Smart City Gnosys

Smart city article details

Title	Optimizing The Environmental Design And Management Of Public Green Spaces: Analyzing Urban Infrastructure And Long-Term User Experience With A Focus On Streetlight Density In The City Of Las Vegas, Nv
ID_Doc	40904
Authors	Shen X.; Kong J.; Song Y.; Wang X.; Mosey G.
Year	2025
Published	Information Fusion, 118
DOI	http://dx.doi.org/10.1016/j.inffus.2024.102914
Abstract	In Las Vegas and many other desert cities, the unique climatic conditions, marked by high daytime temperatures, naturally encourage residents to seek outdoor recreational activities during the cooler evening hours. However, the approach to streetlight management has been less than optimal, leading to inadequate illumination in public parks after dark. This lack of proper lighting compromises not only the safety but also the enjoyment opportunity of these spaces during the night, a time when they could offer a much-needed respite during summer heat. Recent scholarship has highlighted the deterrence of park usage due to poor design of the street lighting, pointing to a broader issue in urban planning that requires attention to adapt infrastructures to local climates for the benefit of public health and well-being. This study seeks to contribute to the existing scholarship on park lighting by utilizing diverse data sources and creating longitudinal measures to examine how population behaviors in urban parks vary over time in different locations. It seeks to explore the impact of park users’ demographics, particularly variations across race and income levels, and the density of street lighting on the nighttime usage of public green spaces by using the time fixed effect method. It aims to understand how demographic diversity among park users and the physical environment, specifically street lighting density, influences patterns of nighttime activities in public parks. Using this analysis, we develop an improved predictive model for determining the density of street lighting in public green spaces by comparing multiple types of machine learning models. This model will consider the demographic diversity of users and the observed patterns of nighttime usage, with the goal of enhancing accessibility, safety, and utilization of these spaces during nighttime hours. The significance of this research contributes to the broader objective of creating resilient, healthy, and inclusive cities that cater to the well-being of their residents. © 2024
Author Keywords	Energy efficiency; Machine learning; Multimodal data fusion, Sustainable development of smart cities; Streetlight density; Time Fixed Effect

Similar Articles

Id	Similarity	Authors	Title	Published
35050	0.888	Gravitz-Sela S.; Shach-Pinsly D.; Bryt O.; Plaut P.	Leveraging City Cameras For Human Behavior Analysis In Urban Parks: A Smart City Perspective	Sustainability (Switzerland), 17, 3 (2025)
49286	0.874	Martinez J.; Mahajan S.	Smart Cities And Access To Nature: A Framework For Evaluating Green Recreation Space Accessibility	IEEE Access, 11 (2023)
56970	0.868	Rozman Cafuta M.	The Sustainability Coefficient Of Urban Open Space Illumination Compliance As A Subjective Indicator Of Environmental Comfort	Applied Sciences (Switzerland), 14, 22 (2024)
43911	0.862	Mashhadi A.; Winder S.G.; Lia E.H.; Wood S.A.	Quantifying Biases In Social Media Analysis Of Recreation In Urban Parks	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 (2020)
425	0.853	Ahsissene S.; Rhziel F.Z.; Raissouni N.	A Bibliometric Analysis Of Outdoor Thermal Comfort Research In Smart Cities: Trends And Methodologies	Solar Energy and Sustainable Development, 2024, Special Issue (2024)
23402	0.851	Pincetl S.; Gustafson H.; Federico F.; Fournier E.D.; Cudd R.; Porse E.	Energy Use In Cities: A Roadmap For Urban Transitions	Energy Use in Cities: A Roadmap for Urban Transitions (2020)