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

Abstract

The COVID-19 pandemic has changed the ways in which we shop, with significant impacts on retail and consumption spaces. Yet, empirical evidence of these impacts, specifically at the national level, or focusing on latter periods of the pandemic remain notably absent. Using a large spatio-temporal mobility dataset, which exhibits significant temporal instability, we explore the recovery of retail centres from summer 2021 to 2022, considering in particular how these responses are determined by the functional and structural characteristics of retail centres and their regional geography. Our findings provide important empirical evidence of the multidimensionality of retail centre recovery, highlighting in particular the importance of composition, e-resilience and catchment deprivation in determining such trajectories, and identifying key retail centre functions and regions that appear to be recovering faster than others. In addition, we present a use case for mobility data that exhibits temporal stability, highlighting the benefits of viewing mobility data as a series of snapshots rather than a complete time series. It is our view that such data, when controlling for temporal stability, can provide a useful way to monitor the economic performance of retail centres over time, providing evidence that can inform policy decisions, and support interventions to both acute and longer-term issues in the retail sector.

Harnessing mobility data to capture changing work from home behaviours between censuses

“…For example, it appears that the least deprived and most urbanised LADs across England were those to experience the greatest uplift in WFH, whilst those more deprived and mainly rural LADs were those that experienced the smallest. There remains a question about the recording of Residential activity in rural areas and its impacts on these trends, given existing literature illustrating the urban bias of mobility datasets (e.g., Ballantyne et al., 2022). However, by presenting the change this way, accounting for both the 2021 Census and Google mobility estimates, the impact of such bias appears to be minimal, as WFH changes in mobility data are closely associated with census changes.…”

“…These changes to work from home (WFH hereafter) patterns are likely to persist beyond the end of the COVID‐19 pandemic. Although the degree of this persistence remains unknown (Deole et al., 2023), previously published research has utilised a wide pool of novel data sources to quantify changes in mobility as a result of COVID‐19 disruptions (Ballantyne et al., 2022; Gibbs et al., 2022; Rowe et al., 2023; Trasberg & Cheshire, 2021). This raises the question as to whether these mobility datasets can be an effective replacement for official statistics in the intercensal period.…”

“…While these data provided valuable near‐real‐time measurements of behavioural changes, mobility data collected as a side effect of other commercial services include uncertainties in the data generation process regarding the size and representativeness of the sample of individuals from which these data were collected (Buckee et al., 2020; Grantz et al., 2020; Wesolowski et al., 2013). Research has identified biases in aggregated measures of mobility, relating to the demographics of users of certain applications which generate the data, as well as the negative biases for specific regions and locations where these data do not remain stable over time (Ballantyne et al., 2022; Coston et al., 2021; Dodge & Nelson, 2023). There is also evidence of variation in the relationship between aggregated human mobility indicators and the incidence of COVID‐19 for data collected in urban and rural areas, likely reflecting varying qualities of data underlying aggregated measures (Kishore et al., 2022).…”

How consumer behaviours changed in response to COVID-19 lockdown stringency measures: A case study of Walmart

“…personal risk appraisals) ( Wang et al, 2022 ). In many places, consumers altered the location of where they chose to shop, tending to avoid larger retailers and crowded high street centres ( Lashgari & Shahab, 2022 ) in favour of smaller stores ( Li et al, 2020 ), local retail centres ( Ballantyne et al, 2022 ) and out-of-town based shopping alternatives ( Sundström et al, 2021 ). A study using mobile phone records for retail foot traffic in busy commuter zones in the US found a significant fall in consumer traffic even before legal restrictions were enacted ( Goolsbee & Syverson, 2021 ).…”

Detecting behavioural bias in GPS location data collected by mobile applications

“…Human mobility data derived from mobile phones are increasingly used to measure economic activity (1,2), predict the spread of disease (3)(4)(5)(6), forecast travel demand (7,8), measure responses to natural disasters (9,10), and understand human social dynamics (11,12). There are a number of sources of mobility data used in these applications, ranging from Call Detail Record Data, which estimates mobility based on mobile phone connections to nearby cell towers, to GPS data, which is collected by GPS sensors in smartphone devices.…”