Can Enhanced LED Street Lighting Contribute to Reduce Violent Crime?

To prioritize light installations at locations identified as high-risk places for criminal behavior during nighttime hours, PSE&G partnered with NPSC in a project to replace obsolete HPS lighting with brighter and more energy-efficient LED light installations. To determine the areas in the greatest need for illumination, NPSC analysts utilized all reported violent crimes (aggravated assault, murder, robbery, and sexual assault) during nighttime hours over a 5-year time span. The project, which is currently in its implementation phase, offers an excellent opportunity to assess the impact of improved lighting conditions on violent crime across all five wards of the City of Newark.

Clinton Ave. (Upper Clinton Hill), Newark NJ

The geography of crime, a theoretical framework for understanding crime variations across space and time (Glaeser et al., 1996), has been widely studied. Whereas in the past, the geography of crime was largely based on the relationship of crime to social characteristics of a neighborhood and its residents (Vandeviver & Bernasco, 2017), recent studies have shifted their focus to the presence of criminogenic environmental features. Understanding where crime concentrates is imperative for a proactive approach to public safety. But understanding why crime concentrates at particular locations provides yet another layer of opportunity for remediation. An array of environmental risk factors impact crime trends, such as abandoned properties, hotels, bars, restaurants, convenience stores, etc. (Piza et al., 2017). Lighting conditions also have an impact on crime. For example, motor vehicle thefts are more prone to poorly lit areas (Levy & Tartaro, 2010). Thus, “Improved street lighting is widely thought to be an effective means of preventing crime, second in importance only to increased police presence” (Clarke, 2008).

At night, the perception of safety increases in well-lit areas by creating an enhanced perception of safety. Lighting improves visibility and decreases concealment in public settings, thus contributing to increased safety. In this sense, crime and light present a unique opportunity for public safety authorities and policymakers in their efforts to balance perceived community safety and energy waste concerns (Svechkina, A. et al. 2020). One’s unique perception of safety can be significantly affected by the immediate physical environment and the perceived potential for victimization (Haans & de Kort, 2012).

According to Xu et al. (2018), improving street lighting can be an affordable and effective way to reduce crime. Their study, conducted in 2014, examined the spatial relationship between street light density and violent crime, as well as neighborhood social disorganization. Their findings suggest that higher street light density has a positive effect at reducing crime victimization. Additionally, their study suggests that certain crime types have particular temporal characteristics. (Xu et. al, 2014).

The impact of poor lighting on violent crime was further analyzed in a randomized control experiment in New York City during the spring and summer of 2016, in which temporary high-power streetlights were randomly installed at selected public housing developments. A total of 80 housing developments were prioritized and chosen for this study based on outdoor/nighttime index of crime for the past two years. The study found that allocating temporary streetlights to public housing developments led to a 36% reduction in nighttime outdoor index crime (Chalfin et al, 2019). 

Clinton Ave. (Clinton Hill), Newark NJ

Whereas the relationship between crime and lighting conditions have been widely studied, authors like Haans & de Kort (2012) sought to understand the relationship between lighting and the perception of safety. Their study’s goal was to determine the impact of smart lighting, meaning lights that have the capacity to adapt to the environment on perceived safety. Three safety cues were evaluated: (1) People’s ability to clearly see their surroundings, (2) their ability to escape, and (3) the presence of hiding spaces for offenders. The study, conducted in The Netherlands, suggests that pedestrians feel safer at locations in which lighting conditions provide a clear overview of their surroundings, given that potential threats typically happen in close proximity (Haans & Kort, 2012). Moreover, a later study validated Haans’ and Kort’ arguments suggesting that brighter locations are associated with higher levels of reassurance, described as the confidence pedestrians may gain from walking along well lit roads (Fotios et. al, 2014).

Improved lighting is thought to have such a significant impact on crime, that the U.S. Department of Justice through its Office of Community Oriented Policing Services (COPS) released a guide for improving street lighting as a means to reduce crime in residential areas. The effect of enhanced lighting can be attributed to different factors, including informal surveillance by neighbors and police presence being more visible to offenders. Moreover, better lighting conditions can also increase community cohesiveness as well as promote recreational outdoor use of spaces (Clarke, 2008). Notably, the guide recommends developing joint community-police  strategies to address crime through lighting improvement. While crime prevention efforts have largely been the backbone of law enforcement operations, recent data-informed community engagement programs have shifted the focus away from traditional police-centric responses to crime. Understanding where and why crime happens enables community stakeholders to implement actionable change in their communities. Through data-informed community engagement (DICE), community stakeholders can participate in public safety efforts in a collective effort that combines data analytics with valuable community insights.

Through the use of shared data analytics, research, and a demonstrated commitment to community empowerment, the Newark Public Safety Collaborative (NPSC) seeks to continue bringing actionable public safety solutions to the City of Newark. 

References

Clarke, Ronald (2008). Improving Street Lighting to Reduce Crime in Residential Areas. U.S. Department of Justice Office of Community Oriented Policing Services. https://cops.usdoj.gov/RIC/Publications/cops-p156-pub.pdf

Chalfin, A., Parker, L., Lerner, J., & Hansen, B. (2019). Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City. AEA Randomized Controlled Trials. doi: 10.1257/rct.4186-1.0

Fotios, Steve & Unwin, J. & Farrall, Stephen. (2014). Road lighting and pedestrian reassurance after dark: A review. Lighting Research and Technology. 47. 10.1177/1477153514524587.

Glaeser, E. L., Sacerdote, B., & Scheinkman, J. A. (1996). Crime and social interactions. Cambridge, MA: National Bureau of Economic Research.

Haans, A., & Kort, Y. A. D. (2012). Light distribution in dynamic street lighting: Two experimental studies on its effects on perceived safety, prospect, concealment, and escape. Journal of Environmental Psychology, 32(4). doi: 10.1016/j.jenvp.2012.05.006

Levy MP, Tartaro C. Auto Theft: A site-survey and analysis of environmental crime factors in Atlantic City, NJ. Security journal. 2010;23(2):75-94. doi:10.1057/palgrave.sj.8350088

Piza E, Feng S, Kennedy L, Caplan J. Place-based correlates of Motor Vehicle Theft and Recovery: Measuring spatial influence across neighbourhood context. Urban studies (Edinburgh, Scotland). 2016;54(13):2998-3021. doi:10.1177/004209801666429

Svechkina, A., Trop, T., & Portnov, B. A. (2020). How much lighting is required to feel safe when walking through the streets at night? Sustainability, 12(8), 3133. doi:http://dx.doi.org.proxy.libraries.rutgers.edu/10.3390/su12083133

Vandeviver C, Bernasco W. The geography of crime and crime control. Applied geography (Sevenoaks). 2017;86(September):220-225. doi:10.1016/j.apgeog.2017.08.012

Xu, Y., Fu, C., Kennedy, E., Jiang, S., & Owusu-Agyemang, S. (2018). The impact of street lights on spatial-temporal patterns of crime in Detroit, Michigan. Cities, 79, 45–52. doi: 10.1016/j.cities.2018.02.021