Cheng, Tao

Wang, Xinglei

Zhang, Xianghui

English

Dagstuhl Research Online Publication Server

The Ups and Downs of London High StreetsThroughout COVID-19 Pandemic: Insights fromFootfall-Based Clustering AnalysisXinglei Wang1 #SpaceTimeLab for Big Data Analytics, University College London, UKXianghui Zhang #SpaceTimeLab for Big Data Analytics, University College London, UKTao Cheng #SpaceTimeLab for Big Data Analytics, University College London, UKAbstractAs an important part of the economic and social fabric of urban areas, high streets were hit hardduring the COVID-19 pandemic, resulting in massive closures of shops and plunge of footfall. Tobetter understand how high streets respond to and recover from the pandemic, this paper examinesthe performance of London’s high streets, focusing on footfall-based clustering analysis. Applyingtime series clustering to longitudinal footfall data derived from a mobile phone GPS dataset spanningover two years, we identify distinct groups of high streets with similar footfall change patterns. Byanalysing the resulting clusters’ footfall dynamics, composition and geographic distribution, weuncover 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 localand nonlocal visitors. This research addresses gaps in the existing literature by presenting a holisticview of high street responses throughout the pandemic and providing in-depth analysis of footfallchange patterns and underlying causes. The implications and insights can inform strategies for therevitalisation and redevelopment of high streets in the post-pandemic era.2012 ACM Subject Classification Applied computing → SociologyKeywords and phrases High street, performance, footfall, clustering analysis, COVID-19Digital Object Identifier 10.4230/LIPIcs.GIScience.2023.80Category Short PaperFunding Xinglei Wang: the author’s PhD research is jointly funded by China Scholarship Counciland the Dean’s Prize from University College London.1 IntroductionOver the past 3 years, the COVID-19 pandemic, a global public health crisis of unprecedentedscale, has brought about substantial changes to urban environment [7]. Although the COVID-19 is no longer a public health emergency of international concern [9] by the time this paperwas written, the long-term effects linger and continue to shape urban landscapes.Among the most affected urban areas are spaces of consumption such as high streetswhich are often the heart of local communities, serving as centres for commerce, socialinteraction, and cultural activities. Important as they are, high streets across the UK suffereda devastating blow during the pandemic, with over 17500 chain stores and other venuesclosing in Great Britain [2] and footfall decreasing by over 80% [5]. Given the pivotal role of1 Corresponding author© Xinglei Wang, Xianghui Zhang, and Tao Cheng;licensed under Creative Commons License CC-BY 4.012th International Conference on Geographic Information Science (GIScience 2023).Editors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise; Article No. 80; pp. 80:1–80:6Leibniz International Proceedings in InformaticsSchloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany80:2 The Ups and Downs of London High Streets Throughout COVID-19 Pandemichigh streets in the economic and social fabric of urban systems, it is of great importance toexplore the impacts of pandemic on high streets’ performance to comprehend the broaderconsequences on local economies, businesses, and communities.Existing studies have utilised various forms of data to assess the economic performanceof consumption spaces, such as vacancies [3] and footfall [8]. Within the COVID-19 context,Enoch et al. used footfall data to analyse the impact of COVID-19 pandemic on six highstreets in England [4]. Ballantyne et al. used a mobile phone app location dataset to examinethe recent recovery of retail centres from the pandemic [1]. Although they provided empiricalevidence of the impact of the pandemic, there are some limitations remained to be addressed.Firstly, the COVID-19 pandemic and its aftereffects last several years, with several rounds ofnational lockdowns enacted, but existing studies do not cover the whole period, thus cannotpresent the full picture of the responses of high streets. Secondly, existing literature onlyfocus on the change of footfall counts, lacking in-depth analysis of the change patterns andits underlying causes. To fill the research gaps, we examine and evaluate the performanceof London’s high streets during the pandemic using longitudinal footfall data. Specifically,footfall data spanning over two years was calculated from a mobile phone GPS dataset andtime series clustering was applied to generate multiple groups of high streets with similarpatterns. By analysing the distinctive footfall change patterns of resulting clusters and theirgeographic distribution, we unravelled the varying responses of different high streets to thedisruption and the spatial patterns of different clusters of high streets. Furthermore, we linkedthe clustering results to the existing typology of retail centres and gained further insight intohow the different composition of clusters corresponds to their performance. Lastly, we delvedinto the cause of particular change patterns by looking at the number of local and nonlocalvisitors.In the following sections, we describe the dataset used in this study, followed by a briefintroduction of the methods employed. We present and analyse the results, discuss theirimplications, and provide recommendations for policymakers and urban practitioners. Weconclude the paper by summarising the main findings and pointing out directions for futurework.2 DataMobile phone GPS trajectory data: it is a large-scale mobility dataset which containsmillions of anonymous users’ mobile phone GPS trajectory data (collected from tens oflocation-based service apps) provided by Location Sciences under GDPR compliance.The dataset spans 3 years, and we define our study period from the first Monday ofFebruary 2020 (03/02/2020) to the last Sunday of April 2022 (24/04/2022), spanning 812days (116 weeks). The number of unique devices in London in February 2020 exceeds610,000. The data collection method and sampling rate over the whole country remainsconsistent throughout the study period.High street boundary dataset: provided by the Greater London Authority2, thisis a shapefile containing the boundaries of 616 London high streets located outside theCentral Activity Zone.Lower Layer Super Output Areas (LSOAs): It is a geographic hierarchy designed toimprove the reporting of small area statistics in England and Wales. This study utilisedthe LOSAs dataset created in the most recent 2021 census and only those within GreaterLondon area were included.2 https://data.london.gov.uk/dataset/gla-high-street-boundariesX. Wang, X. Zhang, and T. Cheng 80:33 MethodsIn this section, we present the workflow of footfall calculation and give a brief introductionto the K-means clustering method.Footfall calculationThe workflow consists of the following steps:1. Home detection: obtain the LSOA-level home location of each individual, which is denotedas home_lsoa. Here, the home of a person is defined as the LSOA where they generatethe greatest number of GPS points during night time (e.g., 22:00-07:00).2. Stop detection: to get the stop which is where people remain stationary for more than aspecific amount of time (we set 5 minutes as the threshold in this study).3. Identity inference: infer the identities (being one of resident and non-resident) of thepeople visiting a certain high street. If the home_lsoa of a person is one of the LSOAsthat intersects with the high street, then this person is considered as a local resident,otherwise, a non-resident.4. Footfall calculation: join people’s stops with high street boundaries and calculate thefootfall w.r.t resident and non-resident by day and sum the 2 types to get the overallfootfall.The output is daily footfall counts on each of the high street in London. We furtheraggregate the daily footfall into weekly ones by summing over 7 days of each week. Thisstep can not only smooth the time series but also reduce the length of it, which can improvethe results of clustering. We also normalise the footfall counts to convert them into relativevalues between 0 and 1, which is an essential pre-processing step for clustering.K-means time series clusteringWe employ a K-means time-series clustering algorithm to cluster the time-series of weeklyoverall footfall on the high streets. We use the Euclidean distance as the metric for clustering.One advantage of this method is that the distance between any two objects is not affectedby the addition of new objects to the analysis, which may be outliers [6]. Elbow method andthe Silhouette score are used to identify the optimal cluster number K.4 ResultsHigh street clusters and their spatial distribution and compositionThree high street clusters are identified based on the time-series pattern in footfall. Theshare of high streets in each cluster are 39 %, 33 % and 28 %, respectively. Figure 1 showsthe footfall time series for the entire study period across the three clusters. Cluster 1 had thesmallest drop when the first national lockdown came into effect. But after the third nationallockdown, it remained the lowest while the other two (especially Cluster 3) recovered better.Cluster 2 and 3 exhibited similar trend, but Cluster 3 surpassed Cluster 2 (and Cluster 1)significantly during the “stepping out of lockdown” period, reaching its pre-pandemic level.The results demonstrate the varied ability of high streets to weather crises.Figure 2 shows the spatial distribution of the clusters where some degree of spatialclustering is notable. Most of the high streets in Cluster 1 are located in inner London area,while Cluster 3 finds more high streets in outer London. Combining the spatial distributionGISc ience 202380:4 The Ups and Downs of London High Streets Throughout COVID-19 PandemicFigure 1 Time-series pattern of the three identified high street clusters (the pink shades indicatethe period of three national lockdowns).and performance, We can draw the crude conclusion that high streets located at the peripheryof the city tend to recover better than those closer to the city centre. High streets in Cluster2 are more evenly distributed, but are relatively bigger in sizes.To gain more information about what types of high streets each cluster contains, wefurther look into the composition of each cluster by linking the high streets with RetailCentre Typology provided by CDRC 3. Figure 3 shows the composition of each cluster, wherewe can see that Cluster 1 has the highest proportion of small local centres compared toCluster 2 and 3. In Cluster 2, only 38.7% of the high streets are small local centres, thelowest among the three clusters, while higher percentages of district centre and town centreare found in this cluster. As for Cluster 3, the composition is very similar to Cluster 1, butthe proportion of high streets located in the outer London area is much higher than that ofCluster 1 (referring to Figure 2).Local and nonlocal visitorsIt is of great interest to us to uncover the underlying cause that made the three clustersaffected so disproportionately by the pandemic. In particular, the compositions of Cluster 1and 3 are very similar, yet they have such distinctive responses during multiple rounds oflockdowns and after-lockdown recovery period. With the question in mind, we calculatedthe number of local visitors (residents) and nonlocal visitors (non-residents) in each clusterand present the result in Figure 4.Clearly, the stronger resilience Cluster 1 showed during the first national lockdown isowing to the preservation of local residents, while its downfall in the recovery phase is largelydue to the continued loss of local residents (possibly because of people moving out of city[10]). The rise of Cluster 3 after the third national lockdown is much explained by the rapidincrease in the number of both local residents and nonlocal visitors.3 https://data.cdrc.ac.uk/dataset/retail-centre-boundaries-and-open-indicatorsX. Wang, X. Zhang, and T. Cheng 80:5Clusters of high streetCluster 1Cluster 2Cluster 3London boroughsInner LondonOuter LondonLegendFigure 2 Spatial distribution of high streets in three clusters.Figure 3 The proportion of different high streets in three clusters.Figure 4 The local (resident) and nonlocal (non-resident) visitors in three clusters.GISc ience 202380:6 The Ups and Downs of London High Streets Throughout COVID-19 Pandemic5 Discussion and conclusionsBy analysing the full trajectory of high street footfall, we made a significant discovery thatthe resilience of high streets towards pandemic is very complicated both in space and time:in general, high streets located at the periphery of the city have recovered better and thosewhich endured better through lockdowns may not recover well in post-pandemic period. Wealso made the first attempt to uncover the underlying cause of such varied responses ofhigh streets. The interesting finding that local residents is the “key to success” highlightsthe importance of community engagement for London high streets. Policymakers and localauthorities might consider organising more local events and activities, as well as launchinginitiatives such as community-led regeneration projects, to help strengthen the high streets’attractiveness, bring back residents and create a sense of ownership and pride among them.In conclusion, this paper is a first step towards the quantification and clustering ana-lysis of high streets performance throughout the COVID-19 pandemic. By identifying thevariations in footfall among different high streets, it provides evidence-based insights fordecision-making processes related to urban regeneration, infrastructure development, and theformulation of policies that support local businesses. Policymakers can tailor interventionsand allocate resources more effectively, ensuring a targeted approach that addresses theunique characteristics and needs of each high street. For future work, we will incorporatemore contextual features into our clustering analysis, such as catchment demographics andbuilt environment information to investigate the mechanisms in which high street responseto disruptions.References1 Patrick Ballantyne, Alex Singleton, and Les Dolega. Using unstable data from mobile phoneapplications to examine recent trajectories of retail centre recovery. Urban informatics, 1(1):21,2022.2 BBC. Pandemic impact ’yet to be felt’ on high streets. https://www.bbc.co.uk/news/business-56378667, 2021. Accessed: 2023-05-22.3 Les Dolega and Alex Lord. Exploring the geography of retail success and decline: A casestudy of the liverpool city region. Cities, 96:102456, 2020.4 Marcus Enoch, Fredrik Monsuur, Garyfalia Palaiologou, Mohammed A Quddus, Fiona Ellis-Chadwick, Craig Morton, and Rod Rayner. When covid-19 came to town: Measuring theimpact of the coronavirus pandemic on footfall on six high streets in england. Environmentand Planning B: Urban Analytics and City Science, 49(3):1091–1111, 2022.5 Retail Gazette. Uk footfall drops to lowest level on record. https://www.retailgazette.co.uk/blog/2020/04/uk-footfall-drops-to-lowest-level-on-record/, 2020. Accessed:2023-05-22.6 Ali Javed, Byung Suk Lee, and Donna M Rizzo. A benchmark study on time series clustering.Machine Learning with Applications, 1:100001, 2020.7 Ka Yan Lai, Chris Webster, Sarika Kumari, and Chinmoy Sarkar. The nature of cities andthe covid-19 pandemic. Current Opinion in Environmental Sustainability, 46:27–31, 2020.8 Christine Mumford, Cathy Parker, Nikolaos Ntounis, and Ed Dargan. Footfall signaturesand volumes: Towards a classification of uk centres. Environment and Planning B: UrbanAnalytics and City Science, 48(6):1495–1510, 2021.9 WHO. Who chief declares end to covid-19 as a global health emergency. https://news.un.org/en/story/2023/05/1136367, 2023. Accessed: 2023-05-22.10 Elias Willberg, Olle Järv, Tuomas Väisänen, and Tuuli Toivonen. Escaping from cities duringthe covid-19 crisis: Using mobile phone data to trace mobility in finland. ISPRS internationaljournal of geo-information, 10(2):103, 2021.

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

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

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