OMB Approval No. 0348-0043 

1.TYPE OF SUBMISSION 

Application Non-Construction 

APPLICATION FOR 

3. DATE RECEIVED BY STATE 

2. DATE SUBMITTED 

07/23/2009 

STATE APPLICATION IDENTIFIER 

APPLICATION IDENTIFIER 

4. DATE RECEIVED BY FEDERAL AGENCY FEDERAL IDENTIFIER 

5. APPLICANT INFORMATION 

Legal Name Organizational Unit
 

Temple University
 College of Liberal Arts 

(city, state, and zip code) Name and telephone number of the person to be contacted on matters involving 

this application

19122-6099 

7. TYPE OF APPLICANT 

6. EMPLOYER IDENTIFICATION NUMBER (EIN) State related inst of higher ed
 


 

8. TYPE OF APPLICATION 9. NAME OF FEDERAL AGENCY
 

New
 National Institute of Justice 

10. CATALOG OF FEDERAL DOMESTIC ASSISTANCE 11. DESCRIPTIVE TITLE OF APPLICANT'S PROJECT
 

Number: 16.560
 CAN VIDEO SURVEILLANCE REDUCE CRIME?� 

QUANTIFYING THE SIZE AND GEOGRAPHIC EXTENT� CFDA Title: National Institute of Justice Research, Evaluation, OF CCTV’S IMPACT and Development Project Grants 

12. AREAS AFFECTED BY PROJECT 

Philadelphia, PA 

13. 14. CONGRESSIONAL DISTRICT(S) OF PROPOSED PROJECT 

Start Date: Ending Date: a. Applicant b. Project 

01/01/2010 12/31/2011 PA01 PA01 

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EXECUTIVE ORDER 12372 PROCESS? 

Program is not covered by E.O. 12372 

a. Federal $486,161 

b. Applicant $0 

c. State $0 

d. Local $0 

e. Other $0 

f. Program Income $0 17. IS THE APPLICANT DELINQUENT ON ANY FEDERAL DEBT? 

g. Total $486,161 N

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Director 

c. Telephone number 

d. Signature of Authorized Representative e. Date Signed 

Previous Editions Not Usable Standard Form 424 (Rev 4-88) 

Prescribed by OMB Circular A-102 
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CAN VIDEO SURVEILLANCE REDUCE CRIME?

QUANTIFYING THE SIZE AND GEOGRAPHIC EXTENT

OF CCTV’S IMPACT

Department of Criminal Justice | Temple University
 
      

                           

                           

                         

                             

                                   

                           

                         

                             

                             

            

                             

                     

                           

                             

                           

                         

                        

                             

                           

                  

Abstract 

Closed Circuit Television (CCTV) is an applied technology that seeks to reduce crime by

increasing the perceived risk of capture (Ratcliffe 2006: 8). While there are no national

estimates on the extent of CCTV across America, newspaper accounts suggest that CCTV

cameras are being implemented at a rate never seen before. For example, San Francisco has

spent close to $1m on 74 cameras at 25 locations, and a further 25 cameras are planned (Bulwa

2008); and Washington DC plans a $4.5 million expansion of its surveillance system (Klein

2008). This rapid and unprecedented expansion of video surveillance technology is not just

limited to the major urban areas (Farrington & Welsh 2007). Reductions in technology cost and

a perception that CCTV is a cost‐effective crime prevention tool have driven investment in video

surveillance in municipal areas across America.

For all this enthusiasm for video surveillance, there has been a lack of high quality,

independent evaluation studies (Eck 1998, Greenberg & Roush 2009). Astoundingly, when

Farrington and Welsh (2007) conducted a recent meta‐analysis of evaluations of CCTV in city

and town centers, they could only find a single study from the US that was methodologically

and statistically robust enough to warrant inclusion in their report. This single US study

examined an undisclosed number of unmonitored cameras in three areas of Cincinnati, Ohio;

cameras that used technology dating from early 1999 (Mazerolle et al. 2002).

Towns and cities across the US are spending a phenomenal amount of money on CCTV.

Given the current enthusiasm, there is an urgent need for a large‐scale, empirically‐sound and

methodologically‐robust evaluation of CCTV cameras in the urban environment.

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In light of a recent successful pilot project, we propose to conduct a large‐scale, multi‐

method, quasi‐experimental research study that will be spatially‐tailored to the viewshed of

each of 220 cameras in Philadelphia, PA. Unlike traditional spatial approaches that simply

measure crime out from a camera to an arbitrarily‐selected distance, individually constructed

polygons for each camera site will be nested within buffer areas to detect displacement and/or

diffusion of benefits to surrounding areas. Advanced interrupted time‐series models that can

incorporate varying implementation dates of different cameras will be populated with controls

for seasonality and long‐term crime trends, and four years of prior crime history in the city.

Field observations at each site and qualitative interviews with police camera operators across

varying shifts will combine with the quantitative work to produce a definitive evaluation, and

create a best practice guide for crime prevention practice with CCTV.

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Resubmit response 

This application was submitted one year ago as application 2008‐91640‐PA‐IJ with the same

project name (Can video surveillance reduce crime? Quantifying the size and geographic extent of

CCTV’s impact). For section 1 (Understanding of the Problem and Its Importance) and section 2 (Quality

and Technical Merit) the reviewers found “No relevant weaknesses were evident in this applicant’s

response to this section of the Application”.

For section 3 (Impact of the Proposed Project) the reviewers wrote that “While not a true

weakness, there appears to be no immediate potential for commercialization of this project’s

deliverables”.

Response: There is already sufficient commercialization of CCTV to fulfill the nation’s need for

video surveillance systems. The broader question, that we feel is much more pertinent, is; what is the

size and geographic extent of any disorder and crime benefits of CCTV, if any? If a strong

methodological study such as we propose is capable of demonstrating a benefit, then there will be an

increase in cities pursuing commercial CCTV systems.

For section 4 (Capabilities, Demonstrated Productivity, and Experience of Applicants) the

reviewers found “No relevant weaknesses were evident in this applicant’s response to this section of the

Application”.

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For section 6 (Dissemination Strategy) the reviewers found “Nothing was mentioned in the

application that would indicate any special efforts to be taken to get their findings to the nation’s city

managers”.

Response: This is a perfectly reasonable criticism and we have this year included two

dissemination activities that would target the nation’s city managers. First, we plan to publish an

article regarding this research in the Leadership Bulletin of the National Executive Institute Associates,

a publication that is copied to graduates of the FBI National Academy and members of both the Major

Cities Chiefs’ Association and the Major County Sheriffs’ Association. Secondly, we will seek to present

the findings of our research at the annual meeting of the International City/County Management

Association, a national body based in Washington DC with a large membership. Their annual meeting

for 2011 will be in Wisconsin, and a budget item for this is included.

Finally, we would like to express our thanks to the reviewers from the first review, who summarized our

application thus “NIJ should have the highest level of confidence that the solicitation requirements will

be met, if not exceeded”. In the intervening period, we have worked even more closely with the

Philadelphia Police Department and are currently engaged in a significant city‐wide foot beat policing

experiment with them. The experiment will conclude by the time this project would commence, but it

demonstrates that we maintain excellent relations with the Police Department and NIJ should feel as

confident in the completion of this research as the reviewers felt last year.

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Contents 

ABSTRACT .................................................................................................................................................. III
 

RESUBMIT RESPONSE .................................................................................................................................. V
 

CONTENTS................................................................................................................................................. VII
 

PURPOSE, GOALS, AND OBJECTIVES............................................................................................................. 1
 

OBJECTIVES ...........................................................................................................................................................2
 

REVIEW OF RELEVANT LITERATURE.............................................................................................................. 3
 

RESEARCH DESIGN AND METHODS .............................................................................................................. 9
 

A LONGITUDINAL STUDY EMPLOYING INTERRUPTED TIME SERIES ANALYSES .......................................................................10
 

A COMPARATIVE STUDY WITH NON‐CAMERA LOCATIONS...............................................................................................13
 

A SPATIAL STUDY EXAMINING POTENTIAL DISPLACEMENT OR DIFFUSION...........................................................................14
 

REGULAR FIELD OBSERVATIONS OF CAMERA LOCATIONS TO CONFIRM IMPLEMENTATION CRITERIA.........................................18
 

INTERVIEWS WITH CAMERA OPERATORS AND KEY STAKEHOLDERS....................................................................................20
 

EVALUATION SUMMARY ............................................................................................................................21
 

IMPLICATIONS FOR CRIMINAL JUSTICE POLICY AND PRACTICE ....................................................................22
 

MANAGEMENT PLAN AND ORGANIZATION ................................................................................................23
 

DISSEMINATION STRATEGY ........................................................................................................................25
 

FIGURES .....................................................................................................................................................28
 

REFERENCES...............................................................................................................................................30
 

OTHER APPLICATION REQUIREMENTS ........................................................................................................33
 

DATA ARCHIVING STRATEGY ....................................................................................................................................33
 

LIST OF KEY PERSONNEL..........................................................................................................................................34
 

RÉSUMÉS OF KEY PERSONNEL ..................................................................................................................................35
 

LIST OF PREVIOUS AND CURRENT NIJ AWARDS ............................................................................................................38
 

LETTER OF COOPERATION/SUPPORT FROM THE PHILADELPHIA POLICE DEPARTMENT..........................................................38
 


 


 

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Purpose, goals, and objectives 

The history of CCTV technology is one of rapid evolution from static, low resolution

cameras, to high quality technology solutions that can pan, tilt and zoom at the command,

through either wireless interface or fiber optic cable, of remote operators usually connected to

a police radio network. Although camera technology continues to evolve, the vast majority of

camera systems planned for the US are similar to the technology to be evaluated in this

research project; pan, tilt and zoom (PTZ) cameras connected to a police control room. This

level of technology is fully developed and readily applicable in an operational criminal justice

setting, and is either currently or soon to be utilized across the country in many towns and

cities. That the maturity of the technology is apparent makes the lack of a high quality

evaluation ever conducted even more difficult to fathom.

Therefore, the purpose of this study is to conduct the first, large‐scale, multi‐method

evaluation of CCTV using robust interrupted time‐series techniques applied to recorded crime,

in the United States.

The goal of this research is to use advanced time‐series and micro‐spatial evaluation tools

that are capable of measuring crime within the specific viewsheds of CCTV cameras to estimate

the prevention benefits of cameras on recorded property crime, violent crime, and disorder

incidents.

A second goal is to complement the quantitative research with qualitative studies that

enhance our understanding of the statistical outcome with a contextual understanding of why

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crime increased or decreased in camera and buffer areas, based on field observations at

camera sites and interviews with key personnel and stakeholders.

Objectives

•	 Map the exact environment of each of 220 cameras across Philadelphia, PA

•	 Establish exact spatial viewsheds for each camera, and liaise with district officers

and camera operators to map the likely displacement zones around each camera

•	 Build monthly time series models of disorder events, property crime and violent

crime for up to four years prior to camera implementation

•	 Continue regular visits to all camera and buffer areas during the implementation

period to monitor the context of the urban environment

•	 Assess changes in violent crime, property crime and disorder activity in target areas,

buffer areas and control areas using a variety of robust statistical methods (ARIMA

time series analysis, Weighted Displacement Quotient analysis, comparison analysis

with control sites)

•	 Conduct qualitative interviews with camera operators, police department

leadership, and patrol (district) officers in police districts with cameras

•	 Disseminate findings not only within academia, but throughout the practitioner

community with publications and presentations specifically targeted to IT managers

and police executives rather than college professors

•	 Complete final report and accompanying publications

•	 Archive data for future project replication

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Review of relevant literature 

The vast majority of CCTV schemes are currently being implemented in public areas of cities

and towns. For example, Washington DC has already spent $4m on street intersection cameras,

and looks to spend about the same again expanding the camera network (Klein 2008), and New

York City has already spent over $25m on a public surveillance camera network. These

implementations are indicative of a growing trend toward the use of CCTV to improve public

safety in city centers and public areas. Philadelphia, Pennsylvania, is joining this trend with an

anticipated implementation of 220 cameras by June 2009 (this process is about 50% complete

and ahead of schedule). As a result, this project proposes to examine cameras implemented in

public areas and street intersections across Philadelphia, PA. Thus, while there is a modest

research history of CCTV evaluations in public housing areas, on public transport and in car

parks, this literature review will concentrate on city and town center camera implementations.

From the earliest wide‐scale implementation of CCTV technology in the 1980s, many

evaluations have lacked independence and have been either conducted by city agencies or

technology companies involved in the scheme, both of whom may be perceived to have vested

interests in the evaluation outcomes (Ratcliffe 2006). The earliest independent evaluation of a

CCTV implementation dates from King’s Lynn, UK (Brown, 1995) where 19 cameras were

installed at public car parks across the city. Like many studies that followed, this evaluation was

methodologically weak and lacked controls for low numbers of initial crimes and for long‐term

temporal trends. As reported in Appendix A of Ratcliffe (2006), numerous studies have been

conducted that, while independent, range in methodological quality from weak to strong.

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Methodologically weak studies have lacked control areas, controls for seasonal variation, or

have lacked any measure of potential displacement (or diffusion).

The existing evaluation literature has considerable variation in not only methodology, but

also outcome measures and independent variables. Some examine the impact of cameras on

crime within a defined distance of CCTV cameras (Harada et al., 2004), while others surveyed

residents in camera areas for their perceptions of how crime has changed (Squires, 2003).

Other studies have interviewed key stakeholders (Hood, 2003) or examined emergency room

attendance levels related to assaults (Sivarajasingam, Shepherd, & Matthews, 2003).

Irrespective of the numerous universally‐glowing reports of CCTV in trade magazines and

technology periodicals, the number of statistically sound and independent evaluations is

substantially smaller. Farrington and Welsh’s (2007) systematic review identified four essential

criteria for inclusion in their study:

1.	 CCTV was the main intervention examined;

2.	 The outcome measure was crime;

3.	 The evaluation had a minimum methodological design that incorporated at least before‐

and‐after measures of crime in experimental and comparable control areas; and

4.	 There was a minimum number of crimes (20) recorded in the experimental area prior to

the CCTV implementation.

Within these basic evaluation criteria, they were able to identify 44 studies, but only 22 that

were applicable to city centers and public urban areas. Of these 22, 17 were conducted in the

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UK, 2 is Scandinavia, and 3 from the US; however, the 3 from the US are actually on 3 locations

in Cincinnati, Ohio, and are reported in one single journal article (Mazerolle et al. 2002).

Furthermore, this single study does not use crime as an outcome measure, but calls for service.

While the study conducted by Lorraine Mazerolle and colleagues is methodologically sound and

empirically sophisticated, the use of calls for service rather than recorded crime basically means

that there has not been a single study of CCTV in America that uses recorded crime in public

space as an outcome measure in a manner that satisfies Farrington and Welsh’s systematic

review criteria.

Since our application last year there has, however, been one US study of note. Greenberg

and Roush (2009) evaluated a CCTV system at a private apartment complex in New York, NY.

Though hampered by low crime counts, their methodologically sound evaluation found that

CCTV may be ‘moderately effective’ in preventing minor crimes but the results for serious crime

were unclear because of the dearth of serious crime to begin with. It is worth noting that even

writing in 2009, they noted a ‘paucity of American studies’ (2009: 5) and on calling for more

research they pointed out that ‘because the benefits of reducing major crime are greater than

of reducing lesser crimes, we suggest that future research focus on areas in which serious crime

rates are higher’ than in their researched location (2009: 22). The results from their private

apartment complex may not translate to the public space environments that we seek to

examine in our study and where most CCTV systems are implemented. Fortunately, with 220

cameras across Philadelphia, we anticipate significantly greater levels of violent and serious

crime on which to test the effectiveness of CCTV.

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Of the CCTV reviews from overseas, the vast majority stem from the UK. As Farrington and

Welsh report from the 22 studies included in their review, ten had positive outcomes as regards

a reduction in crime (or equivalent measure, for example calls for service), five had undesired

outcomes, and the remainder had null or mixed results. While it may be tempting to simply

translate these results to the US (albeit the results are too mixed in terms of outcome and

methodology), there is a problem with that approach. As Andersson notes ‘There is also good

reason to weigh the international results and experiences against our own history and our

current situation as regards the use of CCTV, which are very different from those of countries

like the United Kingdom, for example, which has long been developing large‐scale CCTV

surveillance as a means of combating terrorism’ (Andersson 2007: 6).

The UK’s attempt to combat terrorist attacks from the Provisional IRA spurred numerous

technology solutions, such as CCTV across the public transport network. This camera network

dates back to the mid‐1980s, but evaluations of these cameras have limited applicability to the

modern US experience: the camera technology has advanced significantly during the

intervening years, and although we are in a post‐9/11 homeland security era that has expanded

the number of cameras in cities such as New York, the vast majority of towns and cities across

the country still implement surveillance technology to combat street crime and property

offenses rather than as a counter‐terrorism measure. Moreover, violent crime rates (especially

shootings and homicides) in the UK are significantly lower than in the US and the crime dynamic

(in terms of the mix of street offenses and public disorder crime) is not as transferable to the US

(Gill & Springs 2005). Finally, the street pattern in much of the US (often built on a grid pattern

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rarely seen in the UK) better favors the linear view scheme of CCTV cameras, suggesting that

cameras in the US may have greater applicability to the existing geography.

The literature on CCTV rarely addresses the theoretical perspective of camera benefit; in

other words, how does a surveillance system reduce crime? Drawing on the literature of

environmental criminology, Ratcliffe (2006) suggests that cameras may work to prevent crime if

two criteria are met; the offender is aware that the camera may be watching their activity, and

secondly that the offender perceives that the risk of capture by police may outweigh the

benefits of the crime they are considering. As crime prevention is therefore a feature of

offender perception, it may be that although the cameras may only be able to see a certain

amount of public space, offenders perceive that the cameras can observe their activity to a

greater or lesser range. The choice is therefore to define evaluation areas based on possible

offender perception of camera range, or on the actual area that the camera can view. In this

research we take the approach of mapping the actual areas that the cameras can see. This is

because offender perception is not possible to measure without extensive and expensive

interviewing, and secondly because offender perception will vary from person to person.

Fortunately, as explained in a later section, the weighted displacement quotient analysis is able

to incorporate a measure for diffusion or displacement, in the event of a misspecification of the

surveillance zone. This will allow us to measure any diffusion of benefits or displacement to

the neighboring area around each camera, and to limit the impact of areas where the boundary

of the target area does not exactly match with offender perception. This approach is

significantly preferable to the ‘standard’ method of estimating camera impact.

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If there is a ‘standard’ method, then it is to use a simple buffer that does not reflect the

underlying geography of the urban mosaic around camera locations. For example, a number of

studies have mapped crime a certain distance from the cameras; Harada and colleagues’ (2004)

study in a Tokyo neighborhood examined crime out to 50 meters (164 feet); Mazerolle and

colleagues (2002) buffered regions to 200, 500 and 1,000 feet radially out from cameras; Sarno

et al. (1999) buffered out for 200 meters (656 feet); and a recent DC police evaluation

examined crime within 250 feet of cameras, but did not consider displacement (Klein 2008).

The problem with examining crime by counting events within a certain set distance from

every camera is that camera implementation has a significant impact on the viewshed of

cameras, and no two cameras have the same range of vision. For example, road signs, trees,

camera height and camera technology all enhance or limit the viewable range of CCTV cameras.

Thus a robust approach is one where the actual viewable geography of the streetscape under

surveillance becomes the experimental area, an area that is tailored to each camera. Mapping

the actual viewshed of each camera, as we propose to do, is the most empirically‐sound

approach, yet it has not been conducted in any evaluation study of merit.

In summary, the literature review finds the following key points:

•	 There is a complete lack of empirically‐sound, crime‐focused evaluations of public street

CCTV cameras in the United States (US).

•	 Much of the existing literature from overseas may lack relevance to the US

environment, given the different crime dynamic from British urban centers to US cities.

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•	 Much of the evaluation literature lacks tailoring to the individual viewsheds of the

cameras, simply employing a fixed distance around cameras – a distance that varies by

study.

•	 Existing evaluations are often methodologically weak, lacking controls for seasonality,

limited historical data on which to estimate pre‐camera trends, and rarely explicitly

testing for displacement.

The study we propose resolves all of these issues, and does so over more cameras than ever

before examined.

Research design and methods 

Our research proposal builds on our experience with a pilot project conducted in the latter

half of 2007. In conjunction with the Philadelphia Police Department, a pilot evaluation of 18

cameras at 10 pilot sites across Philadelphia was conducted during 2007. For the pilot, we used

two years of crime data, geocoded and mapped to individually‐constructed polygon areas for

exact camera viewsheds. These viewsheds were established by site visits and by tracking and

panning each camera during visits to the control room in Philadelphia Police Department

Headquarters. The quantitative analysis was conducted using Hierarchical linear modeling

(HLM), a type of statistical analysis that recognizes nested data structures. At the most basic

level this can be thought of, for example, as individuals nested within a neighborhood or police

beats nested within police districts. This analysis also applies to repeated observations across

individuals or locations. In the pilot study, the analysis looked at time nested within camera

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locations. The research incorporated controls for long‐term trends and seasonality and found

that the introduction of cameras was associated with a 13 percent reduction in crime. This

research has been accepted for publication in Justice Quarterly (Ratcliffe et al, in press). Based

on our experiences with the police and the pilot project as well as the clear need for a more

expansive evaluation, we now seek to conduct an extended research project on a much greater

scale.

We propose a multi‐method, multi‐pronged approach for this extended project. Our

research design employs a number of approaches to evaluation the impact of CCTV cameras,

specifically;

•	 A longitudinal study employing interrupted time series analyses;

•	 A comparative study with non‐camera locations;

•	 A spatial study examining potential displacement or diffusion;

•	 Regular field observations of camera locations to confirm implementation criteria; and

•	 Interviews with camera operators and key stakeholders to interpret the quantitative

results.

A longitudinal study employing interrupted time series analyses

We will employ two time‐series methodologies to cross‐reference the quantitative impact

of CCTV implementation on recorded crime and calls for service (CFS). It is important to

examine both crime and CFS data. Recorded crime is often the measure by which municipalities

measure success of a crime prevention intervention, while CFS are a useful indication of police

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workload and much of the minor public disorder that plagues urban areas but that does not rise

to the level of recorded crime. We will examine both data sources.

One methodology is Auto‐Regressive Integrated Moving Average (ARIMA) time series

modeling. The analysis of a time series as the quasi‐experimental design to be employed in this

study is effectively a test of the null hypothesis, in our case, that the introduction of CCTV

cameras had no significant impact on crime. The null hypothesis is tested by comparing the pre

and post series. Time series analysis can also be used to estimate the impact that the

intervention had on the series. In other words, if the introduction of CCTV cameras has a

positive (or negative) significant impact on crime rates within the tailored viewshed of a

camera, what is the estimate of that impact? For example, when a police operation to reduce

burglary was examined using ARIMA modeling, it was found that the police had reduced

burglaries in one Australian city by about 50 per week (Makkai et al. 2004, Ratcliffe 2008).

When examining time series, more common approaches such as Ordinary Least Squares

(OLS) regression are statistically flawed and should be avoided, because time series data violate

the assumption of independence of individual events. In other words, a time series is often

comprised of measurements that are influenced by previous periods in the series (Ross 1982,

Yaffee & McGee 2000). This could be due to seasonal variation (we know that violent crime

usually increases in summer months) or interventions (rising crime rates often trigger a police

reaction) (Langworthy 1989, Lawton et al. 2005). The advantage of ARIMA modeling over other

types of regression discontinuity approach is the ability of ARIMA models to incorporate a

measure of temporal causation such that previous event counts have an impact on future

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values (Block 1984, Box & Jenkins 1976, Chatfield 1989). ARIMA modeling has been used in

numerous evaluations of crime reduction schemes; including burglary prevention operations

(Ratcliffe 2002), vehicle theft prevention (Krimmel & Mele 1998), heroin crackdowns

(Degenhardt et al. 2005), and Compstat (Mazerolle et al. 2007).

A limitation of ARIMA modeling is that the pre‐intervention model requires as many as 40

to 50 measurement points in order to establish a pre‐intervention trend. For the pilot project,

we were forced to use an alternative approach due to a limited historical trend history. For the

pilot, we employed Hierarchical Linear Modeling (HLM). As stated earlier, this is a type of

statistical analysis that recognizes nested data structures, and looked at time nested within

camera locations.

Using HLM for the pilot study had a number of practical benefits. First, it included variables

that statistically controlled for seasonal effects on crime. Failing to control for seasonal effects

(i.e. the difference in crime between winter and summer) can limit the value of any findings.

This could be especially true for street crimes, because people spend more time outside when

the weather is warmer. Secondly, the analysis controlled for preexisting temporal trends at

each camera location. For example, if regeneration was taking place near a camera location,

this could sometimes mean that crime was slowly falling, an additional effect to the seasonal

variation. Failing to control for these pre‐camera implementation trends could result in under‐

or over‐estimating the cameras’ effects on crime patterns.

The results from HLM are easily converted to Incident Rate Ratio (IRR) scores and this is

hugely beneficial in converting rather dense statistical outcomes into measures that the public

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and policy‐makers can understand. For example, a finding that the introduction of CCTV

cameras was associated with a 13% reduction in street crime in the viewshed of CCTV cameras,

is a great deal easier to explain than large tables of (often incomprehensible) data.

In the proposed study, we will use both methods. We have arranged for crime and call for

service data going back to 2003, thus enabling the study to model enough months of crime pre‐

intervention that we will be able to construct ARIMA models. Furthermore, we will also

construct a global model using the same HLM approach as before for both continuity with the

pilot study and because the use of HLM as a regression discontinuity model will act as a cross‐

check on the ARIMA approach.

A comparative study with non‐camera locations

Beyond the two time series techniques, comparative methodologies are a common way to

conduct quasi‐experimental studies. This normally involves a control group measure from

which to compare changes between groups. The study cannot randomly assign cameras to

street corners because the CCTV project is being implemented under considerable media and

political oversight; a problem not uncommon to previous attempts to evaluate CCTV (Honess &

Charman 1992). As a result, the cameras planned for the city will be implemented in areas of

the highest crime activity or along important commercial corridors. Furthermore, if cameras

were introduced to lower crime areas for the purposes of a social science experiment, the cost

of subsequently relocating the cameras to high crime areas would be prohibitive.

Therefore to compensate for this, we will identify near‐equivalent locations for the major

camera sites through ‘loose coupling’ multiple criteria decision‐making (Jankowski 1995) as a

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pre‐cursor to using propensity matching to identify comparison sites, due to propensity

matching’s ability ‘to correct for sample selection bias due to observable differences between

the treatment and comparison groups’ (Dehejia & Wahba 2002: 151). Given the constraints

that cameras are likely to be implemented in high crime areas, and that comparison areas have

to reside outside of the buffer areas for any cameras, we will aim to match with replication, a

process by which the nearest comparison unit is used, irrespective of whether it is also a

comparison unit for another camera. This has a number of advantages, including that it will

most likely improve the precision of the estimates, and reduces the likelihood of comparison

area order selection introducing selection bias into the process (Rosenbaum, 1995).

We will also improve the process by reducing the dimensionality of the comparison areas by

identifying major crime types and then using generalized socio‐economic demographics derived

from broad indices. This capacity to limit the number of criteria entering the matching

procedure will allow a number of variables to enter the decision‐making process without

permitting a single factor to over‐influence the selection process. Direct regression

measurements will then inform the outcome of this part of the study.

A spatial study examining potential displacement or diffusion

CCTV cameras may or may not have an impact in the target area; however, it is possible that

cameras may also have an impact on neighboring areas that are not directly under the

surveillance of a camera. The term displacement is used to indicate when cameras displace

crime activity out of their view and into nearby areas (Cornish & Clarke 1989, Green 1995,

Ratcliffe 2002, Weisburd & Green 1995). This can happen when offenders decide the risk of

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operating within the sight of a camera is too great, and move criminal activity to a nearby

location. Barr and Pease (1990: 278) considered displacement as both “a frustrating side effect”

and “a predictable effect of specific policies and...a manipulative tool of crime control”;

however others have argued that there are still net benefits to a crime prevention initiative

even if displacement occurs (Ratcliffe 2002, Clarke & Weisburd 1994). This is because the

amount of crime displaced rarely, if ever, exceeds or even equals the amount of crime reduced

at the target site (Hesseling 1994).

It is also possible that a diffusion of benefits can occur (Clarke & Weisburd 1994, Ratcliffe &

Makkai 2004, Weisburd et al. 2006). This happens when the beneficial effect of a crime

prevention initiative spills over into surrounding areas. While many crime prevention

practitioners assume that displacement is the most likely outcome, research suggests that

diffusion of benefits is a more likely outcome from a successful crime prevention initiative.

In this study, Bowers and Johnson’s (2003) weighted displacement quotient (WDQ) will be

employed to determine whether or not crime displacement or diffusion of benefits has taken

place. The determination of a WDQ first requires the researcher to determine three operational

areas; the target area where the crime reduction strategy has been deployed (in this case, CCTV

camera viewsheds), a buffer area that is estimated to be the most likely location that crime

would be displaced to, and a control area that acts as a check on general crime trends that are

affecting the region in general. The equation for the weighted displacement quotient is as

follows,

WDQ = (Bt1/Ct1 ‐ Bt0/Ct0) / (At1/Ct1 ‐ At0/Ct0) (1)

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where A is the count of crime events in the target area, B is the count of crime events in the

buffer area, C is the count of crime events in the control area, t1 is the time of the intervention,

and t0 is the pre‐intervention time period (Bowers & Johnson 2003). The examination of the

difference between the buffer and control areas from the pre‐intervention to the intervention

(or post‐intervention) period provides the measure of displacement or diffusion into the buffer

area, while the differences between the target and control area ratios at both times provides

the measure of success for the intervention (Johnson and Bowers 2003). The equation at (1) is

therefore comprised of both a Buffer Displacement Measure (Bt1/Ct1 ‐ Bt0/Ct0) and a Success

Measure (At1/Ct1 ‐ At0/Ct0). A positive Buffer Displacement Measure is indicative of potential

displacement, while a negative value indicates possible diffusion of benefits. A positive Success

Measure indicates that crime did not improve as a result of the intervention, while a negative

Success Measure suggests the police operation (or other form of intervention) was successful in

reducing crime.

This approach requires the designation of two areas around each camera site. The first area

is designated the target area ‐ the area where the cameras are expected to have a positive

effect. For pan, tilt and zoom (PTZ) cameras, we will work (as we did for the pilot study) with

Philadelphia Police Department (PPD) officers to map the individual viewsheds of the cameras

by panning and zooming the cameras and discussing active viewing areas with the officers.

We will then designate buffer areas around each camera. Cornish and Clarke (1987)

conceptualized displacement from the perspective of rational choice theory. From their

examination of the choices made by offenders in the decision to commit crimes and their

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choice of targets, they believed that displacement could best be explained and understood by

concentrating on the offender’s personal decisions and choices made in the commission of

crime. The decisions, opportunities, costs and benefits associated with particular offenses, in

Cornish and Clarke’s view, work to establish the confines of displacement within different

categories of offense. If this is the case, then it becomes less plausible to justify a generalized

approach to displacement. In other words, standard buffers that spread out from a CCTV

camera are not appropriate. Rather, the theoretical groundwork requires practitioners and

researchers to be open to the possibility that displacement may or may not occur with different

spatio‐temporal (and other displacement typology) characteristics for different crime types and

situations.

This raises the specter that displacement areas ‐ places or distances that offenders would

naturally be displaced to ‐ may not exist; with the corollary that the spatial context of the wider

environment surrounding a crime prevention operation must be considered. These areas are

likely to be places in the surrounding neighborhood of the cameras where crime activity could

potentially be displaced. The buffer area is also a zone where potential diffusion of benefits

could occur. This can happen when the cameras exert a benefit to surrounding areas beyond

their target area, and happens when offenders move out of the general area of the camera, or

offenders at unviewed areas curtail their activity because they think the camera can still see

them.

We will visit each site with PPD officers and discuss the spatial constraints of likely buffer

areas with patrol officers familiar with the location on the ground. This local contact‐local

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context approach enables us to incorporate into our target and buffer areas similar sets of

‘crime prone’ characteristics that attracted the need for a camera in the first place,

characteristics that would be likely targets for spatial displacement.

Finally, as a control on general trends in the surrounding areas beyond the target and buffer

area, we designate the surrounding police district(s) as the control area. The control area varies

for each camera.

A unique aspect of this application over every other attempt to examine the level of crime

around CCTV cameras is the customization of the camera viewshed area that we will conduct

for each camera. The identification of target and buffer areas that actually reflect camera

viewsheds and likely offender response is not an issue that can be left to generic measures such

as uniform buffers around each camera. Rather, we will examine individual camera viewsheds,

as was done in our pilot study in Philadelphia. Figure 1 shows a Temple University graduate

student working with a Philadelphia Police Department officer to map the exact contours of the

viewable area. From this, discussion with district officers enabled us to map unique areas, as

shown in Figure 2. In this second figure, the cameras locations are identified as white crosses,

while tight polygons show the limited viewshed of cameras along surrounding streets. The

viewshed is unique for each camera, aptly demonstrating the limitation of fixed buffers, an

approach commonly taken in the research literature.

Regular field observations of camera locations to confirm implementation

criteria

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Initial visits to camera locations are essential. As Pawson and Tilley pointed out, too few

evaluations of crime prevention schemes fail to understand the underlying context of the

environment of the evaluation (Pawson & Tilley 1994, Pawson & Tilley 1997). These initial visits

will be made with patrol officers from the PPD district in which the camera is situated. We will

combine the findings of discussions with the patrol officers and with camera operators at police

headquarters to determine the size and shape of both viewshed (target) and buffer areas.

Regular follow‐up visits are planned to every camera location every month along with

contact with District personnel. This is important in order to establish if the environment has

changed in any way; for example, have new stores opened, has the road been closed for

repairs, have flowering trees impeded the view of the camera? Some of this information can be

gathered from the contact with camera operators at Police Department Headquarters (see

Figure 1), however there are benefits to accessing local knowledge that are essential to getting

an improved understanding of the context of the ‘set space’ (to adapt a gang research term

from Tita et al. 2005) around each camera.

It is also valuable to maintain regular contact with District personnel to see if they have

noticed changes in offender behavior in the area, an expected adaptation given the (albeit

limited) research in this area (Mazerolle et al. 2002). Over time, this rich source of qualitative

information will inform the results of the quantitative study, and will directly feed into the

interpretation of the findings of the research.

Patrol officers will be selected by the PPD with the criteria of finding officers in each District

who are familiar with the area, have worked there for some time, and who are knowledgeable

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about both the crime patterns for the camera area, as well as the known offenders for each

site. Researchers will also attend the PPD Compstat meetings. These meetings are now closed

to the public and media because individual offenders and criminal intelligence matters are now

discussed at the meeting, however the research team has long had a strong relationship with

the leadership of the PPD and we have open access to this meeting.

Interviews with camera operators and key stakeholders

In furtherance of the site visits and contact with District personnel at the camera locations,

we will interview camera operators at the beginning and end of the project, as well as the

leadership of the Philadelphia Police Department and information technology managers for the

city. These will be in‐depth, semi‐structured interviews, structured around Greenhalgh et al.’s

(2004) conceptual model for innovation diffusion and will identify the relevant roles of key

factors at the intrapersonal, agency and contextual levels. These key person interviews will

generate suggestions about potential cultural, structural, and incentive changes that may

facilitate better integration of CCTV technology into the operational planning of the city. Most

importantly, they will provide a rich, qualitative picture of the use of cameras across the city, a

picture that will inform and influence our interpretation of the quantitative findings.

Topics for discussion will emphasize the relationship and levels of contact with local police,

an important finding from British qualitative research into CCTV implementation (Goold, 2004),

and operating conditions regarding numbers of cameras viewed at any one time and the time

spent on each screen – again, an issue identified in the research literature (Fyfe & Bannister

1996).

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Evaluation summary 

As with most CCTV implementations, the Philadelphia experiment is unable to take

advantage of a randomized control experiment due to the constraints of politics and cost. There

are political imperatives in that the study cannot randomly assign cameras to trial locations that

are not in the worst crime hotspots because the CCTV project is being implemented under

considerable media and political oversight: this is an important project for the city.

Furthermore, if cameras were introduced to lower crime areas for the purposes of a social

science experiment, the cost of subsequently relocating the cameras to high crime areas would

be prohibitive and not a cost that can be incorporated into this grant.

However, the multi‐method design that we have proposed addresses all of the

requirements of a strong quasi‐experimental design and would rank highly on the Maryland

Scale of Evaluation Research (Farrington 2002, Sherman et al. 1998) and specifically address

issues of selection bias in the camera locations. The use of ARIMA interrupted time series

analysis and HLM (employed here as a regression discontinuity tool) both address issues of

temporal trend relationships that are not reliably measured by normal regression methods. The

techniques we will employ are both statistically robust and will result in meaningful and reliable

findings that can be converted into measurable crime reduction outcomes that practitioners

and crime prevention professionals considering CCTV will be able to understand.

Furthermore, the identification through multi‐criteria propensity matching of comparable

street corners will allow a direct comparison with quasi‐randomized control locations, and the

introduction of Bowers and Johnson’s Weighted Displacement Quotient as the mechanism to

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evaluate displacement or diffusion will address a particularly common question directly and

with methodological rigor, based on customized spatial parameters at each camera location.

Finally, the scale of the implementation across Philadelphia – the sixth largest city in the

country – provides sufficient levels of criminal activity and locations to establish reliable

measures that are statistically valid.

Implications for criminal justice policy and practice 

We will be able to explain the benefit (or not) of CCTV cameras on property crime, violent

crime, and disorder activity. We will also be able to comment on the setting and operability

conditions that favor greatest CCTV benefit as well as address implementation issues. The

qualitative information will provide a rich vein of contextual intelligence that, when analyzed,

will provide a more rounded picture of the pros and cons of CCTV implementation and flesh out

the story underlying the quantitative evaluation that forms the core of this application.

By addressing property crime and disorder activity along with violent crime, we will be able

to increase the value of the analysis to cities and towns that do not necessarily have the violent

crime issues that plague the larger cities in the country. In other words, while we benefit from

evaluating this technology in a large and diverse city, the findings will be of benefit to the whole

country.

The financial benefits to cities and towns of a more effective implementation of CCTV are

potentially staggering, though difficult to estimate. This research will provide the first concrete

measure of crime reductions both in the immediate camera viewshed and in the surrounding

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area for the US. This information will provide crucial inputs to cost‐benefit analyses conducted

to answer whether CCTVs benefits outweigh the costs. The research will also be able to confirm

or deny the common ‘myth of displacement’ (Ratcliffe 2002), and clearly articulate the

implications of camera implementation for not just camera areas (i.e., target areas), but also

adjacent areas (i.e., buffer areas) and even the surrounding neighborhoods.

Management plan and organization 

This grant is designed to commence in January 2010. The current implementation strategy

of the CCTV cameras in Philadelphia, PA, is due to complete installation of 220 cameras well in

advance of that time (please see letter from Philadelphia Police Department), however even if

there is a delay for a few months, the management plan and timeline are able to cope with a

delay. The evaluation methodology is designed to be robust enough and to a level of

sophistication that it can still function when cameras come online at different times.

Four staff will be employed on this project; Dr Ratcliffe, Dr Groff and two graduate research

assistants from the Department of Criminal Justice at Temple University. Ratcliffe and Groff will

lead the project and share administrative and graduate student supervisory responsibilities.

This builds some redundancy into the management of the project, and also maximizes the

likelihood that the project will be completed on time – no phase is dependent on a single

person.

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Early tasks involve re‐establishing contact and confirming data arrangements with the

Philadelphia Police Department, and identifying and interviewing the liaison officers we will

work with in all of the police districts where cameras function (up to 25 police districts).

Fieldwork commences early, in the second month of the project, where we visit and map

CCTV camera locations, and work at police headquarters and the PPD camera operators to

identify viewsheds for cameras. Furthermore, we will liaise with the district officers to confirm

or correct the areas chosen to be both target and buffer areas.

Workload will be distributed between Ratcliffe and Groff, each accompanied by a graduate

student. This spreads the workload, but it also enables the graduate students to be intimately

involved with every aspect of the project. The Department of Criminal Justice at Temple

University is regularly ranked in the top 10 of Criminology and Criminal Justice graduate

programs, and integration of our graduate students into all of our projects is one way that we

mentor future researchers.

Quantitative work commences with pre‐implementation data gathering and processing

prior to the building of the initial ARIMA time series models of the pre‐intervention time series.

We will also work with SPSS and ArcGIS to conduct multi‐criteria decision making and

propensity matching for comparison sites. Much of this work is scheduled for the summer of

2010, when both Ratcliffe and Groff can dedicated their full time activity to these tasks. At the

same time, we will continue regularly monthly visits to camera sites (predominantly the work of

the graduate students). For safety reasons, researchers will conduct camera site visits of buffer

and target zones in pairs.

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After completion of the required end of year report, we will complete data gathering for at

least one year of post‐implementation at every site. These data will feed into the complete

ARIMA interrupted time series analysis and we will be able to use comparison analysis with all

of the matching sites identified from the propensity matching. With the completion of the

Weighted Displacement Quotient analysis we will be in a position to compile the initial

evaluation results and report these back to the Philadelphia Police Department and to the city

Deputy Mayor for Public Safety by the summer of 2011. We hope that early results will be

available by the time of the IACP annual conference, and the NIJ technology day that precedes

the conference.

With the initial quantitative findings, we will interview District liaison officers regarding the

findings and also interview camera operators regarding the outcomes identified. The final

report will be a comprehensive mixed model evaluation (quantitative/qualitative).

Dissemination strategy 

The target audience for the findings of this research is crime prevention practitioners, city

managers and police officers looking to CCTV to solve their crime problems. As a result, the

traditional model of disseminating academic research is largely unhelpful in getting these

findings disseminated to the target audience.

However, academic research (both at conferences and in peer‐reviewed journals) does

provide an opportunity to establish the legitimacy for the research that can then be used to

enhance the decisions that city managers make. As a result, we will initially seek to disseminate

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the findings on at least one peer‐reviewed academic journal article, and at the annual meeting

of the American Society of Criminology.

With the reassurance that this academic ‘check‐up’ provides, we will then move to a second

dissemination phase that will include publications targeted to the audience we seek. For

example, we anticipate being able to submit articles to Law Enforcement Technology magazine,

Police Chief magazine, and to attend conferences that are targeted more towards our audience.

In this case we anticipate presenting the findings at the Academy of Criminal Justice Sciences

conference, and at the National Institute of Justice technology day that precedes the

International Association of Chiefs of Police conference.

The nation’s city managers are clearly a key specific audience, and we will seek two avenues

to spread this research. First we aim to publish an article regarding this research in the

Leadership Bulletin of the National Executive Institute Associates, a publication that is copied to

graduates of the FBI National Academy and members of both the Major Cities Chiefs’

Association and the Major County Sheriffs’ Association. Secondly, we will seek to present the

findings of our research at the annual conference meeting of the International City/County

Management Association, a national body based in Washington DC with a large membership.

Their annual meeting for 2011 will be in Wisconsin, and a budget item for this is included.

Finally, we will establish a web site housed at the Department of Criminal Justice web site at

Temple University that details the research and contains links to the final report and other

publications. This will be a resource for practitioners at towns and cities that are starting the

process with a web search. The aim of this is to bring the research and the results to the notice

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of people who use web searches as their predominant research tool. The link from the

Department of Criminal Justice will ensure that within a few weeks, web search bots will find

and catalogue the pages.

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Figures 

Figure 1. Temple University graduate student Travis Taniguchi (left) works with a detective

from the Philadelphia Police Department to establish individual viewpoint parameters for each

PTZ camera in the city during the pilot project. Each camera view must be examined to identify

obstructions, viewsheds, realistic distance estimates and camera operability constraints.

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Figure 2. Target and Buffer areas around three CCTV cameras near Temple University

campus, Philadelphia PA. The white cross shows the camera location, while the immediate

irregular shaped cross centered on the camera shows that each camera has a distinct and

unique viewshed area. The larger heavy line indicates the estimated buffer area, again unique

in shape and size to each camera site.

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Ratcliffe, J.H. (2006) 'Video surveillance of public places' (Washington DC: Center for Problem

Oriented Policing).

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Ratcliffe, J.H. (2008) Intelligence‐Led Policing (Cullompton, Devon: Willan Publishing).

Ratcliffe, J.H. and Makkai, T. (2004) 'Diffusion of benefits: Evaluating a policing operation',

Trends and Issues in Crime and Criminal Justice, 278, pp. 1‐6.

Ratcliffe, J.H., Taniguchi, T. & Taylor, R.B. (in press) ‘The crime reduction effects of public CCTV

cameras: A multi‐method spatial approach’, Justice Quarterly.

Rosenbaum, P. (1985) Observational Studies (New York: Springer Verlag).

Ross, H.L. (1982) 'Interrupted time series studies of deterrence of drinking and driving', in J.

Hagan (Ed) Deterrence Reconsidered (Thousand Oaks, CA: Sage) pp. 71‐97.

Sarno, C., M. Hough, and M. Bulos (1999). Developing a Picture of CCTV in Southwark Town

Centres: Final Report. London: Criminal Policy Research Unit, South Bank University.

Sherman, L.W., Gottfredson, D., MacKenzie, D., Eck, J., Reuter, P. and Bushway, S. (1998)

'Preventing Crime: What works, what doesn't, what's promising' (Washington DC:

National Institute of Justice).

Sivarajasingam, V., J. P. Shepherd, and K. Matthews (2003). “Effect of Urban Closed Circuit

Television on Assault Injury and Violence Detection.” Injury Prevention 9(4):312‐316.

Squires, P. (2003). An Independent Evaluation of the Installation of CCTV Cameras for Crime

Prevention in the Whitehawk Estate, Brighton. Brighton (United Kingdom): Health and

Social Policy Research Centre.

Tita, G.E., Cohen, J. and Engberg, J. (2005) 'An ecological study of the location of gang “Set

Space”', Social Problems, 52:2, pp. 272‐299.

Weisburd, D. and Green, L. (1995) 'Measuring immediate spatial displacement: methodological

issues and problems', in J.E. Eck and D. Weisburd (Eds) Crime and Place (Monsey, NY:

Criminal Justice Press) pp. 349‐361.

Weisburd, D., Wyckoff, L.A., Ready, J., Eck, J., Hinkle, J.C. and Gajewski, F. (2006) 'Does crime

just move around the corner? A controlled study of spatial diffusion and diffusion of

crime control benefits', Criminology, 44:3, pp. 549‐591.

Yaffee, R.A. and McGee, M. (2000) Introduction to Time Series Analysis and Forecasting (San

Diego, CA: Academic Press).

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Other application requirements 

Data archiving strategy

We intend to utilize an archiving strategy that both enables replication of the study at some

future point, while retaining the vital requirement of masking and not retaining or recording

the individual details of crime victims or survey respondents. An additional requirement is ease

of data retrieval. We therefore seek a strategy that combines ease of retrieval with data storage

or aggregate crime data and de‐identified survey and interview responses.

We will archive the ArcGIS (ESRI) shapefiles for each camera target area, buffer area and

control area in the standard Pennsylvania south projected coordinate system. We will then

retain an Excel spreadsheet with individual tabs identifying camera areas and a standard flat‐

file row/column database with monthly counts of crime frequency for the various crime types

examined in this study (violent, property, disorder, all crime). Crime counts will be aggregated

by count across all camera sites from 2004 to the end of the project. A simple numeric identifier

will link crime frequency counts to shapefiles. This will enable external oversight and

replication, while still retaining the project aims of simple retrieval.

Interview data will be retained as contemporaneous notes, redacted to remove any

identifying information or personal identifiers. The only linking information will be an

interviewee identifier that is in both final report and in the archived interview notes.

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List of key personnel

Dr Jerry H. Ratcliffe

Professor, Department of Criminal Justice, Temple University, Philadelphia PA.

Dr Elizabeth Groff

Assistant Professor, Department of Criminal Justice, Temple University, Philadelphia, PA.

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Résumés of key personnel

Jerry Ratcliffe

Dr Jerry Ratcliffe is Professor of Criminal Justice with the Department of Criminal Justice,

Temple University, Philadelphia. In a previous life he was a police officer with the Metropolitan

Police in London (UK) where he served for a number of years on patrol duties, in an intelligence

and information unit, and as a member of the Diplomatic Protection Group. Due to a severe

winter mountaineering accident while ice climbing in the Scottish Highlands, he left the police

after 11 years of service. He completed a B.Sc. with honors in Geography and GIS at the

University of Nottingham (UK) and has a Ph.D. from the same institution.

As a lecturer in policing (intelligence) with Charles Sturt University based at the New South

Wales Police College in Australia, he ran graduate programs in criminal intelligence, and for a

number of years coordinated Australia's National Strategic Intelligence Course. Dr Ratcliffe was

also a senior research analyst with the Australian Institute of Criminology, where he conducted

one of the first evaluations of an intelligence‐led policing operation. In 2007, Dr Ratcliffe was

awarded the Professional Service Award for outstanding contributions to criminal intelligence

analysis by the International Association of Law Enforcement Intelligence Analysts (IALEIA).

He has published over 50 research articles and four books including: 'Strategic Thinking in

Criminal Intelligence' (Federation Press, 2004, second edition 2009); 'GIS and Crime Mapping'

(Wiley, 2005) and 'Policing Illegal Drug Markets' (Criminal Justice Press, 2005). His most recent

book, ‘Intelligence‐Led Policing' was published by Willan Publishing in March 2008. Recent

research articles have examined shooting patterns in Philadelphia, street corner drug dealing in

Camden, NJ, and drug gang investigations by the New Jersey State Police.

Of particular relevance to this research project, is that Professor Ratcliffe is author of the (COPS

Office) Center for Problem‐Oriented Policing’s response guide, Video Surveillance of Public

Spaces. This report combined a meta‐analysis of the available literature on CCTV with a

practitioner’s guide. The report‐writing process identified a lack of quality CCTV evaluations,

that this proposed project is an attempt to rectify.

A full resume with publications in available on request (but not included in this application as

with publications and conference presentations it runs to more than 20 pages), or online at

www.jratcliffe.net.

Elizabeth Groff

Dr Elizabeth Groff joined the Temple University Department of Criminal Justice in August 2007.

Prior to joining the Criminal Justice Department, Dr. Groff was from 2002 to 2007 a Senior

Research Associate at the Institute for Law and Justice (ILJ). From 1998‐2002 she was a Social

Science Analyst at the National Institute of Justice’s Crime Mapping Research Center. She has

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also been the GIS Coordinator for the Charlotte‐Mecklenburg Police Department in Charlotte,

NC. Dr. Groff received her B.S. and M.A. degrees in geography from the University of North

Carolina at Charlotte and Ph.D. in geography from the University of Maryland at College Park.

She also has an M.A. in criminology and criminal justice from the University of Maryland at

College Park.

Dr. Groff’s primary research interests are in crime and place; modeling geographical influences

on human activity; agent‐based modeling as a methodology; crime prevention; and policing.

Her current research projects include: (1) a micro level longitudinal study of crime in Seattle,

Washington with Dr. David Weisburd and Dr. Sue‐Ming Yang; (2) exploring the use of simulation

models for understanding street robbery and testing theory; (3) examining the impacts of

technology acquisitions in law enforcement agencies with Dr. Tom McEwen; and (4) a micro

level longitudinal study of the place characteristics related to where juveniles commit crime in

Seattle, Washington with Dr. David Weisburd and Dr. Nancy Morris.

Journal Publications:

Groff, E. R. (2008). Adding the Temporal and Spatial Aspects of Routine Activities: A Further

Test of Routine Activity Theory. Security Journal, 21(1‐2): 95‐116.

Groff, E. R. 2007. 'Situating' Simulation to Model Human Spatio‐Temporal Interactions: An

Example Using Crime Events. Transactions in GIS, 11(4):507‐530.

Groff, E. R. 2007. "Simulation for Theory Testing and Experimentation: An Example Using

Routine Activity Theory and Street Robbery." Journal of Quantitative Criminology 23:75‐

103.

Groff, E. R. and T. McEwen. 2007. "Integrating Distance Into Mobility Triangle Typologies."

Social Science Computer Review 25:210‐238.

Groff, E.R., B. Kearley, P. Beatty, H. Fogg, H. Couture, and J. Wartell. 2005. "A Randomized

Experimental Study of Sharing Crime Data with Citizens: Do Maps Produce More Fear?"

Journal of Experimental Criminology 1:87‐115.

Beckman, K., L. Wyckoff, E.R. Groff, and P. Beatty. 2004. “Trends in Police Research: A Cross‐

sectional Analysis of the 2001 Literature,” Police Practice and Research 5 (2): 165‐189,

2004.

Groff, E.R., and N.G. La Vigne. 2001. “Mapping an Opportunity Surface of Residential Burglary,”

Journal of Research in Crime and Delinquency 38 (3):257‐278.

Book Chapters:

Groff, E. R., D. Weisburd and N. Morris (Forthcoming 2008). Where the Action is at Places:

Examining Spatio‐Temporal Patterns of Juvenile Crime at Places Using Trajectory

Analysis and GIS. In D. Weisburd, W. Bernasco and G. Bruinsma (eds), Putting Crime in

its Place: Units of Analysis in Spatial Crime Research. Springer‐Verlag.

Groff, E. R. 2008. Characterizing the Spatio‐Temporal Aspects of Routine Activities and the

Geographic Distribution of Street Robbery. In L. Liu & J. Eck (Eds.), Artificial Crime

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Analysis Systems: Using Computer Simulations and Geographic Information Systems.

Idea Group: Hershey, PA.

Groff, E.R., and T. McEwen. 2005. “Disaggregating the Journey to Homicide.” In F. Wang (ed.),

Geographic Information Systems and Crime Analysis. Idea Group: Hershey, PA.

Groff, E.R., and N.G. LaVigne. 2002. “Forecasting the Future of Predictive Crime Mapping.” In N.

Tilley (ed.), Analysis for Crime Prevention (Vol. 13, pp. 29‐58). Criminal Justice Press:

Monsey, NY.

Groff, E.R., and N.G. La Vigne. 2001. “Evolution of Crime Mapping in the United States: From

the Descriptive to the Analytic.” In A. Hirschfield and K. Bowers (eds), Mapping and

Analysing Crime Data: Lessons From Research And Practice (pp. 203‐221). Taylor and

Francis: New York.

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List of previous and current NIJ awards

Principal investigator, Ratcliffe, JH, “A stand‐alone application to detect the near repeat

phenomenon in local and regional crime data”, National Institute of Justice 2006‐07, grant

number 2006‐IJ‐CX‐K006, $95,350.

Co‐Principal investigator, Rengert, GF and Ratcliffe, JH, “Geographic Tools to Improve Officer

Safety in Correctional Institutions”. National Institute of Justice grant 2002‐05, grant number

2002‐MU‐MU‐0016, $397,394.

Co‐Principal Investigator, Weisburd, DL, Groff, ER and Jones, G, "Smart Police Deployment:

Evaluating the Use of Automated Vehicle Locator Technologies in Policing." National Institute of

Justice grant 2007‐2009, grant number 2007‐IJ‐CX‐K153, $235,421.

Principal Investigator, Groff, ER, “Modeling the Dynamics of Street Robbery to Inform Policy and

Prevention.” National Institute of Justice grant 2005‐2008, grant number 2005‐IJ‐CX‐0015,

$141,163.

Co‐Principal Investigator, Weisburd, DL, Groff, ER, "Explaining Developmental Crime

Trajectories at Places: A Study of ‘Crime Waves’ and ‘Crime Drops’ at Micro Units of

Geography." National Institute of Justice grant 2005 – 2008, grant number 2005‐IJ‐CX‐0006,

$271,882.

Co‐Principal Investigator, McEwen, T and Groff, ER, “Distance to Crime: Analyzing and Mapping

Violent Crime.” National Institute of Justice grant 2003 – 2006, grant number 2003‐IJ‐CX‐0150,

$100,487.

Letter of cooperation/support from the Philadelphia Police Department

See next two pages

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CLARIFICATION ON GRADUATE STUDENT TUITION

In the original application, we budgeted for $43,176 of graduate student tuition to support our 

graduate research assistants. Since that time, Temple University has clarified the tuition rates for 

the forthcoming year. In light of that, our revised calculation is as follows:

Graduate student tuition per credit hour is set at $590 for in‐state students and $861 for out of 

state (see College of Liberal Arts figures at 

http://www.temple.edu/bursar/about/tuitionrates.htm). We budgeted on the lower rate.

Temple graduate classes are three credit hours per class, therefore each class is 3 x 590 = 1770.
 

We support them up to three classes per semester, therefore each semester 3 x 1770 = 5310.

There are two semesters per year = 2 x 5310 = 10,620 per year per student. 

There are two students allocated to the project therefore year 1 cost is 2 x 10,620 = 21,240.
 

We are unable to predict the likely increase in tuition given the current financial climate; however a 

conservative estimate of a 3% rise in tuition costs for the second year of the project would increase 

the year two tuition from 21, 240 x 1.03 = 21,877.

Adding year one and the year two projection results in 21,240 + 21,877 = 43,117.