Spatiotemporal patterns and influencing factors of human migration networks in Chinese cities during covid-19 easing measures

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Volume Title

Insinööritieteiden korkeakoulu | Master's thesis

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Date

2024-06-10

Department

Major/Subject

Spatial Planning and Transportation Engineering

Mcode

Degree programme

Master's Programme in Spatial Planning and Transportation Engineering (SPT)

Language

en

Pages

98+11

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Abstract

This study investigates the spatiotemporal patterns and determinants of population flow in China when it transitioned from its "zero-COVID" policy to a more relaxed approach. Utilizing migration data from the Gaode Maps platform and various analytical methods, including clustering analysis, spatial autocorrelation, fixed effects modelling, and Geographically and Temporally Weighted Regression (GTWR), the research provides a comprehensive understanding of the complex changes in migration and multidimensional factors shaping migration patterns. Before the policy shift, China’s strict "zero-COVID" strategy resulted in unprecedented domestic and international travel restrictions that significantly shaped mobility trends. As the country eased these restrictions, there was a notable increase in both the desire and intensity of travel, especially compared to the same period in the previous year. The study also highlights regional disparities, with migration indices being higher in the economically vibrant eastern coastal areas compared to inland regions, reflecting the uneven geographical distribution of opportunities and resources.  The fixed effects model identifies the overall impact of key determinants such as population size, economic output, healthcare infrastructure, COVID-19 incidence, industrial composition, and trade activity by controlling for unit-specific and time-specific confounders. We found that cities with larger populations and more hospital beds tended to have higher inflows and outflows. Moreover, the GTWR model reveals the spatial and temporal heterogeneity in these relationships. To investigate the nuanced effects of these factors, our study divided the sample into two subsets based on GRP size: the top 25% representing large cities and the bottom 25% representing small cities. The GTWR model was then applied separately to each subset. Our comparative analysis shows that large cities are significantly influenced by public health crises and economic factors leading to substantial changes in population migration patterns. In contrast, healthcare infrastructure played a more crucial role in small cities. Economic factors were still important but had a less pronounced impact compared to large cities.  The research findings emphasize the need for flexible, region-specific strategies in public health and urban planning to manage population mobility and mitigate impacts as public health policies evolve. These insights can inform preparedness measures for monitoring and managing population movements in response to shifts in public health policies, helping policymakers address the unique challenges and opportunities of different urban areas.

Description

Supervisor

Akbar, Prottoy

Thesis advisor

Akbar, Prottoy

Keywords

human migration, covid-19, spatial analysis, GTWR

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https://urn.fi/URN:NBN:fi:aalto-202406164366

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[dipl] Insinööritieteiden korkeakoulu / ENG