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Street Light Outages, Public Safety and Crime Attraction

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Abstract

Objectives

For more than one hundred years, street lighting has been one of the most ubiquitous capital investments in public safety. Prior research on street lighting is largely limited to ecological studies of very small geographic areas, creating substantial challenges with respect to both causal identification and statistical power. We address limitations of the prior literature by studying a natural experiment created by short-term disruptions to municipal street lighting.

Methods

We leverage a natural experiment created by the differential timing of the repair of nearly 300,000 street light outages in Chicago. By conditioning on street segment fixed effects and focusing on a short window of time around the repair of a street light outage, we can credibly rule out confounding factors due to area-specific time trends as well as street segment-level correlates of crime.

Results

We find that outdoor nighttime crimes change very little on street segments affected by street light outages, but that outages cause crime to spill over to nearby street segments. Effects are largest for robberies and motor vehicle theft.

Conclusions

Despite strong environmental and social characteristics that tend to tie crime to place, we observe that street light outages are sufficiently salient to disrupt longstanding patterns. While the impact of localized street light outages can reverberate throughout a community, the findings imply that improvements in lighting can be defeated by the displacement of crime to adjacent spaces and therefore do not necessarily suggest that localized investments in municipal street lighting will yield a large public safety dividend.

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Notes

  1. Oil lamps were used to improve nighttime public safety in the Greco-Roman world at least as far back as 500 B.C. (Ellis 2007).

  2. These impacts may be further mediated by the extent to which the composition of individuals who spend time outdoors changes.

  3. Studies included in their systematic review utilize a differences-in-differences research design and, as such, have both pre- and post-intervention data and a control group which did not receive the intervention. Among the eight U.S. studies, lighting was found to be broadly effective in Atlanta, Milwaukee, Fort Worth and Kansas City and ineffective in Portland, Harrisburg, New Orleans and Indianapolis. Among the five U.K. studies, lighting was considered to be effective in Bristol, Birmingham, Dudley, and Stoke. In the fifth location (Dover), the improved lighting was confounded with other public infrastructure improvements.

  4. While Welsh and Farrington’s review refers to treatment groups as “experimental” and “control” groups, all of these studies are actually observational.

  5. Research by Domínguez and Asahi (2017) finds similar effects in Chile.

  6. Because of its central importance in interpreting empirical estimates, testing for spatial displacement has received a great deal of attention in experimental and quasi-experimental studies of hot spots policing (Sherman and Weisburd 1995; Braga and Bond 2008; Braga et al. 2014; Groff et al. 2015; Blattman et al. 2017) disorder reduction (Braga and Bond 2008; Branas et al. 2011; MacDonald et al. 2016; Branas et al. 2018), closed circuit television cameras (Waples et al. 2009; Welsh and Farrington 2009; Piza et al. 2014, 2015) and other place-based interventions (Grogger 2002; Ridgeway et al. 2019). Of course, displacement can also take the form of crime, target or tactical “switch” (Johnson et al. 2014) and can also include temporal displacement.

  7. Measuring spatial displacement is challenging for a number of reasons, chief among them that it is unclear a priori where crime might go upon being displaced. Will crime merely be pushed “around the corner” (Weisburd et al. 2006; Blattman et al. 2017) or will it migrate to some more distal area which shares one or more key characteristics with the treated area? Given the difficulty of exhaustively testing for all forms of spatial displacement, the norm in the empirical literature is to focus on adjacent areas (Guerette and Bowers 2009).

  8. The review by Guerette and Bowers (2009) finds little evidence of either spatial displacement or diffusion of benefits in most applications. However, constraints on statistical power mean that displacement is not always detectable even when it exists.

  9. Short et al. (2010) refer to this idea as a “reaction-diffusion” model of crime.

  10. In the administrative data, outages that affect 1–2 lights are called “single outages” and outages that affect more than two lights are called “multiple outages.” More granular information on the precise number of street lights out is not available.

  11. https://www.chicago.gov/city/en/depts/311.html.

  12. Among major outages, which is where we observe crime effects, we are told by municipal officials in Chicago that light outages are almost all “completely out.” Among minor street light outages, approximately 1 in 5 complaints is for lights “going on and off.” The administrative data unfortunately does not distinguish between the two types of outages. Hence, to the degree that 20% of these minor outages provides only minimal treatment, our estimates could potentially be attenuated. Happily, we can back out the degree of attenuation under the assumption that flickering lights have no treatment effect. Assuming that 80% of the minor outages are, in fact, treated, estimates would be attenuated by a factor of \(\frac{1}{0.8}\) = 1.25. As the estimated effects are extraordinarily small, this will not substantively affect our estimates. For example, for index crimes arising from a minor light outage, the estimated treatment effect is − 0.1%. For robbery, it is 2.2%. Multiplying these estimates by 1.25, yields estimates of − 0.125% and 2.75%, respectively. These differences are substantively very small and are more than consistent with sampling error. As such, even if we make the assumption that “flickering outages” are associated with no effects, our estimates would not be substantively different.

  13. When a city employee addresses the outage, they also check all nearby street lights.

  14. At first glance, the ubiquity of outages affecting more than two street lights might seem unusual. However, it is important to note that, in Chicago, it is typically the case that a number of lights are connected to each other in a “group.” Hence, an electrical issue can disable multiple street lights on a given street segment at the same time.

  15. Approximately 4% of post-repair periods experience a new outage and, as such, are exposed to the treatment. In order to avoid introducing post-treatment bias into our models, we follow Chalfin et al. (2020) and report intention-to-treat effects which evaluate the effect of an initial outage and, as such, are if anything conservatively estimated.

  16. https://data.cityofchicago.org/Transportation/Street-Center-Lines/6imu-meau.

  17. The choice of a 50-foot buffer is common in the empirical literature that rely on the geocoding of crimes to blocks or street segments (e.g., (Ratcliffe 2012).

  18. The dataset contains two variables with a time related to the incident: the time the crime was reported and when the incident report was updated by the police. For this study we use the time when the crime was reported to the police, not the update time.

  19. See: http://aa.usno.navy.mil/faq/docs/RST_defs.php.

  20. http://aa.usno.navy.mil/data/docs/RS_OneDay.php.

  21. We consider any report that takes greater than 180 days to resolve to be a data error and exclude it from the data.

  22. Results are similar when burglaries—an indoor crime with some of the characteristics of an outdoor crime—are excluded from the data.

  23. Sometimes crime counts are modeled using negative binomial regression models due to concerns about overdispersion in the data. For several reasons, we prefer Poisson regression in this context. First, tests for overdispersion do not distinguish between overdispersion and misspecification (see Berk and MacDonald (2008); Blackburn (2015)). Consequently, it is a priori unclear when overdispersion actually exists and is therefore an issue. Second, Poisson regression is first order equivalent to negative binomial regression when robust standard errors are used—as we do. Finally, negative binomial regression yields inconsistent estimates when fixed effects are used in a model (Lancaster 2000). This is not an issue for Poisson regression (Allison and Waterman 2002). As our models do include fixed effects, the Poisson regression model is a more appropriate choice.

References

  • Akers RL (1990) Rational choice, deterrence, and social learning theory in criminology: the path not taken. J Crim Law Criminol 81:653

    Article  Google Scholar 

  • Allison PD, Waterman RP (2002) Fixed-effects negative binomial regression models. Sociol Methodol 32(1):247–265

    Article  Google Scholar 

  • Armitage R (2002) To cctv or not to cctv. A review of current research into the effectiveness of CCTV systems in reducing crime 8

  • Armitage R, Monchuk L, Rogerson M (2011) It looks good, but what is it like to live there? Exploring the impact of innovative housing design on crime. Eur J Crim Policy Res 17(1):29–54

    Article  Google Scholar 

  • Atkins S, Husain S, Storey A (1991) The influence of street lighting on crime and fear of crime. Home Office London

  • Ayres I, Levitt SD (1998) Measuring positive externalities from unobservable victim precaution: an empirical analysis of Lojack. Q J Econ 113(1):43–77

    Article  Google Scholar 

  • Barr R, Pease K (1990) Crime placement, displacement, and deflection. Crime Justice 12:277–318

    Article  Google Scholar 

  • Becker GS (1968) Crime and punishment: an economic approach. J Polit Econ 76(2):169–217

    Article  Google Scholar 

  • Berk R, MacDonald JM (2008) Overdispersion and Poisson regression. J Quant Criminol 24(3):269–284

    Article  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 Delinquency 48(1):33–57

    Article  Google Scholar 

  • Bester CA, Conley TG, Hansen CB (2011) Inference with dependent data using cluster covariance estimators. J Econom 165(2):137–151

    Article  Google Scholar 

  • Blackburn ML (2015) The relative performance of Poisson and negative binomial regression estimators. Oxford Bull Econ Stat 77(4):605–616

    Article  Google Scholar 

  • Blattman C, Green D, Ortega D, Tobón S (2017) Pushing crime around the corner? Estimating experimental impacts of large-scale security interventions, National Bureau of Economic Research Washington, DC

  • Bogar S, Beyer KM (2016) Green space, violence, and crime: a systematic review. Trauma Violence Abuse 17(2):160–171

    Article  Google Scholar 

  • Bound J, Brown C, Mathiowetz N (2001) Measurement error in survey data. In: Handbook of eonometrics, vol 5, pp 3705–3843, Elsevier

  • Braga AA, Bond BJ (2008) Policing crime and disorder hot spots: a randomized controlled trial. Criminology 46(3):577–607

    Article  Google Scholar 

  • Braga AA, Papachristos AV, Hureau DM (2014) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 31(4):633–663

    Article  Google Scholar 

  • Branas CC, Cheney RA, MacDonald JM, Tam VW, Jackson TD, Ten Have TR (2011) A difference-in-differences analysis of health, safety, and greening vacant urban space. Am J Epidemiol 174(11):1296–1306

    Article  Google Scholar 

  • Branas CC, South E, Kondo MC, Hohl BC, Bourgois P, Wiebe DJ, MacDonald JM (2018) Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear. Proc Natl Acad Sci 115(12):2946–2951

    Article  Google Scholar 

  • Brantingham PJ, Brantingham P (2013) 5. crime pattern theory. Environ Criminol Crime Anal:78

  • Brantingham PJ, Brantingham PL, Andresen MA (2017) The geometry of crime and crime pattern theory. Environ Criminol Crime Anal 2

  • Brantingham PL, Brantingham PJ (1999) A theoretical model of crime hot spot generation. Stud Crime Crime Prevent

  • Carr JB, Doleac JL (2018) Keep the kids inside? Juvenile curfews and urban gun violence. Rev Econ Stat 100(4):609–618

    Article  Google Scholar 

  • Chalfin A, Hansen B, Lerner J, Parker L (2020) Reducing crime through environmental design: evidence from a randomized experiment of street lighting in New York City. J Quant Criminol

  • Chalfin A, Hansen B, Weisburst EK, Williams MC et al (2020) Police force size and civilian race. Technical report, National Bureau of Economic Research

  • Chalfin A, Kaplan J, Cuellar M (2020) Measuring marginal crime concentration: a new solution to an old problem. J Res Crime Delinquency 0022427820984213

  • Chalfin A, McCrary J (2018) Are US cities underpoliced? Theory and evidence. Rev Econ Stat 100(1):167–186

    Article  Google Scholar 

  • Clarke R (2008) Improving Street lighting to reduce crime in residential areas. Citeseer

  • Clarke RV (1980) Situational crime prevention: theory and practice. Br J Criminol 20:136

    Article  Google Scholar 

  • Clarke RV (1983) Situational crime prevention: its theoretical basis and practical scope. Crime Justice 4:225–256

    Article  Google Scholar 

  • Clarke RV (1995) Situational crime prevention. Crime Justice 19:91–150

    Article  Google Scholar 

  • Clarke RV (2009) Situational crime prevention: theoretical background and current practice. In: Handbook on crime and deviance, Springer, pp 259–276

  • Clarke RV, Weisburd D (1994) Diffusion of crime control benefits: observations on the reverse of displacement. Crime Prevent Stud 2:165–184

    Google Scholar 

  • Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 588–608

  • Cook PJ (1986) Criminal incapacitation effects considered in an adaptive choice framework. The Reasoning Criminal. Springer Verlag, New York, pp 202–216

  • Cornish DB, Clarke RV (1987) Understanding crime displacement: an application of rational choice theory. Criminology 25(4):933–948

    Article  Google Scholar 

  • Cozens P, Davies T (2013) Crime and residential security shutters in an Australian suburb: exploring perceptions of ‘eyes on the street’, social interaction and personal safety. Crime Prevent Commun Saf 15(3):175–191

    Article  Google Scholar 

  • Cozens P, Hillier D (2012) Revisiting Jane Jacobs’s ‘Eyes on the Street’for the twenty-first century: evidence from environmental criminology. In: The urban wisdom of Jane Jacobs, Routledge, pp 202–220

  • Cozens P, Love T (2009) Manipulating permeability as a process for controlling crime: balancing security and sustainability in local contexts. Built Environ 35(3):346–365

    Article  Google Scholar 

  • Cozens P, Love T (2015) A review and current status of crime prevention through environmental design (CPTED). J Plan Literature 30(4):393–412

    Article  Google Scholar 

  • Cozens PM, Saville G, Hillier D (2005) Crime prevention through environmental design (CPTED): a review and modern bibliography. Property Manag 23(5):328–356

    Article  Google Scholar 

  • Doleac JL, Sanders NJ (2015) Under the cover of darkness: how ambient light influences criminal activity. Rev Econ Stat 97(5):1093–1103

    Article  Google Scholar 

  • Domínguez P, Asahi K (2017) Crime time: how ambient light affect criminal activity. Available at SSRN 2752629

  • Eck JE (1993) The threat of crime displacement. Crim Justice Abstracts 25:527–546

    Google Scholar 

  • Ellis S (2007) Shedding light on late Roman housing. In: Housing in late antiquity, Brill, vol 3.2, pp 283–302

  • Evans WN, Owens EG (2007) COPS and crime. J Publ Econ 91(1–2):181–201

    Article  Google Scholar 

  • Farrell G (2015) Crime concentration theory. Crime Prevent Commun Saf 17(4):233–248

    Article  Google Scholar 

  • Farrington DP, Welsh BC (2002) Improved street lighting and crime prevention. Justice Q 19(2):313–342

    Article  Google Scholar 

  • Felson M, Poulsen E (2003) Simple indicators of crime by time of day. Int J Forecasting 19(4):595–601

    Article  Google Scholar 

  • Griffiths E, Tita G (2009) Homicide in and around public housing: Is public housing a hotbed, a magnet, or a generator of violence for the surrounding community? Soc Prob 56(3):474–493

    Article  Google Scholar 

  • Groff ER, Ratcliffe JH, Haberman CP, Sorg ET, Joyce NM, Taylor RB (2015) Does what police do at hot spots matter?: The Philadelphia policing tactics experiment. Criminology 53(1):23–53

    Article  Google Scholar 

  • Grogger J (2002) The effects of civil gang injunctions on reported violent crime: evidence from Los Angeles County. J Law Econ 45(1):69–90

    Article  Google Scholar 

  • Guerette RT, Bowers KJ (2009) Assessing the extent of crime displacement and diffusion of benefits: a review of situational crime prevention evaluations. Criminology 47(4):1331–1368

    Article  Google Scholar 

  • Herbert D, Davidson N (1994) Modifying the built environment: the impact of improved street lighting. Geoforum 25(3):339–350

    Article  Google Scholar 

  • Jacobs J (1961) The death and life of great American cities. Cities 321–25

  • Johnson SD, Guerette RT, Bowers K (2014) Crime displacement: what we know, what we don’t know, and what it means for crime reduction. J Exp Criminol 10(4):549–571

    Article  Google Scholar 

  • Keizer K, Lindenberg S, Steg L (2008) The spreading of disorder. Science 322(5908):1681–1685

    Article  Google Scholar 

  • Kondo M, Hohl B, Han S, Branas C (2016) Effects of greening and community reuse of vacant lots on crime. Urban Stud 53(15):3279–3295

    Article  Google Scholar 

  • Lancaster T (2000) The incidental parameter problem since 1948. J Econom 95(2):391–413

    Article  Google Scholar 

  • Lee J, Park S, Jung S (2016) Effect of crime prevention through environmental design (CPTED) measures on active living and fear of crime. Sustainability 8(9):872

    Article  Google Scholar 

  • Loughran TA, Mulvey EP, Schubert CA, Fagan J, Piquero AR, Losoya SH (2009) Estimating a dose-response relationship between length of stay and future recidivism in serious juvenile offenders. Criminology 47(3):699–740

    Article  Google Scholar 

  • MacDonald J, Branas C, Stokes R (2019) Changing places: the science and art of New Urban Planning. Princeton University Press

  • MacDonald JM, Klick J, Grunwald B (2016) The effect of private police on crime: evidence from a geographic regression discontinuity design. J R Stat Soc Ser A (Stat Soc) 179(3):831–846

    Article  Google Scholar 

  • Marchant PR (2004) A demonstration that the claim that brighter lighting reduces crime is unfounded. Br J Criminol 44(3):441–447

    Article  Google Scholar 

  • Meade B, Steiner B, Makarios M, Travis L (2013) Estimating a dose-response relationship between time served in prison and recidivism. J Res Crime Delinquency 50(4):525–550

    Article  Google Scholar 

  • Newman O (1972) Defensible space. Macmillan, New York

    Google Scholar 

  • Painter K (1994) The impact of street lighting on crime, fear, and pedestrian street use. Secur J 5(3):116–124

    Google Scholar 

  • Painter K (1996) The influence of street lighting improvements on crime, fear and pedestrian street use, after dark. Landscape Urban Plan 35(2–3):193–201

    Article  Google Scholar 

  • Painter K, Farrington DP (1999a) Improved street lighting: crime reducing effects and cost-benefit analyses. Secur J 12(4):17–32

    Article  Google Scholar 

  • Painter K, Farrington DP (1999b) Street lighting and crime: diffusion of benefits in the Stoke-on-Trent project. Surveillance Publ Space CCTV Street Lighting Crime Preven 10:77–122

    Google Scholar 

  • Painter KA, Farrington DP (2001) The financial benefits of improved street lighting, based on crime reduction. Light Res Technol 33(1):3–10

    Article  Google Scholar 

  • Pfaf J (2017) Locked in: the true causes of mass incarceration-and how to achieve real reform. Basic Books

  • Piza EL, Caplan JM, Kennedy LW (2014) Analyzing the influence of micro-level factors on CCTV camera effect. J Quant Criminol 30(2):237–264

    Article  Google Scholar 

  • Piza EL, Caplan JM, Kennedy LW, Gilchrist AM (2015) The effects of merging proactive cctv monitoring with directed police patrol: a randomized controlled trial. J Exp Criminol 11(1):43–69

    Article  Google Scholar 

  • Piza EL, Welsh BC, Farrington DP, Thomas AL (2019) CCTV surveillance for crime prevention: a 40-year systematic review with meta-analysis. Criminol Public Policy 18(1):135–159

    Article  Google Scholar 

  • Priks M (2015) The effects of surveillance cameras on crime: evidence from the Stockholm subway. Econ J 125(588):F289–F305

    Article  Google Scholar 

  • Ratcliffe JH (2002) Aoristic signatures and the spatio-temporal analysis of high volume crime patterns. J Quant Criminol 18(1):23–43

    Article  Google Scholar 

  • Ratcliffe JH (2012) The spatial extent of criminogenic places: a changepoint regression of violence around bars. Geograph Anal 44(4):302–320

    Article  Google Scholar 

  • Reppetto TA (1976) Crime prevention and the displacement phenomenon. Crime Delinquency 22(2):166–177

    Article  Google Scholar 

  • Ridgeway G, Grogger J, Moyer RA, MacDonald JM (2019) Effect of gang injunctions on crime: a study of Los Angeles from 1988–2014. J Quant Criminol 35(3):517–541

    Article  Google Scholar 

  • Robinson MB (2013) The theoretical development of ‘CPTED’: twenty-five years of responses to C. Ray Jeffery. J Criminol Crim Law 8:427–462

    Google Scholar 

  • Roman CG, Chalfin A (2008) Fear of walking outdoors: a multilevel ecologic analysis of crime and disorder. Am J Prevent Med 34(4):306–312

    Article  Google Scholar 

  • Roman CG, Knight CR, Chalfin A, Popkin SJ (2009) The relation of the perceived environment to fear, physical activity, and health in public housing developments: evidence from Chicago. J Public Health Policy 30(1):S286–S308

    Article  Google Scholar 

  • Roman JK, Reid SE, Chalfin AJ, Knight CR (2009) The DNA field experiment: a randomized trial of the cost-effectiveness of using DNA to solve property crimes. J Exp Criminol 5(4):345

    Article  Google Scholar 

  • Roncek DW, Maier PA (1991) Bars, blocks, and crimes revisited: linking the theory of routine activities to the empiricism of ‘hot spots’. Criminology 29(4):725–753

    Article  Google Scholar 

  • Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(5328):918–924

    Article  Google Scholar 

  • Sherman LW, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27(1):27–56

    Article  Google Scholar 

  • Sherman LW, Gottfredson DC, MacKenzie DL, Eck J, Reuter P, Bushway S et al (1997) Preventing crime: What works, what doesn’t, what’s promising: A report to the United States Congress. National Institute of Justice Washington, DC

  • Sherman LW, Weisburd D (1995) General deterrent effects of police patrol in crime ‘hot spots’: a randomized, controlled trial. Justice Q 12(4):625–648

    Article  Google Scholar 

  • Short MB, Brantingham PJ, Bertozzi AL, Tita GE (2010) Dissipation and displacement of hotspots in reaction-diffusion models of crime. Proc Natl Acad Sci 107(9):3961–3965

    Article  Google Scholar 

  • Skogan WG (1990) Disorder and decline: Crime and the spiral of decay in American cities

  • Stinson BM (2018) Newport Firsts: A Hundred Claims to Fame (RI). Arcadia Publishing

  • Tien JM (1979) Street lighting projects. National Institute of Law Enforcement and Criminal Justice, Law Enforcement

  • Tocco P (1999) The night they turned the lights on in Wabash. The Indiana Magazine of History 350–363

  • Waples S, Gill M, Fisher P (2009) Does CCTV displace crime? Criminol Crim Justice 9(2):207–224

    Article  Google Scholar 

  • Weisburd D (2015) The law of crime concentration and the criminology of place. Criminology 53(2):133–157

    Article  Google Scholar 

  • Weisburd D, Bushway S, Lum C, Yang S-M (2004) Trajectories of crime at places: a longitudinal study of street segments in the city of seattle. Criminology 42(2):283–322

    Article  Google Scholar 

  • Weisburd D, Green L (1994) Defining the drug market: The case of the jersey city dma system. Evaluating public policy initiatives, Drugs and crime, pp 61–76

  • Weisburd D, Groff ER, Yang S-M (2012) The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press

  • Weisburd D, Groff ER, Yang S-M (2014) Understanding and controlling hot spots of crime: the importance of formal and informal social controls. Prevent Sci 15(1):31–43

    Article  Google Scholar 

  • Weisburd D, Wyckoff LA, Ready J, Eck JE, Hinkle JC, Gajewski F (2006) Does crime just move around the corner? a controlled study of spatial displacement and diffusion of crime control benefits. Criminology 44(3):549–592

    Article  Google Scholar 

  • Weisburd S (2016) Police presence, rapid response rates, and crime prevention. Rev Econ Stat 1–45

  • Weisburst EK (2018) Safety in police numbers: evidence of police effectiveness from federal COPS grant applications. Am Law Econ Rev 21(1):81–109

    Article  Google Scholar 

  • Weitzer R, Tuch SA, Skogan WG (2008) Police-community relations in a majority-Black city. J Res Crime Delinquency 45(4):398–428

    Article  Google Scholar 

  • Welsh BC, Farrington DP (2008) Effects of improved street lighting on crime. Campbell Syst Rev 13:1–51

    Google Scholar 

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

    Article  Google Scholar 

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Acknowledgements

We thank Patricio Dominguez and Jason Lerner for their helpful feedback on a prior version of this manuscript. We are especially grateful to John MacDonald and David Weisburd and to three anonymous referees for providing particularly extensive and helpful feedback.

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  1. Department of Criminology, University of Pennsylvania, 558 McNeil Building, Philadelphia, PA, 19104, USA

    Aaron Chalfin

  2. Princeton University, Princeton, NJ, USA

    Jacob Kaplan

  3. The Pennsylvania State University, State College, PA, USA

    Michael LaForest

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Chalfin, A., Kaplan, J. & LaForest, M. Street Light Outages, Public Safety and Crime Attraction. J Quant Criminol 38, 891–919 (2022). https://doi.org/10.1007/s10940-021-09519-4

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