An open source delineation and hierarchical classification of UK retail agglomerations

Abstract

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.

Local Data Spaces: Leveraging trusted research environments for secure location-based policy research in the age of coronavirus disease-2019

“…We used the Mid-Year Population Estimates to get information on local demographics (ONS, 2022c), the Access to Healthy Assets & Hazards (AHAH) provided underlying small area context to health challenges and amenities (Green et al, 2018), and the Index of Multiple Deprivation (IMD) is able to give insights into local relative measures of deprivation (MHCLG, 2019). Openly available geographic data on local business density and retail activity was provided through the CDRC Business Census (CDRC, 2022) and Retail Centre delineations (Macdonald et al, 2022). The geographic elements of these sectors are important to consider as lockdowns particularly impacted retail and hospitality which are often concentrated in city centers and high streets and were notably hard hit during the pandemic.…”

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

“…For example, if we look specifically at Regional Centres, the largest in size and typically the most diverse in retail offer (Macdonald et al, 2022 ), they exhibited a significant increase in share in the lead up to Christmas 2021, an expected trend given that these centres comprise the largest variety of retailers, products and ancillary activities, better fulfilling the needs of Christmas shoppers. However, what is most interesting is that following Christmas 2021, Regional Centres exhibited the most notable decline in share of activity from the baseline, suggesting that this specific function of retail centre has become less popular over the last year, relative to other retail centre functions, mirroring much of the literature seen earlier in the pandemic (Ballantyne et al, 2022 ; Frago, 2021 ).…”

“…We must however remain cautious of these trends, especially given they are based on exploratory analysis and modelling, which did not account directly for the impacts of seasonality, weather and holiday periods (Lyu et al, 2022;Rose & Dolega, 2022), and is based on trends for a subset of the major retail centres across the UK. Further research should seek to identify what additional knowledge can be generated about retail centre recovery by focusing on retail centres in London, or those 'Small Local Centres' , which comprise the largest proportion of retail centres in the UK (Macdonald et al, 2022). However, perhaps the greatest consideration we must make relates to the underlying limitations of the mobility data used in this study, which often has a tendency to introduce generational and/or spatial biases, as identified by Trasberg and Cheshire (2021).…”

“…It is important to note however that we are unable to identify the specific sources of data used to construct it (i.e., apps), as that information is commercially sensitive (CDRC, 2021a ). For the purposes of our research in examining the response of retail centre activity, and to minimise disclosure risk, the mobility dataset was appended to the latest iteration of the CDRC retail centre boundaries (Macdonald et al, 2022 ); the nested H3 cells within each centre boundary were derived and joined with the corresponding mobility data, keeping only data within the centre boundary, before calculating the total number of devices within each retail centre at the weekly scale, as a proxy measure for retail centre activity, to smooth variation at the daily level.…”

A new commercial boundary dataset for metropolitan areas in the USA and Canada, built from open data

“…Commercial areas - what, where, and how do we define them? Despite the consistent demand to create a generalisable definition of commercial areas from researchers, government agencies, and related stakeholders, the related studies tend to focus on specific types, such as retail agglomerations 1 , 2 , or functions like downtown regions 3 , 4 , Commercial Centres (CCs) and Central Business Districts (CBDs) 5 . However, commercial activities are not limited to just retail and specific functions, meaning a more consistent perspective could facilitate a holistic understanding of these commercialised zones.…”