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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.
Chen et al. (Thu,) studied this question.