Academic journal article Cityscape

Rethinking Food Deserts Using Mixed-Methods GIS

Academic journal article Cityscape

Rethinking Food Deserts Using Mixed-Methods GIS

Article excerpt

Introduction

British researchers first popularized the term food desert in the mid-1990s (Cummins and Macintyre, 1999; Wrigley, 2002). Since that time, it has become an increasingly common way to refer to neighborhoods where nutritious foods-most often defined as fresh produce and meats- are unavailable, of poor quality or overly expensive. In the United States, several policy initiatives have been based on this research. Pennsylvania's Fresh Food Financing Initiative, which began in 2004, was one major response to this research, providing grants and loans to improve food-related infrastructure in areas with low food access (Pennsylvania Fresh Food Financing Initiative, 2014). Many of these funds were used to expand or create new supermarkets. President Barack Obama expanded this model at the federal level by creating the Healthy Food Financing Initiative (HHS, 2010). Along with the creation of these federal and state programs, several U.S. cities have created initiatives to improve food access in low-income neighborhoods, including the creation of a food policy task force by the U.S. Conference of Mayors (Boston Mayor's Office, 2012).

Current research on food deserts primarily makes use of an approach based on Geographic Information Systems (GlS)-based analysis that relies on the proximity of supermarkets to residential areas (Black, Moon, and Baird, 2014; Caspi et al., 2012). This methodology is conceptually clear and relatively easy to implement. It requires census data and a listing of major food retailers, both widely available, in addition to data on health outcomes such as body mass index, or BMI, or reported food consumption. Recent research shows little or no association between food deserts and these health outcomes, however, which puts into question the efficacy of this spatial analytical approach (Cummins, Flint, and Matthews, 2014; Lee, 2012).

This article describes an alternative methodology, one that moves from measures of food proximity to the food-provisioning practices of urban residents. This mixed-methods study combines Global Positioning System (GPS) data on daily mobility, food-shopping diaries, georeferenced photos, and semistructured qualitative interviews. It identifies the role of other major factors affecting food access, including perceived neighborhood disorder and store quality, the role of social networks, and the effect of available transit options. In contrast to approaches that privilege only objective analysis of geospatial data, this method is also more explicitly participator)', including the voices and perspectives of urban residents. It thus provides a useful lens on the daily food provisioning of urban households and the factors that shape them.

Measuring Food Access

Early research on food deserts mostly used market-basket studies, comparing the availability and price of goods across store types and neighborhoods (Block and Kouba, 2007; Cummins and Macintyre, 2002; Hendrickson, Smith, and Eikenberry, 2006). This research often documented discrepancies in food price and quality between lower and middle-to-upper-class neighborhoods. This labor- and time-intensive research limits analysis, however, because it usually assesses only a small number of neighborhoods. As a result, spatial analysis of food-store distribution across urban areas has become increasingly common (Apparicio, Cloutier, and Shearmur, 2007; Zenk, et al., 2005). In this approach, proximity to healthy food sources-most often supermarkets-is combined with measures of social deprivation, such as poverty level, racial composition, and/or vehicle access. The U.S. Department of Agriculture's (USDA's) own Food Access Research Atlas is arguably the most widely used example of this approach (USDA Economic Research Service, 2014). This online tool1 provides an interactive national map showing the locations of all low-access, low-income census tracts, which are defined using only two variables: poverty level and distance to the nearest supermarket. …

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