Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan

Liu, Xiaoyan; Yang, Saini; Huang, Xiao; An, Rui; Xiong, Qingrong; Ye, Tao

doi:10.1016/j.cities.2022.104104

Cited by 6 publications

(3 citation statements)

References 66 publications

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“…To show the application of the MC method, we use a dataset containing movements of all China Unicom subscribers in Beijing from 1-31 August 2018, and 1-31 May 2021, amounting to ca 11.57 × 10 6 users and 1.8 × 10 9 trips, where a trip is an individual's single visitation from an origin to a destination. China Unicom is one of the three major ICT providers in China and Beijing, whose trip data have provided insights into many socio-economic aspects such as tourism and local imbalanced developments [26][27][28][29]. We note that already one month of data is sufficient to reveal key elements of the evolving urban structure (see below).…”

Section: A Census For Human Mobilitymentioning

confidence: 98%

Mobility census for monitoring rapid urban development

Xiu,

Wang,

Gross

et al. 2024

J. R. Soc. Interface.

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Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

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Section: A Census For Human Mobilitymentioning

confidence: 98%

Mobility census for monitoring rapid urban development

Xiu,

Wang,

Gross

et al. 2024

J. R. Soc. Interface.

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“…The urban recovery from COVID-19 is also crucial to the human sustainability 14 . Understanding relationships between human mobility and recovery is important to the promotion of effective urban recovery which is in line with sustainable development 15 . Furthermore, global warming has also led to melting glaciers, which could unleash ancient viruses 16 .…”

Section: Introductionmentioning

confidence: 99%

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19

Zhang

2023

Sci Rep

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This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

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“…Coarse temporal scales, such as annual or quarterly changes in economic indicators, can reflect the impact of disasters on cities ( Rose, 2007 ), but may not reveal their adaptive and dynamic adjustment ability. Research has shown that urban economic development is closely linked to social connectedness ( Yaish & Andersen, 2012 ), which can be better characterized by fine-grained big geographical data, including mobile device data, social media data, and GPS signaling data ( Di Clemente et al, 2018 ; Huang, Li, Jiang, et al, 2020 ; Li et al 2021 ; Liu et al, 2023 ). Such data can provide valuable insights into the functioning of social networks and help understand how urban populations respond to pandemics or other disasters.…”

Section: Introductionmentioning

confidence: 99%