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8/10/2019 Papazian the Lens of Law Enforcement
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Vol. 4 | Spring 2013 Sanford Journal of Public Policy
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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
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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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References Cited
Biale, Noam. (2008, June 25). What Criminologists and Others Studying
Cameras Have Found.American Civil Liberties Union. Retrievedfrom http://www.aclu.org/technology-and-liberty/expert-ndings-
surveillance-cameras.
Block, Richard and Carolyn Rebecca Block. (1995). Space, place and
crime: hot spot areas and hot places of liquor-related crime. In
John E. Eck and David Weisburd (Eds.), Crime and Place: Crime
Prevention Studies, Volume 4, (pg. 145-183). Monsey, NY:
Criminal Justice Press.
Brantingham, Patricia and Paul Brantingham. (1981).Environmental
Criminology. Beverly Hills, CA: Sage Publications.
Cook, Philip, and John MacDonald. (2010, April). Public Safety through
Private Action: An Economics Assessment of BIDs, Locks, and
Citizen Cooperation.National Bureau of Economic Research.
Retrieved from http://www.nber.org/papers/w15877.
Crime Data. (2012).Denver Police Department Data Analysis Unit.
Retrieved from http://data.denvergov.org/dataset/city-and-county-of-
denver-crime.
Denver Maps. (2012). City and County of Denver. Retrieved from http://
www.denvergov.org/maps.
Environmental Systems Research Institute. (2011).ArcGIS Desktop Help:How Kernel Density works.Retrieved from http://webhelp.esri.com/
arcgiSDEsktop/9.3/index.cfm?TopicName=How%20Kernel%20
Density%20works.
HALO Policy in Police Operations Manual. (2011).Denver Police
Department. Retrieved from http://www.denvergov.org/Portals/720/
documents/OperationsManual/119.pdf.
HALO Street Cameras. (2012).Denver Police Department. Retrieved
from http://www.denvergov.org/police/PoliceDepartment/
SafetyPrevention/StreetCameras/tabid/442831/Default.aspx.
Papazian: The Lens of Law Enforcement
8/10/2019 Papazian the Lens of Law Enforcement
23/24
Vol. 4 | Spring 2013 Sanford Journal of Public Policy
131
Maher, Jared Jacang. (2009, June 18). Smile! You could be on the Denver
Police Departments candid camera. Westword. Retrieved from
http://www.westword.com/2009-06-18/news/smile-you-could-be-
on-the-denver-police-s-candid-camera.
Martinez, Lieutenant Ernie. (2011, August 8). Personal Interview.
Negative Binomial Regression. (2012). University of California at Los
Angeles Statistical Consulting Group. Retrieved from http://statistics.
ats.ucla.edu/stat/stata/dae/nbreg.htm.
Osher, Christopher. (2010, June 6). Denvers surveillance systemdraws praise, concerns.Denver Post. Retrieved from http://www.
denverpost.com/ci_15236766.
Waples, Sam,Martin Gill, and Peter Fisher. (2009, May). Does CCTV
displace crime? Criminology and Criminal Justice, 9(2), 207-224.
Weisburd, David, Wim Bernasco, and Gerben Bruinsma. (2009).Putting
Crime in its Place.New York, NY: Springer Publications.
Welsh, Brandon, and David Farrington. (2009). Public Area CCTV and
Crime Prevention: An Updated Systematic Review and Meta-
Analysis.Justice Quarterly, 26(4), 716-745.
Wyckoff, Chris. (2011, August 16). Personal Interview.
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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