Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
School of Geography and Environment, Jiangxi Normal University, Nanchang, China.
Front Public Health. 2023 Apr 5;11:1128889. doi: 10.3389/fpubh.2023.1128889. eCollection 2023.
This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.
Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.
Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.
Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.
本研究旨在为形成超级传播环境的空间风险提供科学依据。
本研究聚焦于六种常见的城市设施（酒吧、电影院、健身房和健身中心、礼拜场所、公共图书馆和购物中心），首先测试不同类型的设施和不同位置的访客移动特征是否存在系统差异。本研究在芝加哥、中国香港、伦敦、圣保罗、首尔和苏黎世收集了详细的人类移动和其他位置数据。然后，考虑到设施集聚、访客特征和人口密度，将设施分为四个潜在空间风险（PSR）类别。最后，根据空间风险类别和活动性质，利用核密度函数为每个城市推导出风险曲面。
人类移动性分析的结果反映了不同城市中各种设施的地理和文化背景、交通特征和人们的生活方式。在六个全球城市中，地理集聚是酒吧的风险因素。对于其他城市设施，缺乏集聚是一个风险因素。根据空间风险图，在每个城市都确定并讨论了一些超级传播的高风险区域。
在风险模型中整合活动-出行模式有助于识别吸引高移动性访客并有利于超级传播的区域。基于研究结果，本研究提出了一种基于场所的非药物干预策略，平衡控制疫情和城市人口的日常生活。
