Public street lighting service standard assessment and achievement
Introduction
Street lighting is an important component of the built environment, contributing to the charm and character of neighborhoods and business districts alike but also serving to make areas safer in various ways. Lighting can reduce crime and violence as well as decrease the likelihood of pedestrian, bike and/or vehicle accidents. Yet, there are quantifiable costs to providing public lighting, consuming significant tax dollars to maintain and operate not to mention the environmental impacts attributable to electricity generation/usage, product manufacturing, etc. Of course there are many non-quantifiable impacts as well, such as physiological disruptions, general altering and reshaping of ecosystems, and contributing to light pollution. Because of the contrast of benefits and impacts, street lighting is a curious public service. Traditionally little systematic planning of spatial efficiency has gone into street lighting, especially once lights have been installed. Increasingly, however, communities across the globe are re-thinking street lighting in various ways. Some communities have turned them off to save money. Others have begun to explore more energy efficient alternatives. What has not yet happened is system-wide re-assessment of street lighting that takes into account competing objectives and concerns.
The benefits of nighttime lighting are very compelling, no doubt justifying their widespread adoption. Street lighting reduces/eliminates opportunities for criminal behavior that can be attributable to urban layout and structure, but also the fear of crime [22]. The reason for this is that light increases surveillance potential, improving visibility and making perpetrator detection more likely. Beyond this, lighting can reduce the chance of pedestrian, bike and vehicle (or some combination thereof) accidents [23], [31]. Finally, lighting enhances community pride, neighborhood cohesiveness, and informal social control [18].
The impacts of artificial nighttime lighting are also undeniable, consuming nearly 20% of total global electricity and accounting for greenhouse gas emissions of some 1900 Mt of CO2 per year [43]. Of course, electricity consumption translates into real operational costs for public street lighting. Continuing economic challenges are proving to be motivators for cities and communities (Detroit, Colorado Springs, Santa Rosa, Rockford and others in the U.S. and Surrey, Essex, Northamptonshire and others in the UK) to dim, turn off or remove street lighting in order to save thousands to millions of dollars per year [4], [5], [14], [21]. Beyond direct operational costs, lighting has been found to have serious physiological impacts on humans as well as animal and plant populations [19], [25], [44]. For humans, this includes disrupting sleep patterns, increased risk of cancer and degraded air quality. For animals, the impacts of artificial light have been linked to adverse changes in feeding, reproduction and migration patterns [29], [45].
Nighttime lighting of streets is an interesting planning problem given the compelling need for light yet there are competing considerations having to do with costs and impacts. To support a balanced assessment as well as contribute to informed planning and management, this paper details a framework for assessing/planning public street lighting based on location modeling. The next section offers a literature review, including street lighting standards relied upon in planning as well as spatial optimization modeling research. This is followed by details regarding the applied analytical framework, including the specification of the location model. Case study results are then presented. The paper ends with discussion and concluding comments.
Section snippets
Background
The provision of public street lighting is essentially regulated through local planning standards and guidelines derived from state/federal agencies. A prominent resource for establishing standards and guidelines is the U.S. Department of Transportation, Federal Highway Administration, and in particular the FHWA Lighting Handbook [31]. This handbook details appropriate street light layouts, such as one-sided, opposite, staggered, median, etc., depending on the type of road but also based on
Methods
The needs of an area/neighborhood dictate good placement of public street lights. In particular, lighting requirements are location dependent, taking into account streets, sidewalks, travel patterns, behavioral characteristics, safety, security, etc. Given this, a range of detailed spatial information is necessary to support analysis and planning, hopefully translating to a good understanding of needs. Further, processing geographically based data is essential for deriving properties and
Street lighting
Issues associated with public street lighting were investigated using an existing system serving a neighborhood, primarily focusing on service efficiency relative to established standards and guidelines. The analysis was carried out on an Intel i7 (2.70 GHz) computer running Windows 7 64 bit with 16 GB of RAM. ArcGIS 10.2.2 was utilized for data creation, management, manipulation, analysis and display. A Python library, Shapely, was relied upon to structure the integer program, implementing a
Discussion and conclusions
Maintaining standards and guidelines associated with the provision of public street lighting is both important and necessary. Lighting enhances safety and security in neighborhoods, as well as contributes to its charm and character. Cities establish street lighting standards as a mechanism for addressing these concerns, but doing so in a manner that manages costs and impacts on the environment.
The primary benefits of lighting are human safety and security. Such benefits are embodied in
Acknowledgments
This research benefited from seed funding provided by the College of Liberal Arts and Sciences as well as the School of Geographical Sciences and Urban Planning at Arizona State University. The authors also thank Dr. Ran Wei (University of Utah) for assistance with model solution quality assurance.
Alan Murray (BS, MA, PhD UC Santa Barbara) is Professor in the Department of Geography at the University of California at Santa Barbara. He formerly held appointments at Drexel University, Arizona State University and Ohio State University. He is currently an editor of International Regional Science Review, associate editor for Socio-Economic Planning Sciences and Annals of the Association of American Geographers, and former editor of Geographical Analysis. His research and teaching interests
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Alan Murray (BS, MA, PhD UC Santa Barbara) is Professor in the Department of Geography at the University of California at Santa Barbara. He formerly held appointments at Drexel University, Arizona State University and Ohio State University. He is currently an editor of International Regional Science Review, associate editor for Socio-Economic Planning Sciences and Annals of the Association of American Geographers, and former editor of Geographical Analysis. His research and teaching interests include: geographic information science; health informatics; land use planning; urban, regional, and natural resource planning and development; quantitative methods; infrastructure and transportation systems; spatial optimization; location modeling; databases and data structures; spatial representation; and techniques to support interactive planning and decision making. He is the author of two books and over 200 research articles, book chapters and proceedings papers.
Xin Feng is a Ph.D. student in the College of Computing and Informatics at Drexel University. She completed her B.S. and M.S. in Geographical Information System at Wuhan University and Peking University, respectively. Her current research interests include geographic information science; urban, regional, and natural resource planning and development; spatial optimization; and remote sensing.