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

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2025-01-18 DOI:10.1016/j.inffus.2024.102914
Xiwei Shen, Jie Kong, Yang Song, Xinyi Wang, Grant Mosey
{"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","authors":"Xiwei Shen, Jie Kong, Yang Song, Xinyi Wang, Grant Mosey","doi":"10.1016/j.inffus.2024.102914","DOIUrl":null,"url":null,"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.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"102 1","pages":""},"PeriodicalIF":14.7000,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.inffus.2024.102914","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
优化公共绿地的环境设计和管理:分析城市基础设施和长期用户体验,重点关注内华达州拉斯维加斯市的路灯密度
在拉斯维加斯和许多其他沙漠城市,独特的气候条件,以白天的高温为特征,自然鼓励居民在凉爽的晚上寻求户外娱乐活动。然而,路灯管理的方法并不理想,导致天黑后公园的照明不足。缺乏适当的照明不仅影响了安全,也影响了这些空间在夜间的享受机会,而在夏季炎热的时候,它们可以提供急需的喘息机会。最近的学术研究强调了由于糟糕的街道照明设计而阻碍了公园的使用,这指出了城市规划中一个更广泛的问题,即需要注意使基础设施适应当地气候,以造福公众健康和福祉。本研究旨在通过利用不同的数据来源和创建纵向测量方法来研究城市公园中不同地点的人口行为如何随时间变化,从而为现有的公园照明研究做出贡献。它试图通过使用时间固定效应方法来探索公园用户的人口统计数据的影响,特别是种族和收入水平的差异,以及街道照明密度对夜间公共绿地使用的影响。它旨在了解公园使用者和自然环境的人口多样性,特别是街道照明密度,如何影响公园夜间活动的模式。利用这一分析,我们通过比较多种类型的机器学习模型,开发了一种改进的预测模型,用于确定公共绿地中街道照明的密度。该模型将考虑用户的人口多样性和观察到的夜间使用模式,目标是提高夜间空间的可达性、安全性和利用率。这项研究的重要性有助于实现更广泛的目标,即创建有弹性、健康和包容的城市,以满足居民的福祉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
期刊最新文献
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 DF-BSFNet: A bilateral synergistic fusion network with novel dynamic flow convolution for robust road extraction KDFuse: A high-level vision task-driven infrared and visible image fusion method based on cross-domain knowledge distillation SelfFed: Self-adaptive Federated Learning with Non-IID data on Heterogeneous Edge Devices for Bias Mitigation and Enhance Training Efficiency DEMO: A Dynamics-Enhanced Learning Model for multi-horizon trajectory prediction in autonomous vehicles
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1