License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.80
URN: urn:nbn:de:0030-drops-189754
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18975/
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Wang, Xinglei ; Zhang, Xianghui ; Cheng, Tao

The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis (Short Paper)

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LIPIcs-GIScience-2023-80.pdf (4 MB)

Abstract

As an important part of the economic and social fabric of urban areas, high streets were hit hard during the COVID-19 pandemic, resulting in massive closures of shops and plunge of footfall. To better understand how high streets respond to and recover from the pandemic, this paper examines the performance of London’s high streets, focusing on footfall-based clustering analysis. Applying time series clustering to longitudinal footfall data derived from a mobile phone GPS dataset spanning over two years, we identify distinct groups of high streets with similar footfall change patterns. By analysing the resulting clusters' footfall dynamics, composition and geographic distribution, we uncover the diverse responses of different high streets to the pandemic disruption. Furthermore, we explore the factors driving specific footfall change patterns by examining the number of local and nonlocal visitors. This research addresses gaps in the existing literature by presenting a holistic view of high street responses throughout the pandemic and providing in-depth analysis of footfall change patterns and underlying causes. The implications and insights can inform strategies for the revitalisation and redevelopment of high streets in the post-pandemic era.

BibTeX - Entry

@InProceedings{wang_et_al:LIPIcs.GIScience.2023.80,
  author =	{Wang, Xinglei and Zhang, Xianghui and Cheng, Tao},
  title =	{{The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{80:1--80:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18975},
  URN =		{urn:nbn:de:0030-drops-189754},
  doi =		{10.4230/LIPIcs.GIScience.2023.80},
  annote =	{Keywords: High street, performance, footfall, clustering analysis, COVID-19}
}

Keywords: High street, performance, footfall, clustering analysis, COVID-19
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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