A framework for delineating the scale, extent and characteristics of American retail centre agglomerations
Patrick Ballantyne; Alex Singleton; Les Dolega; Kevin Credit (2022). Environment and Planning B: Urban Analytics and City Science, 49(3), 1112-1128. DOI: 10.1177%2F23998083211040519
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.
Extended Summary
This research develops a comprehensive framework for identifying and classifying retail centre agglomerations across American cities, using Chicago as a test case to extend existing UK methodological approaches. The study addresses the need for systematic, data-driven methods to understand retail spatial organisation in the United States, where formal retail centre definitions have been limited compared to established UK frameworks. Using SafeGraph location data covering 106,058 retail establishments across the Chicago Metropolitan Statistical Area, the research combines advanced clustering algorithms (Hierarchical-DBSCAN) with hexagonal spatial indexing (H3) to automatically identify retail centre boundaries. This methodology successfully delineated 1,599 distinct retail centres, ranging from small neighbourhood clusters to major destinations like Chicago’s Loop district. The paper develops a non-hierarchical classification system using machine learning techniques, identifying five main retail centre groups: large multipurpose centres and historic retail cores, popular comparison destinations, leisure strips, independent service centres, and small local convenience centres. Each category exhibits distinct characteristics in terms of store composition, visitor patterns, and catchment area demographics. The classification reveals how retail centres serve different functions within urban spatial structures, from downtown business districts attracting diverse visitors to neighbourhood convenience centres serving local populations. Importantly, the research demonstrates the framework’s practical utility by analysing COVID-19 impacts on different retail centre types throughout 2020. The analysis reveals significant variations in how the pandemic affected different retail formats, with large city centre agglomerations experiencing sustained visitor reductions while smaller, local convenience centres maintained or increased their share of retail visits. This suggests consumers shifted towards more local shopping patterns during lockdown restrictions. The study’s broader significance lies in providing urban planners, policymakers, and retail analysts with robust tools for monitoring retail sector evolution in American cities. As traditional retail faces challenges from e-commerce growth and changing consumer behaviours, understanding spatial patterns of retail agglomeration becomes crucial for urban policy decisions. The methodology offers a scalable approach that could be extended to analyse retail centre performance across the entire United States, supporting evidence-based planning for retail development and urban regeneration strategies.
Key Findings
- Hierarchical-DBSCAN combined with H3 hexagonal indexing successfully identified 1,599 retail centres across Chicago Metropolitan Statistical Area
- Non-hierarchical classification revealed five distinct retail centre types with different spatial patterns and demographic catchments
- COVID-19 impacts varied significantly by retail centre type, with large centres experiencing sustained visitor reductions
- Small local convenience centres increased market share during pandemic as consumers shifted to local shopping patterns
- Framework demonstrates scalability potential for nationwide retail centre analysis and urban planning applications
Citation
@article{ballantyne2022framework,
author = {Patrick Ballantyne; Alex Singleton; Les Dolega; Kevin Credit},
title = {A framework for delineating the scale, extent and characteristics of American retail centre agglomerations},
journal = {Environment and Planning B: Urban Analytics and City Science},
year = {2022},
volume = {49(3)},
pages = {1112-1128},
doi = {10.1177%2F23998083211040519}
}