Spatial and social disparities in the decline of activities during the COVID-19 lockdown in Greater London

Abstract

We use data on human mobility obtained from mobile applications to explore the activity patterns in the neighbourhoods of Greater London as they emerged from the first wave of COVID-19 lockdown restrictions during summer 2020 and analyse how the lockdown guidelines have exposed the socio-spatial fragmentation between urban communities. The location data are spatially aggregated to 1 km2 grids and cross-checked against publicly available mobility metrics (e.g. Google COVID-19 Community Report, Apple Mobility Trends Report). They are then linked to geodemographic classifications to compare the average decline of activities in the areas with different sociodemographic characteristics. We found that the activities in the deprived areas dominated by minority groups declined less compared to the Greater London average, leaving those communities more exposed to the virus. Meanwhile, the activity levels declined more in affluent areas dominated by white-collar jobs. Furthermore, due to the closure of non-essential stores, activities declined more in premium shopping destinations and less in suburban high streets.

Planning for social distancing: How the legacy of historical epidemics shaped COVID-19's spread in Madrid

“…The densest area of Madrid, the historic city centre, was less affected by disease than the inner suburbs ( Menéndez and Higueras García, 2020 ). However, socio-economic factors like overcrowding ( Gómez Giménez, 2020 ), and a large proportion of ‘essential workers’ who reported to their workplaces even during the lockdown, could have facilitated the spread of the virus ( Glodeanu et al, 2021 ), as was observed in other cities ( Trasberg and Cheshire, 2021 ).…”

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

“…Overall, the re-increase in activities after the COVID-19 lockdowns has been a mixed experience. For some, it has been a chance to return to normalcy, while for others, it has been a source of anxiety and worry [ 59 ].…”

Using unstable data from mobile phone applications to examine recent trajectories of retail centre recovery

“…A large body of research is emerging that uses mobility data obtained from mobile phone applications to investigate human behaviour during the COVID-19 pandemic, notably changes in human mobility and internal migration (Kang et al, 2020), and the compliance of social distancing measures (Oliver et al, 2020). In addition, such data has been used to monitor the performance of consumption spaces and the wider retail sector during the pandemic (Trasberg & Cheshire, 2021;Ballantyne et al, 2021;Ballantyne et al, 2022).…”

“…Location data from smartphones face similar challenges to other consumer datasets in that they are often unrepresentative of particular social groups (e.g. generational biases), or of particular areas due to differences in access to mobile devices/internet (Trasberg & Cheshire, 2021;Parsons, 2020). In addition, such data often faces significant temporal limitations, depending on the sample of devices and applications used to collect it, and their representativeness of the general population (Gibbs et al, 2021).…”

“…In addition, existing studies have utilised various forms of data to assess the economic performance of consumption spaces, such as vacancies (Frago, 2021;Dolega & Lord, 2020) and footfall (Philp et al, 2022;Ntounis et al, 2020). The utility of mobility data in answering such questions was first identified in Trasberg and Cheshire (2021), providing significant scope for the use of similar data, such as the Geolytix aggregated in-app location dataset (CDRC, 2021a), to unpack how such responses have manifested in later phases of the COVID-19 pandemic once the key limitations of such data have been addressed. Thus, within this context, we explore the utility of a mobility dataset for exploring spatio-temporal trends of retail centre recovery, demonstrating in particular how we can use retail centre definitions and new forms of data as geographic data tools, to better understand the response of the wider retail sector to the pandemic.…”