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Analyzing the Influence of Micro-Level Factors on CCTV Camera Effect

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Abstract

Objectives

Despite the popularity of closed circuit television (CCTV), evidence of its crime prevention capabilities is inconclusive. Research has largely reported CCTV effect as “mixed” without explaining this variance. The current study contributes to the literature by testing the influence of several micro-level factors on changes in crime levels within CCTV areas of Newark, NJ.

Methods

Viewsheds, denoting the line-of-sight of CCTV cameras, were units of analysis (N = 117). Location quotients, controlling for viewshed size and control-area crime incidence, measured changes in the levels of six crime categories, from the pre-installation period to the post-installation period. Ordinary least squares regression models tested the influence of specific micro-level factors—environmental features, camera line-of-sight, enforcement activity, and camera design—on each crime category.

Results

First, the influence of environmental features differed across crime categories, with specific environs being related to the reduction of certain crimes and the increase of others. Second, CCTV-generated enforcement was related to the reduction of overall crime, violent crime and theft-from-auto. Third, obstructions to CCTV line-of-sight caused by immovable objects were related to increased levels of auto theft and decreased levels of violent crime, theft from auto and robbery.

Conclusions

The findings suggest that CCTV operations should be designed in a manner that heightens their deterrent effect. Specifically, police should account for the presence of crime generators/attractors and ground-level obstructions when selecting camera sites, and design the operational strategy in a manner that generates maximum levels of enforcement.

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Notes

  1. Under the deterrence doctrine, CCTV is most likely to prevent crime when an offender believes cameras may be monitoring their activity and perceives this attention to put them at increased risk of apprehension. This has implications regarding the units of analysis utilized in CCTV research. As articulated by Ratcliffe et al. (2009), “the difficulty with offender perceptions is that they are not measurable without extensive and expensive interviewing. Furthermore, the resultant offender perception will most likely vary from person to person. In other words, while the range of a CCTV camera—as perceived by a criminal—is in the eye of the beholder, finding and interviewing suitable beholders is beyond the budget of most studies, and the results are likely to be quite variable” (751). However, while Ratcliffe et al. presented viewsheds as an alternative to the “offender perception” approach, the concepts certainly overlap. Deterrence can only be realistically expected where a potential offender’s conception of “space” and a CCTV camera’s line-of-sight coincide. In this sense, a camera’s surrounding environment comprises a “spatial node” cogitatively identified by pedestrians as a singular “place” (Lynch 1960). It is within such an area an offender would most likely perceive a heightened level of risk. Given the limited visual extent of cameras, the area immediately visible to CCTV is probably the geography in which offender perception of camera presence is at its peak. Furthermore, viewsheds likely more accurately reflect street-level perceptions of potential offenders than the types of units of analysis traditionally utilized in CCTV research (e.g. “neighborhoods” and “buffers”).

  2. When manually controlled by a user, each camera has the ability to see further than what is visible in panning mode. However, the panning mode was digitized as the viewshed for two reasons. One, given the large camera to operator ratio all of the cameras are in “panning mode” more often than they are actively controlled by an operator. Secondly, constructing the viewshed based on a camera’s possible view would lead to areas significant distances away from the camera being designated as part of the viewshed. For example, Newark officials demonstrated to us that a camera on top of an office building was able to view airline logos on airplanes parked at Newark Liberty International Airport over a mile away. Creating viewsheds based on this capacity would lead to an over-estimation of CCTV coverage, similar to the problem encountered when aggregate geographies serve as units of analysis.

  3. A small level of disagreement occurred regarding the visible extent of a handful of rooftop cameras. In these instances, the commander believed the viewsheds extended over too large an area, leading the researchers to redraw the viewsheds to a more concise area. However, all of the rooftop viewsheds were part of a group of cameras that were excluded from the analysis (which will be discussed later on), rendering this small disagreement between the researchers and commander moot.

  4. We originally considered excluding these areas from the final viewsheds. However, we decided against this approach in recognition of the imprecise nature of crime location data. For example, at one camera site the northwest corner of an intersection was obstructed from view by a building awning. A manual review of the crime reports for 10 incidents occurring at this intersection found that officers did not denote the precise corner where the offense took place in a single instance. This prevented us from identifying whether the incidents occurred on the obstructed corner, or one that was visible to CCTV. Since police officers commonly record incident locations as intersections rather than specific addresses (Braga et al. 2011: 15) we decided against excluding the obstructed areas from the viewsheds.

  5. The Newark Police Department recorded the installation date of 11 cameras as 6/8/2007, coinciding with the official formation of the Video Surveillance unit. However, according to those directly involved with the camera deployment, installation of these cameras occurred during a “test phase” spanning several months in 2006 with intermittent monitoring of the cameras beginning as early as February 2007. Two additional cameras were unable to transmit footage to the control room for over a year after their installation, likewise leading to their exclusion.

  6. While some cameras were in place for longer than 1 year, having uniform pre/post periods allows for a more accurate comparison of sites. Previous research has found that place-based police interventions sometimes exhibit “deterrence decay” after their initial deployment (Sherman 1990), including CCTV (Mazerolle et al. 2002). Restricting the study period to 1-year ensures that camera effectiveness is measured in a uniform way, by testing the initial impact of each camera while excluding any existing deterrence decay effects.

  7. While being captured in police reports as an indoor crime, burglary certainly is significantly related to outdoor activities of offenders, who must first breach the outer structure of a property in order to get indoors. However, we decided against including burglary in the analysis due to potential inaccuracies in Newark’s 2007 Burglary GIS files. In 2008, Newark Police officials conducted an audit of all burglary and indoor theft incidents occurring in 2006 and 2007 to ensure the proper classification of each incident. With the introduction of an electronic Records Management system, it was discovered that the “upgrading” or “downgrading” following the initial investigation of property crime oftentimes was not reflected in the crime statistics. While the crime reports and UCR statistics were corrected to reflect the results of the audit, Newark officials were unclear whether the 2007 GIS files were updated.

  8. While Caplan et al. (2011) calculated LQs based on city-wide crime and geography figures, we chose to utilize each viewshed’s surrounding precinct as its control area. This is in recognition of the fact that local crime levels are highly contingent upon localized factors, including the influence of localized police practices, which may vary across precincts. By comparing viewsheds with encompassing precincts, we ensure that the control areas were susceptible to the same organizational forces that affected the viewsheds (Ratcliffe et al. 2009: 752–753).

  9. Hypothetically, CCTV cameras may displace crime by a distance greater than the reach of the catchment zone. In such cases, the “displaced” crime incidents would get calculated in the LQ statistics. However, empirical research has demonstrated that the likelihood of spatial displacement decreases as the distance from the target area increases (Bowers and Johnson 2003; Eck 1993). Therefore, a distance of only a block or two is generally accepted as an appropriate boundary for a test of displacement (Weisburd and Green 1995). Our catchment zones adhere to this principle while also following the approach of previous CCTV studies (Caplan et al. 2011; Ratcliffe et al. 2009). Therefore, we consider the risk of displaced crimes occurring outside of the catchment zone to be commensurate with that of previous crime-and-place studies.

  10. Researchers measured the area of building footprints falling within each catchment zone by utilizing the “clip” function of ArcToolbox and then calculating the total square footage of the resulting shapefile.

  11. While some of the above features can be categorized together based on certain similarities, disaggregating these micro-features minimizes potential threats to content validity that can surface through considering different areas as if they were the same (Stucky and Ottensmann 2009). For example, since bars and liquor stores have different hours of operation in Newark (liquor stores close at 10:00 p.m. while bars may remain open well after 2:00 am) they likely have differing influence on crime occurrence despite both being classified as “liquor establishments.”

  12. Bars, liquor stores, schools, transit stops, “at-risk” housing, and parking lots.

  13. Take-out eateries, corner stores, and general retail shops. Since the Newark Police Department was not in possession of this data, we were unable to cross validate the data to test their validity.

  14. Bars, liquor stores, schools, general retail shops, corner stores, take out eateries and fast food restaurants, and public transit stops.

  15. The CompStat unit informed us that they received this information from the City of Newark’s Office of Housing Assistance.

  16. The final study area excluded the portion of Newark comprised of Newark Liberty Airport and the (shipping) Port of Newark, which are outside of the Newark Police Department’s jurisdiction. Outside of the airport and port, the area is almost entirely comprised of highways and vacant land, with activity primarily taking the form of long-distance motor vehicle traffic with little-to-no pedestrian activity. Due to these reasons, coupled with the fact that no CCTV cameras were installed in this area, this area was excluded from the final study area.

  17. See Piza (2012: 80–85) for a more in-depth demonstration of the LQ calculation for the environmental features.

  18. While total “enforcement actions” are measured in respect to the CCTV operation, observations of unrelated police activity are restricted to arrests. This is due to city-wide enforcement data being unavailable for all other enforcement actions (e.g. “summonses”) prior to 2009.

  19. Given the similarities between Caplan et al. (2011) and the current study, a comparison of their respective ΔLQ distributions appears in the “Appendix” section.

  20. A “ladder of powers” (Tukey 1977) function performed in STATA 12.0 identified the square transformation as the only procedure to approximate a normal distribution.

  21. Due to space constraints, results of the OLS diagnostic tests are not presented in text, but are available from the lead author upon request.

  22. While Caplan et al. (2011) measured displacement on an aggregate level they did not calculate displacement measures for the individual viewsheds. Therefore, we are unable to compare the displacement findings with the earlier work of Caplan et al. (2011).

References

  • Armitage R (2002) To CCTV or not? A review of current research into the effectiveness of CCTV systems in reducing crime. National Association for the Care and Resettlement of Offenders, London

    Google Scholar 

  • Babyak M (2004) What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 66:411–421

    Google Scholar 

  • Bernasco W, Block R (2011) Robberies in Chicago: a block-level analysis of the influence of crime generators, crime attractors, and offender anchor points. J Res Crime Delinq 48(1):33–57

    Article  Google Scholar 

  • Block R, Block C (1999) The Bronx and Chicago: street robbery in the environs of rapid transit stations. In: Goldsmith V, McGuire PG, Mollenkopf JH, Ross TA (eds) Analyzing crime patterns: frontiers of practice. Sage, Thousand Oaks, CA

    Google Scholar 

  • Bowers K, Johnson J (2003) Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity. J Quant Criminol 19(3):275–301

    Article  Google Scholar 

  • Braga A, Weisburd D, Waring E, Mazerolle L, Spelman W, Gajewski F (1999) Problem-oriented policing in violent crime places: a randomized controlled experiment. Criminology 37(3):541–580

    Article  Google Scholar 

  • Braga A, Hureau D, Papachristos A (2011) The relevance of micro places to citywide robbery trends: a longitudinal analysis of robbery incidents at street corners and block faces in Boston. J Res Crime Delinq 48(1):7–32

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1981) Environmental criminology. Sage, Beverly Hills, CA

  • Brantingham PL, Brantingham PJ (1993) Nodes, paths and edges: consideration on the complexity of crime and the physical environment. J Environ Psychol 13:3–28

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1995) Criminality of place: crime generators and crime attractors. Eur J Crim Policy Res 3(3):1–26

    Google Scholar 

  • Brantingham PL, Brantingham PJ (1998) Mapping crime for analytic purposes: location quotients, counts and rates. In: Weisburd D, McEwen T (eds) Crime mapping and crime prevention. Crime prevention studies, vol 8, pp 263–288

  • Britt C, Weisburd D (2010) Statistical power. In: Piquero A, Weisburd D (eds) Handbook of quantitative criminology. Springer, New York, NY

    Google Scholar 

  • Brown B (1995) CCTV in town centres: three case studies. Crime detection and prevention series, paper 68. Home Office, London

  • Cameron A, Kolodinski E, May H, Williams N (2008) Measuring the effects of video surveillance on crime in Los Angeles. Report prepared for the California Research Bureau. USC School of Policy, Planning, and Development

  • Caplan J (2011) Mapping the spatial influence of crime correlates: a comparison of operationalization schemes and implications for crime analysis and criminal justice practice. Cityscape 13(3):57–83

    Google Scholar 

  • Caplan J, Kennedy L, Petrossian G (2011) Police-monitored cameras in Newark, NJ: a quasi-experimental test of crime deterrence. J Exp Criminol 7(3):255–274

    Article  Google Scholar 

  • Chainey S (2000) Optimizing closed-circuit television use. In: La Vigne N, Wartell J (eds) Crime mapping case studies: successes in the field, vol 2. Police Executive Research Forum, Washington, DC

    Google Scholar 

  • Chakravart I, Laha R, Roy J (1967) Handbook of methods of applied statistics, vol 1. Wiley, Hoboken, NJ

    Google Scholar 

  • Clarke R, Weisburd D (1994) Diffusion of crime control benefits. In: Clarke R (ed) Crime prevention studies, vol 2. Criminal Justice Press, Monsey, NY, pp 165–183

    Google Scholar 

  • Cook T, Campbell D (1979) Quasi-experimentation: design and analysis issues for field settings. Rand McNally, Chicago, IL

    Google Scholar 

  • De Souza E, Miller J (2012) Homicide in the Brazilian favela: does opportunity make the killer? Br J Criminol 52:786–807

    Article  Google Scholar 

  • Ditton J, Short E (1999) Yes it works. No it doesn’t: comparing the effects of open-street CCTV in two adjacent Scottish town centres. In: Tilley N, Painter K (eds) Surveillance of public space: CCTV, street lighting and crime prevention. Crime prevention studies, vol 10. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Eck J (1993) The threat of crime displacement. Crim Justice Abstr 253:527–546

    Google Scholar 

  • Eck J (1994) Drug markets and drug places: a case-controlled study of spatial structure of illicit dealing. Unpublished Ph.D. Dissertation, University of Maryland, College Park

  • Eck J (2002) Preventing crime at places. In: Sherman L, Farrington D, Welsh B, Mackenzie D (eds) Evidence-based crime prevention. Routledge, New York, NY, pp 241–294

    Google Scholar 

  • Eck J, Weisburd D (eds) (1995) Crime and place. Crime prevention studies, vol 4. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Farrington D, Gill M, Waples S, Argomaniz J (2007) The effects of closed-circuit television on crime: meta-analysis of an English national quasi-experimental multi-site evaluation. J Exp Criminol 3:21–28

    Article  Google Scholar 

  • Faul F, Erdfelder E, Buchner A, Lang A (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41(4):1149–1160

    Article  Google Scholar 

  • Felson M (1995) Those who discourage crime. In: Eck J, Weisburd D (eds) Crime and place: crime prevention studies, vol 4. Police Executive Research Forum, Washington, DC

    Google Scholar 

  • Felson M (2002) Crime and everyday life, 3rd edn. Sage, Thousand Oaks, CA

    Google Scholar 

  • Freundschuh S, Egenhofer M (1997) Human conceptions of spaces: implications for GIS. Trans GIS 2(4):361–375

    Article  Google Scholar 

  • Gill M, Spriggs A (2005) Assessing the impact of CCTV. Home Office Research Study No. 292, London

  • Gill M, Turbin V (1998) CCTV and shop theft: towards a realistic evaluation. In: Norris C, Moran J, Armstrong G (eds) Surveillance, closed circuit television, and social control. Ashgate, Brookfield, VT

  • Gill M, Spriggs A, Allen J, Hemming M, Jessiman P, Kara D (2005) Control room operation: findings form control room observations. Home Office, London

    Google Scholar 

  • Gill M, Rose A, Collins K, Hemming M (2006) Redeployable CCTV and drug-related crime: a case of implementation failure. Drugs Educ Prev Policy 13(5):451–460

    Article  Google Scholar 

  • Groff E, La Vigne N (2001) Mapping an opportunity surface of residential burglary. J Res Crime Delinq 38:257–278

    Article  Google Scholar 

  • Guerette R, Steinus V, McGloin J (2005) Understanding offending specialization and versatility: a re-application of the rational choice perspective. J Crim Justice 33(1):77–87

    Article  Google Scholar 

  • Hamilton L (2013) Statistics with STATA. Updated for version 12. Cengage Brooks/Cole, Boston, MA

    Google Scholar 

  • Ittelson W (1973) Environment perception and contemporary perceptual theory. In: Ittelson W (ed) Environment and cognition. Seminar, New York, pp 1–19

    Google Scholar 

  • Johnson S, Bowers K, Birks D, Pease K (2009) Predictive mapping of crime by ProMap: accuracy, units of analysis, and the environmental backcloth. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York

    Google Scholar 

  • Kennedy L, Caplan J, Piza E (2011) Risk clusters, hotspots, and spatial intelligence: risk terrain modeling as an algorithm for police resource allocation strategies. J Quant Criminol 27(3):339–362

    Article  Google Scholar 

  • Keval H, Sasse A (2010) “Not the usual suspects”: a study of factors reducing the effectiveness of CCTV. Secur J 23(2):134–154

    Article  Google Scholar 

  • King J, Mulligan D, Raphael S (2008) CITRIS Report: the San Francisco community safety camera program. An evaluation of the effectiveness of San Francisco’s community safety cameras. Research in the Interest of Society. Center for Information Technology Research in the Interest of Society, University of California, Berkeley

  • La Vigne N, Lowry S (2011) Evaluation of camera use to prevent crime in commuter parking lots: a randomized controlled trial. Urban Institute, Justice Policy Center, Washington, DC

    Google Scholar 

  • La Vigne N, Lowry S, Markman J, Dwyer A (2011) Evaluating the use of public surveillance cameras for crime control and prevention. US Department of Justice, Office of Community Oriented Policing Services. Urban Institute, Justice Policy Center, Washington, DC

  • Lynch K (1960) Image of the city. MIT Press, Cambridge, MA

    Google Scholar 

  • MacDonald J (2002) The effectiveness of community policing in reducing urban violence. Crime Delinq 48:592–618

    Article  Google Scholar 

  • MacKinnon J, White H (1985) Some heteroscedasticity consistent covariance estimators with improved finite sample properties. J Econ 29:53–57

    Article  Google Scholar 

  • Madensen T, Eck J (2008) Violence in bars: exploring the impact of place manager decision-making. Crime Prev Community Saf 10(2):111–125

    Article  Google Scholar 

  • Maxfield M, Babbie E (2001) Research methods for criminal justice and criminology, 3rd edn. Wadsworth/Thompson Learning, Belmont, CA

  • Mazerolle L, Hurley D, Chamlin M (2002) Social behavior in public space: an analysis of behavioral adaptations to CCTV. Secur J 15(3):59–75

    Article  Google Scholar 

  • McClendon M (1994) Multiple regression and causal analysis. F. E. Peacock Publishers, Itasca, IL

    Google Scholar 

  • Myers P (2002) The management of identity in bodegas: stigma and microeconomics in Brooklyn. J Ethn Subst Abuse 1(3):75–93

    Article  Google Scholar 

  • Norris C, Armstrong G (1999) CCTV and the social structuring of surveillance. In: Tilley N, Painter K (eds) Surveillance of public space: CCTV, street lighting and crime prevention. Crime prevention studies, vol 10. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Norris C, McCahill M (2006) CCTV: beyond penal modernism? Br J Criminol 46:97–118

    Article  Google Scholar 

  • Oberwittler D, Wikström P (2009) Why smaller is better: advancing the study of the role of behavioral contexts in crime causation. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York

    Google Scholar 

  • Phillips C (1999) A review of CCTV evaluations: crime reduction effects and attitudes towards its use. In: Tilley N, Painter K (eds) Surveillance of public space: CCTV, Street lighting and crime prevention. Crime prevention studies, vol 10. Criminal Justice Press, Monsey, NY

    Google Scholar 

  • Piza E (2012) Identifying the ideal context for CCTV camera placement: an analysis of micro-level features. Doctoral Dissertation submitted to the Graduate School-Newark, Rutgers, The State University of New Jersey

  • Piza E, O’Hara B (2012) Saturation foot-patrol in a high-violence area: a quasi-experimental evaluation. Justice Q. Advance online publication. doi:10.1080/07418825.2012.668923

  • Piza E, Caplan J, Kennedy L (2012) Is the punishment more certain? An analysis of CCTV detections and enforcement. Justice Q. Advance online publication. doi:10.1080/07418825.2012.723034

  • Ratcliffe J (2006) Video surveillance of public places. Problem-oriented guides for police. Response guide series. Guide No. 4. US Department of Justice Office of Community Oriented Policing Services. Center for Problem-Oriented Policing

  • Ratcliffe J (2010) Crime mapping: spatial and temporal challenges. In: Weisburd D, Piquero A (eds) Handbook of quantitative criminology. Springer, New York, NY

    Google Scholar 

  • Ratcliffe J (2012) The spatial extent of criminogenic places: a changepoint regression of violence around bars. Geogr Anal 44:302–330

    Article  Google Scholar 

  • Ratcliffe J, Taniguchi T, Taylor R (2009) The crime reduction effects of public CCTV cameras: a multi-method spatial approach. Justice Q 26(4):746–770

    Article  Google Scholar 

  • Roncek D (2000) Schools and crime. In: Goldsmith V, McGuire P, Mollenkopf J, Ross A (eds) Analyzing crime patterns: frontiers of practice. Sage, Thousand Oaks, CA, pp 153–165

    Chapter  Google Scholar 

  • Sarno C, Hough M, Bulos M (1999) Developing a picture of CCTV in Southwark town centres: final report. Criminal Policy Research Unit, South Bank University, London

    Google Scholar 

  • Scott M, Dedel K (2006) Assaults in and around bars, 2nd edn. Problem-Oriented Guides for Police. Problem-specific Guides Series. Guide No. 4. US Department of Justice, Office of Community Oriented Policing Services, Center for Problem-Oriented Policing, Washington, DC

  • Sherman L (1990) Police crackdowns: initial and residual deterrence. In: Tonry M, Morris N (eds) Crime and justice: a review of research, vol 12. University of Chicago Press, Chicago, pp 1–48

    Google Scholar 

  • Sherman L, Rogan D (1995) Effects of gun seizures on gun violence: ‘hot spots’ patrol in Kansas City. Justice Q 12:673–694

    Article  Google Scholar 

  • Sivarajasingam V, Shepherd J, Matthews K (2003) Effect of urban closed circuit television on assault injury and violence detection. Inj Prev 9:312–316

    Article  Google Scholar 

  • Smith G (2004) Behind the scenes: examining constructions of deviance and informal practices among CCTV control room operators in the UK. Surveill Soc 2(2/3):376–395

    Google Scholar 

  • Smith M, Clarke R (2000) Crime and public transport. Crime Justice 27:169–233

    Google Scholar 

  • Squires P (2000) CCTV and crime reduction in Crawley. Health and Social Police Research Center, Brighton

    Google Scholar 

  • Stucky T, Ottensmann J (2009) Land use and violent crime. Criminology 47(4):1223–1264

    Article  Google Scholar 

  • Taylor R, Harrell A (1996) Physical environment and crime. National Institute of Justice, Washington, DC

    Google Scholar 

  • Tukey J (1977) Exploratory data analysis. Addison-Wesley, Reading, MA

    Google Scholar 

  • UCLA (2007) Chapter 2: regression diagnostics. In: Stata web books: regression with Stata. University of California Los Angeles: Academic Technology Services, Statistical Consulting Group

  • US Census Bureau (2011). Quick facts from US Census Bureau. Retrieved on 12 March 2011 from http://quickfacts.census.gov

  • Vittinghoff E, McCulloch C (2007) Relaxing the rule of ten events per variable in logistic and cox regression. Am J Epidemiol 165(6):710–718

    Article  Google Scholar 

  • Waples S, Gill M (2006) The effectiveness of redeployable CCTV. Crime Prev Community Saf 8:1–16

    Article  Google Scholar 

  • Weisburd D, Green L (1995) Measuring intermediate spatial displacement: methodological issues and problems. In: Eck J, Weisburd D (eds) Crime and place: crime prevention studies, vol 4. Criminal Justice Press, Monsey, NY, pp 349–361

  • Weisburd D, Morris N, Ready J (2008) Risk-focused policing at places: an experimental evaluation. Justice Q 25(1):163–200

    Article  Google Scholar 

  • Welsh B, Farrington D (2002) Crime prevention effects of closed circuit television: a systematic review. Home Office, London (Research Study No. 25)

  • Welsh B, Farrington D (2007) Closed-circuit television surveillance and crime prevention: a systematic review. National Council for Crime Prevention, Stockholm

    Google Scholar 

  • Welsh B, Farrington D (2009) Public area CCTV and crime prevention: an updated systematic review and meta-analysis. Justice Q 26(4):716–745

    Article  Google Scholar 

  • Winge S, Knutsson J (2003) An evaluation of the CCTV scheme at Oslo central railway station. Crime Prev Community Saf Int J 5(3):49–59

    Article  Google Scholar 

  • Zanin N, Shane J, Clarke R (2004) Reducing drug dealing in private apartment complexes in Newark, NJ. A final report to the US Department of Justice, Office of Community Oriented Policing Services on the Field Applications of the Problem-Oriented Guides for Police Project

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Acknowledgments

This research was supported by the National Institute of Justice, Grant No. 2010-IJ-CX-0026. We express our gratitude to Sergeant Marvin Carpenter and the surveillance operators of the Newark Police Department for their generous support. We also wish to acknowledge the research assistants that worked on this project for their diligence and dedication: Kayla Crager, Andrew Gilchrist, and Christopher Perez. Finally, we thank a number of our colleagues who gave us valuable advice regarding the research methodology and statistical analysis of this project: Robert Apel, Anthony Braga, Elizabeth Griffiths, and Joel Miller.

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Authors and Affiliations

  1. School of Criminal Justice, Rutgers University, Newark, NJ, USA

    Joel M. Caplan & Leslie W. Kennedy

  2. Department of Law and Police Science, John Jay College of Criminal Justice, New York, NY, USA

    Eric L. Piza

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  1. Eric L. Piza
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Correspondence to Eric L. Piza.

Appendix

Appendix

The reader may be interested in how the ∆LQ distribution of the current study compares to that of Caplan et al. (2011). However, the differing scope of these works makes comparisons somewhat limited. Specifically, auto theft and theft from auto are the only two crime types included in both studies. In addition, Caplan et al. (2011) analyzed 73 of Newark’s 146 cameras that were installed at the time. Therefore, we are restricted to discussing the auto theft and theft from auto findings for the cameras included in both studies.

Thirty-four viewsheds experienced reduced levels of auto theft in the Caplan et al. study, with 35 auto theft reductions being observed in the current study. Differences were more pronounced in respect to theft from auto; 41 viewsheds exhibited reduced crime levels in the Caplan et al. study while only 35 exhibited a reduction in the current study. These differences are likely due to the differing methodologies of the two studies, specifically in regards to the LQ formulas and the units of analysis. LQs employed by Caplan et al. controlled for city-wide geography and crime totals while precinct-level data was used in the current study. In addition, while the current study excluded incidents in the surrounding catchment zone from the LQ formula, Caplan et al. included such incidents. Finally, the different approaches to viewshed creation resulted in units of analysis significantly different in size, as confirmed via an independent samples t test (p < 0.01). The average area of viewsheds in the Caplan et al. study was more than twice the size of our viewsheds (268,635 vs. 112,615 ft2). These methodological differences mean that measures of success are not uniform across studies. Therefore, comparison of findings may be more of an “apples-to-oranges” situation than one may expect given the identical study setting.

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Piza, E.L., Caplan, J.M. & Kennedy, L.W. Analyzing the Influence of Micro-Level Factors on CCTV Camera Effect. J Quant Criminol 30, 237–264 (2014). https://doi.org/10.1007/s10940-013-9202-5

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