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

doi:10.11821/dlxb202011017

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. 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. 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/Abstract

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

童昀, 马勇, 刘海猛. 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[J]. 地理学报, 2020, 75(11): 2505-2520.

TONG Yun, MA Yong, LIU Haimeng. The short-term impact of COVID-19 epidemic on the migration of Chinese urban population and the evaluation of Chinese urban resilience[J]. Acta Geographica Sinica, 2020, 75(11): 2505-2520.

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参考文献 33

[1]	Gong Shengsheng, Xie Haichao, Chen Fahu. Spatiotemporal change of epidemics and its relationship with human living environments in China over the past 2200 years. Scientia Sinica: Terrae, 2020,50(5):719-722.
	[ 龚胜生, 谢海超, 陈发虎. 2200年来我国瘟疫灾害的时空变化及其与生存环境的关系. 中国科学: 地球科学, 2020,50(5):719-722.]
[2]	Zhang Yunfei. To promote coordination of the construction between "Beautiful China" and "Healthy China". Journal of Fujian Normal University (Philosophy and Social Sciences Edition), 2020(2):21-26, 167.
	[ 张云飞. 统筹推进“美丽中国”建设和“健康中国”建设: 基于防控新型冠状病毒感染肺炎疫情阻击战的思考. 福建师范大学学报(哲学社会科学版), 2020(2):21-26, 167.]
[3]	Liu H M, Fang C L, Fang K. Coupled human and natural cube: A novel framework for analyzing the multiple interactions between humans and nature. Journal of Geographical Sciences, 2020,30(3):355-377.
[4]	Zhen Feng, Wang Bo. Rethinking human geography in the age of big data. Geographical Research, 2015,34(5):803-811.
	[ 甄峰, 王波. “大数据”热潮下人文地理学研究的再思考. 地理研究, 2015,34(5):803-811.]
[5]	Cheng Changxiu, Shi Peijun, Song Changqing, et al. Geographic big-data: A new opportunity for geography complexity study. Acta Geographica Sinica, 2018,73(8):1397-1406.
	[ 程昌秀, 史培军, 宋长青, 等. 地理大数据为地理复杂性研究提供新机遇. 地理学报, 2018,73(8):1397-1406.]
[6]	Yi Jiawei, Wang Nan, Qianjiale, et al. Spatio-temporal responses of urban road traffic and human activities in an extreme rainfall event using big data. Acta Geographica Sinica, 2020,75(3):497-508.
	[ 易嘉伟, 王楠, 千家乐, 等. 基于大数据的极端暴雨事件下城市道路交通及人群活动时空响应. 地理学报, 2020,75(3):497-508.]
[7]	Long Yan, Mao Mingrui, Mao Qizhi, et al. Fine-scale urban modeling and its opportunities in the "Big Data" era: Methods, data and empirical studies. Human Geography, 2014,29(3):7-13.
	[ 龙瀛, 茅明睿, 毛其智, 等. 大数据时代的精细化城市模拟: 方法、数据和案例. 人文地理, 2014,29(3):7-13.]
[8]	Tong Yun, Ma Yong, Liu Jun, et al. Evolution and prediction of the spatial pattern of hotel industry supported by big data: A case study of Wuhan. Tourism Tribune, 2018,33(12):76-87.
	[ 童昀, 马勇, 刘军, 等. 大数据支持下的酒店业空间格局演进与预测: 武汉案例. 旅游学刊, 2018,33(12):76-87.]
[9]	Deng Chuxiong, Song Xiongwei, Xie Binggeng, et al. City network link analysis of urban agglomeration in the middle Yangtze River basin based on the Baidu Post Bar data. Geographical Research, 2018,37(6) : 1181-1192.
	[ 邓楚雄, 宋雄伟, 谢炳庚, 等. 基于百度贴吧数据的长江中游城市群城市网络联系分析. 地理研究, 2018,37(6):1181-1192.]
[10]	Zhong Weijing, Wang De. Urban space study based on the temporal characteristics of residents' behavior. Progress in Geography, 2018,37(8):1106-1118.
	[ 钟炜菁, 王德. 基于居民行为周期特征的城市空间研究. 地理科学进展, 2018,37(8):1106-1118.]
[11]	Pei Tao, Liu Yaxi, Guo Sihui, et al. Principle of big geodata mining. Acta Geographica Sinica, 2019,74(3):586-598.
	[ 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质. 地理学报, 2019,74(3):586-598.]
[12]	Zhu Binghua, Wang Ligui, Sun Yansong, et al. Progress in researches on surveillance and early warning of infectious diseases based on big data. Chinese Journal of Public Health, 2016,32(9):1276-1279.
	[ 祝丙华, 王立贵, 孙岩松, 等. 基于大数据传染病监测预警研究进展. 中国公共卫生, 2016,32(9):1276-1279.]
[13]	Ginsberg J, Mohebbi M H, Patel R S, et al. Detecting influenza epidemics using search engine query data. Nature, 2009,457(7232):1012-1014. pmid: 19020500
[14]	Polgreen P M, Chen Y L, Pennock D M, et al. Using internet searches for influenza surveillance. Clinical Infectious Diseases, 2008,47(11):1443-1448. doi: 10.1086/593098 pmid: 18954267
[15]	Matthew W. Ebola: Can big data analytics help contain its spread? 2014-10-15. http://www.bbc.com/nems/business-29627831.
[16]	Xin Yan, Bio Diaspora. Epidemic spread prediction based on big data. New Economy Weekly, 2014(11):44-49.
	[ 辛妍, Bio Diaspora. 基于大数据的疫情扩散预测. 新经济导刊, 2014(11):44-49.]
[17]	Zhang Y Z, Bambrick H, Mengersen K, et al. Using big data to predict pertussis infections in Jinan city, China: A time series analysis. International Journal of Biometeorology, 2020,64(1):95-104. doi: 10.1007/s00484-019-01796-w pmid: 31478106
[18]	Li Deren, Shao Zhenfeng, Yu Wenbo, et al. Public epidemic prevention and control services based on big data of spatiotemporal location make cities more smart. Geomatics and Information Science of Wuhan University, 2020,45(4):475-487, 556.
	[ 李德仁, 邵振峰, 于文博, 等. 基于时空位置大数据的公共疫情防控服务让城市更智慧. 武汉大学学报·信息科学版, 2020,45(4):475-487, 556.
[19]	Zhou Chenghu, Pei Tao, Du Yunyan, et al. Big data analysis on COVID-19 Epidemic and suggestions on regional prevention and control policy. Bulletin of the Chinese Academy of Sciences, 2020,35(2):200-203.
	[ 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. 中国科学院院刊, 2020,35(2):200-203.]
[20]	Kraemer M U G, Yang C H, Gutierrez B, et al. The effect of human mobility and control measures on the COVID-19 Epidemic in China. Science, 2020,368(6490):493-497. doi: 10.1126/science.abb4218 pmid: 32213647
[21]	Lai S, Ruktanonchai N W, Zhou L, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature, 2020,585:410-413. doi: 10.1038/s41586-020-2293-x pmid: 32365354
[22]	Zhao Ziyu, Wei Ye, Wang Shijun, et al. Measurement of directed alternative centricity and power of directed weighted urban network: A case of population flow network of China during "Chunyun" period. Geographical Research, 2017,36(4):647-660.
	[ 赵梓渝, 魏冶, 王士君, 等. 有向加权城市网络的转变中心性与控制力测度: 以中国春运人口流动网络为例. 地理研究, 2017,36(4):647-660.]
[23]	Feng Zhangxian, Zhang Yu, Wei Ye, et al. Spatial-temporal pattern and dynamic mechanism of population flow of Changchun city during Chunyun period based on Baidu migration data. Economic Geography, 2019,39(5):101-109.
	[ 冯章献, 张瑜, 魏冶, 等. 基于百度迁徙数据的长春市春运人口流动时空格局与动力机制. 经济地理, 2019,39(5):101-109.]
[24]	Xu Teng, Yao Yang. Urban population migration and housing price fluctuation: An empirical research based on the census data and Baidu migration data. Journal of Jiangxi University of Finance and Economics, 2018(1):11-19.
	[ 徐腾, 姚洋. 城际人口迁移与房价变动: 基于人口普查与百度迁徙数据的实证研究. 江西财经大学学报, 2018(1):11-19.]
[25]	Ye Qiang, Zhang Lixuan, Peng Peng, et al. The network characteristics of urban agglomerations in the middle reaches of the Yangtze River based on Baidu migration data. Economic Geography, 2017,37(8):53-59.
	[ 叶强, 张俪璇, 彭鹏, 等. 基于百度迁徙数据的长江中游城市群网络特征研究. 经济地理, 2017,37(8):53-59.]
[26]	Jiang Xiaorong, Wang Shenglan. Research on China's urban population mobility network: Based on Baidu migration big data. Chinese Journal of Population Science, 2017(2):35-46, 127.
	[ 蒋小荣, 汪胜兰. 中国地级以上城市人口流动网络研究: 基于百度迁徙大数据的分析. 中国人口科学, 2017(2):35-46, 127.]
[27]	Lai Jianbo, Pan Jinghu. Spatial pattern of population flow among cities in china during the Spring Festival travel rush based on "Tencent Migration" data. Human Geography, 2019,34(3):108-117.
	[ 赖建波, 潘竟虎. 基于腾讯迁徙数据的中国“春运”城市间人口流动空间格局. 人文地理, 2019,34(3):108-117.]
[28]	Liu Wangbao, Shi Enming. Spatial pattern of population daily flow among cities based on ICT: A case study of "Baidu Migration". Acta Geographica Sinica, 2016,71(10):1667-1679.
	[ 刘望保, 石恩名. 基于ICT的中国城市间人口日常流动空间格局: 以百度迁徙为例. 地理学报, 2016,71(10):1667-1679.]
[29]	Ta Na, Zeng Yutian, Zhu Qiuyu, et al. Relationship between built environment and urban vitality in Shanghai downtown area based on big data. Scientia Geographica Sinica, 2020,40(1):60-68.
	[ 塔娜, 曾屿恬, 朱秋宇, 等. 基于大数据的上海中心城区建成环境与城市活力关系分析. 地理科学, 2020,40(1):60-68.]
[30]	Anselin L. Local indicators of spatial association: LISA. Geographical Analysis, 1995,27(2):93-115.
[31]	Xu Dong, Huang Zhenfang, Huang Rui. The spatial effects of haze on tourism flows of Chinese cities: Empirical research based on the spatial panel econometric model. Acta Geographica Sinica, 2019,74(4):814-830.
	[ 徐冬, 黄震方, 黄睿. 基于空间面板计量模型的雾霾对中国城市旅游流影响的空间效应. 地理学报, 2019,74(4):814-830.]
[32]	Wang Jinfeng, Ge Yong, Li Lianfa, et al. Spatiotemporal data analysis in geography. Acta Geographica Sinica, 2014,69(9):1326-1345.
	[ 王劲峰, 葛咏, 李连发, 等. 地理学时空数据分析方法. 地理学报, 2014,69(9):1326-1345.]
[33]	Liu Haimeng. The "change" and "unchanged" of urban development path in the post-epidemic era. China Development Observation, 2020(Z7):123-125.
	[ 刘海猛. 后疫情时代城市发展路径的“变”与“不变”. 中国发展观察, 2020(Z7):123-125.]

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

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

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