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

Walmart is a major player in the US retail sector and was one of the grocery corporations that bucked the trend of declining retail sales at the start of the COVID-19 pandemic in 2020. Particularly in the initial stages of the pandemic, governance priorities focussed on restricting the movement of people and closing non-essential retailers and service providers to slow the spread of the virus and keep people safe. This paper investigates the impact of non-pharmaceutical interventions, in the form of lockdown stringency measures, on consumer purchasing behaviours for essential goods over the onset of the pandemic. Focussing on both instore and online sales outcomes for Walmart in the US, we examine changes between pre-pandemic trends in two different sales outcomes, sales transactions and total spend, and trends in 2020. We then employ a series of multi-level regression models to estimate the impact that imposed stringency measures had on these sales outcomes, at both national and state level. Results indicate that nationally consumers were making fewer, larger physical shopping trips and huge increases in online sales was seen ubiquitously across the country. Novel and expansive insights from such a wide-spread retailer, such as Walmart, can help retailers, stakeholders and policy makers understand changing consumption trends to inform business strategies and resilience planning for the future. Furthermore, this study highlighted the value of examining spatial trends in sales outcomes and hopes to influence greater consideration of this in future research.

Ageing in Place Classification: Creating a geodemographic classification for the ageing population in England.

Population ageing is one of the most significant demographic changes underway in many countries. Far from being a homogenous group, older people and their experiences of ageing are diverse. A better understanding of the characteristics and geography of the older population, including the older workforce, is important. It allows policymakers and stakeholders to better adapt to the opportunities and challenges that the ageing population brings. This paper describes the implementation of the Ageing in Place Classification (AiPC) in England. AiPC is a multidimensional geodemographic classification, and it employs a wide range of spatially representative attributes of older people's sociodemographic characteristics and their living environment at the small area level. The openly available product provides valuable insights that can be implemented in both local and national contexts, in particular to improve service delivery and inform targeted policy interventions. AiPC is readily updateable with the arrival of new Census data; the concept and framework are also transferable to other countries.

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

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.

An open source delineation and hierarchical classification of UK retail agglomerations.

Town centres and high streets typically form the social and commercial cores of UK cities and towns, yet, there is no uniform definition of what a town centre or high street is. In this study the spatial delineations of retail agglomerations are generated using open-source data for England, Wales, Scotland and Northern Ireland. The extent and boundaries of these physical retail areas are identified based on the density and connectivity patterns of individual retail units over space. A high resolution hexagonal grid is superimposed over spatial clusters of retail points and a network-based algorithm used to identify mutually exclusive tracts. Agglomerations are then pruned and fine-tuned according to a series of heuristic rules. Our retail agglomerations represent local commerce areas with shopping amenities and are assigned to a hierarchical classification ranking from the largest Regional Centres, Major Town Centres and Town Centres, down to Small Local Centres and Retail Parks. The classification into one of eleven hierarchies is based on a combination of relative rank in the local area and absolute size of retail units within the area. These retail agglomeration boundaries, hierarchical classification and lookups form an open-source spatial data product available for wide use and research implementation.

Archetypes of Footfall Context: Quantifying Temporal Variations in Retail Footfall in relation to Micro-Location Characteristics.

The UK retail sector is constantly changing and evolving. The increasing share of online sales and the development of out-of-town retail provision, in conjunction with the 2008-09 economic crisis, have disproportionately impacted high streets and physical retail negatively. Understanding and adapting to these changes is fundamental to the vitality, sustainability and prosperity of businesses, communities and the economy. However, there is a need for better information to support attempts to revitalise UK high streets and retail centres, and advances in sensor technology have made this possible. Footfall provides a commonly used heuristic of retail centre vitality and can be increasingly estimated in automated ways through sensing technology. However, footfall counts are influenced by a range of externalities such as aspects of retail centre function, morphology, connectivity and attractiveness. The key contribution of this paper is to demonstrate how footfall patterns are expressed within the varying context of different retail centre architypes providing both a useful tool for benchmarking and planning; but also making a theoretical contribution to the understanding of retail mobilities. This paper integrates a range of contextual data to develop a classification of footfall sensor locations; producing three representations of sensor micro-locations across Great Britain: chain and comparison retail micro-locations, business and independent micro-locations and value-orientated convenience retail micro-locations. These three groups display distinct daily and weekly footfall magnitudes and distributions, which are attributed to micro-locational differences in their morphology, connectivity and function.