Abstract
Retail centres are important tools for understanding the distribution and evolution of the retail sector at varying geographical scales. This paper presents a framework through which formal definitions and typologies of retail centres, such as those in the UK, can be extended to the US. Using Chicago as a case study and data from SafeGraph, we present a retail centre delineation method that combines Hierarchical-DBSCAN with ‘H3’, and demonstrate the usefulness of a non-hierarchical approach to retail classification. In addition, we show that the dynamicity and comprehensibility of retail centres make them an effective tool through which to better understand the impacts of COVID-19 on retail centre ‘health’, demonstrating significant scope for a comprehensive delineation of the scale, extent and characteristics of American retail centre agglomerations, providing a tool through which to monitor the evolution of American retail.
Highlights
The contemporary physical retail environments of cities and urban areas have complex form and function, evolving in response to a multiplex of pressures
We demonstrate the utility of the retail centre framework proposed in this paper, by using it to contribute to a growing evidence base in a rapidly emerging field of research in retail – COVID-19
Using data from SafeGraph, retail centres were delineated through integration of HDBSCAN and ‘H3’, and the functional ecologies of the 1,599 retail centres were presented as a ‘two-tier’ classification, constructed using the partitioning around medoids (PAM) algorithm
Summary
The contemporary physical retail environments of cities and urban areas have complex form and function, evolving in response to a multiplex of pressures. Major shopping developments were delineated, such as Woodfield Mall (WC3) and Woodfield Green (WC27), as well as some smaller centres This was arguably only possible through integration of network distances, the most effective way to understand Chicago’s urban structure (Pan et al, 2018). In the example below for Chicago CBD (Figure 5), the centre boundaries seemed to align closely with the spatial ‘signature’ created by the ‘patterns’ data (5a) and encompassed the majority of census blocks identified as having a high proportion of retail employment (5b), with those not encompassed being sites of small retail centres (
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