Chen, Meixu
ORCID: 0000-0003-2712-5551, Liu, Yunzhe, Ye, Zi
ORCID: 0000-0001-5190-5211, Wang, Siqin and Zhang, Wenjing
(2024)
Vivid London: Assessing the resilience of urban vibrancy during the COVID-19 pandemic using social media data
Sustainable Cities and Society, 115.
p. 105823.
ISSN 2210-6707, 2210-6715
Abstract
Since COVID-19, the focus on urban resilience has intensified, particularly on cities' ability to adapt and recover while maintaining essential functions and liveability; however, few studies have examined the resilience of urban vibrancy during such health crises. This study investigates urban vibrancy resilience in Inner London during the COVID-19 pandemic using multi-sourced social media data (geo-tagged Twitter and Flickr). We propose an analytical framework based on space-time permutation scan statistics (STPSS) to identify spatiotemporal urban areas of interest (ST-AOIs), examining their spatial, temporal, and contextual characteristics. Our findings show that central neighbourhoods with transport hubs, educational and healthcare facilities, eateries, and financial centres exhibit greater resilience. These areas adapt by shifting active periods in response to disruptions. Additionally, we assess the varying resilience capacities of different types of points of interest. This research provides actionable insights for urban planners and policymakers by demonstrating how identifying characteristics of robust urban vibrancy can contribute to the resilience of cities and communities, particularly under normal conditions after COVID-19. The findings offer concrete strategies for integrating social media data into urban planning processes, enabling more responsive and adaptive governance that meets the dynamic needs of urban populations.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Urban vibrancy, Urban resilience, Social media data, Crowdsourcing, Spatiotemporal clustering |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Environmental Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 26 Sep 2024 07:19 |
| Last Modified: | 28 Feb 2026 14:52 |
| DOI: | 10.1016/j.scs.2024.105823 |
| Open Access URL: | https://www.sciencedirect.com/science/article/pii/... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3184747 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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