Authors: Patrick Ballantyne*, Geographic Data Science Lab, University of Liverpool, Alex Singleton, University of Liverpool, Les Dolega, University of Liverpool, Jacob MacDonald, Geographic Data Science Lab, University of Liverpool
Topics: Business Geography
Keywords: Retail Centres, Graph Theory, Machine Learning, Geographic Data Science, Huff Model
Session Type: Virtual Paper
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 5
Presentation File: No File Uploaded
Concentrations of individual retail units and their associated activities, formally defined as ‘retail centres’, are important tools for understanding the distribution and evolution of the retail sector at varying geographical scales. Having ways to measure and monitor the location, characteristics and health of retail centres has become acutely important in the second decade of the 21st century, owing to the visibility of challenges posed by the 'retail apocalypse' (Boerschinger, Pansch, & Lupini, 2017; Isidore, 2017), and the COVID-19 pandemic (Roggeveen and Sethuraman, 2020).
In this paper, we present an update on a working set of retail centre definitions for the US, comprising a two-tier, ‘non-hierarchical’ classification, and accompanying set of retail centre catchments. We demonstrate the usefulness of H3 geometries and graph theory in delineation of retail centre extents, both in terms of accuracy, and also interpretability/replicability. We show that a data-driven, non-hierarchical approach to classification based on four domains (Dolega et al., 2019), provides the most nuanced way to represent relationships between centres, and build a modified Huff model to accurately determine the geographical reach of the centres. The centres, characteristics and catchments are explored, and some interesting regional differences in retail centre geographies across the US are unpacked.