Journal of Geo-information Science ›› 2022, Vol. 24 ›› Issue (10): 1941-1956.doi: 10.12082/dqxxkx.2022.220302

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Intercity Travel Resilience, Recovery Patterns and Influencing Factors of Resilience in Chinese Cities in the Context of COVID-19

GOU Yichao1,3,4(), WEI Ming2,3,*(), WANG Jiaoe1,3,4, WANG Chengjin1,3,4   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
    4. Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101
  • Received:2022-05-13 Revised:2022-06-22 Online:2022-10-25 Published:2022-12-25
  • Contact: WEI Ming;
  • Supported by:
    National Natural Science Foundation of China(42071151)


Since the outbreak of the COVID-19 epidemic in 2020, the intercity travel in China has been significantly affected. With the popularity of big data, spatiotemporal modeling and analysis are widely used in epidemic and transportation research. In the post epidemic era, residents' intercity travel shows a certain recovery mode under the influence of local epidemic. The recovery mode and resilience of intercity travel reflects the resilience of cities and can provide information for cities' epidemic prevention and control. Exploring different urban modes and factors affecting the resilience of intercity travel under the influence of epidemic situation has practical significance for normalized epidemic prevention and control management. Based on the migration big data, this paper describes the differentiation pattern of intercity travel resilience under the COVID-19 epidemic from different perspectives, summarizes the time series model, and explores the factors affecting intercity travel resilience. Four indicators, namely fluctuation ratio, recovery ratio, resilience, and recovery index, are constructed to measure the resilience of intercity travel. The results show that: (1) During the epidemic period, residents' resilience to travel shows certain spatial variation. On the whole, the eastern region is the best, followed by the western region and the central region, and the northeast region is the worst; (2) The temporal patterns of intercity travel in epidemic cities are consistent with "resilience triangle" of the typical model. According to the propagation mode and correlation of the epidemic, the specific temporal patterns can be classified into five types: Relative independence mode, intermediate fluctuation mode, starting-point correlation mode, end-point correlation mode, and bidirectional restraint mode, showing different curve forms and characteristics; (3) The resilience of intercity travel is affected by complex factors. When the epidemic wave and regional variables are controlled, economic and transportation factors have a significant impact on the recovery of intercity travel. There may be a U-shaped relationship between per capita GDP and industrial structure and the resilience of intercity travel. When the economic development reaches a certain level, the supporting effect of economy on the resilience of intercity travel becomes increasingly prominent. There is a positive correlation between high-speed rail and airport and the resilience of intercity travel, which plays an important role in increasing the resilience of intercity travel. The results of this study indicate that the application of spatiotemporal big data to analyze the mode and mechanism of urban recovery in the post epidemic era is a novel research method. Subsequent research can further explore the spatiotemporal pattern and mode mechanism of epidemic recovery, in order to provide scientific basis and guidance for epidemic prevention and control of cities.

Key words: COVID-19, migration, recovery ability, temporal variation modes, resilience, risk, big data, econometric model, China