Epidemiological-survey-based multidimensional modeling for understanding daily mobility during the COVID-19 pandemic across urban-rural gradient in the Chinese mainland

ABSTRACT

Human mobility survey data usually suffer from a lack of resources for validation. Epidemiological survey records, which are released to the public as a containment measure by local authorities, provide place visitation details validated by the authority. This study collected and analyzed the epidemiological survey reports published by local governments in the Chinese mainland, between January 2020 and November 2021. To reveal the mobility patterns during the COVID-19 pandemic across the urban-rural gradient in China’s mainland, we derived key mobility indicators from the epidemiological survey data from rural to megacities. We then applied exploratory factor analysis to identify latent factors that affected people’s mobility. We found that the pandemic poses varying impacts across the urban-rural gradient in the Chinese mainland, and the mobility patterns of middle and small cities are more influenced. Our results also showed that the pandemic did not enlarge gender gap in people’s mobility, as gender was not a significant driving factor for explaining people’s quantity of out-of-home activities as well as extent of life space, while age group and city levels were significant. Overall, we argue that the epidemiological survey data are valuable data sources for daily mobility modeling, especially for relevant studies to understand human mobility patterns during the pandemic.

KEYWORDS:

• Human mobility
• epidemiological survey
• exploratory factor analysis
• gender gap
• urban-rural gap
• COVID-19
• pandemic

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

1. https://api.map.baidu.com/lbsapi/webservice-geocoding.htm.

2. https://www.yicai.com/.

Additional information

Funding

This work was supported by the Central China Normal University startup fund [grant numbers 31101222126; 31101222127].

Notes on contributors

Feng Zhao

Feng Zhao is an Associate Professor of the Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province in College of Urban and Environmental Sciences, Central China Normal University. Her research interests are vegetation dynamic monitoring and environmental health.

Zixuan Dai

Zixuan Dai is currently studying in College of Urban and Environmental Sciences for her bachelor’s degree in Physical Geography at Central China Normal University. She will pursue her master’s degree in Natural Resources at Wuhan University. Her research interests are human mobility and transportation analysis.

Wenyu Zhang

Wenyu Zhang is currently studying for her bachelor’s degree in Geographical Information Science at College of Urban and Environmental Sciences, Central China Normal University. Her research interests are urban science and smart city.

Yiting Shan

Yiting Shan is currently studying for her bachelor’s degree in Geographical Sciences at College of Urban and Environmental Sciences, Central China Normal University. Her research interests are human mobility.

Cheng Fu

Cheng Fu is a Lecturer of the Department of Geography, University of Zurich. His research interests are place modeling, human mobility, and map generalization.