How Retailer Hierarchy Shapes Food Accessibility: A Case Study Using Machine Learning Method to Delineate Service Areas and Hierarchical Levels of Food Retailers

Bi Yu Chen, Chenxi Fu, Donggen Wang, Tao Jia and Jianya Gong

Annals of the American Association of Geographers, 2024, vol. 114, issue 4, 611-632

Abstract: Accurate evaluation of food accessibility is the prerequisite for developing sustainable food policies. Most existing studies have evaluated food accessibility by setting a single service area size for all food retailers across a study area. In reality, service area sizes can vary significantly among different types of food retailers in different geographical regions, thus forming a retailer hierarchy. In this study, we propose a new machine learning method to delineate service areas and hierarchical levels for all food retailers in a large study area. Based on the proposed method, a comprehensive case study of 79,419 food retailers was carried out in Wuhan, China. This study revealed three hierarchical levels of food retailers in Wuhan. Retailers at higher positions in the hierarchy had fewer entities but larger service areas. The hierarchical levels of food retailers can be accurately determined by fifteen attractiveness factors. These results underscore the dominant role of middle- and upper-level retailers in determining food accessibility; that is, they accounted for only 6.9 percent of total retailers but contributed to 96.3 percent of total accessibility. Ignoring the hierarchical structure of food retailers will introduce significant bias in food accessibility evaluations.

Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24694452.2023.2294892 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:raagxx:v:114:y:2024:i:4:p:611-632

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/raag21

DOI: 10.1080/24694452.2023.2294892

Access Statistics for this article

Annals of the American Association of Geographers is currently edited by Jennifer Cassidento

More articles in Annals of the American Association of Geographers from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().