地理学报 ›› 2020, Vol. 75 ›› Issue (11): 2505-2520.doi: 10.11821/dlxb202011017

• COVID-19疫情影响分析 • 上一篇    下一篇

COVID-19疫情对中国城市人口迁徙的短期影响及城市恢复力评价

童昀1(), 马勇2(), 刘海猛3   

  1. 1. 海南大学旅游学院,海口 570228
    2. 湖北大学商学院,武汉 430062
    3. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2020-03-03 修回日期:2020-10-03 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 马勇
  • 作者简介:童昀(1991-), 男, 安徽合肥人, 博士, 讲师, 研究方向为区域绿色发展与生态旅游经济。E-mail: tongyuntour@163.com
  • 基金资助:
    国家社会科学基金项目(19BJL036);国家自然科学基金项目(41801164)

The short-term impact of COVID-19 epidemic on the migration of Chinese urban population and the evaluation of Chinese urban resilience

TONG Yun1(), MA Yong2(), LIU Haimeng3   

  1. 1. Tourism College of Hainan University, Haikou 570228, China
    2. Business School of Hubei University, Wuhan 430062, China
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2020-03-03 Revised:2020-10-03 Online:2020-11-25 Published:2021-01-25
  • Contact: MA Yong
  • Supported by:
    National Social Science Foundation of China(19BJL036);National Natural Science Foundation of China(41801164)

摘要:

新型冠状病毒肺炎(COVID-19)疫情对中国国民经济和社会发展产生剧烈冲击。科学评价中国受新冠疫情短期影响及恢复情况并揭示其时空特征,可为常态化疫情防控阶段的经济形势研判和城市恢复提供有力支撑。基于2020年1月13日—4月8日百度迁徙大数据,通过构建恢复指数(RRI)和恢复缺口(RGI)等指标,从多尺度揭示中国受COVID-19疫情短期影响的逐日特征、阶段特征以及时空格局。结果发现:① 疫情未影响春节前返乡迁徙,节后恢复经历恢复停滞期、快速恢复期、平稳恢复期,全国总体恢复程度由恢复停滞期不足20%上升至快速恢复期末60%左右,3月3日开始进入平稳恢复期,恢复指数达70%以上,完全恢复至历史同期水平仍需较长时间。② 疫情对周末和节假日城市间交往活动影响显著,中部和东北地区尤为明显。③ 疫情影响的区域差异性明显,相对恢复程度西部>东部>中部>东北地区。④ 城市间恢复程度差异显著,节后至4月8日呈现南高北低空间格局。结合疫情程度,广州、深圳、重庆处于高确诊高恢复聚类,河北、天津、黑龙江、河南、安徽、湖南处于低确诊低恢复聚类。⑤ 随着疫情有效控制,城市层面恢复缺口由京津冀、长三角、珠三角等城市群的大规模成片劳动力迁入缺口,转变为国家中心城市和部分省会城市的点状缺口。本文研究结果表明时空大数据在重大突发公共卫生事件实时影响评价方面具有较好应用前景。

关键词: COVID-19, 城市韧性, 时空演化, 迁徙大数据, 人地关系, 中国

Abstract:

The COVID-19 epidemic in 2020 has a severe impact on China's national economic and social development. Evaluating the short-term impact of the COVID-19 epidemic and the recovery of China's economy and society, as well as revealing its spatiotemporal characteristics, can provide a strong support for the economic situation research and urban restoration of the normalized epidemic prevention and control stage. Based on Baidu migration big data from January 13 to April 8 in 2020 and that of the same period in history, this paper constructs the Relative Recovery Index (RRI) and Recovery Gap Index (RGI). Furthermore, it reveals the daily characteristics, stage characteristics, and spatiotemporal patterns of the short-term impact of the COVID-19 epidemic at multiple scales. The results are as follows: (1) The outbreak did not affect the travel rush before the Spring Festival. The process after the Spring Festival experienced a recovery stagnation period, a rapid recovery period, and a slow recovery period. The overall degree of recovery nationwide rose from less than 20% during the stagnation period to about 60% at the end of the rapid recovery period. The slow recovery period began on March 3, with a recovery index of over 70%. It will take a long time to fully recover to the historical level. (2) The intercity activities on weekends and in holidays were significantly weakened, especially in the central and northeastern regions. (3) The impact of the epidemic on each region is significantly different, in terms of the RRI, the western region > eastern region > central region > northeastern region. (4) The degree of recovery varies significantly between cities. From the Spring Festival to April 8th, the spatial pattern was high in the south and low in the north. According to the severity of the epidemic, Guangzhou, Shenzhen and Chongqing are in the cluster of High confirmed case-High recovery; Hebei, Tianjin, Heilongjiang, Henan, Anhui and Hunan are in the cluster of Low confirmed case-Low recovery. (5) With the effective control of the epidemic, the recovery gap has shifted from the large-scale insufficiency of labor force in the urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta into the insufficiency in the central cities and some provincial capital cities. The results of this paper show that the use of spatiotemporal big data for real-time impact assessment of major public health emergencies have good application prospects.

Key words: COVID-19, urban resilience, spatiotemporal evolution, migration big data, human-land relationship, China