Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: before, during, and after COVID-19ston County, South Carolina: Before, During, and After COVID-19.

IF 1 4区 医学 Q4 HEALTH CARE SCIENCES & SERVICES
Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-23 DOI:10.4081/gh.2025.1363
Wish Shao, Abolfazl Mollalo, Navid Hashemi Tonekaboni
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引用次数: 0

Abstract

While the COVID-19 pandemic significantly disrupted urban mobility in general, its effects on spatio-temporal foot traffic patterns remain insufficiently explored. This study addresses this issue by analysing foot traffic dynamics across various regions of Charleston County, South Carolina, before, during and after the pandemic. We examined changes across nine distinct stages of the pandemic from 2018 to 2022 at the sub-county level, utilizing point of interest data and public health records. Various machine learning models, including Random Forest, were employed to predict foot traffic trends, achieving high predictive accuracy with an R2 value of 0.88. Our findings reveal varying foot traffic patterns across the county. Prior to the pandemic, foot traffic was generally consistent across county subdivisions, maintaining steady levels in each area. The onset of the pandemic led to significant decreases in foot traffic across most subdivisions, followed by gradual recovery, with some areas surpassing pre-pandemic levels. These results underscore the need for tailored crisis management and urban planning, particularly in midsized counties with similar structures to inform more effective resource allocation and improve risk management in public safety during public health crises.

南卡罗来纳州查尔斯顿县:2019冠状病毒病之前、期间和之后的人流量动态时空分析
尽管2019冠状病毒病大流行总体上严重扰乱了城市交通,但其对步行交通时空格局的影响仍未得到充分探讨。本研究通过分析大流行之前、期间和之后南卡罗来纳州查尔斯顿县各个地区的人流量动态来解决这一问题。我们利用兴趣点数据和公共卫生记录,研究了2018年至2022年次县级大流行的九个不同阶段的变化。采用Random Forest等多种机器学习模型对人流量趋势进行预测,预测准确率较高,R2值为0.88。我们的发现揭示了全国各地不同的步行交通模式。在大流行之前,各个县的客流量基本一致,每个地区保持稳定水平。大流行的开始导致大多数细分区域的客流量大幅减少,随后逐渐恢复,一些地区超过了大流行前的水平。这些结果强调需要有针对性地进行危机管理和城市规划,特别是在结构类似的中型县,以便在公共卫生危机期间为更有效的资源分配提供信息,并改善公共安全方面的风险管理。
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来源期刊
Geospatial Health
Geospatial Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
2.40
自引率
11.80%
发文量
48
审稿时长
12 months
期刊介绍: The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.
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