Food Ethics Research Paper

Neighbourhood food environments revisited: When food deserts meet food swamps

Meng Yang* Economics and Management School, Wuhan University

Haoluan Wang* Department of Agricultural and Resource Economics, University of Maryland

Feng Qiu Department of Resource Economics and Environmental Sociology, University of Alberta

Key Messages

• We identify three types of vulnerable neighbourhoods: food deserts, food swamps, and those with overlaps of food swamps and food deserts.

• We adopt customized regression models to investigate associations between neighbourhood characteristics and different food store availability.

• We propose tailored strategies to effectively and efficiently improve food environments.

This study uses service area–based coverage and various count regression methods to assess neighbour- hood‐level healthy and unhealthy food environments, and food access associated with different socio‐ economic groups in Edmonton, Canada. We identify three types of vulnerable neighbourhoods according to different food environments: food deserts (i.e., neighbourhoods lack sufficient access to healthy foods); food swamps (i.e., neighbourhoods have excess access to unhealthy foods); and those with overlaps of food swamps and food deserts. We also identify neighbourhoods with superior access to healthy foods (i.e., food oases). Additionally, our results from regression analyses indicate: (1) child population is negatively associated with both healthy and unhealthy food resources; (2) good access to public transportation is associated with good coverage of all healthy food outlets and convenience stores; and (3) deprived neighbourhoods with higher percentages of minority populations have better coverage of both healthy and unhealthy foods in general. The results from this study can help the City of Edmonton identify the key neighbourhoods with high potential for local business and the hotspot neighbourhoods that require particular support. Tailored strategies are proposed to effectively and efficiently improve food environments with limited resources.

Keywords: neighbourhood food environment, food desert, food swamp, food oasis, service area

Les environnements alimentaires de quartiers réexaminés : lorsque les déserts alimentaires rencontrent les marécages alimentaires

La présente étude utilise la couverture de l’aire de service et diverses méthodes statistiques afin d’évaluer les environnements alimentaires de quartiers et l’accès à la nourriture de différents groupes socioéconomiques, à Edmonton au Canada. Ainsi, nous identifions trois types de quartiers vulnérables en vertu de différents environnements alimentaires, c’est‐à‐dire les déserts alimentaires (les quartiers qui manquent d’un accès suffisant à

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DOI: 10.1111/cag.12570

© 2019 Canadian Association of Geographers / L'Association canadienne des géographes

Correspondence to / Adresse de correspondance: Feng Qiu, Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, AB T6G 2H1. Email: [email protected] *Yang and Wang equally share the senior authorship.

une nourriture saine), les marécages alimentaires (les quartiers qui ont un accès excessif à une nourriture malsaine) et ceux qui chevauchent les marécages alimentaires et les déserts alimentaires. Notre typologie contient également des quartiers ayant un accès supérieur à une nourriture saine, soit des oasis alimentaires. De plus, les résultats des analyses de régression font ressortir les éléments qui suivent: (1) la population enfantine est négativement associée aux deux ressources de nourriture saine et malsaine ; (2) l’accès à la nourriture du transport public est associé à une bonne couverture des points de vente de nourriture saine et des dépanneurs, et (3) les quartiers défavorisés ayant un pourcentage plus élevé de populationsminoritaires ont généralement unemeilleure couverture de nourriture saine et malsaine. Les résultats de cette étude pourront aider la ville d’Edmonton et les entreprises locales à intervenir dans les quartiers qui nécessitent un soutien particulier. À cet égard, des stratégies ciblées sont proposées pour améliorer les environnements alimentaires ayant des ressources limitées.

Mots clés : environnement alimentaire de quartier, désert alimentaire, marécage alimentaire, oasis alimentaire, aire de service

Introduction

Healthy food intake is essential to overall health status and is reported to reduce the risk of nutrition‐related chronic diseases such as obesity and type 2 diabetes (Camhi et al. 2015; Swan et al. 2015). There is growing evidence that physical access to different types of food outlets substan- tially influences dietary patterns and weight status at the population level (Morland et al. 2006; Moore et al. 2009; Morland and Evenson 2009). A report that systematically reviews 19 Canadian commu- nity food assessments found a positive relation- ship between geographic access to non‐nutritious food sources and obesity rate, especially among children and youth (Health Canada 2013). Increas- ingly, the community food environment has be- come one of the most pressing public health concerns in Canada. Neighbourhood food environ- ments are often studied through the lens of the accessibility to different types of food resources and there are mainly two streams of food outlets in literature. One refers to food retailers that can supply healthy and nutritious foods at relatively affordable prices, such as supermarkets and local grocery stores (see Walker, Keene, and Burke 2010 for a review). The other type is unhealthy food sources such as fast food restaurants and conve- nience stores that mainly sell fast food and non‐ perishable items (e.g., Fleischhacker et al. 2011; Black et al. 2014).

Another strand of research on food environ- ments is the investigation of the associations between a neighbourhood’s food availability and its socio‐demographic characteristics (Sharkey et al. 2009; Lamichhane et al. 2013). In general, the availability of food retailers has been shown

to vary according to the neighbourhood’s socio‐ economic status, depending on study areas. For example, fewer retail sources of healthy foods (e.g., supermarkets) and more sources of unhealthy foods (e.g., fast food restaurants and convenience stores) are found to be located in neighbourhoods with higher proportions of low‐income and ethnic‐ minority residents relative to more affluent neigh- bourhoods or those with fewer minorities in the United States (US) (see Black et al. 2014 for a review). Comparatively, in Canada, more deprived neighbourhoods have greater access to both healthy and unhealthy food outlets, with some variations across study regions (Smoyer‐Tomic et al. 2008; Black et al. 2011; Polsky et al. 2014). Therefore, an in‐depth investigation of the associa- tion between subpopulations and their food avail- ability is essential for government and interest groups to implement specific policies for commu- nities in need.

This paper comprehensively assesses neighbour- hood food environments and investigates associa- tions between neighbourhood characteristics and different food stores availability. We make the following contributions to the food environment literature. First, most existing studies only focus on one aspect or one type of food environment such as investigating food deserts or food swamps in a specific place. This study conducts a comprehen- sive assessment of the food environment in Edmonton, Canada. We study both healthy and unhealthy food stores and identify four types of neighbourhoods based on different food environ- ments: food deserts, food swamps, overlaps of food deserts and food swamps, and food oases. This is critical because different types of environ- ments require different strategies to mitigate the

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136 Meng Yang, Haoluan Wang, and Feng Qiu

vulnerability or to improve the diet environment. For example, strategies for a food swamp with good access to affordable healthy food retailers would be quite different from strategies for a food swamp with no convenient access to healthy food nearby.

In addition to the food environment assess- ment, we also study the association between the socio‐economic characteristics of the neighbour- hoods and the food environment. The empirical techniques we have employed make two addi- tional contributions to the literature. First, using the service area–based counts to represent the number of food outlets in regression analysis addresses the edge effects which are often criti- cized in the literature. Second, we have tested and adopted customized regression models for dif- ferent food retailers. Models considered and adopted in the empirical analysis include Poisson regression, negative binomial regression, and zero‐inflated Poisson regression. The results from the regression analyses therefore offer a more nuanced (less biased) understanding of the physical food environment in the study area and can provide better empirical support for future policy designs.

Literature review

Literature on food environment

Vivid descriptions of different food environments come from various ecological terms, such as “deserts,” “oases,” and “swamps” (Taylor and Ard 2015). Among them, the oldest and the most common term is the “food desert.” The term was originated in the United Kingdom in the 1990s to describe areas with limited food store accessi- bility. Since then, a variety of studies have been developed and put forward the definition of food deserts. In early 2002, Cummins and Macintyre defined food deserts as areas where foods are relatively unavailable and expensive in their study. Guy and David (2004) suggested a food desert is an area where residents have no access to food outlets or only have access to low‐quality food sources. Gregory et al. (2011, 160) defined it as “an area in which residents’ access to healthy, afford- able food is highly restricted, for example, because of the absence of food retailers in a low‐

income urban neighbourhood.” Among the var- ious definitions, the most influential definition of food desert is the one introduced by the United States Department of Agriculture (USDA). The department defined a food desert as: “An area with limited access to affordable and nutritious food, particularly such an area composed of predominantly lower income neighbourhoods and communities” (USDA 2009). In addition, the USDA has taken into account the population parameter as a criterion to define food deserts in real business. For example, one measure of food deserts is defined as low‐income census tracts where a significant number of people (at least 500) or share of the population (at least 33%) live greater than one mile from the nearest super- market, supercentre, or large grocery store for an urban area or greater than 10 miles for a rural area (USDA 2015).

The metaphor of food deserts inverts the idea of food oases (Gregory et al. 2011). A few studies have introduced the concept of food oases to describe neighbourhoods that have good access to healthy food outlets, which are usually represented by supermarkets and grocery stores (Short et al. 2007; Walker, Butler, et al. 2010; Krizan et al. 2015). Washington State Department of Health (2019) provided an updated theoretical definition of a food oasis: “any place where people have the best possible access to healthy options and eating environments. Access includes financial and phy- sical access to healthy foods and drinks that are high quality, affordable, culturally acceptable, and meet the nutritional needs of the people in the community.” Of course, how to define the best possible financial and/or physical access to healthy eating environments in empirical investigations remains controversial.

To capture the idea that unhealthy food access is also important, Rose et al. (2009) came up with the term “food swamps” to describe low‐income urban communities that have a plethora of fast food restaurants and convenience stores that sell unhealthy food. Rose et al. (2009) considered the food swamp to be an especially valuable concept to describe neighbourhood food environments since the excess amount of unhealthy food sources would “inundate” or “swamp out” the healthy food choices that residents have. Luan et al. (2015) and Hager et al. (2016) defined food swamps as areas where residents have access to

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Food deserts meet food swamps 137

large amounts of energy‐dense snack foods while having limited healthy food options in their study areas.

Some researchers also proposed the idea of “food mirage” to describe the phenomenon where resi- dents have access to healthy food outlets, but they find them expensive and therefore travel long distances to shop in discount supermarkets (Short et al. 2007; Breyer and Voss‐Andreae 2013). Breyer and Voss‐Andreae (2013) proposed the definition of food mirage as an area that has abundant grocery stores but prices that are beyond the reach of low‐ income households and demonstrated the impor- tance of identifying foodmirages since the effect of a food mirage is the same as that of a food desert.

This study focuses on identifying three types of vulnerable food environments at the neighbourhood level: food deserts; food swamps; and most impor- tantly, the overlaps of food deserts and food swamps as these neighbourhoods represent the most unfa- vourable food environment in terms of geographic access. In addition, we also identify the food oases using three different definitions to provide a com- plete view of the diet environment of the City.

Commonly used methods to identify food environment

Identification of food deserts, food oases, and food swamps often involves two basic criteria: (i) accessibility to food outlets and (ii) socio‐economic status of neighbourhoods or census tracts (Appa- ricio et al. 2007; Jiao et al. 2012; Slater et al. 2017). Access has typically been measured as the physical distance between the centroids of spatial units of analysis and the nearest food outlets. Two common distance‐based measurements that have been uti- lized are Euclidean distance and road network distance (Smoyer‐Tomic et al. 2006; Larsen and Gilliland 2008; Wang et al. 2016). Euclidean dis- tance is the most straightforward method to calculate the straight‐line distance between two points in Euclidean space. Morton and Blanchard (2007) identified food deserts in US counties by calculating the Euclidean distance between resi- dents’ living areas and supermarkets and super- centres. Walker, Butler, et al. (2010) calculated the distance between the centroid residential zip codes and large chain supermarkets in Pittsburgh, Penn- sylvania; they identified food deserts as geographic areas in which residents have no access to a large

chain supermarket within 0.5 miles and food oases as areas in which residents have access to at least a large chain supermarket within 0.5 miles. With the improvement of network data availability, more researchers have adopted the road network ana- lysis to calculate distance as it provides more accurate estimates by incorporating actual travel impedances such as directional turns, traffic volumes, and speed limits (McEntee and Agyeman 2010; McKenzie 2014; Wang et al. 2014; Wang et al. 2016). McEntee and Agyeman (2010) measured the distance between every residence and the closest food retailers and calculated the mean distance to food retailers within census tracts. The authors defined food deserts in rural Vermont as areas where residents live more than 10 miles from food retail and observed that around 4.5% of the state population lived in food deserts. Smoyer‐Tomic et al. (2006) and Wang et al. (2014) used the shortest network distance measure to calculate actual distances between neighbourhoods’ cen- troids and supermarkets and identified food de- serts by combing neighbourhoods with high popu- lation need and low supermarket accessibility in Edmonton. Slater et al. (2017) calculated the shortest distance from the centroid of dissemina- tion blocks to the nearest grocery stores. The authors defined food deserts in Winnipeg as areas where the lowest‐income quintile population live greater than 500m from a grocery store and found that a large proportion of the Winnipeg population lived in food deserts.

However, distance‐based measurement some- times cannot accurately depict residents’ actual food availability when areas are clustered with food stores. For example, clusterings can occur in the case of food swamps, when there are areas where residents have access to abundant unhealthy food sources. Therefore, researchers have pro- posed the coverage method to more accurately capture neighbourhood food availability (Smoyer‐ Tomic et al. 2006; Wang et al. 2014; Lu and Qiu 2015; Luan et al. 2015). Lu and Qiu (2015), Smoyer‐ Tomic et al. (2006), and Wang et al. (2014) adopted the coverage method to measure neighbourhoods’ food availability by creating road network buffers based on the centre of study areas and then counting the number of total food stores within a threshold distance (e.g., 1 km). Luan et al. (2015) created a 4‐km road network (approximates a 5‐minute driving distance) buffering distance

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138 Meng Yang, Haoluan Wang, and Feng Qiu

from dissemination area centroids and then calcu- lated the relative healthy food access (the number of healthy food outlets divided by the sum of healthy and unhealthy food outlets) in each buffer area in the Region of Waterloo, Ontario. The authors assumed areas where the relative healthy food access is greater than 0% and less than 10% are food swamps, and areas where the relative healthy food access is equal to 0% are food deserts (Luan et al. 2015). Their results suggested that food swamps are more prevalent than food deserts in the Region of Waterloo (Luan et al. 2015).

Several studies (e.g., Larsen and Gilliland 2008; Russell and Heidkamp 2011; Jiao et al. 2012) came up with the service area method to measure food availability, which is the reverse idea of coverage method. Compared to the coverage method, the service area method focuses on creating buffer areas on the basis of food outlets instead of neighbourhood areas. A service area around each food outlet indicates that individuals living within it can be served by the food outlet (Larsen and Gilliland 2008). Larsen and Gilliland (2008) created a service area of 1 km based on each supermarket to assess the level of supermarket access in London, Ontario, and found the existence of food deserts. In a case study of New Haven, Connecticut, Russell and Heidkamp (2011) analyzed the severity of the food desert in this community by mapping ¼‐mile, ½‐mile, and 1‐mile road network service areas around major supermarkets and grocery stores. Jiao et al. (2012) combined the physical and economic access to supermarket criteria and the income level of residents to estimate the food deserts in Seattle‐King County, Washington. The physical access was measured by creating five service areas (1 mile from supermarket or 10‐ minute travel time to a supermarket by either walking, bicycling, riding transit, or driving) for each supermarket and the economic access was assessed by stratifying supermarkets into low‐, medium‐, and high‐cost (Jiao et al. 2012).

Some recent development in the food environment literature

Recently, Cooksey‐Stowers et al. (2017) adopted alternative ways to identify food swamps and food deserts in the US. The authors also addressed the endogeneity problems associated with food envir- onments since individuals may self‐select into

certain neighbourhoods. To solve this problem, the authors used a two‐stage least squares regression to analyze the relationship between obesity and the presence of food swamps and food deserts, and utilized highway exits as the instrument for food outlets access. The regression results showed that the presence of a food swamp is a stronger predictor of obesity rates than the presence of a food desert among US adults. The results indicated that the typical ordinary least squares (OLS) regression analyses may have underestimated the effect of food swamps and food deserts on people’s obesity rates. Su et al. (2017) obtained the daily travel time from each community to each healthy food store under four transport modes (walking, public transit, private car, and bicycle) in Shenzhen, China, and used this information to develop four travel time indicators tomeasure healthy food accessibility. The authors further defined food deserts as areas with lower healthy food accessibility and disadvantaged socio‐economic characteristics, and mapped the food deserts with respect to this definition. The authors also examined the relationship between healthy food accessibility and socio‐economic char- acteristics and found significant social inequalities in healthy food accessibility via walking and public transition in Shenzhen. Helbich et al. (2017) identi- fied the food deserts and examined food inequal- ities in Amsterdam, the Netherlands. This research addressed the spatial autocorrelation in the clus- tering when locating food deserts, and tested the associations among supermarket accessibility, prop- erty prices, and the percentage of native Dutch people per area. The correlation test results showed accessibility differs by region; areas with high property prices and a larger percentage of native Dutch people had a higher supermarket density. However, the authors found no evidence that the healthy food supply in Amsterdam was insufficient in disadvantaged areas.

Study area

As a medium‐sized North American city, Edmonton, Alberta provides an interesting case study because of its unique city structure and increasing policy focus on community food environment. The City of Edmonton has made substantial efforts to create a favourable food environment for Edmontonians. Established in 2012, the Edmonton Food Council

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Food deserts meet food swamps 139

launched the City’s food and agriculture strategy called “Fresh” (City of Edmonton 2012). One of the five goals in the strategy is to develop neighbour- hoods into healthier and more food‐secure commu- nities. The City and the Province of Alberta have paid particular attention to children and adoles- cents partially because of the increasing childhood (including adolescent) obesity epidemic (Health Canada 2013). School‐based health promotion pro- grams have been established that aim to improve healthy living habits of students and to sustain capacity for healthy environments in school com- munities—and these have kept expanding across the city. For example, the Alberta “Project Pro- moting active Living and healthy Eating in Schools” (APPLE Schools) is a school‐wide intervention that was launched in 2008. Fung et al. (2012) and Vander Ploeg et al. (2014) reported that APPLE Schools have increased students’ vegetable and fruit intake by 10% and that students are 40% less likely to be overweight. In addition to educational campaigns and various nutrition programs, such as cooking clubs, the actual availability of fresh foods and unhealthy food in the neighbourhood is a key factor influencing household food consumption. The re- sults from this study could provide useful informa- tion to further promote the program and develop tailored strategies.

Several prior studies on food access assessment have been done in Edmonton. Two of these focused on fresh food accessibility and the identification of food deserts. Smoyer‐Tomic et al. (2006) looked at supermarket accessibility, and identified nine food desert neighbourhoods across the city based on low accessibility and high‐need criteria. Wang et al. (2014) introduced community gardens and farmers’ markets into the healthy food analysis, and their results indicated that community gardens and farmers’ markets can help alleviate the food desert problem to some extent. Smoyer‐Tomic et al. (2008) explored the association between neighbourhood socio‐economic status and exposure to both super- markets and fast food outlets. Their results showed that the odds of exposure to fast food outlets were greater in areas with deprived subpopulations.

However, previous studies in Edmonton ignored convenience stores as a common source of less healthy food in the literature (Smoyer‐Tomic et al. 2008). Furthermore, all prior studies were based on nearest‐distance calculation to describe neighbour- hood food accessibility. The distance‐based

approach ignores the edge effect (Sadler et al. 2011); it also relies on the neighbourhood centroid to represent the whole neighbourhood food envir- onment, which might be less realistic. The adoption of the service area method, which focuses on the areas that can be served by certain food stores, offers a solution to the edge effect problem and can even potentially explore the heterogeneous accesses within a community.

Data and methods

Data

There were four sets of food stores in this study that can be divided into two streams: healthy and unhealthy food outlets. Healthy food outlets included supermarkets and local grocery stores, and unhealthy food outlets included convenience stores and fast food restaurants. All of the food store data repre- sented the situation in 2013 and were from DMTI Spatial Inc., which is a commercial company offering location‐based data in Canada. Supermarkets were assumed to provide a full range of food products (e.g., fruit, vegetables, and meat and dairy products). These full‐service supermarkets are mainly the outlets of chain stores such as Sobeys, Safeway, Superstore, and Walmart. Local grocery stores or specialty shops also sell fresh fruits and vegetables, meat, or fish and other seafood. Store information was further con- firmed by verifying stores’ official websites. Non‐ relevant shops, such as drugstores and liquor stores, were excluded from these two categories. Fast food restaurants were defined as quick‐serving food out- lets that offer relatively limited menus and food preparation options (e.g., burgers, sandwiches, pizzas), where patrons pay before receiving meals. In this study, they were primarily the outlets of franchised stores such as A&W, KFC, McDonald’s, Subway, and Wendy’s. Stores that do not provide food services on a regular basis or non‐food restaurants, such as bars and inns, were excluded from the analysis. Convenience stores were considered outlets that sell a limited selection of daily living items and offer less healthy, sugar‐ and energy‐intense food commodities. Based on the classification in the DMTI database, these stores were mainly some chain stores such as 7‐Eleven and Mac’s and gas station food stores. In the final dataset, we had 82 supermarkets, 40 local grocery stores, 783 fast food restaurants, and

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140 Meng Yang, Haoluan Wang, and Feng Qiu

199 conven

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