit) =

0+

1*T

1+

2*A

t+

3*(T

t*A

t)

count of crime = Yit= exp(

0+

1*T

1+

2*A

t+

3*(T

t*A

t))

count of crime = Yit= exp(

0)*exp(

1*T

1)*exp(

2*A

t)*exp(

3*(T

t*A

t))....

There are three key variables in this approach. The treatment dummy

variableAit= 1 for sites that actually received a HALO camera andA

it= 0 for

the control sites. The time period dummy variable Tit

= 1 for observations

occurring after the cameras were installed and Tit= 0 in the period before

installation. The interaction term of Tit*A

it= 1 for observations corresponding

to treatment areas after the intervention.

In a negative binomial regression, the log of the outcome variable

(the count of crime) is modeled as a linear combination of the predictor

variables. The incident rates ratio for a predictor variable can be calculated

by taking the exponent of its coefcient. In this sense, the incident rates

ratio has a multiplicative effect in the y-scale of crime count (UCLA 2012).

But, when a dummy variable is zero, the exponentiation of the coefcient

equals one, and the predictor variable has no multiplicative effect.

(2)

(3)

(1)


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To review, the unit of observation is the count of crime in each

individual viewshed (whether real or hypothetical) for a period of two

years. There are 44 sites located in Denver Police District #6 that were

eventually treated by installing HALO cameras in 2008. These sites arelisted inthe Appendix. Therefore, a count of crime was computed in each

real viewshed after installation and in each hypothetical viewshed before

installation for a total of 88 observations in the treatment group. There are

44 sites located in Denver Police District #6 that have been selected to serve

as the control. These sites were never treated with a HALO camera but

were selected through a Monte Carlo process. A count of crime is computed

in each hypothetical viewshed in the post-period and in each hypothetical

viewshed in the pre-period for a total of 88 observations in the comparisongroup. Thus, a sum total of 176 observations are used in this analysis.

Table 1: Derivation of the Difference-in-Difference Estimate (3)

Coefficient Calculation

0 a

1 c - a

2 b - a

3 (d - b) - (c - a)

Area 1:

HALO Viewshed

(Treatment)

Area 0:

non-HALO Sites

(Control)

Time Period 0:

Before Intervention

Jan. 2006 to Dec. 2007

b a

Time Period 1:

After Intervention

Jan. 2010 to Dec. 2011

d c

Dummy Variable A

DummyVariable

T

Results

To determine if the HALO intervention had an effect on reducing

crime, I examined the coefcients for the Difference-in-Difference

estimates (3) for each specic category of crime. I developed a separate

econometric model for each specic category of property crime: auto theft,

burglary, larceny, and theft from motor vehicles. All of the coefcients for

the Difference-in-Difference estimates (3) are negative. The coefcient fortheft from motor vehicles is negative and statistically signicant even with

robust standard errors. The percent change in the incident rate of theft from

motor vehicles is roughly a 50% decrease in the treated area between the

two time periods relative to the change in the control area.



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Figure 6: Map of Crime Incidents Overlaid on Top of the Viewsheds of a Subset of the HALO

Cameras and Control Sites

However, none of the other coefcients is statistically signicant,which could be due to sample size. The statistically signicant coefcient

for theft from motor vehicles gives causal evidence that the HALO cameras

did reduce crime for that category in downtown Denver. Figure 7 displays

time series data of all categories of property crime in the downtown area,


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Table 2: The Difference-in-Difference Estimates (3) for Categories of Property Crime

Note: Robust Standard Errors are listed in parentheses

Arson is also a category of property crime. However, arson is excluded because there are very few incidents of

arson in downtown Denver.

Table 3: Tabulation of Property Crime Counts for All Sites Grouped Together

Categories of Property Crime Coefcient P>|z| 95% Conf. Interval

Auto Theft-0.272

(0.350)0.438 -0.958 0.415

Burglary-0.245

(0.658)

0.710 -1.536 1.045

Larceny-0.242

(0.390)0.536 -1.006 0.523

Theft From Motor Vehicle-0.689

(0.331)0.038 -1.339 -0.040

Categories of

Property CrimeTreatment

Before

Treatment

After|

%

Change

Control

Before

Control

After

%

Change

in %

Change

Auto Theft 87 43 -50.6% 74 48 -35.1% -15.5%

Burglary 21 14 -33.3% 27 23 -14.8% -18.5%

Larceny 264 353 +33.7% 158 269 +70.3% -36.6%

Theft From

Motor Vehicle174 106 -39.1% 178 216 +21.3% -60.4%

while Figure 8 displays time series data for just thefts from motor vehicle

in the downtown area. Figure 8 shows a clear divergence of the treatment

versus the control in the period after the HALO intervention.



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Figure 7: All Property Crime in Viewsheds of HALO Cameras vs. Control Sites in Downtown

Denver over Time

Figure 8: Theft from Motor Vehicles Crime in Viewsheds of HALO Cameras vs. Control

Sites in Downtown Denver over Time

0

20

40

60

80

100

120

140

2006 2007 2008 2009 2010 2011

CountofCrimeInciden

tsperYear

Control

Treatment


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Alternative Approach

Diffusion of the treatment effect to control intersections would

undermine the above economic approach. Diffusion likely did not occur

here because each control intersection is at least one block removed (~100yards) from its nearest treatment intersection, while HALO cameras are

only visible from a distance of about 50 yards. However, criminals might

be deterred from committing transgressions in central Denver irrespective

if a camera is watching them. It is possible that the high concentration

of cameras in the city center creates a halo effect, where the treatment

diffuses throughout the entire downtown area rather than only impacts

certain intersections. In the case of a virtuous diffusion, the result would beto reduce the estimated effect sizes because the treatment would spill over

into the control.

Since the econometric approach can only detect a localized effect of

the treatment, crime density hot spot maps are created to complement the

regressions and analyze the effect of the intervention on a larger scale. This

approach draws insight from the environmental criminology research of

Brantingham and Brantingham (1981) in their use of ecological principlesto model crime patterns.

One important element in this approach is to collect data at the lowest

geographic units of analysis. According to Weisburd et al. (2009), crime

maps based upon high units of analysis could be misleading because of

an ecological fallacy. For example, the choropleth map [Figure 1] of

property crime across neighborhoods in Denver has a deceptive quality.

There are some neighborhoods colored light gray because of relatively low

level of property crime, which are adjacent to neighborhoods colored dark

gray because of relatively high level of property crime. It appears that if

one were to walk across the border that one would encounter more crime.

However, there are likely some places in dangerous neighborhoods that

are quite safe, while there are places in safe neighborhoods that are quite

dangerous. Aggregating crime statistics to the neighborhood level distorts

the truthbe cause there are places inside each neighborhood that are not

representative of the whole.

Unfortunately, choropleth maps at all geographic levels (counties,

neighborhoods, census blocks) are susceptible to the ecological fallacy

because administrative boundaries are not based upon crime patterns. The



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Figure 9: Map of Kernel Density Estimate Hot Spots of Property Crime in Denver Before

the Instalation of HALO Cameras (2006-2007)


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Figure 10: Map of Kernel Density Estimate for Property Crimes in Central Denver Between

the Two Time Periods



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solution is to disaggregate to the lowest unit possible, which is the geo

coded incident of a criminal activity. However, a map [Figure 2] displaying

a plethora of push pin crime dots can make it difcult to identify high-

crime areas because multiple crimes at one location will only show up as asingle dot. One solution is to create crime density hot spot maps.

Crime density maps can be used as an alternative evaluation technique

to determine if surveillance cameras have dampened the level of crime.

Waples, Gill, and Fisher (2009) use the concept of a kernel density estimate

in their criminology research on surveillance cameras. Crime in Denver is

concentrated at specichotspots because a lot of crime occurs repeatedly

at the same locations over time. Kernel density estimation (KDE) is a non-parametric procedure that can be used byArcGISto estimate the probability

density function of a random variable (e.g. crime) over a geographic region

(ESRI 2010). Figure 9 illustrates a kernel density estimate of property crime

in downtown Denver before the installation of the HALO cameras (2006-

2007).

Waples et al (2009) use kernel density estimation to create crime

maps before and after the intervention of police cameras to detect if the

hotspots have moved once the cameras are installed. Then, they create

a change detection map to capture the kernel density differences between

the prior and the posterior maps. I replicated their technique to create maps

[Figure 10] of the property crime in Denver before and after the installation

of the HALO police cameras. Similar to the regression approach, the time

period before the intervention (2006-2007) and the time period after the

intervention (2010-2011) both constitute two years and contain the same

calendar months. The colored areas on the change detection map [Figure10] illustrate locations where there is a statistically signicant change in the

kernel density estimate of crime between the two periods.

The map depicts a statistically signicant decrease in the kernel

density estimate of property crime in the northwestern quadrant of

downtown Denver. This is near the location where ve HALO cameras

were installed prior to 2010. This section of the city, known as the LoDo

district (abbreviation for Lower Downtown) is a popular nightlife areabetween two sports stadiums that historically has been plagued by a high

level of crime. However, it would be imprudent to assign the drop in crime

in the LoDo district solely to the HALO camera intervention. It is unwise to

infer a causal relationship from the KDE map because there could be other


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explanations for the drop in property crime. For example, the LoDo district

has recently undergone signicant gentrication. Unlike the difference-

in-difference econometric approach, the kernel density estimation change

detection does not employ a rigorous control. Nonetheless, the map doesdelineate an interesting phenomenon that should be studied in more detail.

Conclusion

In retrospect, this investigation has established a statistically

signicant relationship between the installation of the HALO cameras and

a reduction of thefts from motor vehicles in the viewshed of the cameras

in Denver Police District #6. The difference-in-difference econometricapproach suggests that the relationship is causal. HALO cameras may have

reduced other categories of property crime, but the statistical evidence is

not strong enough to make a causal claim.

There are three potential weaknesses in my methodology. First,

important variables were likely omitted when choosing control sites.

Control sites were selected based on geographic characteristics they had in

common with treatment sites: zoning restrictions, low housing utilization,and proximity to alcohol sales. These geographic characteristics are

somewhat arbitrary, and they are based upon my personal understanding

of downtown Denver. There are other characteristics that may have been

more appropriate. For example, there has been considerable construction in

downtown Denver, which alters travel patterns around the city. Ultimately,

there is no way to select control sites that perfectly match the treatment

sites. Although the control sites are not ideal, I believe they are sufciently

similar to the treatment sites to test the counter factual in the context of aquasi-experiment.

Second, my methodology assumes parallel trends. The treatment and

control sites do not have to be identical for a differences-in-differences

approach. But, in the absence of cameras, crime should otherwise increase

or decrease at the same rate in the treatment sites as in the control sites.

Unfortunately, I cannot prove that this parallel trends assumption holds

true. HALO cameras are clustered around the 16th street mall, the DenverConvention Center, and the Civic Center Park. Those three locations have

no perfect substitutes in the fabric of downtown Denver. However, the

control sites are randomly scattered around the treatment sites as seen in

Figure 4. The control sites are not placed in a suburban strip mall far away



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from downtown. Rather, the control sites are placed as near as possible to

the treatment sites to help ensure the assumption of parallel trends.

Third, there may have been errors in geocoding. Since there were over

250,000 criminal incidents in the database, I could not check every addressindividually to ensure that it was correctly plotted on the map. Instead, I

devoted considerable resources to data cleaning and then used external

software to assign a specic longitude and latitude to each address location

of a criminal incident. There are possible errors in my data, where criminal

incidents are plotted at wrong locations on the map. Any large scale error

in geocoding would cascade into the econometric analysis and corrupt the

statistical results. Unfortunately, there is no metric to gauge the level ofaccuracy in the geocoding. However, I used the web application BatchGeo

to complete this process. BatchGeo is a well-respected geocoding tool that

relies on geo spatial data from Google to plot addresses correctly onto a

map. While it is possible that a minimal number of criminal incidents are

plotted incorrectly, it is unlikely that the errors are large scale enough to

dramatically alter my ndings.

Bearing in mind these potential weaknesses in my methodology, Irecommend three strategies based upon the empirical results:

1. An expansion of new HALO cameras into other areas of Denver

experiencing high levels of theft from motor vehicles. Ideally, the

number of police cameras should be increased until the marginal

benet of crime prevention equals the marginal cost of camera

installation. Future research could be conducted to estimate the

full economic benets and costs of the HALO cameras.2. An upgrade of the information system to cross-reference the

NIBRS crime incident data to actual arrests and convictions.

Publicly available crime data on the Denver Police website does

not link arrests and convictions to usage of the HALO cameras. It

is important to learn if the number of arrests has increased in the

viewshed of the cameras in order to learn their true effectiveness.

3. An implementation of a randomized controlled experiment in thenext phase of the HALO program. Determining the control sites

before the intervention is superior than determining the control

sites after the intervention. This would enable researchers to be

more condent in ascribing causality.


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Number Location Longitude Latitude

1 120 W 14th Ave -104.9888 39.7375

2 12th & Chopper Circle -105.0058 39.74953 12th & Welton -104.9961 39.7408

4 1351 Cherokee -104.9917 39.7377

5 13th & Champa -104.9976 39.7437

6 13th & Cherokee -104.9916 39.7369

7 13th & Delaware -104.9929 39.7401

8 13th & Welton -104.9949 39.7417

9 1450 Bannock -104.9906 39.7388

10 1450 Broadway -104.9874 39.7393

11 14th & Bannock -104.9907 39.7397

12 14th & Broadway -104.9874 39.7383

13 14th & California -104.9946 39.7433

14 14th & Champa -104.9963 39.7447

15 14th & Cherokee -104.9916 39.7385

16 14th & Curtis -104.9972 39.7453

17 14th & Delaware -104.9929 39.7385

18 14th & Larimer -104.9998 39.7473

19 14th & Stout -104.9955 39.7440

20 14th & Welton -104.9937 39.7427

21 15th & California -104.9934 39.7443

22 15th & Curtis -104.9960 39.7463

23 15th & Market -104.9995 39.7489

24 15th & Wazee -105.0013 39.7503

25 15th & Welton -104.9925 39.7436

26 16th & California -104.9922 39.7452

27 16th & Cleveland -104.9878 39.7418

28 16th & Curtis -104.9948 39.7472

29 16th & Lawrence -104.9965 39.7485

30 16th & Market -104.9983 39.7499

31 16th & Wazee -105.0000 39.7512

32 16th & Welton -104.9913 39.7445

33 170 W 14th Ave -104.9898 39.7381

34 17th & Arapahoe -104.9944 39.7488

35 18th & Arapahoe -104.9933 39.7497

36 19th & Market -104.9947 39.7527

37 20th & Blake -104.9943 39.7543

38 27th & Welton -104.9780 39.7548

39 9th & Chopper Circle -105.0069 39.747940 Colfax & Broadway 104.9874 39.7401

41 15th Street & Cleveland -104.9888 39.7410

42 60 W 14th Ave -104.9886 39.7385

43 15th Street and Colfax Avenue -104.9890 39.7401

44 Park Avenue West & Lawrence -104.9881 39.7551

Appendix A: List of the Original 44 HALO Cameras


papazian the lens of law enforcement

Documents