-
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.
-
-
![]()
图 1 武汉三环内的新浪微博求助数据空间分布
Figure 1. Spatial Distribution of Sina Weibo Help Data Within the 3rd Ring Road of Wuhan
![]()
图 3 三甲医院密度的影响效应和显著性范围
Figure 3. Influence Effect and Significance Range of Density of Tertiary Hospital with Grade A
![]()
图 4 商业密度的影响效应和显著性范围
Figure 4. Influence Effect and Significance Range of Business Density
![]()
图 5 建设规模的影响效应和显著性范围
Figure 5. Influence Effect and Significance Range of Construction Scale
![]()
图 7 地铁站点密度的影响效应和显著性范围
Figure 7. Influence Effect and Significance Range of Subway Station Density
![]()
图 8 土地混合使用的影响效应和显著性范围
Figure 8. Influence Effect and Significance Range of Land Mixed Use
表 1 武汉各行政区中新浪微博求助数据与确诊人数的分布
Table 1 Distribution of Sina Weibo Help Data and Confirmed Cases Within Selected Districts of Wuhan
统计项 江岸区 江汉区 硚口区 汉阳区 武昌区 青山区 洪山区 合计 累计确诊人数(2020-03-05)/人 6 521 5 137 6 789 4 661 7 431 2 773 4 652 37 964 新浪微博求助人数/人 72 35 50 37 62 22 49 327 卡方值 9.977 显著性p值 0.126 
下载: 导出CSV
表 2 OLS模型结果
Table 2 Result of the OLS Model
检验变量 回归系数 稳健标准误差 稳健t值 稳健p值 VIF 截距 -0.002 0.034 -0.054 0.957 老龄化/(100人·km-2) 0.279 0.534 5.215 < 0.001*** 2.492 建筑容积率 0.145 0.053 2.729 0.007*** 2.266 三甲医院密度 0.333 0.121 2.754 0.006*** 2.530 地铁站点密度 0.086 0.030 2.886 0.004*** 2.534 商业密度 0.016 0.003 6.325 < 0.001*** 3.603 道路密度 0.002 0.004 0.528 0.598 1.515 土地利用混合度 -0.075 0.038 -1.978 0.048* 1.134 注：***表示p < 0.001，*表示p < 0.05
下载: 导出CSV
表 3 模型诊断指标
Table 3 Models' Diagnosis Indexes
诊断指标 OLS模型 GWR模型 F值 128.498 Wald值 717.899 R2 0.646 0.871 调整R2 0.641 0.822 局部R2 0.263~0.855 AICc 320.042 63.111
下载: 导出CSV
表 4 空间自相关检验
Table 4 Test of Spatial Autocorrelation
检验模型 Moran’s I z值 p值 零模型 0.796 32.886 < 0.001 OLS模型 0.611 25.252 < 0.001 GWR模型 0.314 13.036 < 0.001
下载: 导出CSV
表 5 GWR模型结果
Table 5 Result of the GWR Model
检验变量 回归系数下限 回归系数上限 标准误差下限 标准误差上限 截距 -0.993 0.460 0.071 0.335 老龄化/(100人·km-2) -1.268 1.176 0.087 3.714 6 建筑容积率 -0.672 1.110 0.096 0.738 三甲医院密度 -2.905 2.135 0.157 4.533 地铁站点密度 -0.790 0.414 0.042 0.927 商业密度 -0.012 0.082 0.003 0.038 土地利用混合度 -0.303 0.474 0.078 0.285
下载: 导出CSV
-
[1] 周成虎, 裴韬, 杜云艳, 等.新冠肺炎疫情大数据分析与区域防控政策建议[J].中国科学院院刊, 2020, 35(2):200-203 http://d.old.wanfangdata.com.cn/Periodical/zgkxyyk202002011 Zhou Chenghu, Pei Tao, Du Yunyan, et al. Big Data Analysis on COVID-19 Epidemic and Suggestions on Regional Prevention and Control Policy[J]. Bulletin of Chinese Academy of Sciences, 2020, 35(2):200-203 http://d.old.wanfangdata.com.cn/Periodical/zgkxyyk202002011
[2] 李秉毅, 张琳. SARS爆发对我国城市规划的启示[J].城市规划, 2003, 27(7):71-72 http://d.old.wanfangdata.com.cn/Periodical/csgh200307016 Li Bingyi, Zhang Lin. The Inspiration of Outbreak of SARS to the Urban Planning in China[J]. City Planning Review, 2003, 27(7):71-72 http://d.old.wanfangdata.com.cn/Periodical/csgh200307016
[3] 李煜.城市易致病空间理论[M].北京:中国建筑工业出版社, 2016 Li Yi. The Theory of Urban Susceptible Space[M]. Beijing:China Architecture and Building Press, 2016
[4] Matthew R A, Mcdonald B. Cities Under Siege:Urban Planning and the Threat of Infectious Disease[J]. Journal of the American Planning Association, 2006, 72(1):109-117
[5] Lee V, Aguilera X, Heymann D, et al. Preparedness for Emerging Epidemic Threats:A Lancet Infectious Diseases Commission[J]. The Lancet Infectious Diseases, 2020, 20(1):17-19
[6] 刘滨谊, 郭璁.通过设计促进健康——美国"设计下的积极生活"计划简介及启示[J].国际城市规划, 2006, 21(2):60-66 Liu Binyi, Guo Cong. Promoting Health Through Design:A Brief Introduction of "Active Living by Design", an American National Program[J]. Urban Planning International, 2006, 21(2):60-66
[7] Feng J X, Tang S S, Chuai X W. The Impact of Neighbourhood Environments on Quality of Life of Elderly People:Evidence from Nanjing, China[J]. Urban Studies, 2018, 55(9):2020-2039
[8] 谭纵波.公共卫生突发事件引发的国土空间规划思考[J].中国土地, 2020(3):8-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtd202003003 Tan Zongbo. Thinkings on Land Space Planning Caused by Public Health Emergencies[J]. China Land, 2020(3):8-12 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtd202003003
[9] 石义, 吕维娟.基于公共卫生安全的国土空间规划再认识——结合武汉新冠肺炎疫情防控实际[J].中国土地, 2020(3):4-7 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtd202003002 Shi Yi, Lü Weijuan. Recognition of Land Space Planning Based on Public Health Security-Combined with the Actual Situation of Prevention and Control of COVID-19 in Wuhan[J]. China Land, 2020(3):4-7 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgtd202003002
[10] 唐燕.新冠肺炎疫情防控中的社区治理挑战应对:基于城乡规划与公共卫生视角[J].南京社会科学, 2020(3):8-14, 27 Tang Yan. Challenges and Responses of Community Governance in the Prevention and Control of Novel Coronary Pneumonia:From Perspectives of Urban-Rural Planning and Public Health[J]. Nanjing Journal of Social Sciences, 2020(3):8-14, 27
[11] Griffith D A, Amrhein C G, Desloges J R. Statistical Aanalysis for Geographers[J]. Journal of the American Statistical Association, 1999, 94(446):654
[12] 任致远. SARS与城市座谈会发言摘要[J].城市发展研究, 2003, 10(4):1-7 http://d.old.wanfangdata.com.cn/Periodical/csfzyj200304001 Ren Zhiyuan. Summary of Symposium on Urban Design and Practice[J]. Urban Planning Forum, 2003, 10(4):1-7 http://d.old.wanfangdata.com.cn/Periodical/csfzyj200304001
[13] Wang J F, Meng B, Zheng X Y, et al. Analysis on the Multi-distribution and the Major Influencing Factors on Severe Acute Respiratory Syndrome in Beijing[J]. Chinese Journal of Epidemiology, 2005, 26(3):164-168
[14] Verity R, Okell L, Dorigatti I, et al. Estimates of the Severity of Coronavirus Disease 2019:A Model-Based Analysis[J]. Lancet, 2020, DOI: 10.1016/S1473-3099(20)30243-7
[15] Mcmillen D P. Geographically Weighted Regression:The Analysis of Spatially Varying Relationships[J]. American Journal of Agricultural Economics, 2004, 86(2):554-556
[16] Cervero R.Planned Communities, Self-Containment and Commuting:A Cross-National Perspective[J]. Urban Studies, 1995, 32(7):1135-1161 doi: 10.1080-00420989550012618/
[17] Cervero R. Mixed Land-Uses and Commuting:Evidence from the American Housing Survey[J]. Transportation Research Part A, 1996, 30(5):361-377 doi: 10.1016-0965-8564(95)00033-X/
[18] Zhao P.Spatial Evolution of Job-Housing Relationship and Its Differentiation in Sectors in Central Area of Guangzhou from 2000[J]. Urban Development Studies, 2018, 25(9):108-116 http://d.old.wanfangdata.com.cn/Periodical/csfzyj201809012
[19] 赵鹏军.土地集约利用对可持续城市交通的作用:基于国际文献理论分析[J].城市发展研究, 2018, 25(9):108-116 http://d.old.wanfangdata.com.cn/Periodical/csfzyj201809016 Zhao Pengjun. The Impacts of Land Use Intensification on Urban Transport Sustainability:Theoretical Thinking from Literature Review[J]. Urban Development Studies, 2018, 25(9):108-116 http://d.old.wanfangdata.com.cn/Periodical/csfzyj201809016
[20] 陈春, 陈勇, 于立.为健康城市而规划——建成环境与老年人身体质量指数关系研究[J].城市发展研究, 2017, 24(4):7-13 http://d.old.wanfangdata.com.cn/Periodical/csfzyj201704002 Chen Chun, Chen Yong, Yu Li. Planning for Healthy City:The Influence of the Built Environment on the Body Mass Index of the Elderly[J]. Urban Development Studies, 2017, 24(4):7-13 http://d.old.wanfangdata.com.cn/Periodical/csfzyj201704002
-
期刊类型引用(12)
1. 徐燕飞，胡振琪，陈永春，崔瑞豪，苗伟，冯占杰. 基于遥感生态指数的淮南矿区生态环境质量变化分析. 煤炭工程. 2025(01): 195-202 . 
百度学术
2. 张浩斌，王婉，宋妤婧，苗林光，马超. 基于改进遥感生态指数的干旱内流区生态质量评价——以阴山北麓塔布河流域为例. 生态学报. 2024(02): 523-543 . 百度学术
3. 张昊杰，杨立娟，施婷婷，王帅. GF-6 WFV传感器数据的缨帽变换系数推导. 自然资源遥感. 2024(02): 105-115 . 百度学术
4. 钟安亚，孙娟，胡春明，谷海红. 基于RSEI的北京王平煤矿生态环境修复效果评估及预测. 矿业安全与环保. 2023(04): 89-96 . 百度学术
5. 王新驰，鲁铁定，龚循强，周秀芳. 基于改进遥感生态指数的南昌市生态环境质量监测与评价. 科学技术与工程. 2023(35): 15319-15327 . 百度学术
6. 吴群英，苗彦平，陈秋计，侯恩科，李继业. 基于Sentinel-2的荒漠化矿区生态环境监测. 采矿与岩层控制工程学报. 2022(01): 91-98 . 百度学术
7. 晏红波，杨志高，卢献健，韦晚秋，黎振宝. 基于改进RSEDI的典型喀斯特地区生态环境质量时空变化. 科学技术与工程. 2022(11): 4646-4653 . 百度学术
8. 刘建强，叶小敏，陈鋆. 面向鄱阳湖洪涝风险分析的HY-1C/D卫星CZI影像水体面积与水位关系研究. 华中师范大学学报(自然科学版). 2022(03): 505-512 . 百度学术
9. 史宇骁，李阳，孟翊，赵志远，张婷玉，王栋，袁琳. 1989—2020年长江口九段沙湿地格局演变及影响因素. 应用生态学报. 2022(08): 2229-2236 . 百度学术
10. 梁齐云，苏涛，张灿，王建，周丽丽. 基于改进遥感生态指数的黄山市生态质量评价研究. 地球物理学进展. 2022(04): 1448-1456 . 百度学术
11. 程琳琳，王振威，田素锋，柳亚彤，孙梦尧，杨玉曼. 基于改进的遥感生态指数的北京市门头沟区生态环境质量评价. 生态学杂志. 2021(04): 1177-1185 . 百度学术
12. 陈超，陈慧欣，陈东，张自力，张旭锋，庄悦，褚衍丽，陈建裕，郑红. 舟山群岛海岸线遥感信息提取及时空演变分析. 国土资源遥感. 2021(02): 141-152 . 百度学术
其他类型引用(21)
计量
- 文章访问数: 2962
- HTML全文浏览量: 632
- PDF下载量: 332
- 被引次数: 33
