Spatial–Temporal Urban Mobility Pattern Analysis During COVID-19 Pandemic

Yanggang Cheng · Chao Li · Yong‐Tao Zhang · Shibo He · Jiming Chen

20 citations0/0 files storedIEEE Transactions on Computational Social Systems

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Human Mobility and Location-Based Analysis COVID-19 epidemiological studies Urban Design and Spatial Analysis

Abstract

In response to the repeated outbreaks of the COVID-19, many countries implement the region-specific, multilevel epidemic prevention and control policies. To fully understand the impact of these interventions on urban mobility, it is urgent to analyze spatial–temporal mobility pattern at the neighborhood level and structural changes in urban mobility networks. Here, we construct urban mobility networks among points of interest (POIs), using large-scale anonymous mobility data from de-identified mobile phone users. We comprehensively investigate the changes of urban mobility networks during two waves of the COVID-19 pandemic in Beijing from both graph and subgraph perspectives. Beyond an overall mobility reduction in Beijing, we find that the mobility change is spatially and temporally heterogeneous among different urban regions. We uncover a disproportionately large reduction in long-distance, nighttime, and non-essential travel. This results in a more geographically fragmented, local, and regional network in the pandemic. We demonstrate that these structural changes slow down the spatial spread of the COVID-19 in the mobility network.

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References

2008Understanding individual human mobility patterns 2020Economic and social consequences of human mobility restrictions under COVID-19 2020Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020 2020Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle 2020Aggregated mobility data could help fight COVID-19 2020Lockdowned: Everyday mobility changes in response to COVID-19 2010Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks 2021How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics

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