Identifying Human Mobility Patterns Changes During Public Health Emergencies

ZHONG Leiyang; ZHOU Ying; GAO Song; XIA Jizhe; LI Zhen; LI Xiaoming; YUE Yang; LI Qingquan

doi:10.13203/j.whugis20210400

Volume 49 Issue 7

Jul. 2024

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Geomatics and Information Science of Wuhan University > 2024 > 49(7): 1237-1249. > DOI: 10.13203/j.whugis20210400

ZHONG Leiyang, ZHOU Ying, GAO Song, XIA Jizhe, LI Zhen, LI Xiaoming, YUE Yang, LI Qingquan. Identifying Human Mobility Patterns Changes During Public Health Emergencies[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1237-1249. DOI: 10.13203/j.whugis20210400

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Identifying Human Mobility Patterns Changes During Public Health Emergencies

ZHONG Leiyang^{1, 2, 3, 4,},
ZHOU Ying⁵,
GAO Song⁶,
XIA Jizhe^{1, 2, 3, 4, ,},
LI Zhen^{1, 2, 3, 4},
LI Xiaoming^{1, 2, 3, 4},
YUE Yang^{1, 2, 3, 4},
LI Qingquan^{1, 2, 3, 4}

1.
Key Laboratory for Geo‑Environmental Monitoring of Great Bay Area, MNR, Shenzhen518060, China
2.
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen518060, China
3.
Guangdong Key Laboratory for Urban Informatics, Shenzhen518060, China
4.
Research Institute for Smart Cites, Shenzhen University, Shenzhen518060, China
5.
College of Public Health, Shenzhen University, Shenzhen518060, China
6.
Geospatial Data Science Lab, Department of Geography, University of Wisconsin‑Madison, MadisonWI, 53706

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Received Date: September 16, 2022
Available Online: July 16, 2024

Graphical Abstract

Abstract

Abstract

Objectives
This study aims to evaluate the changes in human mobility during a public health emergency.
Methods
Employing detailed spatiotemporal big data on human mobility from China and the USA, we constructed three mobility indices: Mobility change index, distance change index, and volatility change index. We investigated the mobility characteristics within China's Guangdong-Hong Kong-Macau Greater Bay Area (GBA) and the United States' San Francisco Bay Area (SBA) during the epidemic. The singular value decomposition (SVD) algorithm was applied to identified underlying structures and patterns of mobility in these regions.
Results
The results show that: (1) The GBA outperformed the SBA in terms of human mobility control with a greater decline in movement volumes, smoother volatility in daily movement and shorter average travel distances during the pandemic. (2) Human mobility patterns in the GBA were influenced by both the Chinese Spring Festival holiday and public health policies, from which the daily travel patterns, returning home travel characteristics and returning-to-work characteristics were identified. Human mobility patterns in the SBA show strong regularity (including daily travel characteristics, weekday travel characteristics and weekend travel characteristics), and public health policies do not have profound impacts.
Conclusions
We quantify the changes in human mobility patterns under different epidemic control measures in two Bay Areas of China and the United States, which is essential to assess and identify intervention effectiveness. It also provides important evidences and references for various infectious disease control in the future.

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