ArticlePublisher preview available

Street Light Outages, Public Safety and Crime Attraction

Authors:
Aaron Chalfin at University of Chicago
Aaron Chalfin
Jacob Kaplan
Jacob Kaplan
Jacob Kaplan
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Michael LaForest
Michael LaForest
Michael LaForest
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

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.
Visual schematic of research design. Note: These figures (not drawn to scale) present a visual depiction of our research design. Consider a street light outage that is first reported to municipal authorities at time, s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document}. This outage may have begun on s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document} or it may have begun prior to s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document}. Panel A refers to a street light outage that is longer than seven-days in duration. The outage is repaired at time, s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}. Given this, we study the days that are bounded by the dashed red lines: the pre-repair period are the seven-days between s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{1}$$\end{document} and s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}; the post-repair period are the four-days between s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document} and s3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{3}$$\end{document}. Panel B refers to a street light outage that is less than seven-days in duration—for example, two days. Here, the reported outage date s0=s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0} = s_{1}$$\end{document}, the beginning of the pre-period. We continue to study the days that are bounded by the dashed red lines: the pre-repair period are the days between s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{1}$$\end{document} and s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}; the post-repair period are the four-days between s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document} and s3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{3}$$\end{document}
Visual schematic of research design. Note: These figures (not drawn to scale) present a visual depiction of our research design. Consider a street light outage that is first reported to municipal authorities at time, s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document}. This outage may have begun on s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document} or it may have begun prior to s0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0}$$\end{document}. Panel A refers to a street light outage that is longer than seven-days in duration. The outage is repaired at time, s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}. Given this, we study the days that are bounded by the dashed red lines: the pre-repair period are the seven-days between s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{1}$$\end{document} and s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}; the post-repair period are the four-days between s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document} and s3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{3}$$\end{document}. Panel B refers to a street light outage that is less than seven-days in duration—for example, two days. Here, the reported outage date s0=s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{0} = s_{1}$$\end{document}, the beginning of the pre-period. We continue to study the days that are bounded by the dashed red lines: the pre-repair period are the days between s1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{1}$$\end{document} and s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document}; the post-repair period are the four-days between s2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{2}$$\end{document} and s3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_{3}$$\end{document}
… 
Duration of time between reported and repaired street light outages. Note: Duration is measured as the number of days between the initial reported outage and the date that the outage was repaired by municipal workers. Because outages may be reported sometime after they occur, measured duration is likely an underestimate of the actual duration of an outage. The mean outage duration in the data is 9.3 days; the median duration is 3 days. 90% of outages are resolved within 21 days
Duration of time between reported and repaired street light outages. Note: Duration is measured as the number of days between the initial reported outage and the date that the outage was repaired by municipal workers. Because outages may be reported sometime after they occur, measured duration is likely an underestimate of the actual duration of an outage. The mean outage duration in the data is 9.3 days; the median duration is 3 days. 90% of outages are resolved within 21 days
… 
Figures - available from: Journal of Quantitative Criminology
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Journal of Quantitative Criminology
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
Journal of Quantitative Criminology (2022) 38:891–919
https://doi.org/10.1007/s10940-021-09519-4
1 3
ORIGINAL PAPER
Street Light Outages, Public Safety andCrime Attraction
AaronChaln1· JacobKaplan2· MichaelLaForest3
Accepted: 7 June 2021 / Published online: 6 July 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
Objectives For more than one hundred years, street lighting has been one of the most ubiq-
uitous capital investments in public safety. Prior research on street lighting is largely lim-
ited 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 dis-
placement of crime to adjacent spaces and therefore do not necessarily suggest that local-
ized investments in municipal street lighting will yield a large public safety dividend.
Keywords Street lights· Crime displacement· Place-based interventions
* Aaron Chalfin
achalfin@sas.upenn.edu
1 Department ofCriminology, University ofPennsylvania, 558 McNeil Building, Philadelphia,
PA19104, USA
2 Princeton University, Princeton, NJ, USA
3 The Pennsylvania State University, StateCollege, PA, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... This paper contributes to literature on nighttime light from an economic perspective, such as Henderson, Storeygard, andWeil 2012 andMartínez 2022. It also contributes to literature related to crime theory, see Patricia Paul Brantingham 1995 andLaForest 2021. Moreover, it is innovative because, to the best of the author's knowledge, there has not been an analysis between nighttime light and violence, with a rigorous econometric analysis of these relationships, and also because it presents a theoretical framework explaining the findings, which allows us to gain a better understanding of these violent phenomena. ...
View
View
View
View
Attempting to reduce traffic stop racial disparities: An experimental evaluation of an internal dashboard intervention
Article
  • Apr 2024
Research Summary We conducted a group randomized‐controlled trial of an internal dashboard system deployed by the Michigan State Police to determine its effectiveness in reducing traffic stop racial disparities. Informed by a difference‐in‐differences design, analyses of traffic stop data from 2019–2022 indicated that the dashboard had no impact on traffic stop racial disparities. Additional analyses of traffic stops, crashes, and crime revealed that the dashboard had no “de‐policing” effect on traffic patrols, nor were there any significant changes in traffic safety or crime in treatment patrol areas relative to control patrol areas. Qualitative analyses of interview data from more than 40 troopers in the agency revealed unique barriers to program implementation and opportunities for future improvement. Policy Implications In an era of policing where the capacity and demand for data‐driven decision‐making is on the rise, evidence‐based policy and practice can provide police agencies with informed solutions for addressing traffic stop racial disparities. Yet, the increased demand for evidence‐based reform is fueled by a relatively low supply of evidence‐based research. This study adds to this evidence base by providing unique insights into the effectiveness of a program built specifically to reduce racial disparities in traffic stops, while also highlighting implementation challenges.
View
View
View
View
View
View
Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City
Article
Full-text available
  • Jan 2021
  • J QUANT CRIMINOL
Objectives This paper offers novel experimental evidence that violent crimes can be successfully reduced by changing the situational environment that potential victims and offenders face. We focus on a ubiquitous but understudied feature of the urban landscape—street lighting—and report the first experimental evidence on the effect of street lighting on crime. Methods Through a unique public partnership in New York City, temporary street lights were randomly allocated to 40 of the city’s public housing developments. Results We find evidence that communities that were assigned more lighting experienced sizable reductions in nighttime outdoor index crimes. We also observe a large decline in arrests indicating that deterrence is the most likely mechanism through which the intervention reduced crime. Conclusion Results suggests that street lighting, when deployed tactically, may be a means through which policymakers can control crime without widening the net of the criminal justice system.
View
View
Measuring Marginal Crime Concentration: A New Solution to an Old Problem
Article
  • Jan 2021
  • J RES CRIME DELINQ
Objectives In his 2014 Sutherland address to the American Society of Criminology, David Weisburd demonstrated that the share of crime that is accounted for by the most crime-ridden street segments is notably high and strikingly similar across cities, an empirical regularity referred to as the “law of crime concentration.” In the large literature that has since proliferated, there remains considerable debate as to how crime concentration should be measured empirically. We suggest a measure of crime concentration that is simple, accurate and easily interpreted. Methods Using data from three of the largest cities in the United States, we compare observed crime concentration to a counterfactual distribution of crimes generated by randomizing crimes to street segments. We show that this method avoids a key pitfall that causes a popular method of measuring crime concentration to considerably overstate the degree of crime concentration in a city. Results While crime is significantly concentrated in a statistical sense and while some crimes are substantively concentrated among hot spots, the precise relationship is considerably weaker than has been documented in the empirical literature. Conclusions The method we propose is simple and easily interpretable and compliments recent advances which use the Gini coefficient to measure crime concentration.
View
Safety in Police Numbers: Evidence of Police Effectiveness from Federal COPS Grant Applications
Article
  • May 2019
Understanding the impact of police on crime is critical to designing policies that maximize safety. In this article, I use a novel estimation approach to measure the impact of police hiring, which exploits variation in federal Community Oriented Policing Services (COPS) hiring grants, while also controlling for the endogenous decisions of police departments to apply for these grants. Using data from nearly 7,000 U.S. municipalities, I find that a 10% increase in police employment rates reduces violent crime rates by 13% and property crime rates by 7%. The results also provide suggestive evidence that law enforcement leaders are forward-looking.
View
View
Effects of Improved Street Lighting on Crime
Article
  • Sep 2008
Improved street lighting serves many functions and is used in both public and private settings. The prevention of personal and property crime is one of its objectives in public space, which is the main focus of this systematic review. There are two main theories of why improved street lighting may cause a reduction in crime. The first suggests that improved lighting leads to increased surveillance of potential offenders (both by improving visibility and by increasing the number of people on the street) and hence to increased deterrence of potential offenders. The second suggests that improved lighting signals community investment in the area and that the area is improving, leading to increased community pride, community cohesiveness, and informal social control. The first theory predicts decreases in crime especially during the hours of darkness, while the second theory predicts decreases in crime during both daytime and nighttime. Results of this review indicate that improved street lighting significantly reduces crime. This lends support for the continued use of improved street lighting to prevent crime in public space. The review also found that nighttime crimes did not decrease more than daytime crimes. This suggests that a theory of street lighting focusing on its role in increasing community pride and informal social control may be more plausible than a theory focusing on increased surveillance and increased deterrence. Future research should be designed to test the main theories of the effects of improved street lighting more explicitly, and future lighting schemes should employ high quality evaluation designs with long‐term followups. Abstract Background Improved street lighting is intended to serve many purposes, one of them being the prevention of crime. While street lighting improvements may not often be implemented with the expressed aim of preventing crime – pedestrian safety and traffic safety may be viewed as more important aims – and the notion of lighting streets to deter lurking criminals may be too simplistic, its relevance to the prevention of crime has been suggested in urban centers, residential areas, and other places frequented by criminals and potential victims. Objectives The main objective of this review is to assess the available research evidence on the effects of improved street lighting on crime in public space. In addition to assessing the overall impact of improved street lighting on crime, this review will also investigate in which settings, against which crimes, and under what conditions it is most effective. Search strategy Four search strategies were employed to identify studies meeting the criteria for inclusion in this review: (1) searches of electronic bibliographic databases; (2) searches of literature reviews on the effectiveness of improved street lighting in preventing crime; (3) searches of bibliographies of street lighting studies; and (4) contacts with leading researchers. Both published and unpublished reports were considered in the searches. Searches were international in scope and were not limited to the English language. Selection criteria Studies that investigated the effects of improved street lighting on crime were included. For studies involving one or more other interventions, only those studies in which improved street lighting was the main intervention were included. Studies were included if they had, at a minimum, an evaluation design that involved before‐and‐after measures of crime in experimental and control areas. There needed to be at least one experimental area and one reasonably comparable control area. Data collection & analysis Narrative findings are reported for the 13 studies included in this review. A meta‐analysis of all 13 of these studies was carried out. The “relative effect size” or RES (which can be interpreted as an incident rate ration) was used to measure effect size. Results are reported for total crime and, where possible, property and violent crime categories using (mostly) official data. In the case of studies that measure the impact of improved street lighting programs on crime at multiple points in time, similar time periods before and after are compared (as far as possible). The review also addresses displacement of crime and diffusion of crime prevention benefits. Main results The studies included in this systematic review indicate that improved street lighting significantly reduces crime, is more effective in reducing crime in the United Kingdom than in the United States, and that nighttime crimes do not decrease more than daytime crimes. Reviewers’ conclusions We conclude that improved street lighting should continue to be used to prevent crime in public areas. It has few negative effects and clear benefits for law‐abiding citizens.
View
Changing Places: The Science and Art of New Urban PlanningThe Science and Art of New Urban Planning
Book
  • Oct 2019
The design of every aspect of the urban landscape—from streets and sidewalks to green spaces, mass transit, and housing—fundamentally influences the health and safety of the communities who live there. It can affect people's stress levels and determine whether they walk or drive, the quality of the air they breathe, and how free they are from crime. This book provides a compelling look at the new science and art of urban planning, showing how scientists, planners, and citizens can work together to reshape city life in measurably positive ways. It demonstrates how well-designed changes to place can significantly improve the well-being of large groups of people. The book argues that there is a disconnect between those who implement place-based changes, such as planners and developers, and the urban scientists who are now able to rigorously evaluate these changes through testing and experimentation. It covers a broad range of structural interventions, such as building and housing, land and open space, transportation and street environments, and entertainment and recreation centers. Science shows we can enhance people's health and safety by changing neighborhoods block-by-block. The book explains why planners and developers need to recognize the value of scientific testing, and why scientists need to embrace the indispensable know-how of planners and developers. It reveals how these professionals, working together and with urban residents, can create place-based interventions that are simple, affordable, and scalable to entire cities.
View
Police Presence, Rapid Response Rates, and Crime Prevention
Article
  • Dec 2019
This paper estimates the impact of police presence on crime using a unique database that tracks the exact location of Dallas Police Department patrol cars throughout 2009. To address the concern that officer location is often driven by crime, my instrument exploits police responses to calls outside of their allocated coverage beat. This variable provides a plausible shift in police presence within the abandoned beat that is driven by the police goal of minimizing response times. I find that a 10 percent decrease in police presence at that location results in a 7 percent increase in crime. This result sheds light on the black box of policing and crime and suggests that routine changes in police patrol can significantly impact criminal behavior.
View
View
View