李欣, 周林, 贾涛, 吴昊, 邹宇量, 秦昆. 城市因素对COVID-19疫情的影响——以武汉市为例[J]. 武汉大学学报 ( 信息科学版), 2020, 45(6): 826-835. DOI: 10.13203/j.whugis20200152
引用本文: 李欣, 周林, 贾涛, 吴昊, 邹宇量, 秦昆. 城市因素对COVID-19疫情的影响——以武汉市为例[J]. 武汉大学学报 ( 信息科学版), 2020, 45(6): 826-835. DOI: 10.13203/j.whugis20200152
LI Xin, ZHOU Lin, JIA Tao, WU Hao, ZHOU Yuliang, QIN Kun. Influence of Urban Factors on the COVID-19 Epidemic: A Case Study of Wuhan City[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 826-835. DOI: 10.13203/j.whugis20200152
Citation: LI Xin, ZHOU Lin, JIA Tao, WU Hao, ZHOU Yuliang, QIN Kun. Influence of Urban Factors on the COVID-19 Epidemic: A Case Study of Wuhan City[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 826-835. DOI: 10.13203/j.whugis20200152

城市因素对COVID-19疫情的影响——以武汉市为例

Influence of Urban Factors on the COVID-19 Epidemic: A Case Study of Wuhan City

  • 摘要: 新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)疫情是一次重大的突发公共卫生事件,深入研究城市空间因素对疫情发展的影响对于未来城市安全具有重要意义。武汉市是遭受COVID-19疫情影响最为严重的城市,以武汉市三环内的中心城区作为研究区域,基于COVID-19疫情的新浪微博求助数据,揭示了COVID-19疫情在城市中的空间分布格局及其对不同区域的影响。在此基础上,根据疫情的主要传播途径及相关影响因素,选取社会人口、城市形态、城市设施、城市功能4项指标进行了验证。通过将研究区域进行网格化处理,运用地理加权回归模型对这些因素的效应、空间异质性以及影响范围进行分析,解释并反演出疫情在武汉城市空间中发生、传播、扩散的实际情况和作用机制。结果显示,三甲医院密度、商业密度、地铁站点密度、建设规模、老龄化、土地混合使用对疫情有显著影响。对城市空间因素的分析和验证有利于在未来的突发性公共安全危机中采取有效的城市规划和建筑设计应对,帮助城市决策者制定科学合理的防治策略,提前规避或减小对脆弱性区域和群体的冲击。

     

    Abstract: Coronavirus disease 2019 (COVID-19) is a major public health emergency, it is of great significance to study the influence of urban spatial factors on the development of epidemic situation for the future urban safety issues. Wuhan is affected most heavily by this epidemic situation. Based on Sina Weibo data posted in the core area of Wuhan city, we reveal the spatial distribution pattern of COVID-19 epidemic and its impacts in different urban areas of the city. According to the major suspected transmission routes and related factors of the epidemic, indicators of social population, urban morphology, urban facilities, and urban functions, are selected for validation. Through gridding the research area into uniform analytical units, we reveal the effect, spatial heterogeneity, and influence area of these factors, using the geographical weighted regression model. The result indicates that some factors, e.g. the densities of major hospitals, commercial facilities, subway stations, construction scale, aging, and land-use mixture, present significant influence on the epidemic severity.This research helps to explain and perform the mechanism of occurrence and spread of the epidemic in urban space. The analysis and validation of these urban factors help to adopt effective urban planning and architectural design responses in the future crisis, as help decision makers formulate more scientific and reasonable prevention strategies, and avoid or reduce the impact on vulnerable areas and groups in advance.

     

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