Comparative Analysis of Food Desert Mapping Definitions

Ryan Culligan

Project Advisor: Douglas J. Spieles

Department of Environmental Studies

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Copyright, Ryan Robert Culligan, 2014

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Acknowledgements

I would like to thank my advisor, Dr. Doug Spieles for guiding me through the entire

process of my research from start to finish. It is highly unlikely that I would know how

to use GIS had he not been there to guide me through my research process. I would also

like to thank Dr. Karl Sandin for taking the time out of his busy schedule to be my reader.

Lastly, thank you to the Environmental Studies Department for providing me with the

opportunity to conduct this research.

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Table of Contents

Abstract ............................................................................................................................................ 1

Project Overview .............................................................................................................................. 2

Introduction ..................................................................................................................................... 4

Methods ......................................................................................................................................... 22

Results: ........................................................................................................................................... 33

Discussion ...................................................................................................................................... 40

Bibliography ................................................................................................................................... 51

1

Abstract

Food Desert areas are areas with a perceived lack of access to healthy or

affordable food. Issues that have been noted to include mobility disadvantage, income

disadvantage, health and nutritional problems, social or cultural constraints, and lack of

food accessibility. The basic tenets food desert concept have existed for an extended

period of time, however issues of geographic access have only recently been explored.

This study uses GIS to evaluate a breadth of food desert definitions to examine whether

food desert associated issues have a geographic basis. The results of this study suggest

that geographic food deserts exhibit little to no association with demographic

observations such as percentage of households below poverty and percentage of

individuals with access to transportation by car.

2

Project Overview

This study uses census data from 2011 paired with food retailer location data

attained from various databases to identify geographic food desert areas. The goal of

the study was to determine whether poverty within an area had effects on access to

healthy foods at a grocery store level. While a correlation between poverty and lack of

access to foods is often assumed, there is scant evidence to support the notion, as many

studies are inconclusive about the matter (Walker, Keane, & Burke, 2010).

The geographic area of study for this project was the state of Ohio. This area was

chosen, because it was large enough to evaluate food desert definitions intended for

areas as large as the United States. Factors were examined on a census block level

because it was the highest resolution census demographic area. Food desert areas were

created in ArcMap 10.1, and then compared against each other where statistical

comparisons could be made.

Different geographic definitions exhibited different outcomes in their

demographic selections. Food desert definitions from the United States showed

increased poverty in food desert areas, while a definition from the United Kingdom

showed lower levels of poverty in food desert areas. Furthermore statistics could be

easily changed with subtle manipulation of the food desert categorization methodology

employed in ArcMap 10.1.

These results were contrary to the hypothesis that levels of poverty will be higher in

food desert areas. Geographic accessibility is by no means dissociated from the food

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desert phenomenon. Instead, issues of geographic food accessibility are more likely

symptomatic and often pointed out as a factor of food desert causation within areas

where a plethora of food desert associated factors exist.

4

Introduction

The term food desert was coined by a group of individuals working for the Low

Income Project Team of Nutrition Taskforce in 1995. This task force was operating in

Britain, and their new term was meant to describe areas in which they worked where

foods are too expensive or unavailable to the people living within the areas, for reasons of

systematic built environment structure (Cummins & Macintyre, 1999). The food desert

term attracted both government and media attention and in turn, a new wave of food

desert related studies came about to describe the geographic phenomenon. This media

attention climaxed in the United States with Michelle Obamas pledge to eradicate food

deserts in the United States by 2017 (USDA, 2011). Unfortunately, many of the issues

surrounding food deserts are only loosely related to geographical access, and a majority

of the studies undertaken to address food desert associated problems make omissions

when it comes to the social and economic pressures that underlie food desert problems

in many regions (Donald, 2013).

The geographically delineated food desert definition proposed by Cummins and

Macintyre had broad parameters. Food Deserts within the area of Glasgow they

studied were agreed upon strictly defined as areas with little to no access to grocery

stores. Accessibility problems in Glasgow were speculated to have arisen in the 1980s

due to increased social demands which pressured stores for a greater variety of foods

available for purchase (Cummins & Macintyre, 1999). To meet the demands, grocery

stores with low overhead, due to their location outside the city center, fared well, and

those inside the cities did not, leading to the creation of food desert areas in Glasgow

(Chung & Myers, 1999). Cummins and Macintyres 1999 study took place in urban

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Glasgow. This area was noted to be the center of the relocation and decentralization of

food retailers away from population centers. The relocation of grocery stores, made

sense for city dwellers with easy access to transportation; however, it left many people

unsatisfied and feeling that they had been left with inadequate access to grocery stores.

In wake of the relocation many social groups including the urban-dwelling elderly, poor,

and youth were left with little to no perceived access to quality fresh and nutritious foods

(Cummins & Macintyre, 1999). Food deserts quickly became a hot topic within the

British media, and were popularized in a manner that pushed for governmental action to

address the growing wealth discrepancies (Cummins & Macintyre, 1999). Despite the

publicity and widespread acceptance that the food desert concept received publically,

relatively few studies had been conducted to pragmatically research characteristics of a

food desert area.

The food access studies that did exist at the time of the definition of a food

desert did not focus on geographical access, but rather the ways that food is acquired by

people in different sociological circumstances. These studies have merits in terms of the

data they collected when it comes to the basic understanding of food access disadvantage

in deprived populations. However, they were not wholly conducive to undertaking swift

actions to fix the problems of geographical access on a large scale. Prior to the food

desert concepts inception, food access studies focused primarily on price research and

low-income or racial economic access to food products in the United States (Goodman,

1968). This research was conducted using store-to-store price comparisons between

predominately areas of different demographic makeups. In these models, nutritional

access was not taken into account (Sexton, 1971). Studies conducted on the previously

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listed variables had dramatically different outcomes in comparison to one another with

conflicting results in different cities across the United States, and sometimes within the

same city (Sexton, 1971).

In the city of Philadelphia, Dixon and McLaughton (1968) found that food cost in

grocery stores had a positive correlation with neighborhood per capita incomes (Dixon &

McLaughlin, 1968). The same year, Goodman (1968) found that grocery store prices in

Philadelphia depended on store type, and that 92% of the low income population he

studied left their neighborhoods to shop at more price-competitive stores (Dixon &

McLaughlin, 1968) (Goodman, 1968). Discrepancies in the comparative outcomes

between studies were hugely prevalent in the study of food access. While on one hand

the food access imbalance was undeniable when encountered within an area of study, a

metric of access comparison between multiple areas remained elusive (Sexton, 1971).

By the 1990s the study of food access inequality had further diversified as a

field, and was no longer focused primarily on food access inequality by racial

demographics. It had extended to examine problems of food accessibility through a lens

of nutritional availability, systematic reinforcement of food purchasing behaviors, and

psychological conditions associated with living in food desert areas. This expansion in

the breadth of accessibility knowledge paired with technological revolutions gave rise to

new geographic schema to quantify and delineate food deserts on a geographic basis.

To further understanding of the complex issues surrounding geographic access,

many modern studies have found it beneficial to employ GIS-based approaches to

studying food access and food deserts. GIS; short for Geographic Information systems,

is a mapping software which enables placement and manipulation of spatially oriented

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data. Food desert definitions which employ GIS as a tool simplify complex food

desert associated issues by defining idealized groups of quantifiable information. The

studies then use tools contained within the GIS software to quantify information across

geographic regions with demographic significance.

In their early years, many British food desert studies served as reports

conducted in conjunction with the British government to define areas that needed to have

the geographic food environment addressed. These studies took place slightly after food

deserts were popularized in the mid nineteen nineties and focused on establishing

methods that could geographically define food deserts. Research was essential because

in order for the British Government to enact practical solutions to alleviate the problem of

food deserts, it had to create ways to define food access-deprived urban areas. As

previously mentioned, the definition of a food desert was still in its infancy, and

afflicted regions in Britain had been observed by Government Nutrition Advocates, but

never formally defined (Macintyre & Cummins, 2002). The ambiguity in the definition

of food deserts, and the lack of proper evidence to confirm their existence, prompted

these preliminary studies to stick to geographically based conceptualizations of food

deserts focusing to establish whether the food desert problem was as prevalent on-the-

ground as it had come to be known by popular media sources.

In the wave of food desert studies after the British governments call to action,

different studies took different approaches to better understand food deserts.

Geographic methods of exploring food desert phenomena were limited in the objective

data they could utilize to classify food deserts. In most cases these methods involved

local or regional mapping with analysis of theoretical food availability, or in some cases

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theoretical food availability compared to actual food availability at studied stores. For

the most part, nutritional quality and cultural factors which contribute to food choices

were thought to play a minor role in food accessibility issues and largely ignored.

Unsurprisingly, different methods for mapping food deserts returned different results

on a basis on the metrics they used to define areas that lack readily available or nutritious

food. These different outcomes did not preclude the existence of food deserts on any

scale, but issues associated with identification became more complex, because more often

than not, food desert areas did not align with any predefined geography.

One of these early British government-sponsored food desert studies was The

Location of food stores in urban areas: a case study in Glasgow. This study took a

mapping-based approach to studying food deserts in the city of Glasgow. This

mapping-based approach was conducted by comparing the locations of food outlets in

Glasgow, Scotland with the deprivation categories of the areas they were located in at the

level of postcode regions (Cummins & Macintyre, 1999). The postcode scores were

chosen because they were originally delineated at a scale that most resembled an

appropriate scale for local walking access to residents within a given area, and therefore

could be approximate an appropriate scale for shopping (Cummins & Macintyre, 1999).

Deprivation was calculated using the Carstairs Morriss Deprivation index (DEPCAT).

This index was created in 1991 is commonly used in studies throughout Scotland. The

DEPCAT deprivation score serve as measurements of social and economic deprivation

that take into account overcrowding, male unemployment, low social class, and car

ownership (Carstairs & Morris, 1991). Based on the aforementioned factors, the

DEPCAT5 scores range 1-7 with one being the lowest levels of deprivation and 7 being

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the most deprived. The spatial distribution of the food stores identified through the

Public Register of Food Premises were mapped against the postcode deprivation areas

using MAPINFO and SPSS (Cummins & Macintyre, 1999). The mapping of food stores

against the DEPCAT5 scores in turn amalgamated a large number of the assumed

constituent components of food deserts, allowing the mapping of population density

and income against regional grocery store access. Well aligned with their null-

hypothesis, slightly more stores were found in the more deprived areas, and generally

speaking, food stores were evenly distributed throughout the studied area. The notable

omission from this food desert assessment was actual in-store food item availability.

While store type was taken into account, and from that data food availability was

inferred, in this study no data was compiled concerning on the ground food availability.

This lack of real world data could have contributed greatly to whether or not a place was

deemed a food desert on a basis that store designation type is not equivalent to store

quality of selection and item availability. This assumption was addressed in a follow up

study by Cummins and Macintyre in 2002, which compiled a list of food items that were

prospected for on a store-by-store basis to explore food item availability in more depth.

Like Cummins and Macintyre 1998, Cummins and Macintyre 2002 used the

DEPCAT deprivation score to determine socio-economic strata by postal code in

Glasgow. Cummins and Macintyre wished to further explore the concept of the food

desert as it concerned nutrition and nutrition access within an area, especially

considering that their previous attempt to define food deserts in Glasgow had not

returned relevant connections between deprivation and food store inaccessibility. The

2002 study food accessibility study was influenced heavily by several studies, which had

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established that large food outlets are cheaper than their independently owned

alternatives, and healthy food diets are more expensive than their unhealthy

alternative (Wrigley, Warm, & Margetts, 2002). The 325 studied stores were separated

into a 10-fold classification system that would more thoroughly describe a stores niche

as it applied to the specialty of its products, as the alternative governmental store

classification system that only included 4 potential definitions (small, large, specialized,

and non-specialized). After stores were designated, 57 standard food items were studied

at each of the grocery stores selected from a previous study by Nelson et al., which had

compiled a modest but adequate diet for those living in the UK (Nelson, Mayer, &

Manley, 1993). After data was compiled, it was found that 51 of 57 items were available

in more than 90% of the 325 grocers that were studied. The best indicator for food prices

was not in fact location of the food retailer, but rather retailer designation between large

general grocery stores and small specialty stores.

Many food items did not statistically differ in price, however, and the foodstuffs

that did diverge from consistent pricing were low nutrient density, high energy density

and tended to be less expensive in areas with higher deprivation scores. Despite the

inequalities in food availability that had been colloquially observed in the greater

Glasgow area, Cummins and Macintyres 2002 study showed that when looking at the 57

chosen food items, there were no signs of food availability inequality which could be

directly indicative of a food desert. Despite the cost consistency, Cummins and

Macintyre noted that the small number of foods that were less expensive in lower income

areas including tea cakes, sausages, burgers, chocolate, and frozen French Fries, were all

foods that nutritionists recommend people consume less of. In this case, although the

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price and availability of healthy foods did not differ, the price of low-nutritional value

foods did, pushing low-income consumers towards unhealthy food choices. For the

reason that there was not a substantial difference between food prices or availability

based on geographic or economic geographical strata, Cummins and Macintyre

concluded that although food deserts may exist to a degree in Glasgow and the greater

United Kingdom, they did not appear in a definable way within the context of their study,

because the costs of foods between neighborhoods did not differ enough to statistically or

practically cause healthy or unhealthy foods to become cost prohibitive (Cummins &

Macintyre, 2002). The Cummins and Macintyre 2002 studys outcome was much like

their first study, maybe because there was indeed a high degree of overlap between on-

the-ground food availability and assumed food item availability that was used in their

first study.

Chung and Meyers 1999 took a study approach which mirrored approaches taken

by both Cummins and Macintyre 1998 and 2002. This study looked at both store

location, food item availability, and store type along geographic income strata in

Minnesotas Hennepin and Ramsay counties. Unlike the studies conducted in Glasgow,

Chung and Myers statistically established that all grocery stores were more common in

more affluent areas, and that if stores were found in poorer areas, they were more likely

to be large or chain grocery stores. There was also a statistical difference between chain

and non-chain store food prices, with chain stores coming in at $16.92 less expensive per

standardized staple foods market basket purchase on average than non-chains. Like

other studies, Chung and Meyers concluded that there is little statistical evidence for

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food deserts in the Twin Cities metropolitan area, as the prices of foods failed to differ

from region to region within Hennepin and Ramsay county food outlets.

The quantitative price and geographical access approach taken by Chung and

Meyers 1999 can be seen as a counterpoint to the study conducted by Smith et al. in 2010

looking at environmental influences on food access and shopping and dietary behaviors

in Minnesotans in the same area. Smith et al. first define food deserts in much the

same way as Chung and Meyers do in their study, using the Cummins and Macintyre

2002 definition, among others, to define a food deserts as poor urban areas where

residents cannot buy affordable, healthy food, and regions lacking close proximity to

food outlet (Cummins & Macintyre, 1999). After this definition, Smith et al. take a

drastic turn towards studying both quantitative and qualitative measurements of diet

quality and food access, specifically within two family shelters in the Minneapolis area.

These shelters lacked some or all means of food preparation and storage such as

refrigerators, freezers, ovens, and stoves, and by virtue of their limitations, and enforced

policies that disallow perishable food items. In turn, many food items which would be

considered to be of high nutritional value were negatively reinforced by shelter residents

in favor of low nutritional value, high energy density foods such as crackers, cookies, and

chips (Smith, Butterfass, & Richards, 2009).

Unlike many other studies that tend to focus on the straightforward delineation of

food deserts on a geographic basis, Smith et al. attempt to look at the relationships

between the proximity of low income shelters to food sources and between family

income, food prices, and their effects on dietary choices and health. Measurements taken

included height and weight data from participants as well as daily food consumption

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records and firsthand accounts and food system personal complaints from the participants

of the study (Smith, Butterfass, & Richards, 2009). It was established that 80% of the

studied individuals were clinically overweight. Many people living in the areas felt they

had no easy access to food at a local level in the center of Minneapolis, and also limited

in their food options by price (Smith, Butterfass, & Richards, 2009). Unlike Chung and

Meyers study, this study defined food deserts using data directly gathered from people

who had reportedly lacked access to food while living in inner-city Minneapolis, and in

turn established the existence of a perceived food desert, in spite of a lack of empirical

data to establish higher food costs within an area.

The differences in the Chung and Meyers and Smith et al. studies as to whether or

not food deserts exist in the Minneapolis metropolitan area point towards weaknesses

in the different approaches that have been utilized to study food deserts. On one hand,

there are mapping-based approaches to studying food deserts which generally follow

the approach taken by Cummins and Macintyre 1999. These studies deal with mapping

access to food on a city or regional scale, by looking at food retail outlets, their prices for

individual food items, income levels within the studied area, and other factors particular

to the specific interests of the study authors. While these studies provide insightful data

for the practical application of solutions to food access problems, they rarely find

statistically relevant differences in food cost or accessibility as demonstrated in studies

conducted in Minneapolis in 1999 (Chung & Myers, 1999). A common alternative

means of understanding food deserts is to take a sociological approach to articulating

the problems faced by individuals with low income in areas without accessible foods, as

seen in Smith et al. 2009. In this mode of study, problems with food accessibility are

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directly visible, but without a statistical basis for understanding the geographies

associated with the lack of food access encountered by said people, there is no viable

geographic definition. Both types of study are essential to the understanding of food

deserts, because without a sociological basis for geographic research, the geographic

research would not take place; however, the conclusions of the two study types are often

at odds with each other.

An attempt to visualize the variation in food cost and availability which

synthesizes both local deprivation, food availability, and cultural tastes can be seen in

Donkin et al. 2000. This research took place in a two square kilometer area of London

with a high Carstairs deprivation score. The British government spurred the studys

undertaking after they called for the development of quantitative methods for defining

and mapping access to healthy foods as part of a political agenda (Findlay & Sparks,

2002). Unlike other geographically- and socially-tied studies of food deserts, Donkin

et al. looked at factors that contributed to a healthy and balanced diet, such as the ethnic

makeup of the area, population food preferences, ethnic variations in shopping practices

(bulk or individual), and the monetary concerns of the people living in the deprived area.

From these concerns, one hundred twenty three food items were considered to be

candidates to be checked for local availability and seventy-one food items were identified

as candidates for price study. One hundred ninety-nine outlets within the two kilometer

range were surveyed, of which only one hundred sixty-six outlets stocked some of the

healthy foods identified by Donkin et al. From the data collected, GIS plots were

created with different symbology corresponding to differing costs and levels of

availability. Unlike other studies of the same kind, this study did not attempt to derive a

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universal method for the mapping of food deserts, rather, it sought to create a modular

and scalable toolkit that could be readily applied to different geographies. A modular

tool-kit is beneficial to the study of food deserts because it is vitally important to have

replicability of study methods to help better understand the underlying causes of food

access disparities are attributable to lumped factors unique to each case studied location.

The subjective implications of living in a food desert are of equal importance

because unless they are understood, there is no means through which food accessibility

problems can be practically addressed. Studies such as the Cummins and Macintyre

1998, though important to the study of food deserts, use accessibility and deprivation

as the sole factors to predict diet inequalities. This approach is useful but ignores the

sociological aspects of food deserts, such as attitudes towards healthy eating, fear of

compulsiveness and going over budget, and grocery store familiarity and other

preferences. These seemingly secondary factors contribute to slow adoption of closer,

less expensive, or better-stocked grocery stores. This was the case with a store noted by

Wrigley et al. where after two years of grocery store existence in a food desert area,

only 50% of surveyed shoppers had changed their purchasing habits to reflect the

presence of the store (Wrigley et al. 2004). Habits as examined by Wrigley et al. suggest

that issues of poverty are paramount to the study of food deserts. Money is among the

primary influences food purchases, and unless people are incentivized to make healthy

food purchases from an economic standpoint as for prospects of better health, purchasing

habits are unlikely to change (Wrigley, Warm, & Margetts, 2002).

In the wake of the preliminary studies done in the late 90s and early 2000s,

food deserts have become hot topic issues within American society, and a priority for

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the United States Department of Agriculture (USDA) (Walker et al 2010). The USDA

was first tasked with monitoring and researching the prevalence and causes in 2008 with

the signing of The Food, Conservation, and Energy Act of 2008 (110th Congress, 2008).

With this new responsibility on the part of the USDA came the first nationwide food

accessibility studies undertaken by a country as large as the United States. The USDA

had to wade through many of the inconsistent means of study to define a food desert at a

nationwide scale using a body of research that was not widely meant to scale beyond a

city level, which involved a literature review process that was taken by Walker et al.

2010. The review of existing literature gave many insights into study types that had been

previously conducted in the United States, and what areas of food desert research

needed to be drastically improved. While studies within the United States had thoroughly

explored the concept of geographic access to foods, the real life implications of living in

a food desert had been under-explored by comparison (Walker, Keane, & Burke,

2010).

The imbalance in successful delineation of food desert areas has implications in

the practical changes that the USDA has taken to incentivize the removal of food

deserts in the United States. Based upon the data collected by the USDA, the Obama

administration created the Healthy Food Financing Initiative, which was meant to

eradicate food deserts nationwide by 2017 (Donald, 2013). Part of the strategy to

accomplish this feat is to address built environment problems by partnering with pre-

existing national and regional grocery chains to bring healthier foods to affected areas

(Donald, 2013). In turn, many globalized companies currently plan on entering or re-

entering perceived food deserts in with small-scale box format stores (Donald, 2013).

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However, there are many critics of this strategy, as the approach propagates the existence

of grocery store chains and approaches the food desert problem in terms of food item

economic accessibility and food regulations and policy.

Although it can be argued that chain stores can proliferate to new regions faster

than independent grocers because of their pre-existing distribution networks, and thereby

address food desert-associated problems on a faster timeline, there are many reasons

why this may not be the case. Many grocery store chains are said to propagate the

economic divides that contribute to the formation of food deserts through practices

involving low-wages, anti-union sentiments and the intense cost-pressures they place on

local competing businesses (Donald, 2013). If these cost-cutting factors are present, they

propagate cost reduction strategies on multiple levels, which can arguably further bisect

the food market between high-end and low-cost food markets. This split in retail markets

is important because the local demands in low income areas may not be in line with

healthy foods, and if the stores follow a hybridized model of retail globalization and

tailor their stocks to local demands, they could propagate the consumption of low

nutritional value foods (Donald, 2013) (Wrigley, Warm, & Margetts, 2002).

Low nutritional value foods with high energy density are less expensive on

average than nutritious foods for reasons including government food policies (Donald,

2013) (Drewnowski et al. 2005). Although energy density and food cost are negatively

related to one another, whether a healthier diet costs more to purchase is a point of

scientific contention with many conflicting reports (Drewnowski et al. 2005). The

division between research outcomes in food price studies has contributed to the choice to

promote nutritional education over the arguably more effective, but much more complex

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and costly rehashing of United States food policy to promote purchasing habits involving

foods that are of high nutritional value (Drewnowsky et al. 2004). Food policy is left

largely unaddressed in the Healthy Food Financing Initiative when it comes to

subsidization, but not in food taxation efforts. As a means to improve the diets in the

United States, several efforts have been made to raise taxes on unhealthy foods

(Drewnowski et al. 2005). However, these actions have been received poorly, because

rather than address issues of economic inability to purchase healthier foods, this raises

general food costs, effectively penalizing those who cannot afford more nutritious foods

(Drewnowski et al. 2004).

A typical approach to food desert studies is to use one-time experimental data

to determine whether there are correlative relationships between different groupings of

people living in a small city level geographic area (Cummins & Macintyre, 1999)

(Donkin, Dowler, Stevensonn, & Turner, 2000). This approach is critical to the

preliminary study of food deserts and is often inconclusive, but also often serves as a

base on which to enact policy. Follow up studies are rarely conducted in food desert

literature. Very rarely, do researchers go back into an area after actions have been made

to address food desert associated problems. One of the few studies that has assessed

before and after outside intervention in a food desert area is Cummins et al. 2005.

Like other studies by Cummins et al, the experiment studies the greater Glasgow area

using DEPCAT scoring to determine social deprivation, but unlike his other studies, the

2005 study deals with survey data rather than readily available government census data.

Previous studies by Cummins and Macintyre studied food item availability and

proximity to grocery stores as it related to social deprivation, however, the 2005 study

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used mailed surveys to conduct a psychological inquiry of individual health before and

after large scale food retailers moved into deprived areas of Glasgow (Cummins et al.

2005). The pre- and post-intervention surveys were sent to individuals living in the

studied areas and asked questions that pertained to vegetable and fruit consumption, as

well as psychological health. It was hypothesized that improving geographical access to

foods would positively impact consumption of the healthy foods, and negatively impact

consumption of more shelf stable unhealthy foods. Although one study is hardly enough

to give a decisive answer to whether food desert solutions seeking to improve geographic

access are effective, Cummins et al. 2005 would suggest that this type of solution did not

improve diet quality for people in the intervention regions (Cummins et al. 2005). The

only statistical difference in survey results before and after intervention was a small

improvement in psychological health for those immediately involved in the food desert

intervention projects (Cummins et al 2005).

Cummins et al. 2005 did not come up with any relevant differences in food habits

pre- and post-food desert intervention. Regardless of this outcome, the United States

government has adopted much the same strategies in dealing with addressing food

desert problems, when it partnered with nationwide food outlets to increase availability

(Donald, 2013). The adoption of the built-environment approach to alleviating food

desert concerns could be seen as a warning sign that the governments approach may

help to remove food deserts; however, the government is also pushing food education,

and little to no research has been conducted to determine how built environment and

education approaches work in tandem to alleviate food desert pressures. It is of key

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importance that food deserts are approached from both empirical and subjective angles,

as they can be defined equally well through either means.

An approach that holds promise to the study of food deserts is concept

mapping. This research focuses on making subjective data measures that are of value to

the study of food deserts understandable through programs meant to visually map

relevant yet discreet contributing factors (Walker, Keane, & Burke, 2010). This concept

mapping process is not meant primarily to test previously existing hypotheses, but rather

to serve as an exploratory tool to develop a more complex understanding of the processes

and practices that contribute to the observed food desert phenomena (Jackson & Trochim,

2002). Concept mapping analysis doesnt necessarily show a geographic base to the

food desert problem, but can nonetheless help to assign statistical relevance to

subjective data as well as to model interconnected issues in food supply and demand web.

When combined with geographic mapping, concept-mapping methods of models of food

deserts can provide the information that has been commonly overlooked in the food

desert literature used to inform many policy decisions in recent history (Donald, 2013).

The existence of food deserts has been acknowledged by a multitude of people

dwelling in what they call food deserts, and authorities who have prioritized the

irradiation of the food desert problem. Despite the acknowledgement of food

deserts, practical solutions to food desert associated problems and a tendency to

unilaterally address the problems compound the ill effects of the food deserts.

Although each method of study to understand individual aspects of food desert-

associated problems has its merits, the umbrella structure of the policies to address food

desert issues is typically based on a model of geographic access (Donald, 2013). When

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the United States government pledged to eradicate food deserts by 2017 in the United

States, it became critically important to define what a food desert was so action could

be taken to address the issue (110th Congress, 2008) (Donald, 2013). Without objective

criteria to judge effectiveness, the effort to eradicate food deserts cannot be successful.

This research projects seeks to implement multiple food desert research

definitions in GIS. As previously mentioned, food desert studies have been

inconsistent in their ability to show relevant differences between food desert and non-

food desert areas. The intent of food desert delineation is to reveal differences

between locations in the food landscape. The world is not a uniform place, and by virtue

of this demographic delineations used for food desert research may not be

representative of the intended selection of classification schemes. To evaluate the

effectiveness of different food desert definitions, definitions will be evaluated on a

basis of their ability to select for demographic inconsistencies that are related to an

idealized food desert concept, including:

Rates of poverty are higher in food desert areas.

Access to transportation is lower in food desert areas.

There are less grocery stores in food desert areas.

There are higher numbers of fast food retail locations in food desert

areas.

Although these metrics are simplistic, they hold the potential to reveal weaknesses in

food desert classification schemes.

22

Methods

Table 1:

The basic method of this study was to identify food desert and non-food

desert areas in Ohio. Food deserts were defined by multiple definitions to allow for

comparisons (Table 1). Areas were then compared to one another using their

socioeconomic attributes as a means of evaluation for the validity of each definition.

To analyze and compare geographically based food desert definitions, a consistent

methodology was required to maintain consistency of selection throughout testing.

Different model studies had varying data collection techniques, which used inconsistent

data sources. This study sought to compare multiple definitions of food deserts, and

generalized databases were used to ensure that data sources remained as consistent as

possible. Multiple definitions required the testing of various hypotheses; the hypotheses

tested in this study include:

Definition: Area of Study:

USDA Low Access:

Rural: 10 miles from grocery store

Urban: 1 mile from grocery store

United Kingdom:

Urban: 500 meter from grocery store

USDA Per Capita Access:

Grocery Stores per 1,000 pop. Census Block

Fast Food Location per 1,000 pop. Census Block

Table 1: A list of food desert delineation schema tested in this study. Euclidean distances were

used for distance delineation. Per Capita definitions used census block delineations.

23

1. Census blocks in food desert areas have a higher number of households below

the poverty line and lower percentage of the population with access to

transportation.

2. Access to car transportation will be lower in areas classified as food deserts, as

transportation is necessary to get to food in the absence of public transportation

3. The number of grocery stores per 1,000 individuals is lower in food desert areas

than it is in non-food desert areas.

4. The number of fast food restaurants per 1,000 individuals is higher in food

desert areas than in non-food desert areas.

The state of Ohio was selected as the site of focus in this study. Smaller regions were

considered in preliminary research, but to encompass both urban and rural areas a larger

scale which encompassed both types of area was selected. The breadth of the

information contained in the state of Ohio databases that were compiled also represented

a better analogue for the USDA definition of a food desert, which seeks to model the

Unites States as a whole.

Ohio census demographic and delineation data was acquired from the U.S. Census

Bureau shapefile and linefile, TIGER database 2011. First, Ohio border files were

downloaded from the U.S. Census State Geodatabase file. U.S. Census block data was

collected to enable the comparison of demographic data across the census block areas in

the state of Ohio. The U.S. Census block data is the smallest census delineation with

statistical data, and contains many metrics that are necessary to the creation of economic

based food desert delineation systems, and useful to the cross-comparison of alternate

food desert definitions.

24

After the collection of Census Block data, it was critical to several food desert

definitions to be able to differentiate between urban and rural areas. The U.S. Census

Bureau urban areas 2010 map was used to differentiate between urban and rural areas.

This classification distinguished census blocks on a basis of whether their centroid was

within the Urban Areas shapefile. This shapefile is defined by the presence of urban

clusters of at least 2,500 individuals and urban areas of more than 50,000 individuals with

a population density exceeding 1,000 individuals per square mile. Centroid selection

was used to select census blocks which fell within the aforementioned urban areas.

Business locations and main lines of business were key components of all geographic

food desert definition schemes. To compile a consistent database for the entirety of the

sampled area of Ohio, a consistent data sampling technique was required. Business

information was attained in its entirety from two databases: DatabaseUSA and

ReferenceUSA (ReferenceUSA, 2014); (DatabaseUSA, 2014). Database entries included

business name, latitude and longitude coordinates, main line of business, square footage,

and annual income, as well as various other comparative metrics. The businesses in Ohio

were sampled from databases as defined by their business type designated by their retail

designation codes. Database entries were compiled in a parallel .csv file to enable use in

JMP, GIS, and Excel. Although individual database format was similar, entries were

modified to fit a consistent order before placement in a common .csv file. Business

listings were filtered by address to remove any duplicate entries. Removal was

conducted by hand to verify that no mistake had been made. The main line of business

was compiled for each store in an Excel spreadsheet using five distinct categories:

restaurant, fast food establishment, convenience store, grocery store, and food bank.

25

Separation methodology was largely conducted using predefined identifier codes, but

some hand modifications were made to the dataset to account for mis-designation by

code (ie. Kroger gas station from Grocery Store to Convenience Store). Business

addresses were then geocoded using the Google Maps API and exported be to a .kml

format. This conversion was completed using Google Fusion Tables, which could then

re-exported into a formatted .kml with unique business identifiers that could be used to

match data with ArcMap 10.1. Business locations were then imported into ArcGIS 10.1

using the .kml to shapefile script.

Projections from all sources were redefined using the batch project script in ArcMap

10.1 in accordance with the UTM 17N projection. Layers from U.S. census data

geodatabases were then processed to lie only within the Ohio border to remove

superfluous information outside the chosen study area. The number of businesses per

given census block corresponding to each type of business category were created from

the geocoded business location layer and census block data using ArcMap 10.1s union

function. This in turn, was joined to the census layer and business layer attribute tables to

enable statistical analysis of both layers concurrently.

Maps which visualized poverty and transportation data used Jenk distributions to

visualize the differences in percentage with or without access to a variable for each map.

Jenk distributions were chosen because they isolate discreet groups in data sets by

maximizing variance between separate groups, and minimizing variance within distinct

groups. This method is best for data visualization of this type, because food desert or

non-food desert groups only make sense within the context of the opposite category.

26

Even sampling fails to show differences between deprived and affluent areas (Map 2,

Map 3).

The USDA and five hundred meter United Kingdom low access geographic food desert

definitions were created using the buffer function in ArcMap 10.1. A ten mile buffer was

used for rural areas and a one mile buffer for urban areas to define low access areas

within the USDA food desert definition. These buffers were created and then clipped

to the area of Ohio. Urban grocery stores were buffered twice, as they needed to account

for both the urban 1 mile and rural 10 mile food desert definitions. Buffers that were

created for the 10 mile area were erased from urban areas to remove any compounded

non-food desert areas. Food desert census blocks were then selected by their

centroid, as defined as non-food desert if it was within the area of a defined non-food

desert areas. Lists of the selected census blocks were compiled from the selected

features and turned into binary classifications in Excel by checking lists of shape

identifiers for their presence in exported arrays using Boolean statements. This

methodology was repeated to select for the 500 meter urban food desert definition.

The above mentioned food desert categorizations were then selected from

compiled census block and business data, and attribute tables were exported as .csv files.

The .csv format allowed data to be used in Microsoft Excel and for the Continuous and

Nominal datasets to undergo bivariate fit-line, T-Test, and ANOVA comparisons

between the data sets in JMP. JMP enabled the statistical testing of otherwise subjective

map data, allowed for the comparison and classification of food desert schema

according to their statistically relevant differences.

27

Fast food restaurants per 1000 individuals and grocery stores per 1000 individuals

were studied as a function of the percentage of households living below the poverty line

using a bivariate fit analysis. Fit-lines were created between these relationships, to

determine whether there was a correlation between households living below the poverty

line and decreased access to grocery stores or increased access to fast food establishments

in a given census block. The dataset was created using grocery store point data that had

been buffered in ArcMap 10.1 to account for distances. This dataset was then used to

create a geometric union in ArcMap 10.1, to derive a count of buffers per given census

block. The count was defined by the presence of a buffer shape overlapping the centroid

of a census block shape to define a non-food desert area.

The USDA food desert classification scheme was the first definition to undergo

statistical analysis. The USDA food desert definition uses a 1 mile buffer for urban

areas and 10 mile buffer for rural areas to delineate low access. To evaluate the low

access component of the USDA food desert definition, ANOVA analysis was

performed to distinguish differences between Urban, Rural, and Combined urban and

rural food desert areas by virtue of their food desert centroid selection. This

comparison used percentage of households below poverty line, percentage of population

with access to a car, and number of grocery or fast food locations per 1000 individuals as

comparative metrics. If food desert definitions had different low access and low-

income components, relevant data was selected and combined to enable ANOVA tests

across paired variables. This statistical review process was repeated for all applicable

classification schemes in which comparison was valid.

28

Map 1: The location of grocery

stores in Ohio

29

Map 2: Percentage of Households

below the poverty line by Census

Block. Percentage delineations are

created using Jenk optimization

algorithms.

30

Map 2: Percentage of Households

below the poverty line by Census

Block. Percentage delineations were

created using Jenk optimization for

best viewing results.

31

Map 3: USDA low access food

desert areas are shown in green. A

one mile buffer was used for urban

areas and ten mile buffer for rural

areas.

32

Map 4: United Kingdom low access

food desert areas are shown in

green. A 500 meter buffer was used

for urban areas. White areas are rural

and therefore not taken into account

within this definition.

33

Results:

Statistics were run to

develop an understanding of

the demographic groups in

Ohio before any food desert

classification schemes were

derived from mapping data.

These statistics compared

urban and rural areas without

any food desert or non-food desert distinction. T-tests and ANOVA tests were

performed to analyze whether there were statistical differences between urban and rural

populations. There was no statistical difference between the percentage of the total

population living below the poverty line between urban or rural areas (p = .6132). There

was a statistically relevant difference in percentage of the population over the age of 16

with access to transportation between urban and rural areas. In urban areas, 90.42% of

residents had access to a car transportation, but in rural areas 91.63% of the population

had access to transportation (p = .001).

The number of grocery stores and fast food establishment counts per 1,000 individuals

showed no statistical difference

in number of either of the establishments per 1000 individuals between urban and rural

census blocks (grocery p = .3335, fast food = .5406).

Rural Urban

Percentage of Households

below the poverty line

15.96 15.66

Percentage of Individuals with

access to transportation by car

91.63* 90.42*

Number of Grocery Stores per

1,000 Individuals

.2032 .2293

Number of Fast Food Retailers

per 1,000 Individuals

.3274 .3488

*demarcates statistical significance within a row (

34

To evaluate the effectiveness of food desert categorization, the number of

grocery stores per census block area was examined on a basis of whether a census block

was considered a food desert or non-food desert area for both the USDA and United

Kingdom food desert definitions. There was no statistical difference in the number of

food sources in food-desert areas or non-food desert areas in any selection

methodology. Tested selection methods included block intersection with food desert

area, block centroid in food desert area, and block completely contained within food

desert area. The most statistically relevant selection method was centroid selection

which had extremely an extremely weak difference between the number of grocery stores

per given census block and census block classification (p-value = .1358). The centroid

selection method was used for all following experimentation as it was based on the

USDA definition, and provided better classification of food desert areas than any

feasible alternative.

Table 3:

Definition: Rural Urban Combined

USDA Low Access:

Food Desert 15.83 17.45* 17.10*

Non-Food Desert 20.47 14.98* 15.00*

United Kingdom:

Food Desert: --- 14.96* ---

Non-Food Desert --- 16.64* --- *demarcates statistical significance within a definition column (

35

Urban and rural census blocks designated as USDA food deserts showed a

statistically higher number of households below the poverty line (Table 3). The food

desert census blocks as had a mean of 17.10% of households below the poverty line,

whereas census blocks designated as having high-access to grocery stores had a mean of

15.00% of households below the poverty line (p < .0001).

Urban food deserts and rural food deserts (as defined by the USDA) were

separated from one another for independent analysis. Within the USDA definition, urban

food-desert areas had a statistically higher percentage of households living below the

poverty line. A mean of 17.45% of households in urban food deserts live below the

poverty line, compared to a mean of 14.98% of households in urban non-food desert

areas (Table 2). In rural areas, there was no statistically relevant difference in percentage

of households living below the poverty line regardless of whether the area was

considered a food desert (Table 3).

There was found to be a statistical difference in the percentage of households

living below the poverty line. In food desert areas under the 500 meter United

Kingdom urban food desert definition, a mean of 14.96% of the households were living

below the poverty line (< .0001). In contrast, non-food desert areas had a mean of

16.64% of households living below the poverty line (Table 3).

36

Table 4:

Definition: Rural Urban Combined

USDA Low Access:

Food Desert 91.74 87.47* 88.40*

Non-Food Desert 87.49 91.55* 91.54*

500 meter Urban Definition

Food Desert ---- 92.1 ---

Non-Food Desert --- 88.11 --- *demarcates statistical significance within a definition column (

37

desert census blocks (Table 4). There was no difference in access to transportation by

car between rural food deserts and non-food desert areas.

A comparison between access to transportation by car for persons 16 and older

yielded a statistically relevant difference between the food desert and non-food

desert urban census blocks using a 500 meter food desert definition (p < .0001).

Using this definition, food desert census blocks 92.10% of people had access to

transportation, and in non-food desert areas 88.11% of individuals had access to

transportation by car (Table 4).

38

Chart 1: There was a weak positive correlation between number of grocery store

locations per 1,000 individuals and the percentage of households below the poverty

line in a given census block (p < 0.0001)(r2 = 0.0159) .

Chart 2: There was a weak positive correlation between number of fast food

locations per 1,000 individuals and the percentage of households below the poverty

line in a given census block (p < 0.0001)(r2 = .0021) .

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Percentage of Households Below the Poverty Line

Number Of Fast Food Locations per 1,000 Individuals vs. Percentage of Households Below

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39

The relationship between the percentage of households below poverty in a given

census block and the number of grocery stores per 1,000 individuals and in a given

census bock were shown to have a positive directional relationship (p < .0001) (Chart 1).

The positive directional relationship found across the state of Ohio was significant in

both rural and urban areas. This result was in line with Cummins and Macintyre 1999,

which found a higher number of grocery stores in more deprived areas (Cummings &

Macintyre, 1999).

Another positive directional relationship was found when comparing the number

of fast food restaurants in a given census block with the levels of poverty in the same

census block (p < .0001)(Chart 2). This relationship also remained consistently

significant and positive across urban and rural census blocks. Although it had not been

previously reported in Ohio, this finding mirrors what has been reported in many health

related food desert studies such as (Cummings & Macintyre, 1999).

40

Discussion

The geographically defined food desert definitions recreated in this study

attempt to define food deserts on the basis of lack of ready accessibility to healthy

foods. This type of definition is consistent with the original definition of a food desert

as put forward by the Low Income Task Force in the UK, which defined a food desert

as an area with little to no access to food retail provision (Cummings & Macintyre,

1999). However, geographic studies only examine symptomatic and surface level

evidence which may be indicative of problems within an area. In this aspect, geographic

definitions fail to take into account the extraordinary complexity of the food desert

associated problem-set, including mobility disadvantage, income disadvantage, social

constraints, and nutritional problems to name a few (Guy & David, 2005). However

simplistic a geographic definition is, mapping solutions can serve as valuable assets to

food desert identification prior to on-the-ground investigation. However, this is only

the case so long as the definition that is used for mapping is widely applicable to a huge

range of diverse geographies, beyond the area that it was originally identified.

To encompass a wider range prospective food desert areas, many food desert

definitions contain selection caveats to make themselves more widely applicable to a

range of on-the-sground circumstances. To evaluate comparative criteria among

definitions, many subdivisions within individual datasets were necessary to ensure that

variation in testing outcomes was not due to changes in definitions at different scales (ie.

Food Desert Urban 1 mile compared to Food Desert Rural 10 mile within the USDA

definition).

41

It was found that within the USDA food desert definition using centroid

selection, urban food desert areas had lower percentages of the population in urban

areas with access to transportation and higher percentages of households below poverty

compared to urban non-food desert areas. This data confirmed a food desert

hypotheses within urban areas of Ohio and disproved another. While levels of poverty

were greater in food desert areas, access to transportation was lower, suggesting that

even while there may be greater need for transportation, the need may not be satisfied.

However, no statistically relevant differences between car access or percent of

households below the poverty line were found in rural areas. The United Kingdom 500

meter definition showed a statistically higher percentage of the population living below

poverty line in urban non-food desert areas using centroid selection compared to urban

food desert areas. This means that the selection threshold set at 500 meters within the

European food desert definition had opposite effects of the USDA 1 mile definition,

suggesting that it is too strict of a delineation threshold. Statistically relevant

relationships were also observed between the number of census blocks per 1,000

individuals and number of households below poverty. There were more fast food

restaurants and grocery stores in Urban and Rural areas with higher number of

households below poverty than in census block areas with lower number of households

below poverty. This result was in line with what other food desert studies had found in

their experimentation, and advocated for a weakly directional statement that there were

more grocery and fast food locations in more impoverished areas.

The same dataset was used to calculate food desert delineation buffering and

count of grocery stores per 1,000 individuals. Comparative results of the two tests show

42

separate conflicting results. For food desert buffers, census blocks were chosen on the

basis of whether or not they had a centroid in a food desert area. For the number of

grocery stores per 1000 individuals definition, a count of grocery store data points (in the

form of distance buffers) was created for each census block, and the number of grocery

stores per 1,000 individuals created from the grocery store count and census block

population data. These statistically relevant results are odds with one another. Urban

census block areas with high poverty are more likely to have healthy food locations

such as grocery stores nearby, and also have a higher probability of being in a food

desert area, as defined by the centroid of a given census block existing outside the

bounds of a one mile buffer from any food source. This result can be explained on a

basis of the selection biases created by the buffer distance of selection in each definition

scheme. While count per 1,000 individuals takes into account the number of buffered

locations within distance of individuals, accessibility by distance does not.

Geographic accessibility is a fickle measurement, because small changes in

criteria of data selection or alternatively changes in the distance scale for data selection

can have large implications for which data are selected. Different food desert

delineations can be achieved by using spatial intersection as a selection method. This

was demonstrated when separate selection methodologies were tested to determine their

utility in choosing a viable way to designate a census block as food desert or non-food

desert. Of the three options that were tested, none did a satisfying job of matching and

capturing relevant overlaps between the different geographies, should any have existed.

Even within simple geographic food desert definitions, selection methodology

can change statistical outcomes with seemingly small tweaks. Multiple selections are

43

needed in each dataset to isolate definition variables, but each selection introduces more

previously unseen bias, necessary to each definition, but not relevant in meaningful ways.

Not surprisingly, If the selection methodology is changed in this study, so too are its

results, in some way significantly.

As previously mentioned, food desert selection criteria can have huge impacts

on which census blocks are selected within a given definition. By changing the census

block selection method from centroid with a food desert area to intersection with a

food desert area, or full-shape containment within a food desert area, the statistical

outcomes of a study can be manipulated. The difference between percentages of

population living below the poverty line in urban food desert and urban non-food

desert areas can be increased, or alternately the difference in percentage of households

below the poverty line between rural food desert and rural non-food desert census

block can be made relevant, by changing selection methodology. These selection

choices are all equally viable ways to select for probable food areas but also completely

negate the effects of a buffer distance on large portions of the population. These effects

are so much so that no selection methodology has a relevant relationship with the number

of grocery stores in a given census block area, defeating the purpose of any geographic

low access definition.

Centroid selection is weakest in rural areas. Rural census blocks in Ohio have a

higher land area than urban census blocks in Ohio. Centroids in ArcMap 10.1 are created

by a heuristic algorithm used to find the geometric center of a two dimensional figure, in

this case a shape file. To define an area as a food desert in centroid selection, the

centroid must be encompassed in a buffered area. As areas get larger for census blocks,

44

centroid selection becomes less accurate, by virtue of the fact that the centroid is less

applicable to the relative distances in the combined area. Intersection selection is far less

picky, and any intersection with shapefile boundaries will designate a census block as a

non-food desert, but intersection has no bearing whatsoever on percent cover.

Containment is by comparison to the other two selection methodologies the most strict,

but does not work well in small buffered non-food desert areas, which despite large

percent coverage of census blocks, are not enough to contain shapes.

Food desert definitions often use buffered models to select which populations to

study as food desert areas. Studies including Donkin et. al. 1999 use buffers (500m) to

identify portions with of the map within select distances of identified grocery stores

(Donkin, Dowler, Stevenson, & Turner, 1999). This data analysis method has been

widely adopted in United Kingdom at the recommendation of the government (Furey,

Strugnell, & Mclveen, 2001). However, at scale and with non-consistent delineation

shape of census areas, selection methodologies are highly inaccurate. An alternative

selection method which removes selection from buffers is a cellular automata model.

Such a model was used in Ploeg et al. to model United States food deserts.

In this approach a cellular food desert model uses a grid of a set size and shape

to model relationships between variables which represent real world information. Ploeg

et al. uses a cellular automata model with a set resolution of a kilometer to calculate

population weighted distances from grocery stores in the USDA 1 mile urban 10 mile

rural food desert definition. Cellular automata models are meant to increase distance

resolution and create better accessibility maps.

45

Ploeg et al. uses population information from descending census geographic

levels (tract to block) to infer population density on a resolution as defined by a 2D grid

of set mile size model size to model actual geographic areas. Unlike in modeling using

buffers, cellular modeling calculates distances to and from food sources at a scale defined

by each grid piece centroid rather than a distinct census block. Distance to grocery stores

was calculated using a Euclidian distance measure from the centroid of each non-grocery

store grid-piece to the centroid each grocery store grid-piece. This data is averaged for

census blocks and combined with an assumed population distribution. Census areas are

designated as food deserts in accordance with the results of the combined cells

contained in their abstracted cellular area.

Furthermore, distances can be within a cellular automata model such as this one

can be deceptive, as the kilometer grid projection of the cells in Ploeg et al. does not

fully align well with any large geography. Distance weighting also introduces selection

bias in an automata model. In the Ploeg et al study methods, distance to a food source is

weighted by population in the Plough et al. USDA definition (Ploeg, et al., 2010). This is

meant to help infer block and tract information from grid points. However, block

information is the smallest measurement made accessible by the U.S. Census Bureau (U.

S. Census Bureau, 2014). To determine the proportion of a given census blocks

population living below the poverty line, the population was weighted between the

kilometer square cells in each block. This weighting scheme combined with different

distances for Urban and Rural areas showed that a limitation of the methodology was a

selection of low density urban areas towards classification as food deserts (Ploeg, et al.,

46

2010). Cells didnt scale between distances consistently, and depending on distance

thresholds, maps were be extremely different.

There is also reason to suspect the data reported in the Ploeg et al. model. The

median distance to a supermarket reported to be .86 miles per household (Ploeg, et al.,

2010). Simply stated, there are not enough supermarkets in their sample size of 39,502

United States supermarkets to allow the values to be that low. Using a buffering method

and this studys 1,523 Ohio data points, the same median distance would mean that only

2% of Ohios area would be inhabited, assuming a one mile buffer with no overlap. This

means that more than fifty percent of Ohios households would be living within two

percent of its land (and thereby eligible for consideration within a Euclidean distance

calculation that could result in this median). This raises the question of whether the

downcasting methodology in the Plough et al. study actually worked, or if it

concentrated populations into relatively few half kilometer cells that happened to be

within one mile of food sources.

A possible solution to the inconsistencies in both buffering methods and cellular

automata methods are network based accessibility studies. Studies such as McEntee and

Ageyman (2009) use road networks within a city or state to derive distance from food

sources (McEntee & Agyeman, 2009). This removes almost all distance selection bias,

as points are calculated at a household level to the nearest food source over a road

network (McEntee & Agyeman, 2009). Network calculated distances are longer than

buffered or cellular, as they are no longer Euclidian, but rather reflect actual road

pathways taken by a majority of people. In a 2009 study in Vermont the median distance

to a food source was 4.14 miles (urban and rural), as compared to a median of .83 miles

47

(urban and rural) for the United States in 2010 (McEntee & Agyeman, 2009) (Ploeg, et

al., 2010). This inconsistency points to a underestimate of distance to food sources that

use Euclidean distance as a measure. Network maps are also much harder to generate

than other data types from store and census information, and were noted to be too

computationally taxing to be used in the Ploeg et al. study (Ploeg, et al., 2010).

Regardless of computational time, all small scale network studies generate substantially

higher distance estimates, and to address geographic food desert problems, should they

exist, underestimate of distance should be cited in buffering or automata studies.

Selection bias is important to consider when looking into food desert

delineations, but geographic selection bias in census areas hardly captures the realities of

symptomatic food desert problems. Many geographic food desert classification

schemes are by their very nature tied to the locations in which they are experienced.

Geographic classification schemes, take into account limited quantifiable information to

infer problems that may be experienced on by individuals living in the studied area.

No population is homogenous by virtue of its geography, culture, or

demographics, it is this clear that any of the relevant differences in populations

demonstrated in different classification schema are a result of their carefully tailored

selection bias. In United Kingdom studies such as Donkin et al. mapped food

accessibility and pricing indices in GIS, and had success in finding relevant price

differences between low income and low access areas and high income, high access areas

where Cummins and Macintyre hadnt the same year (Cummings & Macintyre, 1999)

(Donkin, Dowler, Stevenson, & Turner, 1999). This success was attributed to taste

preferences and local knowledge that were introduced to pricing indices that went

48

beyond the pricing of staple foods in a given area (Donkin, Dowler, Stevenson, & Turner,

1999). When definitions are transplanted to new locations, there is a high probability that

they will not work as a tool to select for the causes behind symptomatic problems as they

once did.

The United States Department of Agriculture (USDA) food desert definition is

maintained for general use across the United States. This definition is the broader than a

majority of the food desert definitions. It designed to encompass both rural and urban

areas across the geographic area of the United States. As the United States is a large and

diverse geographic area, in an attempt to diversify classification schemes, the USDA

definition takes into account many variables. In their combined form, the definition is

incredibly unruly and complex, but can point to general issues (such as low income or

low access) in certain areas. In the USDA definition geographic access is calculated

using distance thresholds. Geographic food accessibility is measured on a basis of family

income and vehicle availability. Low access can also mean low access to transportation,

or living more than a set distance from any food provider. After qualifications are made,

urban and rural food desert sub-definitions are reorganized into manageable tracks, to

select for low-income and low-access populations in urban and rural areas.

The broad definition of a food desert in the USDA definition provides

flexibility to the USDA definition, as it can be theoretically applied to many areas, and

still remains relevant with respect to varying factors that are presumably symptomatic of

a food desert area. However, grouping quantifiable traits of food deserts together

allows for many compounded variables at a large scale. While mixed methods

definitions are inclusive of many variables, due to variation in the United States

49

population, a broad definition including many caveats is subject to become useless,

unless basic measurements such as real world network distance are taken into account.

When government agencies seek to define food desert areas, the studies they

conduct studies are purposeful and meant to address relevant inconsistences in the food

landscape (United States Department of Agriculture, 2014). The USDA Food Access

Research Atlas was created as a tool to identify areas to target as a part of what is now

called the Lets Move! initiative (United States Department of Agriculture, 2014).

The Lets Move! initiative has five pillars, one of which is increasing food accessibility

(White House, 2014). After identifying areas of low food accessibility, the Lets Move!

Initiative seeks to develop and equip food retailers of all sorts, which included grocery

stores, convenience stores and farmers markets. However, as shown in this study, in

many areas, there are no differences in accessibility as a function of distance to food

sources. Rather, overall improvement in access to food and food education in all

geographic areas, including food deserts could be a better approach to the food desert

problem.

The problems associated with food deserts are multidisciplinary. Issues that

combine and to create spaces commonly called food deserts include, health and

nutritional problems, income disadvantage, social and cultural constraints, and shortage

of good-quality, low priced food locally (Guy & David, 2005). Food deserts can occur

as a result of one or many of these factors working together to create an area that is in

some observable sense deprived of food availability. Food desert definitions seek to

identify generalized symptoms of low food accessibility. Despite searching for statistical

differences in culture, demographic differences, and distance to food sources, ease of

50

transportation, too many complex variables can be associated with the food desert

problem to calculably find and address specific food desert problems on a large scale.

Food deserts are a broad problem set. Although they were first identified as

geographic areas, it is clear that their definition transcends a simple geographic lack of

food sources. The average percentage of households living below the poverty line in

Ohio is 11.15%, with a median of 11.16%. These values are high enough to warrant

attention regardless of geographic access. Especially considering the best food desert

definitions observed in this study were able to isolate only three percent more households

than the comparative mean. While evidence exists to support a relationship between

distance to food sources and diet quality by means of food choice (Walker, et al., 2011).

A vast body of research supports the relationship between economic food access and diet

quality. Healthy food items cost on average more than energy-dense alternatives

(Drewnowski, Andrienu, & Darmon, 2005). Furthermore, in low income individuals cite

prohibitive costs as a reason for not eating more fruits and vegetables (Jetter & Cassady,

2006). These issues are ones of income and food price rather than geographic location.

However income does come into play with food availability, especially in areas where

residents are dependent on cars for transportation.

51

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