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美国社区的时间概况:社会基础设施场所的时间使用数据集。

Time Profile of U.S. Neighborhoods: Datasets of Time Use at Social Infrastructure Places.

作者信息

Wang Yan, Guo Ziyi

机构信息

Department of Urban and Regional Planning and Florida Institute for Built Environment Resilience, University of Florida, P.O. Box 115706, Gainesville, FL, 32611, USA.

出版信息

Sci Data. 2025 Aug 25;12(1):1476. doi: 10.1038/s41597-025-05504-9.

Abstract

Social infrastructure plays a critical role in shaping neighborhood well-being by fostering social and cultural interaction, enabling service provision, and encouraging exposure to diverse environments. Despite the growing knowledge of its spatial accessibility, time use at social-infrastructure places is underexplored due to the lack of a spatially resolved national dataset. We address this gap by developing scalable Social-infrastructure Time Use (STU) measures that capture length and depth of activity engagement, diversity, and spatial inequality, supported by the first-of-their-kind datasets spanning multiple geographic scales-from census tracts to metropolitan areas. Our datasets leverage anonymized and aggregated foot traffic data collected between 2019 and 2024 across 49 continental U.S. states. The data description reveals variances in STU across time, space, and differing neighborhood socio-demographic characteristics. Validation demonstrates generally robust population representation, consistent with established national survey findings while revealing more nuanced patterns. Future analyses could link STU with public health outcomes and environmental factors to inform targeted interventions aimed at enhancing population well-being and guiding social infrastructure planning and usage.

摘要

社会基础设施通过促进社会和文化互动、提供服务以及鼓励人们接触多样化的环境,在塑造社区福祉方面发挥着关键作用。尽管人们对其空间可达性的认识不断提高,但由于缺乏空间解析的全国性数据集,社会基础设施场所的时间利用情况尚未得到充分研究。我们通过开发可扩展的社会基础设施时间利用(STU)指标来填补这一空白,这些指标能够捕捉活动参与的时长和深度、多样性以及空间不平等性,并得到了从人口普查区到大都市区等多个地理尺度的同类首创数据集的支持。我们的数据集利用了2019年至2024年期间在美国大陆49个州收集的匿名和汇总的行人流量数据。数据描述揭示了STU在时间、空间以及不同社区社会人口特征方面的差异。验证表明,该指标总体上具有强大的人口代表性,与既定的全国性调查结果一致,同时揭示了更细微的模式。未来的分析可以将STU与公共卫生结果和环境因素联系起来,为旨在提高人口福祉以及指导社会基础设施规划和使用的有针对性的干预措施提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d806/12379198/c5ce41be5f62/41597_2025_5504_Fig1_HTML.jpg

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