Spatial Analytics for Enhancing Street Light Coverage of Public Spaces

ABSTRACT The spatial distribution of public area lighting in an urban region greatly influences human activities and safety. Such services are not free, often consuming significant budgetary resources to site, maintain, and operate on an annual basis. Further, lighting has been found to negatively impact the environment by limiting visibility in the form of glare, trespass, and sunglow as well disturbing diurnal rhythms, breeding behavior, and migration patterns of fauna, not to mention impacts and costs attributable to energy generation. Enhancing configurations of nighttime lighting sources is therefore important on many fronts to address fiscal limitations and sustainability concerns. There are many implications for system inefficiency that arise due to growth and development, necessitating systematic evaluation approaches. Light provision can be assessed using spatial optimization. Both benefits and impacts can be taken into account, enabling a range of analytical methods to be brought to bear on decision making and policy associated with public lighting. This article develops a methodology for studying lighting in an urban area based on the use of geographic information systems, spatial statistics and spatial optimization. Relationships between observed patterns of nighttime light are deduced relative to energy costs. Models are developed and applied to identify service tradeoffs to support planning efforts. Application results highlight the utility and insight possible through the use of an integrated spatial analytical framework.

[1]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[2]  D. Farrington,et al.  PROTOCOL: Effectiveness of Programs to Prevent School Bullying , 2008 .

[3]  Alan T. Murray,et al.  Public street lighting service standard assessment and achievement , 2016 .

[4]  A Antal Haans,et al.  Light distribution in dynamic street lighting: Two experimental studies on its effects on perceived safety, prospect, concealment, and escape , 2012 .

[5]  Anja Vogler Business Site Selection Location Analysis And Gis , 2016 .

[6]  H. Herring,et al.  Technological innovation, energy efficient design and the rebound effect , 2007 .

[7]  K. Clarke Getting Started with Geographic Information Systems , 1996 .

[8]  Martin Moeck Constraint Satisfaction Software for Architectural Lighting Design: A Case Study , 2004 .

[9]  Adam Sędziwy,et al.  A New Approach to Street Lighting Design , 2016 .

[10]  Brandon C. Welsh,et al.  Effects of Improved Street Lighting on Crime: A Systematic Review , 2002 .

[11]  António Augusto de Sousa,et al.  Lighting Design: A Goal Based Approach using Optimisation , 1999, Rendering Techniques.

[12]  B. Griefahn,et al.  The Dark Side of Light: A Transdisciplinary Research Agenda for Light Pollution Policy , 2010 .

[13]  U. Territory,et al.  The dark side of light , 2018, Nature.

[14]  Alan T. Murray Maximal Coverage Location Problem , 2016 .

[15]  Aie World Energy Outlook 2000 , 2000 .

[16]  Roadway Lighting , 2014 .

[17]  Pierre Desprairies,et al.  World Energy Outlook , 1977 .

[18]  Alan T. Murray Advances in location modeling: GIS linkages and contributions , 2010, J. Geogr. Syst..

[19]  Dessislava A. Pachamanova,et al.  Optimization of the light distribution of luminaries for tunnel and street lighting , 2008 .