Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Sci Total Environ. 2021 Jun 10;772:145379. doi: 10.1016/j.scitotenv.2021.145379. Epub 2021 Jan 27.
Identifying the space-time patterns of areas with a higher risk of transmission and the associated built environment and demographic characteristics during the COVID-19 pandemic is critical for developing targeted intervention measures in response to the pandemic. This study aims to identify areas with a higher risk of COVID-19 transmission in different periods in Hong Kong and analyze the associated built environment and demographic factors using data of individual confirmed cases. We detect statistically significant space-time clusters of COVID-19 at the Large Street Block Group (LSBG) level in Hong Kong between January 23 and April 14, 2020. Two types of high-risk areas are identified (residences of and places visited by confirmed cases) and two types of cases (imported and local cases) are considered. The demographic and built environment features for the identified high-risk areas are further examined. The results indicate that high transport accessibility, dense and high-rise buildings, a higher density of commercial land and higher land-use mix are associated with a higher risk for places visited by confirmed cases. More green spaces, higher median household income, lower commercial land density are linked to a higher risk for the residences of confirmed cases. The results in this study not only can inform policymakers to improve resource allocation and intervention strategies but also can provide guidance to the public to avoid conducting high-risk activities and visiting high-risk places.
在 COVID-19 大流行期间，识别传播风险较高地区的时空模式以及相关的建成环境和人口特征对于制定有针对性的干预措施以应对大流行至关重要。本研究旨在识别香港不同时期 COVID-19 传播风险较高的地区，并使用个体确诊病例数据分析相关的建成环境和人口因素。我们在 2020 年 1 月 23 日至 4 月 14 日期间在香港的大型街道街区组（LSBG）水平上检测到 COVID-19 的时空集群具有统计学意义。确定了两种高风险地区（确诊病例的居住地和活动场所），并考虑了两种病例（输入病例和本地病例）。进一步研究了确定的高风险地区的人口和建成环境特征。结果表明，交通便利性高、建筑密度高且楼层高、商业用地密度高、土地利用混合度高与确诊病例活动场所的风险较高有关。更多的绿地、更高的家庭中位数收入、更低的商业用地密度与确诊病例居住地的风险较高有关。本研究的结果不仅可以为政策制定者提供信息，以改善资源分配和干预策略，还可以为公众提供指导，避免进行高风险活动和访问高风险场所。
