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Beyond the Label

Permanent Link: http://ufdc.ufl.edu/UFE0044954/00001

Material Information

Title: Beyond the Label a Typology for Assessment and Mitigation of Disparities in the Urban Food Environment
Physical Description: 1 online resource (186 p.)
Language: english
Creator: Leigh, Meredith Summer
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: community food security -- food access equity -- food justice -- food systems planning
Design, Construction and Planning -- Dissertations, Academic -- UF
typology -- urban design principles -- urban food access
Genre: Design, Construction, and Planning Doctorate thesis, Ph.D.
Electronic Thesis or Dissertation
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )

Notes

Abstract: In 2009, 15% of households in the United States were considered food insecure and one in four children faced hunger every day (Nord, 2010). Previous research indicated that, in the U.S., access to healthy foods has been a challenge in many urban areas. Researchers have highlighted the effect of the built environment on rates of obesity and obesogenic disease. In predominantly low-income neighborhoods where there is a paucity of quality healthy food outlets such as supermarkets, obesity rates tend to be higher. Evidence also shows that in areas where poverty rates are high, convenience stores and fast food restaurants abound and supermarkets are scarce. This study argues that, in the literature, there is still no consensus on definitions or measurements of disparities in urban food access and contends that not all “food deserts” share the same characteristics, and therefore cannot be served by the same solutions.  This study’s aim was to develop a spatial typology for classifying disparities in urban food access in order to move beyond the common labeling of an area as a food desert or food insecure. The study was designed as a quantitative exploratory case-study using GIS to map three spatial barriers (proximity, diversity of availability and mobility) to healthy food access and to compare these spatial barriers to three socio-economic measures (income, age and single mothers). Tampa, Florida was used as the study site. The result was a typology of six scenarios that categorize each census block group based on its healthy food access characteristics.  Results of the analysis of spatial barriers and socio-economic data revealed no correlation between any of the spatial factors and income. However, the analysis did reveal that there were a higher percentage of single mothers in neighborhoods with at least one supermarket nearby. There were also more single mothers in neighborhoods where access to public transit and vehicle ownership rates were lower. Householders 65 and older tend to have good mobility and live in areas well-served by a variety of healthy food outlets. Implications for urban planning policy and urban design are discussed as well as directions for future research.
Statement of Responsibility: by Meredith Summer Leigh.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
General Note: Description based on online resource; title from PDF title page.
General Note: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
General Note: Adviser: Carr, Margaret H.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044954:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044954/00001

Material Information

Title: Beyond the Label a Typology for Assessment and Mitigation of Disparities in the Urban Food Environment
Physical Description: 1 online resource (186 p.)
Language: english
Creator: Leigh, Meredith Summer
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: community food security -- food access equity -- food justice -- food systems planning
Design, Construction and Planning -- Dissertations, Academic -- UF
typology -- urban design principles -- urban food access
Genre: Design, Construction, and Planning Doctorate thesis, Ph.D.
Electronic Thesis or Dissertation
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )

Notes

Abstract: In 2009, 15% of households in the United States were considered food insecure and one in four children faced hunger every day (Nord, 2010). Previous research indicated that, in the U.S., access to healthy foods has been a challenge in many urban areas. Researchers have highlighted the effect of the built environment on rates of obesity and obesogenic disease. In predominantly low-income neighborhoods where there is a paucity of quality healthy food outlets such as supermarkets, obesity rates tend to be higher. Evidence also shows that in areas where poverty rates are high, convenience stores and fast food restaurants abound and supermarkets are scarce. This study argues that, in the literature, there is still no consensus on definitions or measurements of disparities in urban food access and contends that not all “food deserts” share the same characteristics, and therefore cannot be served by the same solutions.  This study’s aim was to develop a spatial typology for classifying disparities in urban food access in order to move beyond the common labeling of an area as a food desert or food insecure. The study was designed as a quantitative exploratory case-study using GIS to map three spatial barriers (proximity, diversity of availability and mobility) to healthy food access and to compare these spatial barriers to three socio-economic measures (income, age and single mothers). Tampa, Florida was used as the study site. The result was a typology of six scenarios that categorize each census block group based on its healthy food access characteristics.  Results of the analysis of spatial barriers and socio-economic data revealed no correlation between any of the spatial factors and income. However, the analysis did reveal that there were a higher percentage of single mothers in neighborhoods with at least one supermarket nearby. There were also more single mothers in neighborhoods where access to public transit and vehicle ownership rates were lower. Householders 65 and older tend to have good mobility and live in areas well-served by a variety of healthy food outlets. Implications for urban planning policy and urban design are discussed as well as directions for future research.
Statement of Responsibility: by Meredith Summer Leigh.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
General Note: Description based on online resource; title from PDF title page.
General Note: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
General Note: Adviser: Carr, Margaret H.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044954:00001


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1 BEYOND THE LABEL: A TYPOLOGY FOR ASSESSMENT AND MITIGATION OF DISPARITIES IN THE URBAN FOOD ENVIRONMENT By MEREDITH SUMMER LEIGH A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

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2 2012 Meredith Summer Leigh

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3 To Scout, my sole companion

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4 ACKNOWLEDGMENTS This dissertation represents an accumulation of an experience that has been at once the most challenging and most eyeopening growth experience I could never have anticipated. The process of finding THE question, developing THE methodology, finding the wor ds to express what you have found, and staying motivated was so much of a journey that often felt all too singular in nature. I realized, after some trial and error that I was not totally alone and I did have people I could go to for help when I got stuck in the muck of research. Through the guidance and cooperation of my committee members and some close and very wise friends, I was able to complete this piece of work and I would feel remiss if I did not personally thank them all. Professor Peggy Carr, my committee chair took me in as a student despite being incredibly busy and not having much expertise on my research topic. She took interest, took time to read my numerous drafts and was instrumental in making sure what I had to say made sense. I am thankful for her unwavering patience, support and belief that I could do this. Dr. Paul Zwick served as an invaluable guide and guru when I was ready to sort through piles of data. It was really great to work with him and learn, and learn, and learn. Figuring out how to make GIS and SPSS work for my research actually became fun. I have to thank Tina Gurucharri who, as the professor of my first class in the Master of Landscape Architecture program at University of Florida, chair of my masters thesis committee, and member of this dissertations committee, has been a mentor, and a role model for calm and grace under pressure. No matter how busy, her door was always open and her students always came first.

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5 In working with Dr. Mickie Swisher, this studys focus became very clear. Many hours of discussion on food insecurity and what it really means to have limited access to food served to make me more passionate about my topic and excited to explore it. Our talks really made me think and more importantly, eager to learn more and find a niche. Friendships during the PhD experience become even more important and being able to spend time to commiserate and laugh with my friend Ben Himschoot, helped keep me sane. Plus, I learned a thing or two about statistics that I was able to incorporate into this study thanks to Ben. My family, despite the distance between us, provided me with encouragement and support when I needed it most, when I doubted whether I could, or would want to finish, when I was most drained. My sisters A lison and Erin, helped boost me up and believed in me. They steadfastly believed that I could get through this and also reminded me to not beat myself up if I did not. My m other, Margueritenever doubted my abilities, was always supportive, always just wanted me to be happy and to be able to stand on my own two feet. Well here I stand.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 11 LIST OF ABBR EVIATIONS ........................................................................................... 13 ABSTRACT ................................................................................................................... 14 CHAPTER 1 INTRODUCTION .................................................................................................... 16 Background of the Study ......................................................................................... 16 Research Problem .................................................................................................. 20 Towards a Typology: NonSpatial Barrie rs to Accessing Healthy Foods ................ 21 Purpose and Objectives .......................................................................................... 24 Theoretical Framework ........................................................................................... 24 Conceptual Model ................................................................................................... 25 Significance of the Study ........................................................................................ 26 Study Procedure ..................................................................................................... 27 Frequently Used Terms .......................................................................................... 27 Assump tions ........................................................................................................... 29 Limitations ............................................................................................................... 29 Summary ................................................................................................................ 30 2 REVIEW OF THE LITERATURE ............................................................................ 31 Definitions in the Literature ..................................................................................... 32 Food Deserts .................................................................................................... 33 Food Insecurity ................................................................................................. 33 Food Security ................................................................................................... 34 Community Food Security ................................................................................ 35 The Use of Typologies in Urban Design ................................................................. 36 The Demographics of Poverty and Obesity ............................................................ 37 Challenges of the Food Environment in Urban Areas ............................................. 41 Proximity Challenges in Accessing Healthy Food Outlets ................................ 41 Mobility Challenges in Accessing Healthy Food Outlets ................................... 46 Variety in Availability of Healthy Food Outlets .................................................. 48 Assessing Affordability: Thrifty Food Plan Market Baskets ............................... 50 Planning and Design Professionals and Food Systems Planning ........................... 51 Case Studies .................................................................................................... 55 Summary ................................................................................................................ 56

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7 3 RESEARCH METHODS ......................................................................................... 58 Research D esign .................................................................................................... 59 Study City Selection ......................................................................................... 59 Selection of Census Block Groups ................................................................... 60 Development of the Spatial Typology for Food Access Disparities .................. 61 Instrumentation ....................................................................................................... 61 Collection and Treatment of the Data ..................................................................... 63 Proximity to Healthy Food Outlets .................................................................... 63 Diversity in Availability ...................................................................................... 65 Mobility ............................................................................................................. 67 Summary ................................................................................................................ 68 4 RESULTS ............................................................................................................... 70 Proximity as a Factor in Urban Food Access .......................................................... 70 Diversity in Availability ............................................................................................ 73 Mo bility .................................................................................................................... 76 Comparison of Spatial Factors and Socioeconomic Factors ................................. 79 Comparison of Spatial Factors to Median Income .................................................. 79 Spatial Clustering of Median Income ................................................................ 79 Proximity to Food Outlets ................................................................................. 82 Diversity in Availability of Food Outlet Types .................................................... 84 Mobility to Food Outlets .................................................................................... 86 Comparison of Spatial Factors to Single Female Householders ............................. 88 Proximity to Food Outlets ................................................................................. 88 Diversity of Availability of Food Outlet Types ................................................... 89 Mobility to Food Outlets .................................................................................... 91 Comparison of Spatial Factors to Age .................................................................... 92 Proximity to Food Outlets ................................................................................. 92 Diversity of Availability of Food Outlet Types ................................................... 93 Mobility to Food Outlets .................................................................................... 94 Summary ................................................................................................................ 95 5 DISCUSSION AND CONCLUSION ........................................................................ 96 Summary of the Research and Findings ................................................................. 96 The Urban Food Access Typology .......................................................................... 98 Implications for Current Knowledge and Professional Practice ............................. 105 Conclusions .......................................................................................................... 110 APPENDIX A TERMS AND DEFINTIONS FROM THE LITERATURE ....................................... 115 B FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: PROXIMITY ................ 127

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8 C FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: DIVERSITY OF AVAILABILITY ...................................................................................................... 136 D FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: MOBILITY ................... 150 E FOOD ACCESS AUDIT INSTRUMENT: DATA SUMMARY SHEET .................... 163 REFERENCES ............................................................................................................ 177 BIOGRAPHICAL SKETCH .......................................................................................... 186

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9 LIST OF TABLES Table page 1 1 Factors contributing to access problems used to develop a nonspatial typology .............................................................................................................. 23 1 2 The theoretical framework for this study based on the relationships between Shaws nonspatial barriers to equitable food access and the spatial factors e xamined herein ................................................................................................. 25 3 1 An example of the method used to convert median distances by food outlet type to scores of 1, 2, or 3 .................................................................................. 65 3 2 Interpretation of raw scores for proximity to food outlets for one sample CBG ... 65 3 3 Conditions for scoring CBG diversity of food outlet availability ........................... 66 3 4 Interpretation of raw scores for diversity of availability per CBG ......................... 67 3 5 Conditions for scoring CBG mobility ................................................................... 68 3 6 Interpretation of raw scores for mobilit y for one sample CBG ............................. 68 4 1 Reclassified proximity scores ............................................................................. 72 4 2 Reclassified diversity of availability scores ......................................................... 73 4 3 Reclassified mobility scores ............................................................................... 76 4 4 Variables used to determine correlations in the typology ................................... 79 4 5 Global Moran's I results showing z scores. The p values (significance level) for each nearest neighbor indicates a less than 1% chance that the clustered pattern of median income distribution is a result of a random chance ................ 80 4 6 ANOVA Between median household inc ome for reclassified (14) scores for proximity to food outlets ...................................................................................... 82 4 7 Summary score averages by median income brackets ...................................... 84 4 8 ANOVA between median household income for reclassified (14) scores for diversity in availability to food outlets .................................................................. 85 4 9 ANOVA between median household income for reclassified (14) scores for mobility ............................................................................................................... 87 4 10 ANOVA for reclassified (14) scores for proximity to food outlets and single female householders .......................................................................................... 89

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10 4 11 ANOVA for reclassified (14) scores for diversity of food outlets available and the single female householders .......................................................................... 90 4 12 ANOVA for reclassified (14) scores for mobility and single female householders ...................................................................................................... 91 4 13 ANOVA for reclassified (14) scores for proximity to food outlets and householders 65 and older ................................................................................. 92 4 14 ANOVA for reclassified (14) scores for diversity of food outlets available and the householders 65 and older ........................................................................... 93 4 15 ANOVA for reclassified (14) scores for mobility and householders 65 and older ................................................................................................................... 94 5 1 Possible ranking based upon reclassified sco res ............................................... 99 5 2 Urban food access disparity typology ............................................................... 100 5 3 Results of food access typology as applied to the City of Tampa ..................... 104

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11 LIST OF FIGURES Figure page 1 1 Conceptual model of variables that affect healthy food access in the built environment ........................................................................................................ 26 1 2 Flowchart illustrating the operationalization of this study .................................... 28 2 1 Conceptual diagram of transit oriented development as a n urban design typology .............................................................................................................. 37 2 2 Grap h showing the change in food insecurity rates 20042009 Source: ............. 38 2 3 Graph illustrating the change in poverty rates in the United States and Florida between the 2000 and 2010 census ................................................................... 39 4 1 Raw data s cores show census block groups resulting proximity scores after the completion of data collection ........................................................................ 71 4 2 Map of Tampa CBGs reclas sified by proximity to higher quality food outlets ..... 72 4 3 Map of Tampas census block groups according to their raw data scores for diversity in availability ......................................................................................... 74 4 4 Map of Tampas census block groups according to their reclassified scores for diversity in availability .................................................................................... 75 4 5 Map of Tampas census block groups according to their raw data scores for mobility ............................................................................................................... 77 4 6 Map of Tampa CBGs reclassified categories for mobility ................................... 78 4 7 Distribution curve for clustering analysis using Global Morans I. ....................... 80 4 8 Map of Tampas median income distribution by census block group .................. 81 4 9 Means plot of ANOVA between median income and proximity from SPSS ........ 83 4 10 Line graph showing the relationship between median income and mean scores for proximity to higher quality food outlets ............................................... 84 4 11 Means plot showing the relationship between the four classes of diversity in availability and median household income ......................................................... 85 4 12 Line graph depicting the relationship between diversity and the mean scores by for CBG by income ........................................................................................ 86

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12 4 13 Means plot showing the relationship between the four classes of mobility and median household income .................................................................................. 87 4 14 Line graph depicting the relationship between mobility and the mean scores by for CBG by income ........................................................................................ 88 4 15 Means plot of ANOVA between the mean percentage of single female householders and proximity to higher quality food outlets from SPSS ............... 89 4 16 Means plot showing the relationship between the four classes of diversity of availability and mean percentage of households with a single female as head .. 90 4 17 Means plot showing the relationship between the four classes of mobility and single female householders with families ........................................................... 91 4 18 Means plot of ANOVA between the mean percentage of householders age 65 and older and proximity to higher quality food outlets from SPSS ................. 92 4 19 Means plot showing the relationship between the four classes of diversity in availability and the mean percentage of households with head 65 and older ..... 93 4 20 Means plot showing the relationship between the four classes of mobility and the mean percentage of households with head 65 and older ............................. 94 5 1 The urban food access typology mapped for the City of Tampa ....................... 102 5 2 Comparison of the USDA ERS Food Desert Locator map and the results of this studys typology for Tampas CBGs ........................................................... 107

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13 LIST OF ABBREVIATIONS ANOVA Analysis of Variance CBG Census block group CDC Center for Disease Control and Prevention CFSC Community Food Security Coalition EAPRS Environmental Assessment for Public Recreation Spaces ERS USDA Economic Research Service GIS Geographic Information System LSRO Life Sciences Research Office PEDS Pedestrian Environmental Data Scan SNAP Supplemental Nutrition Assistance Program SPSS IBMs Statistical P rogram for the Social Sciences TND Traditional Neighborhood Development TOD Transit Oriented Development USDA United States Department of Agriculture WIC Women Infants and Children program

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14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy BEYOND THE LABEL: A TYPOLOGY FOR ASSESSMENT AND MITIGATION O F DISPARITIES IN THE URBAN FOOD ENVIRONMENT By Meredith Summer Leig h December 2012 Chair: Margaret Peggy Carr Major: Design Construction and Planning In 2009, 15 % of households in the United States were considered food insecur e and one in four children faced hunger every day (Nord, 2010) Previous research indicated that, in the U.S., access to healthy foods has been a challenge in many urban areas Researchers have highlighted the effect of the built environment on rates of obesity and obesogenic disease. In predominantly low income neig hborhoods where there is a paucity of quality healthy food outlets such as supermarkets, obesity rates tend to be higher. Evidence also shows that in areas where poverty rates are high, convenience stores and fast food restaurants abound and supermarkets are scarce This study argues that, in the literature, there is still no consensus on definitions or measure ments of disparities in urban food access and contends that not all food deserts share the same characteristics, and therefore cannot be served by the same solutions. This studys aim was to develop a spatial typology for classifying disparities in urban food access in order to move beyond the common labeling of an area as a food desert or food insecure The study was designed as a quantitative exploratory casestudy using GIS to map three spatial barriers (proximity, diversity of availability and

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15 mobility) to healthy food access and to compare these spatial barriers to three socioeconomic measures (income, age and single mothers) Tampa, Florida was used as the study site. The result was a typology of six scenarios that categorize each census block group based on its healthy food access characteristics Results of the analysis of spatial barriers and socioeconomic data revealed no correlation between any of the spatial factors and income. However, the analysis did reveal that there were a higher percentage of single mothers in neighborhoods with at least one supermarket nearby. T here were also more single mothers in neighborhoods where access to public transit and vehicle ownership rates were lower. H ouseholders 65 and older tend to have good mobility and live in areas well served by a variety of healthy food outlets. Implications for urban planning policy and urban design are dis cussed as well as directions for future research.

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16 CHAPTER 1 INTRODUCTION Background of the Study According to a 2009 USDA report, about 15 % of households in the United States are considered food insecur e Furthermore, in the same year, one in four children face d hunger every day (Nord, Coleman Jensen, Andrews, & Carlson, 2010) In the face of the Great Recession, the state of poverty and food insecurity has become a growing concern, and recent literature has focused on assessing this phenomenon (Nord et al., 2010; Raja, Born, & Russell, 2008; Walker, Keane, & Burke, 2010) The food system has become the focus of researcher s in a multitude of disciplines including public health, agriculture, clinical health professions, sociology, and geography, to name a few. In the r ealm s of urban and regional planning, landscape architecture and urban design, the U.S. food system has been a relatively understudied field (Potteiger, 2009) However, as the latter three professions have pri marily been concerned with the health, safety, and welfare of people through the built environment, making healthy foods more equitably accessible through changes in design or policy should become a part of that mission through research and practice. The s tudy of food access and food systems for landscape architects has seen increased interest, yet few articles addressing local and community food systems from the designers perspective have been published (Potteig er, 2009) Much of the research found regarding landscape architecture and food systems focused on rural issues, such as sustainable agriculture and concern over changing landuse and urbanization of peri urban agricultural land (Condon, Mullinix, Fallick, & Harcourt, 2010) Landscape architects have been concerned with the science and art of design in the built

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17 environment. They have focused on a broad scope of issues, from green design and sustainability, to urban form solutions such as integrating Smart Growth principles, including Transit Oriented Development (TOD) into new and redeveloped urban areas (ASLA, 2011) These professionals are equipped to handle design solutions at num erous scales. Therefore, solving problems of food access in the built environment is appropriate for their skillset. Allied professionals like u rban and r egional p lanners have shown more interest in the realm as time has passed, as demonstrated by 1999 and 2008 surveys. The surveys were distributed to professional planners in order to assess to what extent they thought food systems ought to become a part of their professional duties (Pot hukuchi, 2000; Raja et al., 2008) In 1999, only 38% of the planners surveyed believed they ought to take on food systems planning as a part of their job. In 2008, about 73% of survey respondents said they wished to address food systems issues (Raja et al., 2008) This suggests that more research on food systems and implications for planning and design is necessary in order to inform the decision making process. Planners, landscape architects and urban designers h ave in common the sustainable, responsible design of the rural and urban built environment, regardless of socioeconomic status, race or culture. They have all begun to acknowledge that problems of access and availability of the urban food system, specific ally the lack of adequate, healthy choices, involve physical, spatial, and structural issues. These professions are positioned to assist in resolving access challenges through intelligent planning, design and policy (Potteiger, 2009) This study will primarily focus on increasing the knowledge of the

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18 characteristics of challenges to food access in urban areas and assessing the involvement of landscape architects in food systems planning, design and research. In 2010, First Lady Michelle Obama launched the Lets Move! campaign focused on solutions for combating and preventing the growing epidemic of childhood obesity in the United States. This mission included educating families about the importance and benefit s of a healthier diet, encouraging more physical activity, providing children with healthier food options through improved school lunch program requirements, and teaching them how to make better food choices ( www.letsmove.gov, 2011) This acknowledgement of the seriousness and scope of obesity and the associated health risks coincided with a growing body of evidence that has indicated a positive relationship between obesity and race, socioeconomic status, and the built environment. Previous research has found that in the urban environment, a higher density of fast food restaurants existed in predominantly black neighborhoods, and within these neighborhoods obesity rates were higher than in nonblack communities (Block, Scribner, & DeSalvo, 2004; Larson, Story, & Nelson, 2009; Stein & Chakraborty, 2010) Studies conducted on access to fast food chain restaurants in predo minantly black and Hispanic neighborhoods in New Orleans, Los Angeles, and Hillsborough County, Florida have shown a higher density of fast food chain restaurants in these neighborhoods (Block et al., 2004; Larson et al., 2009; Morland, Wing, Diez Roux, & Poole, 2002; Short, Guthman, & Raskin, 2007; Stein & Chakraborty, 2010) Some studies also indicated, conversely, that in neighborhoods with better access to healthier food options, obesity rates were lower (Jetter & Cassady, 2006; Larson et al., 2009; K. Morl and et al., 2002) The literature indicated a disparity in access to healthy food in the

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19 United States and suggested that those who live in low income minority neighborhoods were most affected. Three common attributes of food insecurity were identified in the literature. The first was lack of access. For people living in low income urban areas, more often than not, there has been a lack of nearby supermarkets or grocery stores (Kirkup e t al., 2004; Walker et al., 2010) Supermarket corporations opted to head to the suburbs following the exodus of young professionals and families who chose to move away from the urban core (Morland et al., 2002) Studies have shown that 15 minutes is the limit a person may be willing to walk to any destination (ASLA, 2011) and many grocery stores and supermarkets are out of that range for portions of the population (Clifton, 2004; Kirkup et al., 2004; Larson et al., 2009) This coincided with the second factor contributing to food insecurity lack of transportation (Clifton, 2004; Jetter & Cassady, 2006; Larson et al., 2009; Morland et al., 2002; Potteiger, 2009; Stein & Chakraborty, 2010; Walker et al. 2010) Residents of low income, predominantly minority neighborhoods are less likely to have a private automobile than those in middle and upper income neighborhoods and are more likely to base their shopping and eating choices on how much they can carry using alternative modes of transportation (Clifton, 2004; Jetter & Cassady, 2006; Larson et al., 2009; Morland et al., 2002; Stein & Chakraborty, 2010) Finally, affordability was identified as a major contributor to the food choices made by people living in low income urban areas ( Clifton, 2004; Jetter & Cassady, 2006) A family of four can feast on the value menu at a number of fast food restaurants for less than what it would cost to buy fresh produce, meats, whole grain bread and low -

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20 fat milk at the grocery store (Adler et al., 2009; Schlosser, 2001) Since those in the lower income bracket tended to spend more time traveling to and from work, time is short for them and fast food has often been the most convenient and cost efficient method to feed their families (Adler et al., 2009; Schlosser, 2001; Walker et al., 2010) The ability to access quality, nutritious food is co nsidered a basic human need. In the United States, the production of food has not been a roadblock to food security but accessibility and affordability has (Adler et al., 2009; Pollan, 2006; Sachs, 2005; Sen, 1999) Many urban areas across the country have lacked healthy food options. In exchange for supermarkets where food prices have been generally kept low, convenience stores and other food outlets predominated (Jetter & Cassady, 2006; Kirkup et al., 2004) While there have been studies conducted throughout the country that helped characterize challenges to food access l ittle research exists on the urban food environment within Florida cities (Stein & Chakraborty, 2010) Research Problem It has become clear in recent research that food deserts (areas without supermarkets within a reasonable walking distance) exist in both urban and rural areas. Various methods of assessing the food environment have been presented in the literature. However, in several papers addressing food deserts as a phenomenon, no clear definition was offered, as if there was already a commonly accepted definition. Furthermore, whereas most studies out of the United Kingdom (UK) more often than not use the term food desert to describe poor access to healthy foods, studies in the United States (US) more often us e either food security, or food insecurity to describe the same phenomenon.

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21 As mentioned, many researchers correlate poverty with food security or insecurity. However, Shaw (2006) pointed out, not all food access problems are a result of being poor, or of living in inner city areas. Some barriers are attitudinal or psychological in nature. Her proposed classification of food deserts was based on nonspatial factors, but her typology seemed to also lay a good foundation for a typology based on spatial b arriers to accessing healthy foods. She also argued for a move away from the term food desert as a blanket term and towards terms that more precisely described the food environment. The term food desert was, after all, coined as a metaphor, but soon became synonymous with all forms of food access disparities. Therefore, this study will define a typology in terms of food access disparities to avoid the ambiguity of the various definitions presented in the literature for food deserts, food insecurity and food security. In the absence of a typology to clearly define and classify spatial barriers consumers face in attaining a healthy diet, landscape architects are left without a valuable tool that could help them more easily integrate food systems planning and design into practice. This research is an effort to fill that gap and provide an example of how the typology can be applied in an urban setting. Using exploratory qualitative methods, this study examined the food environment of the city of Tampa to develop a spatial typology that can then be used to identify appropriate solutions for the spatial barriers captured in the typology. Towards a Typology: NonSpatial Barriers to Accessing Healthy Foods The phenomenon of urban food access disparities has shown to be a spatial challenge necessitating intervention. However, the literature indicated there are nonspatial barriers that are more psychological and financial in nature that also affect ones

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22 abil ity to access healthier food options. In her 2006 qualitative study based on semi structured interview responses, Shaw found three nonspatial barriers which became the basis of a classification of unsupportive food environments in Leeds and North Lincolns hire in the UK: asset, ability and consumer attitude (Table 11). The types of asset problems one might encounter include ones inabilty to afford the healthier food options which may be readily available or the inability to afford the added cost of alter native modes of transportation in the absence of a private vehicle, or having inadequate storage or cooking facilities in the home (Clifton, 2004; Reisig & Hobbiss, 2 000; Shaw, 2006) In the US, community food security research has focused on food access issues faced by people in low income urban areas, which inherently means individuals with limited assets. For many of the people living in these areas, affordabilit y is an obstacle to overcome. Some consumers face barriers to accessing food brought about by ones inability to overcome obstacles in the urban environment or by their own physical condition. Shaw describes these barriers as ability problems. A challenging topography was provided as an example of this type of barrier as carrying groceries uphill is difficult for some (Shaw, 2006) A 2008 study of accessibilty of grocery stores for the disabled in Chicago found that only 46% of urban stores had ramps or other types of compliance measures and that people who are physically impaired face obstacles in accessing healthy foods due to limited access to stores (Mojtahedi et al., 2008) Elderly people may have special needs, impairments, or may no longer be able to drive to a store and so may face ability challenges based upon their limitations (Lee & Frongillo Jr., 2001)

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23 These are j ust a few examples from the literature where ability problems may play a role in limiting access to healthy foods. Finally, ones mental state of mind, or attitude, may affect ability to access healthy foods. Examples include lack of knowledge about how to prepare healthy meals, or more serious causes such as depression. Longtitudinal studies revealed that low income mothers who suffer from mental health issues, like depression, are less likely to have regular access to healthy foods (HuddlestonCasas, Charnigo, & Simmons, 2009; Lent, Petrovic, Swanson, & Olson, 2009; Shaw, 2006) The association is reciprocal as depression can inhibit ones ability to ac cess healthy foods and the inability to access foods can lead to feelings of depression (HuddlestonCasas et al., 2009; Lent et al., 2009) Table 1 1. Factors contributing t o access problems used to develop a non spatial typology (Shaw, 2006) Contributing factors Definitions Ability anything that physically prevents access to food which a consumer otherwise has the financial resources and the mental desire to buy Asset lack of any financial asse t that prevents consumption of food the consumer can otherwise physically access and has the desire to consume Attitude any state of mind that prevents the consumer from accessing foods they can otherwise physically bring into their home and hav e the necessary assets to procure

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24 Purpose and Objectives This study aimed to develop a typology for classifying spatial disparities in urban food access using Tampa, Florida as the sample city. The study addressed the following objectives: To determine how access to healthy foods differ by neighborhood To determine the spatial barriers to accessing healthy foods To develop a classification for urban healthy food access disparities Theoretical F ra mework This study began as an investigation into strategies for improving equitable access to healthy foods in an urban setting. However, the literature revealed that there is still no consensus on definitions or on how to measure the extent of urban food access disparities. According to Reisig and Hobbiss (2000) the term [food desert] has remained conceptual rather than being an operational term by which geographical areas can be identified, and indeed is proving hard to define given that the ease with which people access food is a function of more than geography (p.138). Shaws proposed nonspatial classification of food deserts served as the basis for this studys theoretical framework (Table 1 1) but in Table 1 2 is augmented with three spatial factors: proximity, diversity of availability and mobility. The spatial factors were selected because they were recurrent factors identified in the literature. The arrows in Table 12 indicate the relationships between S haws nonspatial factors and the spatial factors further examined in this study. This framework was then used to organize the literature review of this dissertation.

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25 Table 1 2. The t heoretical framework for this study based on the relationships between S haws nonspatial barriers to equitable food access and the spatial factors examined herein Non Spatial Factors (from Shaw) Spatial Factors (from common recurrent in the literature) Ability Proximity Diversity of Availability Asset Access to Mobility Attitude No related spatial factor identified Conceptual Model From the literature, it became apparent that healthy food access is unequally distributed in urban areas across the US and that addressing the issue requires a multi faceted approach (Eckert & Shetty, 2011; Jetter & Cassady, 2006; Morland et al., 2002) Low income consumers have fewer options for eating a healthier diet because they face mobi lity and financial challenges as well as limited availability of healthy food choices (Block et al., 2004; Clifton, 2004; Eckert & Shetty, 2011; Jetter & Cassady, 2006; Morland et al., 2002; Nord et al., 2010; Walker et al., 2010) The conceptual model in Figure 11 illustrates how the spatial and nonspatial variables reflected in the theoretical framework relate to the research problem. The research is guided by the premise that not all food deserts share the same characteristics; therefore they cannot be served by the same solutions. In this study, each s patial factor, identified in the theoretical framework in Table 12, will be evaluated independently at the neighborhood (census block group) level, and classified according to the findings using GIS as a mapping and analysis tool.

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26 Figure 11. Conceptual model of variables that affect healthy food access in the built environment Significance of the Study For landscape architects, a system for identifying specific spatial barriers in the urban food environment can help in developing design solutions for new development or urban infill and redevelopment projects. This study offers a unique methodology for classifying unsupportive urban food environments and so is of value to landscape architects as the findings will help design professionals identify and prioritize physical solutions for improving access to healthy foods Although several studies have examined the relationship between fast food outlets and supermarkets and their proximity to low income neighborhoods, this research consider ed neighborhood access to healthy foods from multiple outlets grouped into three classes: Type 3 Outlets: supermarkets, super centers and farmers markets Type 2 Outlets: grocery stores and ethnic or specialty food stores Type 1 Outlets: convenience and dollar stores

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27 The m ethod is intended to be replicable by both academics and design and planning professionals. Study Procedure Figure 12 illustrates how the study was operationalized. The study was conducted in two stages. First, quantitative data collection and mapping was completed, wherein a preliminary typology was developed based upon previous research findings, and tested on a single city. The results were used to create a typology classifying disparities in healthy food access at the census block level. Frequently Use d Terms A food desert is a low income census tract where a large proportion of residents have poor access to a healthy food outlet such as a supermarket or large grocery store (U.S.D.A. Economic R esearch Service, 2011) Household food insecurity describes the inability to acquire enough food for all household members due to lack of money or other resources (Nord et al., 2010) Household food security is mos t consistently defined as access by all people at all times to enough food for an active, healthy life and includes at a minimum: a) the ready availability of nutritionally adequate and safe foods, and b) the assured ability to acquire acceptable foods in socially acceptable ways (Life Sciences Research Office, 1990) Community food security is often described as a condition in which all community residents obtain a safe, culturally acceptable, nut ritionally adequate diet through a sustainable food system that maximizes community self reliance and social justice (Winne, Joseph, & Fisher, 1997) Typology in urban design is a classification system used to identi fy patterns of building, roads and urban form based upon historical models (Walters & Brown, 2004) Thrifty Food Plan Market Basket is market basket of food intended to represent a national standard for a nutritionally balanced diet at a low cost (Dinour, Bergen, & Yeh, 2007) Specialty food stores are places that sell unique food products such as cheese, baked goods or fresh cut meat (U.S. Census Bureau, 2011c)

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28 Figure 12. Flowchart illustrating the operationalization of this study Literature Review Food Security Disparities/Definitions Benefits of a Typology Challenges to food access Preliminary classification system Non Spatial o Ability o Asset o Attitude Spatial o Proximity o Variety (or Selection) o Access to Mobility Spatial factors analyzed for Tampa using GIS mapping and statistics Development of typology for classifying a neighborhoods food accessibility Product 1 Discussion of the purposes and benefits of a spatial typology to define food access

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29 A low income community is a census tract where the poverty rate is 20% or more, or where the median household income is at 80% below the citys median household income. Census tracts are small statistical subdivisions within a county with between 2,500 and 8,000 people. They are relatively homogenous with economic status, population characteristics and living conditions and are permanent delineations for the purpose of statistical comparison over time (U.S. Census Bureau, 2011b) Census block groups are smaller geographic areas within census tracts containing anywhere from 600 to 3,000 people (U.S. Census Bureau, 2011b) Adequate food access was defined in terms of whether or not low income consumers were able to acquire basic goods identified by the Thrifty Food Plan Market Basket (the standard by which Food Stamp allowance have been a llotted) (Bolen & Hecht, 2003; Dinour et al., 2007) within a reasonable walking distance. Assumptions It is assumed that prices for whole foods are lower in supermarkets and large grocery sto res It is also assumed that all residents within a given study area are both willing and able to walk one mile within 15 minutes Limitations This study used Tampa, Florida in Hillsborough County as a case study. As a result there is a risk that some of t he typologies may not be universally applicable. This study was limited to examining food distribution in an urbanized area; rural areas were excluded. This study was limited to spatial and policy considerations that will improve healthy food access in urban areas. Consumer education on what comprises a healthy diet ; current buying habits and consumer level needs assessment were outside the scope of this study. Census block group data used was from the 2010 Census except for the income data and automobile availability. At the time this study was conducted, the only median household income and vehicle availability data available at the census block group level

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30 was from the 2000 census. Income levels may have been higher in 2000 than in 2010 due to the change in the economic environment and inflation. This study was limited to examining the following sociodemographic categories: median household income, single female head of household, householders 65 and over as independent variables. Food outlet mapping included all sources with a fixed location, which sell whole foods for profit; charitable food outlets including Meals on Wheels, community pantries and soup kit chens were excluded Summary The study of food systems and its relationship to the field of landscape architecture is relatively new. However, there is increased interest from industry researchers and professionals in including this issue in future planni ng, research and design projects. Much of the existing literature focuses on the health risks associated with inadequate access to healthy affordable food choices. Although there are no guarantees that simply improving access for lowincome communities wil l lead to healthier diets and lower obesity rates, there is a need to reexamine food systems and their place in the built environment. A dequate access to healthy and affordable food is a basic human need and can be facilitated through improvements in the b uilt environment derived from policy and design.

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31 CHAPTER 2 REVIEW OF THE LITERATURE The purpose of this study was to determine how to best define urban areas with poor or limited access to healthy foods and what measures might be taken by landscape archit ects and planners to improve access to healthy foods. Appropriate definitions and classifications can potentially yield more effective interventions. The research was framed in a spatial context based on the theoretical framework and conceptual model intr oduced in Chapter 1. Evidence from previous research addressing the major obstacles to obtaining a healthy, affordable diet including findings about the increase in poverty, obesity and food insecurity in the United States and in Florida, and evidence regarding the challenges of food access for low income urban areas, and the growing involvement of planning and design professionals in food systems planning are discussed. The organization of the literature review is guided by the conceptual model, which illustrated the challenges to healthy eating for residents in urban areas. It is important to understand how terms used to describe disparities in healthy food access have been defined in order to understand the need for this study. There are at least four ter ms in the literature used to describe the challenges to accessing a healthy diet and several definitions for each term. The first section of the chapter addresses the challenge posed by this ambiguity. The next section establishes the benefits of using a t ypology for both research and practical purposes. In section three, challenges posed by the urban food environment are discussed beginning with proximity as a barrier to healthy food access.

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32 The discussion of spatial challenges is divided into three sections: proximity referring to the distance required to healthy food outlets, diversity of healthy food outlets available, or the limited grocery availability and/or poor quality product, and mobility challenges, which examines the advantages and constraints o f available modes of transportation. An important consideration of this section is the increased cost of accessing healthy foods for low income consumers due to transportation costs and how that has played a role in their nutritional decisions. There have been a growing number of studies examining food costs for the urban poor and it is generally accepted that the poor pay more to eat a healthy diet compared to middle and upper income consumers. This section provides examples of this phenomenon. Underlying the discussion of community food security is the design and planning mandate to create and sustain livable, resilient communities. The final section addresses the role planning and design professionals have played and might play in urban food systems planning. Definitions in the Literature F ood access disparity presents in the literature in four different terms, food deserts, household food in security, household food security, and community food security. Each of these four terms are utilized in food desert, nutritional, and community and public health research. Significantly, though, they were not used interchangeably. Of the 36 sources found to mention at least one of the term s food deserts, food insecurity, food security and community food secur ity, 34 defined at least one of the terms (Appendix A), while two offered no definition at all.

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33 Food Deserts Food desert as a term used to describe areas with no supermarkets first came into use in the early nineties and has since had a variety of uses and definitions in the literature. The problem is that the term has proven difficult to concretely define resulting in a lac k of consensus on its meaning (Reisig & Hobbiss, 2000; Walker et al., 2010) Of all of the definitions examined, those for food desert were the least consistent with all seven sources prov iding different definitions. Food Insecurity Twelve of the articles defined food insecurity in explicit terms with two main themes prevailing : poverty as an indicator or correlate of food insecurity, and food insecurity as a sociospatial, public healt h and policy issue. In the 2010 edition of the annual USDA report on food insecurity in the United States Nord et al (2010) defined food insecurity as the inability to acquire enough food for all household members due to lack of money or other resources. Several other researchers have used Nords definition for food insecurity Nords definition suggests food insecurity is primarily the result of poverty (Bhattacharya, Currie, & Haider, 2004; Che & Chen, 2001; Freedman & Bell, 2009; Nord et al., 2010) According to these sources, t hose considered food insecure are, more often than not, low income, and/ or receive federal assistance in the form of Food Stamps or the S upplemental N utrition A ssistance P rogram (SNAP), Women Infants and Children (WIC) program or free school lunch program (Nord et al., 2010) Much of the research using this definition did not focus on spatial factors related to food insecurity, but instead 1) quantitatively measured food insecurity at a national scale (Carlson, 1999), 2) identified socioeconomic aspects of food insecurity (Le e & Frongillo

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34 Jr., 2001) or 3) examined the correlation between food insecurity and specific health outcomes such as obesity and diabetes (Bhattacharya et al., 2004; Che & Chen, 2001). An Economic Research Service (ERS) publication defined food insecurity as the inabil ity of people to access at all times enough food for an active and healthy life, with no need for recourse to emergency food sources or other extraordinary coping behaviors to meet their basic food needs (Cohen, IQ Solutions, & United States Dept. of Agr iculture Economic Research Service, 2002, p. 2). This definition builds upon the previous most commonly adapted definition provided by the Life Sciences Research Office (LSRO) (1990) which explained food insecurity as a phenomenon which exists whenever the availability of nutritionally adequate and safe foods or the ability to acquire acceptable foods in socially acceptable ways is limited or uncertain (p.1576). There were also articles focus ed on food insecurity that did not offer any definition. In these articles, t he term is used in the context of increasing understanding of the food environment at various scales and has been described as a consequence of the spatial imbalance in the food environment (Thomas, 2 010) Food Security The most consistently defined term was food security. With 13 sources addressing the subject, all offered a sound definition and 9 out of the 13 adapted the definitions from the Life Sciences Research Office (1990) reports which defi ne food security as access by all people at all times to enough food for an active, healthy life and includes at a minimum: a) the ready availability of nutritionally adequate and safe foods, and b) the assured ability to acquire acceptable foods in socia lly acceptable ways (e.g. without resorting to emergency food supplies, scavenging, stealing, and other coping strategies). (p. 1575)

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35 The definitions for the remaining four sources were similar in wording consisting of a combination of keywords such as access by all people at all times to enough nutritionally adequate and safe foods to live an active, healthy life (Carlson, 1999; Cohen, IQ Solutions, & United States Dept. of Agriculture Economic Research Service, 2002; Economic and Social Development Department, 1996; Life Sciences Research Office, 1990; Martinez et al., 2010; Nord et al., 2010; University of WisconsinExtension) Community Food Security There was far less consensus amongst the sour ces that attempted a definition of community food security. Seven sources addressed and offered a definition for the phenomenon; however, there were five different definitions. Notably, most of the definitions provided have basis or were inspired by the Co mmunity Food Security Coalition (CFSC) which defined community food security as a condition in which all community residents obtain a safe, culturally acceptable, nutritionally adequate diet through a sustainable food system that maximizes community self reliance and social justice (Winne et al., 1997, p. 4) The Martinez (2010) and Hamm and Bellows (2003) definition s remai n consistent with the CFSC definition almost to the exact wording, but Cohen (2002) introduces other layers of complexity in the term with concerns the underlying social, economic, and institutional factors (p. 3) that affect a communities ability to access safe and affordable food. The inconsistency of definitions and terms used to describe the spatial imbalance of food access is the foundation of this dissertation and forms part of the theoretical framework upon which this research is based. The next section will highlight the importance of typologies as a basis for understanding complex phenomena.

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36 The Use of Typologies in Urban Design Generally speaking, a typology is a categorization of a variable with like characteristics. In research, it can be used to identify key characteristics of different elements under study. In the discipline of urban design and for planning and design profess ionals, typology has specific meaning. It is an urban design classification system with preferred spatial relationships. Walters and Brown define typology in urban design as patterns of buildings and urban form based upon historical models (Walters & Brown, 2004) A typology though, is not prescriptive and may in fact be interpreted differently by different designers. A typology can inform design but also serves to make design intervention understandable to non des ign professionals (Walters & Brown, 2004) Typologies have been used in the literature to provide a clearer understanding of complex spatial phenomena. One example of an urban design typology is Transit Oriented De velopment or Design (TOD), a design approach focused on creating more compact, walkable communities where population density is higher in the city center, and residents are easily connected to commercial and retail centers as well as higher density residen tial areas through mass transit (Calthorpe, 1993) It is important to note though that the TOD model can also exist without transit as it makes for a more environmentally sensitive and pedestrian friendly neighborhood design (Calthorpe, 1993; Condon, 2010) Figure 21 illustrates, conceptually, how the TOD works as a typology. A core commercial or mixeduse zone surrounds a transit hub which c onnects residents in higher density (approximately 18 dwelling units per acre) residential areas all within 2000 feet (Calthorpe, 1993) A description of the typology developed as a

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37 result of this research is presented later in this chapter, as it is a cornerstone of the methodology used to collect and analyze the data. Figure 21. Conceptual diagram of transit oriented development as an urban design typology. Source: ( Calthorpe, 1993) The Demographics of Poverty and Obesity Since the millenium, poverty in the United States has risen steadily, and food insecurity has become a growing concern. Using Nords definition, food insecure households are those that have diffi culty at some time during the year providing enough food for all their members due to lack of resources (2010) According to a 2009 study on food insecurity in the United States, about 15% of households were considered food insecure. Nearly 6% were reportedly severely food insecure (Nord et al., 2010) Despite their eligibility, only 57% of food insecure households were Food Stamp or federal aid

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38 recipients (Nord et al., 2010) Rates of food insecurity in 2009 were the highest since the USDA began tracking the phenomenon in 1995 (Nord et al., 2010) Food insecurity is more prevalent among households with median incomes at or below the federal poverty line (Larson et al., 2009; Nord et al., 2010) Statistics have shown that food insecurity and poverty more prevalent among black, Hispanic and single parent households (Nord et al., 2010, Bhattachary a, J., Currie, J., & Haider, S., 2004) In Florida, 14.2% of the population reportedly were food insecure in 2009, just below the national rate of 15%. Food insecurity increased 5.3 % from 2004 to 2009 (Figure 2 2). The increase was the largest for any state during that time period (Nord et al., 2010) Figure 22. Graph showing the change in food insecurity rates 2004 2009 Source: (Nord et al., 2010) The national rate of poverty based on a 2009 Census Bureau survey was 14.3% in the United States (Bishaw & McCartney 2010; DeNavas Walt, Proctor, Smith, & U.S. Census Bureau, 2010) The national poverty rate rose one percent from the previous year (Bishaw & McCartney 2010; DeNavas Walt et al., 2010) During the recession 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 2004 2009Food Insecurity Rates Food Insecurity: 20042009 United States Florida

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39 years from 2008 through 2009 31 states had increases in poverty rates, including Florida (Bishaw & McCartney 2010) Figure 2 3 shows how poverty rates in Florida rose to exceed the national rate over a ten year period. In 2003, Florida had 2.7 million people, or 14.9% of the states population, living at or below the poverty line (Bishaw & McCartney 2010; DeNavas Walt et al., 2010) Rates were higher among black and Hispanic populations (U.S. Census Bureau American FactFinder 2011) Figure 23. Graph illustrating the change in poverty rates in the United States and Florida between the 2000 and 2010 census (U.S. Census Bureau American FactFinder, 2011) At a time of risi ng poverty rates, obesity rates have also risen at an a larming rate, especially among the poor. According to the Centers for Disease Control and Prevention, one is considered obese i f their body mass index (BMI) is at or above 30 (Centers for Disease Control and Prevention, 2010) In 1990, no state had an obesity prevalence higher than 15% As of 2010, no state had a rate lower than 20% (Centers for Disease Control and Prevention, 2011) Nationally, o besity rates were highest among non Hispanic blacks (44.1% ) and amongst all Hispanics (37.9% ) (Centers for 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 1999 2008 2009Poverty Rates Poverty Statistics: 19992009 United States Florida

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40 Disease Control and Prevention, 2011) In 2009, 26.6% of the total population was considered overweight or obese in the state of Florida Rates of o besity and obesity related illness es are statistically higher amongst the poor. Poor diets, poor nutrition, and nutritionrelated health conditions are especially prevalent among low income families (Beebout, 2006, p. 5 ) N utritionally deficient, energy dense diets have been linked with higher obesity and diabetes rates (Dinour et al., 2007; Gordon et al., 2011; Powell, Slater, Mirtcheva, Bao, & Chaloupka, 2007) Studies have indicated that a positive relationship exists between obesity and racial and socio economic status (Beebout, 2006; Block et al., 2004; Centers for Disease Control and Prevention, 2011; Larson et al., 2009; Powell et al., 2007) Evidence indicated that low income, less educated women were affected by obesity more than middle and upper income wom en (Centers for Disease Control and Prevention, 2011; National Institutes of Health, 1998) Though obesity rates among men over all income levels vary, for women, rates for those living below the poverty line were higher (Ogden, Lamb, Carroll, & Flegal, 2010) Overall, there was a 29% obesity prevalence among women who earned incomes well above the federal poverty threshold. However, obesity among women living at or below the poverty threshold was 42% (Ogden et al., 2010) Reasons for this phenomenon have been attributed to economics and the availability of lower cost energy dense foods such as refined starches, fats and sugars readily available in packaged snack foods and fast food restaurants ( Block et al., 2004; Drewnowski, 2004; Freedman & Bell, 2009; Galvez et al., 2009; Paeratakul, Ferdinand, Champagne, Ryan, & Bray, 2003) These types of foods metabolize quickly in the body,

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41 convert to sugars which are stored as fat, resulting in weight gain. Several studies have shown that higher food costs are considered an obstacle by low income consumers and, consumption of higher energy foods may be an unintended consequence of a strategy to save money (Drewnowski, 2004, p. 160) L iterature addressing the associated causes of obesity and obesity related illnesses has increase d In addition to the causes mentioned previously, the food system with in the built environment has received a lot of attention in recent research findings. Lack of availability, meaning a limited number of healthy food outlets, and longer travel distances to healthy food outlets have been linked to obesity amongst the urban poor. The lack of availability has made it difficult for the urban poor to achieve a healthy affordable and nutritious diet. The next section addresses research findings regard ing the obstacles faced by low income urban residents where healthy food choices are concerned. Challenges of the Food Environment in Urban Areas Geographic accessibility, mobility or access to transportation, variety in food outlet availability, and affor dability were recurrent issues identified as barriers to healthy food access in the literature and they are also conditions that require consideration when assessing community food security. Proximity Challenges in Accessing Healthy Food Outlets While som e practitioners in the planning and urban design professions have said they feel food systems planning is a public health issue, evidence suggests that the design of the built environment has not only played a role in the ability or inability to access foo d, but also in the rising rates of obesity (Eckert & Shetty, 2011; Pothukuchi, 2000) Previous studies have established a link between characteristics of the built

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42 environment and obesity. Poor public transportation, sprawling cities where the commute to stores spans greater distances and takes more travel time, and inhospitable or unsafe walking and biking conditions are examples of ways that urban design and infrastructure affec t obesity rates (Booth, Pinkston, & Poston, 2005; Jackson, 2003) Historically, the urban food environment consisted of small general stores and specialty shops within easy walking dis tance of urban dwellers. When supermarkets emerged in the early 20th century, the way people shopped for food and household goods changed as the economies of scale principle put a variety of goods under one roof and lowered prices. During the post war peri od of unprecedented economic prosperity and suburban expansion, supermarkets proliferated in suburbia. Many of the small shops in urban centers that had gone out of business with the advent of supermarkets never came back, leaving a void in the food supply in the urban core. Toward the end of the 20th century, the demographic makeup of many inner city neighborhoods was of low income minority residents who were left with few healthy food outlets (Coulton, Chow, Wang, & Su, 1996; Jargowsky, 1994; Kasarda, 1993) In a longitudinal study of inner city economics and demographics, Jargowsky (1994) found that the percentage of blacks living in extreme poverty increased from 37.2 in 1970 to 45.4 in 1990. Historically, it was not uncommon for living conditions to be segregated by race or by economics. Freys 1979 examination of the out migration of whites to the suburbs suggested both racial and nonracial factors in the movement that left central city areas with high concentrations of blacks. The author cited income differences and employment as reasons blacks tended to remain in inner city areas (Frey, 1979) A later study of the migration trends of ethnic groups in major urbanized

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43 areas in the U.S. showed that blacks were predominant in 8 of the 33 cities examined. The largest concentrations were in North and MidAtlantic States, Atlanta, New Orleans, Chicago and Detroit, but the study excluded data specifically on central (inner) c ity demographics (Lieberson & Waters, 1987) Demographic data on national central city demographic makeup has been difficult to find and some authors cite ever changing metropolitan boundaries and continued exurban migration as possible reasons for this gap in the literature (Clark, 1986; Lieberson & Waters, 1987) In Kasardas analysis of 100 urban centers, census tract data revealed that by 1990, 41.6% of blacks lived in abject poverty (1993) The migratory trend for whites, especially the affluent, to the suburbs through the postwar era seemed to peak in the early 1990s as the gap between wealthy and poor continued to widen (Reich, 1991) In a 2002 multi state study, researchers found over three times the number of supermarkets in wealthier zip codes compared to low income zip codes (K. Morland et al., 2002) Studies have shown that the poorest neighborhoods have only 55% of the grocery square footage of wealthier neighborhoods (Cotterill & Franklin, 1995) Ad ditionally, Larson and colleagues (2009) found that there were about half the number of chain supermarkets present in predominantly black neighborhoods compared to nonblack or mixed neighborhoods. Several s tudies conducted in New Orleans, Toledo, Detroit, and Los Angeles found that there were fewer supermarkets in low income minority neighborhoods compared to middle and upper income or predominantly mixed or white neighborhoods and as a consequence, options for achieving a healthy, balanced diet were limited (Algert, Agrawal, & Lewis, 2006; Bodor, Rice, Farley, Swalm & Rose, 2010; Morland et al., 2002; Powell et al., 2007; Walker et al., 2010)

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44 Studies have demonstrated there is a positive correlation between a lower prevalence of obesity and the presence of supermarkets (Morland et al., 2006; Booth et al., 2005). In neighborhoods where supermarkets are more prevalent, consumers have the opportunity to attain a healthy diet because supermarkets often offer the widest variety of fresh, whole foods (Bodor et al., 2010; Eckert & Shetty, 2011; Freedman & Bell, 2009; Jetter & Cassady, 2006; Larson et al., 2009; Powell et al., 2007) According to a recent study of the food system in Toledo, Ohio, residents who had better access to a food outlet carrying a variety of healthy food options made better food choices (Eckert & Shetty, 2011) Neighborhoods with few er supermarkets, such as low income urban areas, were found to have a greater number of convenience stores, corner stores, and fast food restaurants (Bodor et al., 2010; Powell et al., 2007; Walker et al., 2010) These outlets tended to offer far less in the way of healthy food choices (Eckert & Shetty, 2011; Freedman & Bell, 2009; Gordon et al., 2011; Powell et al., 2007; Walker et al., 2010) Evidence has suggested that in several cities nationwide, a disparity in food distribution has persisted with convenience stores and fast food restaurants being more prevalent in low income neighborhoods. Galvez and colleagues found that in an East Harlem neighborhood, a predominantly black, low income community, convenience stores were present in 55% of the census blocks and fas t food restaurants were present in 41% (Galvez et al., 2009, p. 340) Stein and Chakraborty (2010) found that in Hillsborough County, Florida, a higher densit y of fast food restaurants existed near low income, predominantly black and Hispanic neighborhoods. In New Orleans, Block et al. found a higher percentage of fast food restaurants in or near predominantly black

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45 neighborhoods and that for every 10% increas e in fast food restaurant density, median household income per census tract fell 4.8% and the percentage of black residents rose 3.7% (Block et al., 2004, p. 213) In a nationwide study of 28,050 zip codes low income neighborhoods had 1.34 times the number of fast food restaurants than upper income areas (Powell, Chaloupka, & Bao, 2007) However, this study found that nationally, predominantly black neighborhoods had 59.3% fewer fast food restaurants than predominantly white neighborhoods (Powell et al., 2007) The authors acknowledged this difference in results from the previously mentioned studies and suggested that perhaps it was because it was a nationwide study, whereas the other studies examined a single city. Given that fewer options for healthy foods exist in low income communities, those who live within these underserved areas have to travel farther to purchase t heir goods. In a study examining neighborhood healthy food access for food pantry clients in Pomona, California, the findings indicated that 59% of the sample of the population had no stores within walking distance (15 minute walk, or 0.8 km) where a variety of fresh fruits and vegetables could be purchased (Algert et al., 2006) Another study focused on low income, predominantly black neighborhoods in Detroit and found that in the poorest areas with a high proportion of blacks, the nearest supermarket averaged more than a mile away (Zenk et al., 2005) As lowincome residents are less likely to own a vehicle or have daytime access to private transportation, walking has become the primary means of getting places (Clifton, 2004; Dunkley, Helling, & Sawicki, 2004) Therefore, if the healthier food outlet was beyond the 15minute walking distance, the loc al store or

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46 fast food restaurant became the food outlet of choice (Galvez et al., 2009; Larson et al., 2009) Mobility Challenges in Accessing Healthy Food Outlets With grocery stores being out of range for so many low income households, transportation and transportation planning have been identified as critical elements of food systems planning. Most Americans have come to rely on personal vehicles to shop for groceries (Dunkley et al., 2004) In fact, having a vehicle has been found to reduce the amount of time and money spent shopping, as it has become common practice to combine shopping trips for convenience (Clifton, 2004; Dunkley et al., 2004) Cliftons study revealed that those with automobiles had an easier time providing for their families because their shopping was not restricted to specific times of the day, they co uld travel farther to get to better stores and on average could save more on food costs in comparison to families without personal vehicles (Clifton, 2004) This was confirmed by Dunkley et al. who found that c omparison shopping, and thus, saving money, was easier for those with a personal vehicle (Dunkley et al., 2004) Having a car has made traveling greater distances to access better stores with better variety more fe asible (Clifton, 2004; Dunkley et al., 2004) Suburban American communities have been designed to serve the automobile. This combined with the suburban locations of most supermarkets put s low income urban households at a disadvantage. Grocery stores and supermarkets comprise a healthy percentage of the establishments underrepresented in [low income] neighborhoods, due in large part to the trends in supermarket location and the consolidat ion of retail structure over the past thirty five years (Clifton, 2004, p. 403) Compounding the greater travel distances required to access healthy foods, several

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4 7 studies have found that low income consu mers are less likely to have personal transportation to rely upon and instead must utilize alternative forms of transportation such as public transit, taxis, ridesharing or walking (Clifton, 2004; Cotterill & Franklin, 1995; Dunkley et al., 2004; Eckert & Shetty, 2011) Significantly, those without a dependable vehicle have found their ability to maintain a healthy diet inhibited (Clifton, 2004; Dunkley et al., 2004) Interview participants without transportation, which included low income single mothers and married couples with children, spent more time and money traveling than those w ith vehicles and often worked provision shopping into their commute from work (Clifton, 2004). They were also limited in how much they purchased in any shopping event so not to be over b urdened on the bus or on the walk home (Clifton, 2004) As a result of the added expense of using alternative forms of transportation to and from healthy food outlets, low income consumers pay more to achieve a healthy diet than middleand upper income consumers who are more likely to have their own car (Clifton, 2004; Walker et al., 2010) Low income consumers have more often opted to provision shop at stores closer to their homes where costs may be higher and the quality of the goods lower than what could have been purchased at a supermarket (Clifton, 2004; Walker et al ., 2010) The literature also indicated a racial disparity in mobility challenges to accessing healthy food outlets. Bodor and colleagues (2010) examined 175 census tracts in New Orleans and found that there w ere more AfricanAmerican households living below the Federal poverty line 1 than households in mixedrace neighborhoods, and fewer 1 For a family of four, the minimum poverty guideline is $22,350 (Sebelius, 2011)

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48 households had a car (Bodor et al., 2010) According to a 2006 study on food deserts in Chicago, people who live in predominantly black neighborhoods travel the farthest to access a grocery store or supermarket (Mari Gallagher Research & Consulting Group, 2006) In summary, without reliable transportation, accessing healthy food costs low income consumers more, in both time and money. For the sake of convenience, local corner stores have continued to thrive even with higher pri ces and lower quality food simply because residents with limited mobility have had little choice but to patronize these outlets (Clifton, 2004; Dunkley et al., 20 04; Eckert & Shetty, 2011) Variety in Availability of Healthy Food Outlets Food store variety can be defined as either the variety of food outlets available or variety in availability of healthy foods in the stores that are available. This study consid ers the former. Studies have shown a relationship between the types of food stores available and the overall health and well being of the community. Consumers living in neighborhoods without any supermarkets, but with an abundance of liquor, convenience an d small grocery stores are less likely to eat a balanced nutritious diet (Dinour et al., 2007; Larson et al., 2009; Morland et al., 2002; Powell et al., 2007) Furthermore, there is a positive relationship between high fast food restaurant density and a neighborhoods obesity rate (Larson et al., 2009; Powell et al., 2007; Stein & Chakraborty, 2010) A study of neighborhood stores and adolescent obesity in Harlem, New York found that the presence of a fast food restaurant or convenience store near a youths home was positively associated with a higher BMI than those who did not live near one (Galvez et al., 2009) The same study determined that residents without supermarkets close to their home were 25% to 46% less likely to have a healthy diet

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49 than study participants who lived in an area of high supermarket density (Galvez et al., 2009, p. 339) Finally, Larsen and Gilliland found that in a neighborhood determined to be a food desert, the intr oduction of a farmers market had multiple benefits, as not only did it provide an additional quality source from which to shop for healthy foods, but over time prices at other area grocery stores came down in order to be more competitive with the farmers m arket (2009) The farmers market also improved the quality and variety of fresh produce available in the same study. Studies have shown that the availability of a wide variety of healthy foods is limited in s ome urban areas and that there is a direct correlation between neighborhood characteristics and the variety of healthy food available. Bodor and colleagues conducted a study of shelf space allotment for fresh produce at a number of grocery and convenience stores in New Orleans and found that overall there was far more shelf space allotted to packaged snacks than fresh foods and, in mixedrace census tracts there was 27.8% more shelf space allotted to fresh foods as compared to predominantly AfricanAmerican tracts (2010) A focus group study of residents in a low income area revealed that the cost, poor quality and poor healthy food choices were barriers to eating a healthy diet (Hendrickson, Smith, & Eikenberry, 2006) Finally, as a study in England showed, t he built environment plays a major role in food choices (Dunkley et al., 2004) P eople in a low income neighborhood in Leeds, which had been classified as a food desert, were not as satisfied with their small, local stores as generally believed (Dunkley et al., 2004) However, study respondents attested that if healthier, better qualit y food choices were available, they would have

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50 been more inclined to shop closer to home (Dunkley et al., 2004) It is clear from the literature that eating a healthy diet is directly correlated to the availability of healthy food options (Cheadle et al., 1991) Assessing Affordability: Thrifty Food Plan Market Baskets The USDA implemented the Thrifty Food Plan (TFP), a market basket of food intended to represent a national standard for a nutritionally balanced diet at a low cost (Dinour et al., 2007) The TFP is a meal plan representing a minimally nutritious diet at the lowest cost for a family of four with a limited budget (Carlson, Lino, Juan, Hanson, & Basiotis, 2007; Jetter & Cassady, 2006) It is used to determine elegibility for Federal Assistance programs such as SNAP, or food stamps. In the literature, there are several studies testing the actual affordability of TFP market baskets. Based on the market basket prices of the TFP, the studies have revealed that low income urban residents pay more for the standard basket in smaller grocery stores in their o wn areas compared to shopping at a supermarket outside of the neighborhood. A 2006 study of four communities in Minnesota revealed that in the urban areas actual market basket prices were significantly higher (ranging from 26% to 52%) than TFP prices (Hendrickson et al., 2006) Findings indicated consumers considered cost, choice and quality barriers to healthy diets (Hendrickson et al., 2006) In Southern California, Jetter and Cassady compared the shopping experience for staple, whole food items of low income consumers to the same of higher income consumers in order to compare the cost and availability of healthier food items. The objective was to see who paid more to eat a healthy diet. They found that with a set list of items including fresh produce, meat, low fat dairy and whole wheat grain, availability and price differed between the two experiences. In low income neighborhoods the lack

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51 of supermarkets left s hopping limited to small groceries and convenience stores where, if avai lable, the same foods cost more and were of lower quality. The same basket of items cost around 30% more than higher income consumers would pay in their neighborhood supermarkets. Interviews with consumers indicated that the higher prices were a deterr ent to eating healthier diets (Jetter & Cassady, 2006) In summar y, the literature indicated that supermarkets the best source for better quality, lower priced whole foods are far less prevalent in low incom e, minority communitie s, and low income consumers pay more to eat healthier foods whether through time spent traveling to higher quality food outlets, money spent on public transit or taxis where personal vehicles are not available, or higher prices paid at smaller grocery or convenience stores nearby Planning and Design Professionals and Food Systems Planning Integrating food systems planning into land use and transportation planning is a challenge that many community, equity, transit, and city planners have, in the past decade, begun to incorporate into their planning models and comprehensive master plans (Raja et al., 2008) Prior to 2000, food systems planning was not considered an issue by many planning and design professionals and for a long time, it was almost completely neglected. A 1999 survey of practicing planners revealed that the majority felt that food system planning was largely a public health issue, outside of their expertise (Pothukuchi, 2000) The general concensus was that food systems and their many components (i.e., distribution, marketing, consumption, waste management, and processing) were, at best, private sector issues (American Planning Association, 2007; Pothukuchi, 2000) Planners felt the food system had little to do with the built environment. In Pothukichis study, only 38% of respondents felt pla nners should be

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52 more involved in community food planning (Pothukuchi, 2000) However, many at the time expressed an interest in becomi ng more involved in food systems planning and over the course of the decade opinions began to shift (Pothukuchi, 2000; Raja et al., 2008) A follow up survey of planners in 2008 indicated that significantly more planners felt that community and regional food issues should be a priority (Raja et al., 2008) The researchers found that, in fact, 70% of r espondents believed that the preparation and modification of comprehensive plans to include community and regional food issues should be an area in which the planning profession should be significantly involved (Raja et al., 2008, p. 29) With vast growth of suburban development and the shift in thinking toward more sustainable, smarter growth patterns coupled with the exponentially widening income gap and hard economic times of the recent recession, the plight of the urban and suburban poor has become a major focus of media and academic attention (Bedore, 2010) Significantly, it has pushed social equity and the impact of the built environment on health and wellness int o the arena of planning literature and research. Much of the focus of food system research has been on the effect the built environment has had on obesity rates, and obesity related disease, the socioeconomic imbalance of access to healthy food outlets, an d contributions in methods for measuring the food environment (D. Block & Kouba, 2006; Bodor et al., 2010; Hendrickson et al., 2006; Kjellstrom & Mercado, 2008; Larson et al., 2009; McEntee & Agyeman, 2010; Morland, Wing, & Roux, 2002; Morland & Filomena, 2007; Sharkey, 2009) With the increased interest from planners in assessing urban food systems, we have begun to see the impact of planning and design professionals

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53 contributions to wards a more equitable availability of healthy foods in the urban environmen t Planning and design professionals and academics have recognized that the built environment has a lot to do with the food environment. The food environment contains a variety of destinations to which people are required to travel in order to obtain healthy foods (Raja et al., 2008) The American Planning Association (APA) recognized that there is a social aspect of planning that incorporates the design of policy and planning development without discrimination making sure that, to the extent possible, everyones basic needs of access to clean air, fresh water and adequate food supply are met (American Planning Association, 2007; Pothukuchi, 2000) They have also acknowledged that without conscious, equitable planning, policies and implemented regulations could actually increase instances of hunger and food insecurity (American Planning Association, 2007) At a forum in 2009 addressing the issue of food security in the built environm ent Matthew Potteiger, a landscape architect and professor, suggested that landscape architects ought to become more involved in food systems planning, since access to healthy foods has been recognized as a spatial issue at the urban and community level (Potteiger, 2009) Landscape architects and urban designers are uniquely positioned to help improve the imbalance through comprehensive examination of a regions local ecology, history, culture, social and polit ical structure, economics and physical form (Calthorpe & Fulton, 2001; Potteiger, 2009) Strategies implemented at the regional scale can potentially benefit everyone in a community, but it has potential to greatly improve food access and availability for those living in underserved communities

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54 (Calthorpe & Fulton, 2001) The American Society of Landscape Architects (ASLA) has advocated smart growth principles such as those embraced by transit oriented developent (TOD) and designing compact walkable communities as ways in which the profession could directly impact community food systems (ASLA, 2011) Public tra nsit within the city could operate on light rail designed to stop at major transfers and shopping destinations. Busses could take passengers to more destinations throughout the city, and stops for public transit could be no farther than a 15minute walk aw ay from any home. At the neighborhood scale, landscape architects can, and have become involved in the urban agriculture or local community foods movements for the purposes of forging strong er, more resilient communities. T he impact of planners involvement in food systems has begun to show results. In a 2007 policy guide, the APA adopted a set of policies specifically addressing planning professionals role in community and regional food systems It recommended priorities for intervention, such as utilizing land use and transportation planning tools to increase spatial access to programs and facilities that help reduce hunger and food insecurity for residents in impoverished urban and rural communities (American Planning Association, 2007, p. 15) Increasingly, p lanners have been more involved in the decision making process involving the development of new supermarkets, grocery stores, fast food and full service restaurants, and oth er food outlets such as supercenters. They have also been involved in recommending land use that could be designated for urban agriculture or community gardens. Finally, research has shown more attention paid to the ways people adapt to mobility challenges such as not having

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55 private transportation, being elderly or disabled and requiring transportation assistance (Garrett & Taylor, 1999; Gottlieb, Fisher, Dohan, O'Connor, & Parks, 1996) T he prioritization of food systems planning through reformed land use planning in some urban areas through public/private cooperation, public participation, and involvement of the design community, serve as examples of the drive to put food systems research into action. The following case studies exemplify efforts already in progress aimed at alleviating the strain of poor access to healthy food in low income urban areas. Case Studies Philadelphia, Pennsylvania. In 2008, The Food Trust conducted a study and found that the city of Philadelphia had the second lowest number of supermarkets per capita in the country, and the number of supermarkets in the lowest income neighborhoods was 156% less than in its highest income neighborhoods (Raja et al., 2008) This finding led to the formation of a public private collaboration to provide funding incentives to grocery and chain supermarkets to entice them back into the urban core. As a result of the efforts of The Food Trust and Pennsylvanias Fresh Food Financing Initiative (FFFI), as of 2008, 50 supermarket projects had been funded for development in urban areas in Philadelphia, as well as other urban areas throughout the state (Raja et al., 2008). The use of economic development funds has been an important factor in incentivizing improved food access. According to Duane Perry, founder of The Food Trust, in order for policy changes and progress to happen, planning and design professionals should emphasize the need for supermarkets as a solution for improving healthy food access in underserved urban areas (Raja et al.,

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56 2008) To policy makers, increasing the number of supermarkets has become a tangible, workable solution (Raja et al., 2008) Infill Philadelphia: Food Access was a collaborative effort between the design, planning, banking, policy makers, and grocery store operators to explore ways to improve food access to underserved urban areas through design (The Community Design Collaborative, n.d.) Three sites that were abandoned, underused or empty were identified for redevelopment, and conceptual designs were presented in a series of work shops. The end result was a new concept for new grocery coops and a new supermarket designed to be equally accessible to both pedestrians and vehicles. Through the workshops, the Collaborative developed some principles through which design could be used as a tool for improving access to fresh foods (The Community Design Collaborative, n.d.) Summary This study examined the food environment in Tampa, Florida to test a method for classifying dispariti es in healthy food access in an urban setting. The study is founded on the theory that to simply identify a neighborhood as a food desert or to say the residents are food insecure without specifying what makes it so does little to inform the urban designer or planner about the variables at play or ways to resolve the challenges faced by the areas residents. The research was based on the theoretical foundation set forth in the literature, which largely agreed that the three major obstacles to accessing heal thy food were proximity challenges, lack of mobility, and lack of a variety of healthy food outlets. In many low income urban communities, supermarkets and other healthy food outlets exist outside a comfortable walking distance. Studies have found that the poorest communities had less than half the amount of fresh food outlets compared to

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57 wealthier communities. Researchers have also found a racial disparity in food outlet distribution with higher densities of fast food restaurants and fewer supermarkets ava ilable in p redominantly black communities. The literature showed that people who live in poorer census block groups are less likely to have a personal vehicle and, therefore, must rely on other forms of transportation to shop for provisions. This contribut es to higher costs for accessing healthier food outlets for low income consumers. In fact, several studies have concluded that the poor spend more to achieve a healthy diet than middleand upper income consumers. This increased cost has been attributed to either increased cost of time and money spent using alternative modes of transportation or the increased cost associated with shopping at stores within their neighborhoods. The challenge of living in an area where the availability of healthier foods is limited has had public health and social justice implications. The literature demonstrated that having limited access to better foods and making poor dietary choices, such as opting for the cheap, convenient fast food meal, has led to high rates of obesity and obesity related diseases such as diabetes and heart disease (Beebout, 2006; Blair Lewis et al., 2005; Freedman & Bell, 2009; Powell et al., 2007). Both obesity and poverty have disproportionately affected minority communities. Planning and design profes sionals have begun to recognize the role they can play in improving food access through design and planning policy. They have recognized that by integrating food planning into practice by designing for active communities, for instance, they can also have an impact on public health outcomes.

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58 CHAPTER 3 RESEARCH METHODS Introduction This study was guided by two research questions: How can the disparities in urban food access be better defined as to break away from the ambiguity implied by term food desert? a nd what would a typology classifying challenges faced in accessing healthier food outlets look like? In most previous studies of urban food environments geographic information systems software and regression modeling have been used to map and analyze the data. V arious methods have been used to collect the data including local telephone directories and groundtruthing (physically walking around with a G lobal Positioning System and geocoding the location of the food outlets). Largely the latter method has proven to be more accurate (Block et al., 2004; Rose et al., 2009) The USDA Economic Research Service (USDA ERS) as a contribution to the First Lady's Let's Move! Initiative developed a F ood Desert Locator (U.S.D.A. Economic Research Service, 2011) The goal of the Locator was to find out where and how many people reside in areas with poor access to healthy foods. To accomplish this, researchers at the Economic Research Service mapped the location of low income areas based on census tract data and measured distances to supermarkets and large grocery stores (U.S.D.A. Eco nomic Research Service, 2011) food outlets believed to provide healthy foods. According to their definition, only low income census tracts can be also considered food deserts. However, this study will look at the urban food environment in a broader con text, as it will examine the correlation between the defined spatial factors and median household income, and single female head of household, and age. The Locator

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59 was useful as a model for the research design for this dissertation because it provided a baseline definition of a community with poor proximity to supermarkets and grocery stores T his study expanded the scope to include the variety of food outlets and mobility. The following sections explain in detail the research design and procedures followed in collecting and organizing the data, the structure of the research instrumentation and treatment or analysis of the data. Research Design A single city was examined to explore the relationship between income and three factors that affect residents abil ity to access healthy foods: proximity, mobility and diversity of availability. The research employed a combination of quantitative procedures to map and measure healthy food access in an urban area using an assessment instrument derived from the research of others. Study Cit y Selection The City of Tampa is the county seat of Hillsborough County on Floridas Gulf Coast. It was selected as the study city for this dissertation because according to the USDAs Food Desert Locator data, of all of the counties in Florida, Hillsborough County has the highest number of census tracts considered food deserts. Census tracts are statistically delineated geographic areas within a county with populations ranging from 2,500 to 8,000 people. The population within each census tract is generally homogenous (U.S. Census Bureau, 2011b) Census block groups (CBG) are smaller geographic areas containing anywhere from 600 to 3,000 people, and formed the basis for data gathering because detailed socioeconomic data were available for each CBG

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60 including household composition, median income and automobile ownership (U.S. Census Bureau, 2011b) Tampa has a population of 625,570 within 379 CBG s and an overall median income of $40,883 In 2010, it was determined that 18.1% of Tampa residents live at or below the poverty threshold of $22,340 for a family of four (Sebelius, 2011; U.S. Census Bureau American FactFinder, 2011) These statistics suggested an intriguing site for use in the investigation of factors affecting healthy food access. Selection of Census Block Groups While it would have been ideal to use solely 2010 Census data to obtain the most upto date picture of Tampas food environment, this study was limited by the availability of data. Median household income and vehicle availability data were not collected, or were not made publicly available during the 2010 Census at the CBG summary level. Data on these relevant factors were, however, collected during the previous decennial census. A GIS dataset was downloaded from the Florida Geographic Data Library (FGDL) containing the 2000 CBG data for the City of Tampa. These data contained household income, vehicle availability, as well as detailed demographic composition for all block groups. These data were added, using Spatial Join in the ArcGIS toolbox, to the 2010 residential parcel dataset in GI S (acquired from the Hillsborough County property appraisers office). Block groups from the 2010 Census formed the spatial boundaries and available data including population and household composition were compiled in an Excel spreadsheet as well as in the GIS. The 2010 parcel data with the joined 2000 data for income and vehicle availability were then intersected with the 2010 block group boundaries in the GIS to form the completed necessary dataset for analysis. As of the 2010 Census, there were 379 census block groups in the city

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61 boundaries of Tampa, 336 of which were included in this studys sample. Uninhabited block groups, block groups occupied by specialized land uses (i.e. Federal and State land uses), and those split by the city boundary were excluded. Development of the Spatial Typology for Food Access Disparities The data were collected between August 2011 and February 2012. As described in the introduction, three factors affecting urban food access, proximity, variety in availability, and mobility emerged as significant and measurable. Other factors were frequently cited, such as affordability; however, this was not seen as a factor that could be addressed through changes in urban design and so it and other nonspatial factors were not examined. In this study, proximity, or distance to food outlets was examined in terms of median physical distance between occupied residential parcels to a healthy food outlet. Variety in availability was defined in terms of the various ways consumers obtain healthy f oods (i.e. supermarkets, farmers markets, bodegas, supercenters, etc.). Mobility barriers represent the various ways people travel to food outlets. In U.S. cities, the majority of consumers travel by car, public transit, bicycle or by walking to food outlets. Based upon these definitions, and using census block group data to define spatial boundaries, research instrumentation was developed to examine the food environm ent within the city of Tampa. Instrumentation The instrumentation was developed to address this studys objective of examining challenges to food accessibility by neighborhood ( CBG ) and by extension, the relationship between a neighborhoods food accessibility and three socioeconomic variables: median household income, single female head of households and age of

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62 householders. An audit instrument was created for each spatial factor identified above. The audit was modeled after the Pedestrian Environmental Data Scan or PEDS (Clifton, Livi Smith, & Rodrigue z, 2007) audit instrument and an Environmental Assessment for Public Recreation Spaces (EAPRS) developed by Dr. Brian Saelens and colleagues. The EAPRS audit is an in depth assessment tool intended to measure the physical quality of outdoor spaces (Saelens et al., 2006) The procedure requires direct observation and rating for several hundred parameters or elements that would be found in parks and public green spaces. The level of detail of the EAPRS audit was unnecessary for this study; however, the method of scoring the quality of elements provided a replicable example of how to measure the three factors in this study. The PEDS audit instrument was intended to be simple, replicable and concise. Like the EAPRS method, the PEDS instrument focused on specific qualities that make a neighborhood pedestrianfriendly such as environmental or neighborhood attributes like the existence and quality of pedestrian facilities (i.e. sidewalks), roadway attributes and the walking/cycling microclimate. Each of these factors had a checklist or questions that required a yes or no response or a checkmark and then an overall score for the neighborhood ( CBG ) was tabulated. The simplified organization of the PEDS audit was combined with the scoring method of the EAPRS tool to create the instrumentation for measuring the three spatial factors for each CBG for this study. A worksheet was created for each factor with specific attributes to be evaluated within each CBG. For example, the mobility measure evaluates the way people travel to access healthy foods so the audit assessed access to bicycle facilities, public transit and the availability of personal vehicles. Scores of 3

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63 (high), 2 (medium), and 1 (low) were assigned for each attrib ute. Once the audit for each factor was completed the scores were compiled onto a data summary sheet. The complete audit ins trument used is available in Appendices B through E Collection and Treatment of the Data The 2010 census block group boundaries and 2010 parcel datasets were attained from the Hillsborough County property appraisers office. The shapefile representing boundaries of the 2010 CBGs for the City of Tampa established the base map in ArcMap. Next, the 2010 parcel data were added to the GIS and 2000 median household income and vehicle availability data were joined t o the 2010 CBG attribute table. Proximity to Healthy Food Outlets Recall from Chapter 1 that traditional outlets such as supermarkets, supercenters, and farmers markets that were c onsidered the best places to acquire healthy and affordable foods were classified as Type 3. Smaller grocery stores and ethnic or specialty food markets tend to have a more limited availability of fresh fruits, vegetables, meats or fish and prices tend to be higher at these store for the same items one would pay less for at a supermarket; these stores were categorized as Type 2 outlets. Finally, the poorest options for shopping for healthy foods tend to be convenience stores and dollar stores and they were categorized as Type 1. Even though Type 1s are more abundant, prices are much higher, and most of the goods sold are processed and prepackaged. To evaluate each CBGs access to healthy food outlets, a field was added to the attribute table of the food stor e map layer in ArcMap identifying each store as a Type 1, Type 2 or Type 3 outlet. Next, using Spatial Analyst, the distance between all occupied

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64 residential parcels within each CBG to the nearest Type 1, Type 2 and Type 3 food outlet was determined. The m edian distance for all occupied residential parcels within each block group was recorded on the audit worksheet for Proximity resulting in three median distances for each CBG one for each Food Outlet Type. Once the median distances were determined, condi tional statements were created in the worksheet and proximity scores were assigned following the procedure which follows below. In the literature, proximity is most commonly used to describe spatial distance between origins (e.g. homes) and destinations (e.g. supermarkets). Findings of a 2011 study comparing methods of measuring distribution of urban food access indicated that the use of network distance yields the same conclusions as using Euclidian distance (Spark s, Bania, & Leete, 2011) Therefore this study used straight line Euclidian distance to measure proximity from each residential parcel to the nearest Type 3, Type 2 and Type 1 food outlet. The nearest distance for each CBG was recorded on the worksheet and the median distances were used as the scoring parameter. To convert the distances to scores of 1, 2 or 3, a standard of one mile was set as the maximum distance. This is due in large part to the USDA ERS Food Desert definition that sets access to super markets and large grocery stores to within one mile. For this study, distances one mile and farther received a score of 1, as the farther away one lives from a healthy food outlet, the p oorer their access is. CBGs with nearest distances between the median distance for each Type of food outlet, and .99 miles received a 2 and those below the median were scored a 3 (Table 31 ).

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65 Table 3 1 An example of the method used to convert m edian d istances by f ood o utlet t ype to scores of 1, 2, or 3 Distance (d) to: Median (in Miles) Score =3 IF Score =2 IF Score =1 IF Type 3 Outlet 0.56 d < 0.56 0.99 > d > 0.56 d > 1 Type 2 Outlet 0.77 d < 0.77 0.99 > d > 0.77 d > 1 Type 1 Outlet 0.083 d < 0.083 0.99 > d > 0.083 d > 1 E ach CBG received a three digit score for Proximity to Food Outlets (Table 32 ) with the value in the first digit representing the score for proximity to Type 3, the value of the second digit representing proximity to Type 2 and the third digit representing the proximity to Type 1 food outlets In the case of the block group shown, Type 2 outlets (small grocery, ethnic and specialty food stores) had the closest median proximity to occupied residential par cels. Table 3 2 Interpretation of r aw s cores for p roximity to f ood o utlets for one sample CBG Block Group ID Median Proximity to Nearest Type 3 Outlet Median Proximity to Nearest Type 2 Outlet Median Proximity to Nearest Type 1 Outlet Combined Raw Score 120570001011 2 3 2 2 3 2 Diversity in Availability The names and addresses of supermarkets and other food outlets were gathered using intermediate methods. Due to time and financial constraints, it was not feasible to collect the names, locations and geographic coordinates for every food outlet in Tampa thr oug h groundtruthing. Instead secondary sources were used. Internet directory searches were conducted and results were cross referenced with corporate web sites and verified using Google Earth (Google Inc., 2011) satellite imagery and Street View feature. The stores were divided into categories according to the quality and quantity of healthy food content typically found within each store type. All food outlet addresses

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66 were geocoded onto the GIS map of Tampa and quarter mile buffers were drawn around each. Several outlets lie on the boundary between two or more CBG boundaries, however, if the quarter mile buffer touched or overlapped a CBG boundary that store type was considered accessible to the CBG. All food outlets were counted that fell inside or within a quarter mile of the boundary of the block group and their totals recorded according to food store Type on the audit worksheet. The totals were entered on the worksheet and then each block group was assigned a score for each Type of food outlet based on the following criteria: If there were zero stores, the block group received a 1 for that food outlet Type. The median values for the outlet Types in each CBG were used to determine scores of 2 and 3 for each outlet T ype. Table 33 shows how the CBG scores wer e derived for each. Table 3 3. Conditions for scoring CBG diversity of food outlet availability Score Condition Score Type 3 Outlets Median = 1 IF n < 1 1 IF n = 1 2 IF n > 1 3 Type 2 Outlets Median = 1 IF n < 1 1 IF n = 1 2 IF n > 1 3 Type 1 Outlets Median = 3 IF n < 3 1 IF n = 3 2 IF n > 3 3 n= total no. of Type 3, 2 or 1 food outlets For each of the three categories, totals above the median received a score of 3, indicating a high availability of that store type. Totals equal to the median received a 2, indicating a limited to moderate availability of that store type. In the end, each CBG was

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67 assigned a three digit score indicating which type of food outlet was most or least abundant. This was used as a measure of the diversity in healthy food availability (Table 3 4) in each CBG. As with the proximity score column 1 represents availabi lity of Type 3 outlets, column 2 represents availability of Type 2 outlets and the third column represents availability of Type 1 outlets. Table 3 4. Interpretation of raw scores for diversity of availability per CBG CBG ID Score for Type 3 Outlet Score for Type 2 Outlet Score for Type 1 Outlets Combined Raw Score 120570001011 2 3 3 2 3 3 Mobility To assess mobility, d ata for the number of vehicles available by household were gathered from the 2000 Census dataset and compiled onto the audit worksheet in the appropriate columns. To simplify data collection and analysis, this study simply included total percent of households where zero vehicles were available for each CBG. To determine availability of transi t for each block group, routes and transit stops for Hillsborough Area Regional Transit (HART) were added to the GIS map and onehalf mile buffers were drawn around the transit stop points. T he percentages of block group residential parcels within the transit buffers were calculated in GIS and recorded onto the audit worksheet. If a transit buffer touched or overlapped a parcel, that parcel was considered to be within the transit buffer. Finally, existing bike route shape files from the Hillsborough Metropolitan Planning Organization for Transportation were added to the map. For each CBG that had bike lanes within the boundary, the percentage of total road length with bike lanes within the block group was calculated and added to the spreadsheet.

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68 Scoring for each category was accomplished by breaking down percentages into three rankings. For percentage values between one and 33, the CBG was scored a one for the category being evaluated, a two for percentage values between 34 and 66 and a three for percentage v alues 67 and higher. Upon completion, the three digit scores for each block group revealed the neighborhoods with the best mobility, and which ones are in need of intervention most importantly, the scores indicated where the most severe disparities in mobility exist ed and what types of barriers posed the greatest challenges. In the end, there were three three digit scores for each CBG all compiled onto a data summary sheet where column 1 represented the score for bike facility percentage, column 2 represe nted the percentage of household vehicle availability and the third column represented the score for access to transit (Table 35 and 36 ). Table 3 5 Conditions for scoring CBG mobility Bike Facilities Access to Cars Access to Transit n > 66% 3 n > 66% 1 n > 66% 3 66% > n > 33% 2 66% > n > 33% 2 66% > n > 33% 2 n < 33% 1 n < 33% 3 n < 33% 1 Table 3 6. Interpretation of raw scores for mobility for one sample CBG Bike Facilities Access to Cars Access to Transit CBG ID % (n) of Roads w/ Bicycle Facilities Score (3,2,1) % of HH w/ No Veh Score (3,2,1) % HH 0 1/2mi from transit stop Score (3,2,1) Combined Raw Score 120570001011 26.84% 1 4.50% 3 10.15% 1 1 3 1 Summary This chapter highlighted the procedures that were undertaken within this study in order to understand how disparities in urban food access varies from neighborhood to neighborhood; that is, not all areas considered food deserts are deserts for the same

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69 r easons. This method allows for the analysis of the degree to which spatial barriers manifest in the built environment and affect ability to easily access healthy foods The next step was then to compare the spatial results with the socioeconomic makeup of the block group to gain a better understanding of what barriers affect specific social and ethnic groups. The results are presented in Chapter 4. In Chapter 5, implications of the results including a summation of the food access typology will be discussed along with some possible solutions based on case study research that may be appropriate for specific conditions affecting neighborhoods with poor access to high quality healthy food optio ns.

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70 CHAPTER 4 RESULTS There were several decisions which needed to be made once the data were collected and the data summary sheet was completed. The objective was to synthesize the data to provide a straightforward way to visualize the extent to which th e spatial factors affected the neighborhoods of Tampa and combine them in order to compare with socio economic data. This chapter discusses the decisionmaking processes which lead to the Urban Food Access Typology introduced in Chapter 5. In this chapter, the procedure for data analysis is explained followed by the analysis and results for proximity, diversity of availability and mobility as factors in urban food access Each factor was treated individually to remain consistent with the data collection procedures, which allowed the typology to take shape. Proximity as a Factor in Urban Food Access Data for proximity to the three identified types of food outlets were calculated for each block group. The scores were compiled onto the data summary sheet in Exc el. The resulting data tab les can be found in Appendix B. Of the 336 census block groups included in this study, 170, or 50.6% of them have at least one supermarket or supercenter within less than .56 miles of occupied households. The raw scores were mappe d as shown in Figure 41. There were 20 combinations of proximity as a factor to food access ranging from 333 111. The next step was to reclassify the raw proximity scores into four categories ranging from 1 (Poor) to 4 (Excellent) in order to map the results of the data for proximity to food outlets for analysis. The range of scores were reclassified into four groups based upon the assumption that the closer one is to Type 3 or 2 outlets, the

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71 better ones food access would be. The following criteria were developed for use in the reclassification: All CBGs with the shortest median distances (<.56mi) to the nearest Type 3 out let were given a score of four. Block groups with nearest proximity between .56 and one mile to the nearest Type 3 store and with at l east a score of two for proximity to ethnic/specialty foods and small grocery stores (Type 2 outlet) were given a three. CBGs with low proximity (i.e. have only moderate proximity to either a Type 3 or a T ype 2 outlet) were given a two. Any CBG with close proximity to convenience and dollar stores (Type 1 outlet) that yet are far away from any other food outlet type (Type 2 and Type 3) were given a one. Figure 41. Raw data scores show census block groups resulting proximity scores after the completion o f data collection

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72 Table 41 shows the breakdow n of the score reclassification. The results were mapped in Figure 42. The block groups with the best food access based on proximity are concentrated in the center of the city. People living in the northeast a nd southeast appear to live further away from higher quality food outlets. Table 4 1 Reclassified p roximity s cores 4 CBG Count 3 CBG Count 2 CBG Count 1 CBG Count Raw Scores 333 59 233 25 213 13 122 3 332 26 232 17 212 19 113 10 323 20 223 4 211 2 112 15 322 7 222 10 133 18 111 7 313 23 132 23 312 31 311 4 Count Total 170 56 75 35 Figure 42. Map of Tampa CBGs reclassified by proximity to higher quality food outlets

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73 Diversity in Availability Each CBG was assessed on the diversity, or variety, of food outlets available within the CBG boundary plus a mile buffer of the block group boundary. Recall from Chapter Three, the first column for Diversity indicated the score for the number of Type 3 food outlets w ithin the buffer (Refer to Table 32 for a breakdown of the raw score). The second digit in the score indicated the score for the number of Type 2 outlets available, and the third digit was for the number of Type 1 outlets. It was considered ideal to have at least two Type 3 supermarkets or supercenters available to the residents of a given CBG, and diversity was considered adequate if there was at least one Type 3 store and one small grocery or specialty food store within the buffer. The results yielded 20 score combinations which were then reclassified into four categories (Table 42 ). The raw data were m apped as shown in Figure 43. Table 4 2. Reclassified diversity of availability scores 4 CBG Count 3 CBG Count 2 CBG Count 1 CBG Count Raw Scores 333 1 233 6 213 34 123 21 323 2 232 2 212 10 122 13 313 18 223 10 211 6 113 64 312 7 222 3 133 6 112 91 221 1 132 2 111 35 131 1 Count Total 28 22 59 224 The following criteria were developed for use in the reclassification of the raw scores: A four indicated high diversity, with at least two Type 3 outlets and at least one each Type 2 and Type 1 outlet available to meet the nutritional needs of a CBGs residents Category three were block groups with one Type 3 outlet and two or more Type 2 outlets

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74 Category two included block groups where there was one supermarket or two or more ethnic/specialty food store. The remaining block groups comprised category one where there w as no supermarket but there was a small grocery store or ethnic food store. This category is characterized by an abundance of convenience and dollar stores. CBGs which lack all three food store types within the buffer were also included in this category. Figure 43. Map of Tampas census block groups according to their raw data scores for diversity in availability

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75 As Table 42 illustrates, 224 or 66.7% of the block groups completely lack Type 3 outlets within their boundary plus the mile buffer, howev er, are well serviced by Type 1 outlets (convenience and dollar stores). There were 35 census block groups which were not serviced by any food outlet within the city limits. Figure 44 shows the map of Tampa to illustrate the spatial distribution of the reclassified census block groups. Figure 44. Map of Tampas census block groups according to their reclassified scores for diversity in availability

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76 Mobility The block groups were then assessed upon the quality of mobility, or how easily residents would be able to travel independently to any type of food outlet. Mobility consisted of three factors: the percentage of bike lanes provided along the roadways throug hout the city, the percentage of households with no vehicles available, and the percentage of a CBGs occupied households which lie within a half mile radius of a transit stop. Here, public transit was evaluated simply upon the percentage of households that would be serviced by public transit within city limits. Buses provide public transit throughout the city, with the exception of downtown. A streetcar also services the downtown area. Frequency of service and evaluation of routes directly serving grocery stores and supermarkets were excluded from the scope of this study because with bus transfers, all routes could provide access. Bicycle lanes are generally lacking within the city limits. Of the 379 block groups in Tampa, only 84 or 22.2% of them have exis ting roadside bike lanes. The raw scores for mobility resulted in 10 different score combinations as shown in Table 43 The raw data were mapped to reveal the distribution of the mobility scores (Figure 4 5). Recall from Chapter 3 (Table 35) that the fir st digit in the combined score indicated percentage of roadside bike lanes available, the second digit referred to the personal vehicle availability, and the third indicated the score for the percentage of households that were within half a mile from a transit stop within a given CBG. Table 4 3. Reclassified mobility scores 4 CBG Count 3 CBG Count 2 CBG Count 1 CBG Count Raw Scores 231 2 133 198 123 14 113 4 132 38 122 9 112 1 131 58 121 10 111 2 Count Total 2 294 33 7

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77 Figure 45. Map of Tampas census block groups according to their raw data scores for mobility Again, the decision was made to reclassify the scores into four categories (Figure 4 6) It was considered ideal if a block group had a low (x < 33 percent) percentage of households with zero vehicles available. Because of the low density, s prawling nature of the city and l imited availability of alternative modes of transportation, having access to a vehicle to shop for groceries and household goods is essenti al in Tampa. Th e data supports this assumption. I n 296 or 88.1% of the CBGs at least 67% of households own at least one vehicle. Even more significant is the fact that 198 or 58.9% of households in

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78 Tampas CBGs are within a half mile of a transit stop and are also within the top tier for vehicle availability. The raw data were reclassified into four categories based on the following criteria: Category Four CBGs had roadside bike lanes on at least 33% of the roadways and over 67% vehicle ownership by household regardless of public transit availability Category Three block groups were those with more than 67% vehicle ownership and at least 33% of households within the half mile buffer of a transit stop. Category Two block groups were those where vehicle ownership was between 33 and 67 percent, and at least 33% of households were within the half mile buffer of a transit stop. Category One CBGs were areas where vehicle availability was below 33% regardless of public transit availability Figure 46. Map of Tampa CBGs reclassified categories for mobility

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79 The spatial distribution of the mobility reclassification shows most of the city falls into the light green, or above average, category. This is mostly due to the high rates of vehicle ownership. Comparison of Spatial Factors and Socio economic Factors The data summary sheet (Appendix E) was loaded into SPSS (IBM, 2010) and analyzed using oneway analysis of variance (ANOVA) to determine whether there was a correlation between the CBGs with the three spatial food access variables and household characteristics identified in Table 4 4 as dependent variables. The CBGs were reclassified in categories of 14 and mapped for each variable as described above. These reclassified scores were used in the statistical analysis. The results for each spatial factor are explained in the following sections. Table 4 4 Variables used to determine correlations in the typology Independent Variables Dependent Variables Proximity Percentage of Single Female Householder Diversity of Availability Percentage of Householders over 65 years old Mobility Median Household Income Comparison of Spatial Factors to Median Income Spatial Clustering of Median Income In order to explore the potential correlation between median household income and the spatial characteristics of the study, the distribution of median income was evaluated. Global Morans I in ArcMap was used and the results s howed that income was highly s patially clustered. The analysis was performed testing the clustering of income from the 1st nearest neighbor through the 100th nearest neighbor (Table 45 and Figure 47 ) to confirm the results. A look at the map for median income distribution by census b lock group (Figure 48) shows the distribution of income ranges (reclassified by

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80 natural breaks). The map shows that there are clusters of CBGs where median income is within the same range. Table 4 5. Global Moran's I results showing z scores. The p valu es (significance level) for each nearest neighbor indicates a less than 1% chance that the clustered pattern of median income distribution is a result of a random chance Nearest Neighbor 1st 2nd 4th 5th Moran's Index 0.205184 0.254493 0.220488 0.202216 z score 3.042662 5.106305 6.159996 6.341913 p value 0.002345 0.000000 0.000000 0.000000 Nearest Neighbor 10th 20th 50th 100th Moran's Index 0.142839 0.086602 0.063762 0.049197 z score 6.362786 5.662281 7.161971 9.049278 p value 0.000000 0.000000 0.000000 0.000000 A B Figure 47. Distribution curve for clustering analysis using Global Morans I showing that income in Tampa is spatially clustered at the A) 1st Nearest Neighbor and at the B) 100th Nearest Neighbor

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81 Figure 48. Map of Tampas median income (2000 Census) distribution by census block group

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82 Proximity to Food Outlets Statistical analysis revealed no significant relationship between proximity to higher quality food outlets and median household income. Although spatial analysis results using Global Morans I in ArcMap showed that income is indeed spatially clustered there appeared to be no statistical significance between income and proximity to higher quality food outlets when the data were analyzed using ANOVA (confi rmed by Hochberg, LSD, Bonferroni, Tukey HSD and Games Howell post hoc tests) in SPSS. As Table 46 shows, in the City of Tampa, with a pvalue of .777 outside of the .05 confidence interval, the null hypothesis that there is not a correlation between medi an income and the proximity to higher quality food outlets was not rejected. Table 4 6 ANOVA Between m edian h ousehold i ncome for r ec lassified (1 4) scores for proximity to food o utlets Sum of Squares df Mean Square F Sig. Median Household Income (2000 Census) Between Groups 635171923.909 3 211723974.636 .367 .777 Within Groups 191431499978. 421 332 576600903.549 Total 192066671902. 330 335 According to the 2010 American Community Survey results, median household income in Tampa was $40,883 (U.S. Census Bureau, 2011a) The means plot for this analysis (Figure 49) shows that households with mean median income between $38,000 and $39,000 have higher q uality outlets closest to them.

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83 Figure 49. Means plot of ANOVA between median income and proximity from SPSS A secondary analysis was run in Excel using descriptive statistics for the reclassified scores for all three spatial variables and the nine income classes determined by ArcMap in an effort to see if any correlation could be determined by using an alternative calculation method. The mean score for each factor was determined for each income br acket (Table 4 7 ) and then a combined average score for the factors was calculated. The average scores for proximity by income were graphed (Figure 410) showing that those in the highest and lowest income ranges had the lowest average score for proximity meaning they both live the farthest from the higher quality food outlets.

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84 Table 4 7 Summary score averages by median income brackets CBGs by Median Income Mean Proximity (Reclass) Mean Diversity ( Reclass ) Mean Mobility ( Reclass ) Mean Spatial Factor Score 7500 16250 2.810 1.714 2.619 2.381 16251 23580 3.111 1.556 2.933 2.533 23581 29357 3.123 1.456 2.842 2.474 29358 34919 3.125 1.536 2.857 2.506 34920 41027 3.041 1.612 2.878 2.510 41028 49706 3.200 1.550 2.825 2.525 49707 66832 3.161 1.710 2.968 2.613 66833 95977 2.857 1.429 2.905 2.397 95978 141553 2.750 1.500 2.938 2.396 Figure 410. Line graph showing the relationship between median income and mean scores for proximity to higher quality food outlets Diversity in Availability of Food Outlet Types The reclassified data was analyzed in SPSS to test whether there was a relationship between the distribution of food outlets and the aforementioned dependent variables. As with proximity, ANOVA revealed no cor relation between diversity in availability and median income (Table 48 ). The pvalue is .182, well outside of the confidence interval of 95%. Therefore, there is a failure to reject the null hypothesis that 2.500 2.600 2.700 2.800 2.900 3.000 3.100 3.200 3.300 Mean Proximity (Reclass) Mean Proximity (Reclass)

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85 there is no relationship between median household income and how diverse the healthy food outlet availability is in Tampa it makes no difference. Table 4 8. ANOVA b etween m edian h ousehold i ncome for reclassified (1 4) s cores for diversity in a vailabilit y to food o utlets Sum of Squares df Mean Square F Sig. Medi an Household Income (2000 Census) Between Groups 2787261390.43 8 3 929087130.1 46 1.630 .182 Within Groups 189279410511. 893 332 570118706.3 61 Total 192066671902. 330 335 The means plot (Figure 411) shows that there is little difference in income levels in relationship to the variety of food outlets available. The graph shows middle and higher income consumers in area spread out through neighborhoods of all four categories. Figure 4 11. Means plot showing the relationship between the four classes of diversity in availability and median household income

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86 The same secondary analysis as was performed wit h the first factor, proximity, was run in Excel using descriptive statistics for the reclassified scores for all three spatial variables and the nine income classes determined by ArcMap (refer back to Table 47 ). The mean scores for diversity and its relationship to median income were plotted on a line graph which can be seen in Figure 4 12. The plot shows a randomized pattern indicating that there is no real relationship between median income and neighborhoods with a wide variety of healthy food outlets available. Figure 412. Line graph depicting the relationship between diversity and the mean scores by for CBG by income Mobility to Food Outlets The data for mobility was entered into SPSS and compared to the dependent variables using oneway ANOVA. The results for median income were similar for mobility as those for proximity and diversity in availability (Table 49 ). The significance value, .464, falls well outside the confidence interval necessary of .05 to reject the null hypothesis that mobility is correlated with income. In other words, there is no significant 1.250 1.300 1.350 1.400 1.450 1.500 1.550 1.600 1.650 1.700 1.750 Mean Diversity (Reclass) Mean Diversity (Reclass)

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87 r elationship between variables affecting mobility and median income. Post hoc tests performed confirmed there was no mobility category where income made a difference. Table 4 9 ANOVA between median household i ncome for reclassified (1 4) s cores for m obili ty Sum of Squares df Mean Square F Sig. Median Household Income (2000 Census) Between Groups 1476460823.972 3 492153607.9 91 .85 7 .464 Within Groups 190590211078.35 9 332 574066900.8 38 Total 192066671902.33 0 335 The means plot in Figure 4 13 shows the relationship between income and the mobility categories. Though it does show a lower mean median income for those living in Category One CBGs, and higher incomes concentrated in Categories Two through Four, the lack of significance can perhaps be explained by the fact that there only seven CBGs in Category One as opposed to 294 in Category 3. Figure 413. Means plot showing the relationship between the four classes of mobility and median household income

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88 Again, a secondary analysis was per formed in Excel using descriptive statistics for th e reclassified scores as f or all three spatial variables and the nine income classes determined in ArcMap using Jenks (Natural Breaks). The mean scores for mobility and its relationship to median income were plotted on a line graph which can be seen in Figure 4 14. The plot shows a similar pattern to the means plot indicating the lowest income areas have the lowest mobility and the higher income areas with better average mobility scores. Figure 414. Lin e graph depicting the relationship between mobility and the mean scores by for CBG by income Comparison of Spatial Factors to Single Female Householders Proximity to Food Outlets Results of the oneway ANOVA (comparing proximity to the percentage of single female householders by block group) rejected the null hypothesis that there was no correlation (Table 41 0). With a pvalue of less than .001, it is probable that there is a significant relationship between neighborhoods where higher quality food outlets are and where single mothers tend to live. The means plot Figure 415 shows that CBGs 2.400 2.500 2.600 2.700 2.800 2.900 3.000 Mean Mobility (Reclass) Mean Mobility (Reclass)

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89 with about 24% of households headed by single females are category 3 meaning they have moderate proximity to Type 3 food outlets and high proximity to Type 2 outlets. Thu s, the analysis shows a higher proportion of single women with families live in neighborhoods where there is a supermarket, super center or small grocery store nearby. Table 4 1 0 ANOVA for reclassified (1 4) scores for p roximity to food o utlets and sing le female householders Sum of Squares df Mean Square F Sig. Percent of Households with Single Females as Head Between Groups .261 3 .087 6.641 .000 Within Groups 4.352 332 .013 Total 4.613 335 Figure 415. Means plot of ANOVA between the mean percentage of single female householders and proximity to higher quality food outlets from SPSS Diversity of Availability of Food Outlet Types Results of the ANOVA indicated a significance in the relationship between the categories of diversity of food store availability and the mean percentage single female

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90 householders. Table 411 shows the ANOVA results for this demographic For the variable, single female householder, a pvalue or significance level of .004 indicat ed that there was a correlation. Tukey HSD, LSD and Games Howell post hoc tests confirmed that the correlation was with those CBGs in Category Four (Figure 416). Table 4 1 1 ANOVA for reclassified (1 4) s cores for d iversity of food o utlets a vailable and the single female householders Sum of Squares df Mean Square F Sig. Percent of Households with Single Females as Head Between Groups .183 3 .061 4.573 .004 Within Groups 4.430 332 .013 Total 4.613 335 Only about 12% of households, on average, in Category Four CBGs are headed by single females. This indicates that it may be important to single mothers to live close to at least a supermarket, but it may not be as important to have access to multiple types of food outlets. Figure 416. Means plot showing the relationship between the four classes of diversity of availability and mean percentage of households with a single female as head

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91 Mobility to Food Outlets Results of the oneway ANOVA showed significance in the relati onship between mobility and single female householders. Table 412 shows the results of the ANOVA performed in SPSS. Significance levels for single females and senior citizens are less than 0.001 and .05 respectively. This means that the null hypothesis that there is no relationship between mobility and both of these variables was rejected. For single female householders, post hoc tests performed indicated that the significance was with Category Two CBGs. As shown on the means plot in Figure 417, about 27. 5% of households headed by a single female are in CBGs with moderately poor mobility. This may not be as big a negative as it seems as of the 336 CBGs included in this study, 33 or 9.8% fall into Category Two (Moderately Poor Mobility; 3466% vehicle acces s rates). Table 4 12 ANOVA for r eclassified (1 4) s cores for mobility and single female householders Sum of Squares df Mean Square F Sig. Percent of Households with Single Females as Head Between Groups .309 3 .103 7.956 .000 Within Groups 4.304 332 .013 Total 4.613 335 Figure 417. Means plot showing the relationship between the four classes of mobility and single female householders with families

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92 Comparison of Spatial Factors to Age Proximity to Food Outlets In comparing the final dependent variable, percentage of householders 65 years of age and older, the same analysis was conducted and yielded similar results as those for median income. Table 413 shows in the results of the oneway ANOVA there is no significant difference between the mean percentages of householders over the age of 65 and the four classifications of proximity The mean percentages for this variable (shown in the means plot in Figure 418) range from between 9.5% and 10.10% which is not a significant varianc e. One could assume, therefore, that proximity for householders 65 and over is fairly homogenous for all types of food outlets. Table 4 1 3 ANOVA for reclassified (1 4) scores for proximity to food o utlets and householders 65 and older Sum of Squares df Mean Square F Sig. Percent of Head of Households age 65 and over Between Groups .004 3 .001 .401 .752 Within Groups 1.061 332 .003 Total 1.065 335 Figure 418. Means plot of ANOVA between the mean percentage of householders age 65 and older and proximity to higher quality food outlets from SPSS

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93 Diversity of Availability of Food Outlet Types According to the ANOVA results, there was no significant relationship between householders 65 and older and diversity of food outlet type (Table 4 14). With a pvalue of .181, the null hypothesis that there is no relationship between this variable and CBGs with a wider variety of healthy food outlets available was not rejected there is no difference. The means plot (Figure 419) indicates a spli t with a lower percentage (<10% mean) of this household type in Category Two and One neighborhoods (those with the least diversity and fewest stores) and higher percentages (>11%) in Category Three and Four neighborhoods. The lack of correlation could be due to the apparent split, or it could be due to the fact that the mean percentages vary only by two percentage points. Table 4 1 4 ANOVA for reclassified (1 4) s cores for d iversity of food o utlets a vailable and the householders 65 and older Sum of Squares df Mean Square F Sig. Percent of Head of Households age 65 and over Between Groups .016 3 .005 1.636 .181 Within Groups 1.049 332 .003 Total 1.065 335 Figure 419. Means plot showing the relationship between the four classes of diversity in availability and the mean percentage of households with head 65 and older

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94 Mobility to Food Outlets Results of the oneway ANOVA showed significance in the relationship between mobility and householders 65 and older (Table 415). Games Howell a nd LSD post hoc tests for the ANOVA comparing the categories of mobility to the percentage of householders 65 and older indicated that the correlation was in Category Three CBGs. The means plot revealed that census block groups in Category Three had the hi ghest mean percentage of householders 65 and older (Figure 420). This is a positive correlation indicating above average mobility for a higher percentage of householders over 65. It should be noted again though, that 294 or 87.5% of Tampas CBGs fall into this category. Table 4 1 5 ANOVA for reclassified (1 4) s cores for mobility and householders 65 and older Sum of Squares df Mean Square F Sig. Percent of Head of Households age 65 and over Between Groups .025 3 .008 2.628 .050 Within Groups 1.040 332 .003 Total 1.065 335 Figure 420. Means plot showing the relationship between the four classes of mobility and the mean percentage of households with head 65 and older

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95 Summary The data for proximity, diversity of availability and mobility were analyzed in a sequential manner following the same procedure. Analyzing each barrier independently provided the opportunity to understand how each spatial factor affected households by neighborhood. Each factor was tested against three dependent variables, median household income, age, and gender and marital status of the householder to determine if there was a correlation between these characteristics and adequate access to healthy food outlets. Median income, though spatially clustered, had no significant correlation with any of the spatial factors. There was a correlation between proximity and the remaining two variables. Diversity of availability had only a correlation with CBGs with a higher percentage of single female householders but there was no significant relationship with householders over the age of 65. Mobility proved to be correlated with both the dependent variables of single female head of hous ehold and householders over 65. This chapter provided insight on how to interpret the data once collected and organized. Through the use of GIS, Excel, and SPSS the data began to reveal where people in Tampa have convenient access to different types of food outlets whether it is because the nearest store is within walking distance or accessi bility is high because there are more options for where to shop or the neighborhood is well serviced by public transit. The next chapter offers a discussion on how this analysis can be inter preted and how it can serve as the basis of a typology for the classification of urban food access disparities.

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96 CHAPTER 5 DISCUSSION AND CONCLUSION This was a quantitative exploratory casestudy conducted to determine if a typology or classification system could be developed based on spatial challenges households face in accessing healthier food outlets. A single city, Tampa, Florida was used as the study site. The study applied a new methodology to spatially analyze and map the specific challenges faced by residents in each neighborhood. The method can be utilized for determining food accessibility challenges for an entire city, and can also be used to identify whether these factors have affected certain socioeconomic groups more than others. By examining each factor individually, one can better understand the complex ity of the food access challenge and thereby prioritize decisionmaking in design or policy planning. In this chapter, the research findings will be summarized followed by a discussion of the contributions to current knowledge on urban food access as well as implications for research in this area for the field of landscape architecture. The chapter will conclude with recommendations for practice and future research. Summary of the Research and Findings The research employed manual data collection from the U .S. Census Bureau, the Hillsborough County Property Appraiser, Hillsborough Metropolitan Planning Organization, Hillsborough Area Regional Transit, Florida Geographic Data Library, Internet directory listings and web pages, as well as Google Earth satellit e imagery and Street View feature as secondary sources. The data were mapped in ArcMap and Spatial Analyst was used as a tool to complete audit worksheets created in Excel to organize the data. Census block group raw scores were compiled onto a data summar y sheet which served as an efficient way to begin to analyze the data. Viewing all of the

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97 scores in one place, it became clear that there were unique circumstances affecting food accessibility CBG by CBG. The raw scores for proximity, diversity and mobilit y were reclassified into four categ ories representing the degree to which positive factors were found in each CBG. All factor condensed into a manageable four categories, further analysis became possible. These data were entered into SPSS and the independe nt and dependent variables were compared to one another to determine if there was a significant relationship between the characterization of food access and the socioeconomic makeup of the residents. Findings indicated that, in Tampa, there was no signif icant relationship between median household income and any of the food access barriers. A look at the maps generated in GIS showed that despite the lack of statistical significance, several neighborhoods where median income fell below the poverty threshold demonstrated the greatest need for intervention with less than 33% household automobile ownership rate, as well as greater median di stances to transit stops and a lack of supermarkets (Type 3) and small grocery (Type 2) stores. This supports existing food access studies conducted in other cities such as Detroit, and New Orleans where a paucity of supermarkets was prevalent in low income neighborhoods. It should be noted, however, that there were a number of higher income block groups, especially in northeast Tampa and along its western edge (Tampa Bay) where proximity to healthier food outlets was equally as poor. In the typology that follows, these CBGs only scored slightly higher overall than the lower income neighborhoods with the difference attributable to high (> 66%) rates of vehicle o wnership.

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98 However, there were correlations between all three factors and households headed by single women. In Tampa, there were significantly more single women with families living in neighborhoods where mobility was lim ited or moderately poor. Likely reasons for this included a lack of a transit stop within mile walking distance (recall that a CBG was scored based on the percentage of its occupied households within mile of a transit stop; the closer one resides to a transit stop, the better) and/or lower rates of vehicle ownership within the neighborhood. Furthermore, it appeared that there were far fewer single women with families living in neighborhoods with the best variety of healthy food options available. However, the correlations were not all negative. There was a higher percentage of this household type living in neighborhoods where the median distance to a supermarket, a supercenter, or at least a small grocery store was within mile. Householders over the ag e of 65 in Tampa fared better with all three factors. Households under this demographic seemed to be well served by higher quality food outlets throughout the city. Even with mobility, there is a positive correlation. There were higher numbers of households of this type in CBGs where there were both higher rates of vehicle ownership and access to transit stops. This may be a testament to how seniors choose their neighborhoods; perhaps location and proximity to commercial centers is of primary i mportance in their home choice. The Urban Food Access Typology It is important to note that these results were only valid for the City of Tampa. Yet the process of data gathering, analysis and finally, the urban food access typology have a much broader application and demonstrate a straightforward way to visualize the unique food access barriers of each CBG

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99 The proposed typology resulted from examining each of the reclassified CBG maps for the City shown in Figures 42 4 4 and 46 as well as the map for Median Household Income (Figure 4 8) The maps showed significant clustering of CBGs categorized as Poor and Moderately Poor (ones and twos) as well as those classified as Moderate/Adequate and Excellent (threes and fours) in all three factors. The next step taken was t o determine scenarios based upon combinations of proximity, diversity and mobility which occurred by CBG on the maps. To do this, the results for each spatial factor were condensed into two categories: High and Low. CBGs with a 3 or 4 in any of the factors were considered well served by proximity, diversity or mobility; those categorized as 1 or 2 were not. CBGs considered Poor or Moderately Poor, 1 or 2, were defined as Low (L) and those reclassified as Moderate/Adequate and Excellent, 3 or 4, were defined as High. The results were six possible scenarios identifying the quality of healthy food access for a given CBG. Table 5 1 illustrates the six scenarios which then became the basis for the typology proposed in Table 52 The CBGs are reclassified based on this typ ology and mapped in Figure 51. Table 5 1. Possible ranking based upon reclassified scores Combinations Proximity Diversity Mobility HHH High High High HLH High Low High HHL High High Low HLL High Low Low LHH Low High High LLL Low Low Low High (H): CBG reclassified score for any factor was 3 or 4 Low (L): CBG reclassified score for any factor was 1 or 2

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100 Table 5 2. Urban food access d isparity t ypology Typology Spatial Factors Scores Rank Interpretation Recommended Action Low priority for intervention Proximity 3 or 4 H No significant disparities No immediate action necessary. Periodic assessment of food accessibility should be conducted to monitor change in environment. Diversity of Availability 3 or 4 H Mobility 3 or 4 H Low Priority for intervention; Adequate Access to Type 3 Outlet, Limited Diversity Proximity 3 or 4 H Adequate or high proximity to supermarkets or smaller grocery stores; high mobility Mobility is high for these neighborhoods, and there is at least one supermarket or super center serving the neighborhood. Periodic assessment should be conducted to determine need for more diverse food selection or culturally appropriate foods Diversity of Availability 1 or 2 L Mobility 3 or 4 H Moderate Priority in intervention; Limited by Mobility; Otherwise adequately served by a variety of higher quality food outlet types Proximity 3 or 4 H Adequate Proximity & Diversity; Moderately Poor or Poor Mobility Identify ways to increase and/or improve transit to area and redesign neighborhood roads to include bike lanes or convert to bicycle boulevards for better connectivity for the transit dependent Diversity of Availability 3 or 4 H Mobility 1 or 2 L Moderate Priority for intervention; limited by mobility and diversity Proximity 3 or 4 H Proximity is high; diversity and mobility are moderately poor or poor Assess walkability of neighborhood. Determine suitability for improved transit routes and frequency, increase bike lane road coverage through neighborhoods. Consider feasibility of designing for a greater mix of land uses for redevelopment/infill projects Diversity of Availability 1 or 2 L Mobility 1 or 2 L

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101 Typology Spatial Factors Scores PDM Interpretation Recommended Action High Priority for Intervention ; resources limited within neighborhood Proximity 1 or 2 L Limited in higher quality food outlets; limited to convenience/dollar stores; mobility is high/adequate Mobility is high or adequate, so people are able to, and must, travel outside their neighborhood to access healthier foods. Low income neighborhoods with this classification should be top priority for intervention. A community food assessment ought to be conducted to determine feasibility for addition of Type 3 or 2 outlets to the neighborhoods. If population densities are adequate, encourage the introduction of a supermarket, farmers markets, or smaller grocery stores to the area. Diversity of Availability 1 or 2 L Mobility 3 or 4 H High Priority for intervention: Significant disparities in food access Proximity 1 or 2 L Food access is lacking in all factors Investigate further to determine sociodemographic makeup of neighborhood (who is most affected). Is there a s ignificant difference between the makeup of these neighborhoods and others? Determine whether there is sufficient density to promote the addition of supermarkets to the area. Explore possibility of infilling vacant urban areas with a mix of uses and amenities to encourage increased densities that will support both supermarkets and other healthier food outlets and improved transit. Improve walkability of neighborhood through streetscaping. Explore feasibility and/or community desire for community gardens programs, urban agriculture, co ops and farmers markets. Diversity of Availability 1 or 2 L Mobility 1 or 2 L Table 5 2. Continued

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102 Figure 51 The urban food access typology mapped for the City of Tampa

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103 Applying this typology to Tampa (Table 53) 47 (or 13.9%) of the CBGs included in the study classified as Moderate/Adequate or Excellent Food Access for all three spatial factors which meant that there were no significant food access disparities for the residents of those neighborhoods. Of that 47, 15 (or 31.9%) block groups had a median income of under $30,000; 6 (12.8%) of them fell below the poverty threshold. By contrast, 18 (or 5.35%) of the CBGs were classified as having Poor or Moderately Poor Food Access for all three factors and therefore were identified as High Priority Areas due to the significant lack of healthy food access. Within this classification, 15 of the 18 (83.3%) block groups had a median income of less than $30,000; nine of them fell below the poverty threshold. There were 159 (47.3%) block groups included in this study which were Low Priority Neighborhoods; neighborhoods with adequate or high rates of mobility and at least one supermarket or grocery store. Of the total number with this classification, 53 (33.3%) CBGs had median incomes below $30,000, 54 (33.9%) had median incomes between $30,000 and $40,000 and 52 (32.7%) had median incomes above $40,000.This classification characterizes the majority of the census block groups in the city, and it contains a fairly evenly distributed income strata. CBGs classified as Moderate Priority; Limited by Mobility comprised the smallest proportion of block groups. Of the five (1.5%) in the class, four (80%) of them hosted median incomes below $30,000. CBGs categorized as Moderate Priority; Limited by Mobility and Diversity comprised 17 (5.06%) census block groups of which 15 (88.3%) had median incomes under $30,000.

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104 Table 5 3. Results of food access t ypology as a pplied to the City of Tampa Percentage of CBG Median Household Income by Typology Typology Number of CBGs Percentage of CBGs Total Households 23000 $30000 > $30000 Low Priority No significant disparities 47 13.99% 20073 6 12.77% 15 31.91% 32 68.09% Limited diversity 159 47.32% 62462 25 15.72% 59 37.11% 100 62.89% Moderate Priority Limited by Mobility; Otherwise adequately served by a variety of higher quality food outlet types 5 1.49% 2751 1 20.00% 4 80.00% 1 20.00% Limited by mobility and diversity 17 5.06% 6577 2 11.76% 4 23.53% 13 76.47% High Priority Limited resources within neighborhood Adequate mobility 90 26.79% 42385 17 18.89% 31 34.44% 59 65.56% Significant disparities in food access 18 5.36% 5152 9 50.00% 15 83.33% 3 16.67%

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105 The remaining 90 (26.8%) block groups were classified as High Priority; High Mobility, Limited Resources. More than two thirds (67%) of those block groups had median incomes higher than $30,000. It was important to note the median incomes, because it is more likely that the areas with lower median incomes will demonstrate greater need and benefit more from improvements in design of the built environment and planning policy amendments. Implications for Current Knowledge and Professional Practice The typology accomplishes this studys primary objectives of showing how access to healthy foods differs by neighborhood, and th e end result is a CBG classification based on healthy food access barriers. This study supports Shaws contention that not all food access problems are a result of income or of living in inner city areas and moves away from qualifying a given area as food desert or food insecure because they meet a singular criterion. A typology of this nature is multi layered and more comprehensive in assessing neighborhood access to healthy food sources. Through considering other sociodemographic variables in addition to income, a researcher would be able to understand who these spatial barriers affect most and in which neighborhoods. There have been many studies conducted demonstrating the impact th e built environment has had on human health and wellness, and several st udies examining the correlation between obesity and access to healthy foods. However, the development of a typology in food access studies has, to date only been found in a nonspatial context. A food access typology grounded in spatial analysis is a fairl y new concept and it provides the added benefit of examining the disparities from a systematic macro perspective as well as providing a framework for a more detailed assessment of the socio demographic relationship to the spatial context.

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106 The mapped result s of this typology were compared to the maps developed by the USDA ERS Food Desert Locator. The Food Deser t Locator defined a food desert as a low income census tract where at least 33% of the residents live more than one mile away from a supermarket or la rge grocery store in an urban area. The census tracts within the City of Tampa determined to be food deserts by the Locator can be found in Figure 52. A comparison of the Figures 51 and 52 reveals some differences in definitions of what characterizes fo od access disparities. Whereas the Food Desert Locator considered only supermarkets and large grocery stores as a proxy for healthy food access, the methodology for this study considered seven f ood outlets types under the assumption that healthy, whole foods are available at varying degrees of quality and quantity depending on the food store. The other major determinant was income. This study did not characterize food access disparities in terms of median income. It was not a determinant used in the spatial typology. Instead, income was used as a socioeconomic correlate to derive information about the residents who live in areas determined to be low, moderate, or high priority areas. The greatest consistency in the comparison map was found in the southeastern census block groups (identified on Figure 52 as LLH and LLL). Those areas are low income census tracts w h ere at least 33% of the population live more than one mile away from a supermarket according to the Locator map; they are also considered high prio rity areas due to limited food outlet resources within the block group boundaries or there was an overall lack of any type of healthy food outlet according to this study. However, the results of this study contradict the USDAs Locator in central and east Tampa This is largely due to the consideration of median income in their methodology.

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107 Figure 52. Comparison of the USDA ERS Food Desert Locator ma p and the results of t his studys typology for Tampas CBGs LLH LL L HLH HHH HLH HLL HLH HHH LLH HLH HLH LLH HLH

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108 Landscape architects work at multiple scales in designing the built environment, from the regional and city scale, or macroscale, to smaller scale, neighborhood and site specific design, or micro scale. Though this ty pology may not be of practical use at the state and multi state level, it has the potential in form at the regional scale that could encompass county and multi county areas in an urban food access assessment. As this study demonstrates, the typology is def initely applicable at the city level and thereby can aid design professionals in their goal of enhanced urban environments For example, in an area found to be moderate priority with limited mobility, the planning and design of improvements to transit infr astructure would be a viable solution. However, a light ra il system or a new bus route can not only serve one neighborhood. It will instead have the added benefit of potentially improving food access through better connectivity for people in several neighborhoods. Food access issues have begun to gain attention in education and in the professional practice of landscape architecture. The May 2012 issue of Landscape Architecture Magazine included a feature highlighting landscape architects perspective on ways the food system fits into the profession. Several educators, designers, and students replied and of the nine replies published, all of them emphasized that landscape architecture should be focused on urban agriculture, urban farming, community gardens and education as a way to create more sustainable communities for the future. What research in this area that exists from the landscape architectural perspective has focus ed on creating edible cities, agri tourism, and productive landscapes as a way to combat food insecurity, and obesity. As landscape architects increase their interest and involvement in urban food access issues and integrate them

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109 into larger scale projects, the typology here could serve as a valuable tool to prioritize design interventions I f used with a top down approach to design there may be unintended consequences of excluding the public from changes being made to their environment which could result in anger and resentment. A bottom up approach to using the typology would be to facilitate public meetings, or focus groups with residents who live in areas classified as a High Priority; Limited Resource area. Collecting public input on their assessment of their food access challenges, listening to their needs and integrating their desires into research as part of a broader community food assess ment or a design has the added benefit of fostering a vested interest from residents in the success of the final outcome and reducing the likelihood of feelings of marginalization. An urban food access typology could be used to augment urban design typolog ies such as transit oriented development or traditional neighborhood development as well as aid transportation and urban and regional planners in developing more comprehensive, socioeconomically inclusive master plans. It can be of use in both the analysi s and conceptual phase of design as an important social consideration in the research phase of the urban design process. It can also be a useful tool for investigating and mitigating exis ting food access disparities particularly in areas slated for redevel opment and infill. The use of a typology such as this could also be used as a learning tool in design studios at colleges and universities. Along with the growing interest in urban food systems, urban agriculture, and community gardens, landscape architect ure student s often integrate social and cultural research into their designs. This typology can

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110 introduce a new way of thinking about community food systems and the contribution landscape architects can have when it comes to health, wellness and social jus tice in the built environment. Conclusions The theoretical framework of this study began with Shaws nonspatial classification of what she called unsupportive food environments. Her study determined that there were three main barriers to accessing healthy foods: ability, asset and attitude. While Shaws study was qualitative, with results derived from interviews with area residents, this study aimed to augment this foundation with a spatial typology derived through quantitative methods. The results were consistent in that both studies found that poverty is not a primary determinant in ones ability to access healthy foods. With Shaws nonspatial and this study s spatial typology established it has become possible to comprehensively assess the social, physical, spatial and economic barriers that impact ones ability to access foods. The term food desert has not been adequately or concretely defined, nor has the method of measurement been agreed upon in the literature. These two typologies can be used as guide toward more accurately describing and defining areas where access to healthy food is limited leaving the use of the term food deserts inadequate and obsolete. This study reveal ed that, in Tampa, barriers to food accessibility manifests in different CBGs in varying degrees. They impact neighborhoods differently and can reveal intriguing data on settlement patterns. Use of the typology can help reveal how those patterns relate to food accessibility For instance, g iven the predominance of poverty within CBGs classified as H igh Priority for I ntervention, further research would be necessary to determine suitability for redevelopment or infill projects that would

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111 include the addition of a supermarket or at least a small grocery store. In low income neighborhoods in Miami, Florida, Louisville, Kentucky and Milwaukee, Wisconsin, among others, the addition of weekly farmers markets in underused urban spaces near project housing communiti es have improved the quality of healthy foods available to local residents They also provided the added benefit of educating customers on the benefits of healthy eating, how to prepare different nutritious meals, and they have added to the overall sense o f community to otherwise isolated neighborhoods (Raja, et al. 2008). It would be interesting to follow up by identifying housing patterns of demographic groups, such as senior citizens. Is there a reason for why they choose to locate as they do? With the t ypology revealing where the worst food access disparities exist, and the reasons for the disparity identified, it would be intriguing to see what impact the food environment has had on obesity rates, especially childhood obesity rates. It is likely that ch anges in the built environment, i.e. the growth of more walkable communities, would have the biggest impact on the behavior of younger generations who are more adaptable to change. Future study could also test the reliability of this methodology to determ ine whether it can be replicated for other cities of larger and smaller sizes and whether age and income have a stronger correlation with the spatial barriers. Poverty may not have had as significant a statistical correlation in Tampa as it might in other cities given the median income for the city was around $41,000. The typology may be a stronger indicator of poverty in cities with smaller populations or with lower median incomes.

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112 As this study has aimed to contribute to the body of knowledge of landscape architecture and the majority of those in the field are focused on professional practice, it would be important to evaluate the perspective of landscape architects on the role they could play in improving food access in the built environment. Food access solutions can happen at the largescale and small scale. Assuring equitable access to healthy foods at the large scale is probably best addressed with policy planning. Urban growth boundaries establish limitations on development and limit encroachment on n atural resources. In a city with urban growth boundaries, as population increases, densities increase which would create a chain reaction leading to the development of more Type 3 food outlets, and alternative solut ions such as urban agriculture. Having a ccess to healthy food choices is the cornerstone of a healthy diet and healthy body weight. As obesity continues to be a growing concern in the United States, access to healthy food is in the forefront of the discussion when considering possible solu tions. Although eating habits are mostly a learned behavior (and there is little this typology or landscape architects can do to change that) studies have shown that those who opt to eat cheaper, energy dense foods available at fast food restaurants and convenience stores have higher rates of obesity. Using this typology to identify what food outlets exist in neighborhoods with higher rates of obesity would at least be a start in helping make the right food available. Currently, much of the work of landscape architects involved in food systems issues has been focused on work with local governing agencies, neighborhoods groups, and school children to implement community gardens, urban agriculture and urban farming. It would be useful to gauge the value a typology s uch as this would have to

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113 practitioners and see whether it would lead to more involvement in other areas of urban food access issues. In conclusion, the goal of this study was to s pecifically identify and define disparities in the urban food environment by creating a typology to define spatial barriers that consumers face in accessing healthy foods. The typology also determined the extent to which each of the three spatial barriers, proximity, diversity of availability and mobility existed in each neighborh ood as defined by census block group boundaries. The spatial boundaries were identified repeatedly in previous food access studies as obstacles in accessing healthy food and so an assessment tool was developed to measure how they might manifest in a large urban area. Tampa, Florida was chosen as the test city because according to the USDA ERS Food Desert Locator, Hillsborough County had the most census tracts they considered food deserts of any county in the state. The spatial factors were assessed individually, mapped and compared to selected socioeconomic criteria to assess what groups were affected most by proximity, diversity or mobility. In the resulting typology, the CBGs in the test city were classified into one of six scenarios. The typology identif ied a CBG as a Low, Moderate or High Priority area and specified the factors that most affected the neighborhood. The typology proved the complexity of food access charact erization. In the literature, studies examining food access disparities focused narrowly on measuring access to only one or two types of food outlets such as supermarkets or farmers markets. With at least four terms by which food access is identified (food deserts, food security, community food security and food insecurity) there was no concrete definition

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114 or method by which to measure them. This study argued that one cannot label a neighborhood a food desert or food insecure without defining what makes it so. Furthermore, it argued that a lack of a supermarket nearby is not the only challenge in the urban food environment. For those who have the ability to affect physical change in the built environment, knowledge of what needs attention most, whether it be increasing or improving public transit service to an area, or modifying housing and development codes to encourage more mixed income housing or additional food outlets, could help them in making food access a regular consideration in practice. The typolog y is just the beginning and could be a valuable tool for those in the design and planning professions. Several areas for future study were identified that could build upon the typology and determine how it can be integrated in landscape architecture and ur ban design professional practice.

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115 APPENDIX A TERMS AND DEFINTIONS FROM THE LITERATURE Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Alkon, AH; Norgaard, KM (2009) "access to healthy, affordable, culturally appropriate food" (289). Anderson S. Core indicators of nutritional state for difficult to sample populations. J Nutr. 1990;102:15591660 "limited or intermittent access to nutritionally adequate, safe, and acceptable foods accessed in socially acceptable ways "Access by all people, at all times to sufficient food for an active and healthy life [and] includes at a minimum: the ready availability of nutritionally adequate and safe foods, and an assured ability to acquire acceptable foods in socially acceptable ways" APA (2007) Used for the purpose of supporting policy guidelines for professional planners involved in community food systems planning inadequate access to resources with which to meet daily food needs Bedore, Melanie (2010) "communities with poor access to healthy affordable food and whose population is characterized by compounding deprivation and social exclusion"

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116 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Bhattacharya (2003) No set definition. Poverty as a contributor to food insecurity. Food insecurity is described and assessed at 3 levels of severity and contextually used to describe cases where there is lack of money to buy food period. Food insecurity is predictive of poor nutritional outcomes Borjas (2002) No definition, but the examination of food insecurity is under the context that those who are the least secure are that way because of financial constraints.

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117 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Cam p bell, C (1991) Food security is defined as access by all people at all times to enough food for an active, healthy life, and at a minimum includes the following: 1) the ready availability of nutritionally adequate and safe foods and 2) the assured ability to acquire personally acceptable foods in a socially acceptable way. Carlson, SJ (1999) Towards a methodology of measuring large scale (national) food insecurity a limited or uncertain availability of nutritionally acceptable or safe foods "assured access at all times to enough for an active healthy life" (511S) food insecurity may be seen as varying through a (range of severity levels and thus quantifiable in the dimension of the degree of basic need deprivation experienced" (511S)

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118 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Che & Chen (2001) Measuring food insecurity in Canada (nationwide) conceptually addressed as it was associated with health outcomes... Food insecurity in the context of lack on income, a result of poverty Uses the Life Sciences definition for Canadians most likely living in households lacking sufficient money to eat Chilton, Mariana and Donald Rose (2009). "the lack of access to enough quality food for and active and healthy life." (1203) Cohen, Barbara (2002) Provides this definition in as a baseline for planners in conducting community food assessments the inability to access at all times "enough food for an active and healthy life, with no need for recourse to emergency food sources or other extraordinary coping behaviors to meet their basic food needs" (2). "Access by all people at all times for an active, healthy life" including "the ready availability of nutriti onally adequate and safe foods." and "an assured ability to acquire acceptable foods in socially acceptable ways" (3). "concerns the underlying social, economic, and institutional factors within a community that affect the quantity and quality of available food and it affordability of price relative to the sufficiency of financial resources available to acquire it" (3).

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119 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Community Food Security Coalition social justice, local food movement "A condition in which all community residents obtain a safe, culturally acceptable, nutritionally adequate diet through a sustainable food system that maximizes community self reliance and social justice." Cummins S. et al describes "poorer areas where communities have little access to an affordable and healthy diet." (288) Dinour, L (2007) Investigates the role food stamps play in food insecurity and obesity. Mostly lit review uses the Life Sciences Research Office definition for food insecurity US Department of Agriculture, Economic Research Service Web site. Http://ers.usda.gov/Bri efing/FoodSecurity/lab els.htm Low food security: Reports of reduced quality, variety, or desirability of diet. Little or no indication of reduced food intake High Food Security: No reported indications of food acce ss problems or limitations

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120 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Very low food security: Reports of multiple indications of disrupted eating patterns and reduced food intake Marginal Food Security: One or two reported indications typically of anxiety over food sufficiency or shortage of food in the house. Little or no indication of changes in diets or food intake. Freedman, D (2009) Using poverty as the impetus for food insecurity, the authors compared quantitative mapping and inventory of the food environment in Nashville, TN with survey data to assess whether low income consumers had the ability to accurately assess their food environment. Same as Nord et al Gottleib R, Fisher A. Community food security and environmental justice: Searching for a common discourse. Ag Hum Values. 1996;3:2332 A situation in which all community residents obtain a safe, culturally acceptable, nutritionally adequate diet through a susta inable food system that maximizes self reliance and social justice, without resorting to emergency food

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121 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert sources. Hamm & Bellows (2003) "a situation in which all community residents obtain a safe, culturally acceptable, nutritionally adequate diet through a sustainable food system that maximizes community self reliance and social justice" (37) Larsen, Kristin, Gilliland (2009) "socially distressed neighborhoods with poor access to healthy foods" (1158) Lee et al (2001) Food insecurity as an issue associated with lack of money or resources (economics) generally described in terms of adequate food supply in the home and availability, affordability and accessibility of food the elderly can eat as their unique dietary needs change Uses the hous ehold definition used by Nord et al

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122 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Life Sciences Research Office (1990). (Seems to be the most commonly accepted definition) is a phenomenon which exists whenever the availability of nutritionally adequate and safe foods or the ability to acquire acceptable foods in socially acceptable ways is limited or uncertain" (1576) "access by all people at all times to enough food for an active, healthy life and includes at a minimum: a) the ready availability of nutritionally adequate and safe foods, and b ) the assured ability to acquire acceptable foods in socially acceptable ways (e.g. without resorting to emergency food supplies, scavenging, stealing, and other coping strategies)" (1575). Macias (2008) Frames argument in terms of local food systems community agriculture being a viable solution to food insecurity and social exclusion brought on by poverty Same as Nord et al Martinez (2010) In exploring various definitions of "local food systems" particularly in terms of scale and distribution. Defines food security in terms of how it can benefit from local food markets the condition of having "uncertain ability to acquire food in normal ways" (47) "all people at all times have access "to enough food for an active healthy life," and is a necess ary condition for a nourished and healthy population" (47) (Cites Nord et al, 2009) improving access to "safe, healthy, and culturally appropriate food for all consumers" (2).

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123 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Melcarek (2009) [Dissertation] in terms of social justice, proposes steps to integrate urban gardening in low income black neighborhoods in an effort to create more self reliant, food secure communities "sustained access to nutritious food from nonemergency sources" Morton L et al (2005) Social problems of Iowa food deserts: Food insecurity and civic infrastructure "occurs when there is limited or uncertain availability of nutritionally adequate and safe foods in socially acceptable ways." "the ready availability of nutritious and safe food and the assured ability to obtain it through normal sources." (95) "Places where few or no grocery stores exist" are created "when the normal food system is unevenly distributed" they are "places where few or no consumer food stores are available." (9596) National Academies of Science refers to the social and economic problem of lack of food due to resource or other constraints and can also be experienced when food is available and accessible but cannot be used because of physical or other constraints, such as limited fu nctioning by elderly people or those with disabilities

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124 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Nord et al (2010) Annual food security evaluation by the USDAERSFood Security as a result of poverty; food insecure are more often than not on federal assistance, Food Stamps, SNAP, free school lunch program, WIC. When those resources run out, until the next disbursement, many surveyed worried about how they would afford to feed their families. the inability or uncertainty of being able to acquire enough food for all household members due to lac k of money or other resources for food all household members having access at all times to enough food for and active, healthy life Pothukuchi (2000) Food security as a tool for planners CFS "embodies the notion that all residents have access at all times to affordable, highquality food through conventional (and not charity based) sources and through means that are environmentally, economically, and socially acceptable" (121)

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125 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert Reisig and Hobbiss, 2000, p.138 "areas of relative exclusion where people experience physical and economic barriers to accessing healthy food" Russell & Heidkamp (2011) "the ability of a town to provide for their low income resident adequate access to healthy and affordable food" (1201) Short, Anne (2007) "areas absent of the large supermarkets that litter the suburbs" Thomas, Brian Puts food security in spatial context instead of individual household or even community characteristics. The emphasis is on the physical distribution of food retailers. Its the food desertification which leads to food insecurity describes food insecurity in terms of ones inability to physically access food outlets (mobility)

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126 Author Context Food Insecurity (Household) Food Security (Household) Community Food Security Food Desert USDA (food desert locator documentation) "a low income census tract where a substantial number or share of residents has low access to a supermarket or large grocery store" Webb et al (2006 Links food insecurity to lack of money Uses World Food Summit's definition and emphasizes availability, access and utilization as a hierarchy of food security. Wisconsin Food Security Project "the assured access of all people to enough food for an active healthy life; households are food insecure if they have uncertain or limited access to food through normal channels" World Food Summit (1996) solutions to world food security and hunger "...uncertain or limited access to food through normal channels" "a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life."

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127 APPENDIX B FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: PROXIMITY Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570001011 0.8837 2 0.2803 3 0.3329 2 232 120570001012 1.2604 1 0.4503 3 0.3572 2 132 120570001021 1.3343 1 0.4232 3 0.0334 3 133 120570001022 1.3665 1 0.3396 3 0.0347 3 133 120570001023 1.7390 1 0.2667 3 0.2611 2 132 120570001024 1.8345 1 0.1860 3 0.1078 2 132 120570002011 0.9515 2 0.1255 3 0.1011 2 232 120570002012 1.0128 1 0.5151 3 0.0370 3 133 120570002021 0.5586 2 0.3268 3 0.2015 2 232 120570002022 0.2853 3 0.1645 3 0.1484 2 332 120570002023 0.5244 3 0.1340 3 0.3743 2 332 120570003001 0.7805 2 0.7591 3 0.0354 3 233 120570003002 0.9186 2 0.6624 3 0.0671 3 233 120570003003 0.7748 2 0.2831 3 0.2792 2 232 120570003004 0.6001 2 1.0149 1 0.0396 3 213 120570003005 0.9712 2 0.9329 2 0.0957 2 222 120570003006 1.0818 1 0.5854 3 0.0497 3 133 120570004011 1.0824 1 0.6246 3 0.3104 2 132 120570004012 0.9108 2 0.3273 3 0.0544 3 233 120570004013 0.8414 2 0.7322 3 0.2555 2 232 120570004021 0.1640 3 1.1478 1 0.0846 2 312 120570004022 0.7182 2 0.9321 2 0.1692 2 222 120570004023 0.4151 3 0.8741 2 0.0397 3 323 120570005001 1.1434 1 1.2717 1 0.3707 2 112 120570005002 0.8019 2 0.9623 2 0.1838 2 222 120570005003 0.5592 2 0.5550 3 0.1153 2 232 120570005004 0.5232 3 0.4376 3 0.0409 3 333 120570006011 0.1722 3 0.1115 3 0.0651 3 333 120570006012 0.3991 3 0.3939 3 0.0355 3 333 120570006013 0.0851 3 0.0933 3 0.0896 2 332 120570006021 0.0780 3 0.9359 2 0.0229 3 323 120570006022 0.0420 3 1.0330 1 0.0496 3 313 120570007001 0.5395 3 1.1730 1 0.0271 3 313 120570007002 0.6213 2 0.8765 2 0.0087 3 223 120570007003 0.8621 2 0.8008 2 0.1029 2 222 120570007004 0.9259 2 0.6981 3 0.2333 2 232 120570008001 1.2590 1 1.0630 1 0.0549 3 113 120570008002 0.9950 2 0.6733 3 0.0532 3 233

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128 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570009011 1.7604 1 0.5247 3 0.0769 3 133 120570009012 1.8852 1 0.1490 3 0.0500 3 133 120570009013 1.9689 1 0.4085 3 0.2719 2 132 120570009021 2.1058 1 0.0489 3 0.0964 2 132 120570009022 2.2844 1 0.2294 3 0.1369 2 132 120570009023 1.0610 1 0.5274 3 0.3584 2 132 120570010011 1.0327 1 0.8338 2 0.1554 2 122 120570010012 1.0191 1 1.0468 1 0.0469 3 113 120570010021 0.5306 3 0.2079 3 0.0409 3 333 120570010022 0.4074 3 0.2599 3 0.0681 3 333 120570010023 0.2676 3 0.0294 3 0.0280 3 333 120570010024 0.0627 3 0.0857 3 0.0628 3 333 120570011001 0.6837 2 0.3548 3 0.1470 2 232 120570011002 0.7418 2 0.5456 3 0.3184 2 232 120570011003 1.0081 1 0.7714 2 0.1269 2 122 120570012001 0.8860 2 0.3802 3 0.1255 2 232 120570012002 0.7607 2 0.2609 3 0.0307 3 233 120570012003 0.6515 2 0.0422 3 0.0383 3 233 120570013001 0.1860 3 0.0441 3 0.0440 3 333 120570013002 0.3561 3 0.1863 3 0.2170 2 332 120570013003 0.0369 3 0.2667 3 0.0563 3 333 120570013004 0.3215 3 0.0210 3 0.2389 2 332 120570013005 0.2724 3 0.0668 3 0.0351 3 333 120570014001 0.0771 3 0.3354 3 0.0349 3 333 120570014002 0.3382 3 0.5954 3 0.1118 2 332 120570014003 0.1614 3 0.6164 3 0.0175 3 333 120570014004 0.0848 3 0.1674 3 0.0465 3 333 120570015001 0.6070 2 0.2937 3 0.0239 3 233 120570015002 0.5275 3 0.3757 3 0.0388 3 333 120570015003 0.5359 3 0.0716 3 0.0835 2 332 120570016001 0.2375 3 0.0350 3 0.0440 3 333 120570016002 0.0449 3 0.1978 3 0.0297 3 333 120570016003 0.1705 3 0.3495 3 0.0215 3 333 120570017001 0.0666 3 0.2865 3 0.0612 3 333 120570017002 0.3946 3 0.6235 3 0.2940 2 332 120570017003 0.7687 2 0.9323 2 0.1442 2 222 120570017004 0.1209 3 0.7693 3 0.0375 3 333 120570017005 0.5227 3 0.8629 2 0.0948 2 322 120570018001 0.2003 3 0.4606 3 0.0305 3 333 120570018002 0.1472 3 0.4500 3 0.0549 3 333 120570018003 0.4318 3 0.7229 3 0.1346 2 332 120570018004 0.4080 3 0.7158 3 0.0383 3 333

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129 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570018005 0.7648 2 0.9246 2 0.0476 3 223 120570019001 0.5615 2 0.6400 3 0.0689 3 233 120570019002 0.7767 2 0.7389 3 0.2486 2 232 120570019003 0.9484 2 0.4919 3 0.0348 3 233 120570020001 0.3941 3 1.1444 1 0.0605 3 313 120570020002 0.3898 3 0.8028 2 0.0274 3 323 120570020003 0.5074 3 0.5232 3 0.0429 3 333 120570021001 0.0462 3 0.8568 2 0.0405 3 323 120570021002 0.0155 3 0.9526 2 0.0182 3 323 120570021003 0.1701 3 0.7265 3 0.0676 3 333 120570022001 0.1512 3 0.3539 3 0.0457 3 333 120570022002 0.2214 3 0.4499 3 0.0368 3 333 120570022003 0.0545 3 0.8633 2 0.0568 3 323 120570023001 0.6546 2 0.0548 3 0.0542 3 233 120570023002 0.5054 3 0.5360 3 0.2338 2 332 120570023003 0.4309 3 0.7790 2 0.0385 3 323 120570024001 0.2476 3 0.5397 3 0.0686 3 333 120570024002 0.4355 3 0.1421 3 0.0900 2 332 120570024003 0.6321 2 0.3691 3 0.3712 2 232 120570024004 0.4425 3 0.2240 3 0.0655 3 333 120570025001 0.3294 3 0.2959 3 0.0673 3 333 120570025002 0.1148 3 0.6191 3 0.0488 3 333 120570025003 0.3225 3 0.6067 3 0.2449 2 332 120570025004 0.2612 3 0.2251 3 0.0349 3 333 120570026001 0.2243 3 0.4424 3 0.0314 3 333 120570026002 0.8741 2 0.8936 2 0.0454 3 223 120570027001 0.1312 3 0.0356 3 0.0523 3 333 120570027002 0.0575 3 0.1376 3 0.0815 3 333 120570027003 0.1380 3 0.0581 3 0.1198 2 332 120570027004 0.2808 3 0.0265 3 0.0277 3 333 120570027005 0.3749 3 0.0275 3 0.0732 3 333 120570028001 0.2847 3 0.3789 3 0.0431 3 333 120570028002 0.3460 3 0.4182 3 0.3218 2 332 120570028003 0.2982 3 0.2824 3 0.2253 2 332 120570028004 0.4842 3 0.3660 3 0.0438 3 333 120570029001 0.4226 3 1.0360 1 0.0202 3 313 120570029002 0.5336 3 0.8935 2 0.2815 2 322 120570029003 0.9182 2 0.6754 3 0.0405 3 233 120570030001 0.1027 3 0.8013 2 0.0172 3 323 120570030002 0.3590 3 1.1889 1 0.1803 2 312 120570030003 0.5261 3 0.6857 3 0.1737 2 332 120570031001 0.1908 3 0.7727 2 0.0388 3 323

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130 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570031002 0.3696 3 0.5159 3 0.0233 3 333 120570031003 0.6294 2 0.2520 3 0.0458 3 233 120570031004 0.7053 2 0.2455 3 0.0204 3 233 120570032001 0.5386 3 0.5217 3 0.0329 3 333 120570032002 0.7768 2 0.8210 2 0.0465 3 223 120570032003 0.8570 2 0.6551 3 0.0197 3 233 120570033001 0.8541 2 0.3463 3 0.0720 3 233 120570033002 1.0629 1 0.2225 3 0.2823 2 132 120570033003 1.0973 1 0.4966 3 0.1086 2 132 120570034001 1.1565 1 0.0364 3 0.0637 3 133 120570034002 1.0692 1 0.4975 3 0.0238 3 133 120570034003 1.3139 1 0.1762 3 0.0227 3 133 120570035001 1.2582 1 0.0606 3 0.2274 2 132 120570035002 1.4692 1 0.2661 3 0.0389 3 133 120570035003 1.6223 1 0.4253 3 0.0159 3 133 120570035004 1.5673 1 0.6277 3 0.2515 2 132 120570036001 1.0356 1 0.6074 3 0.0581 3 133 120570036002 1.2980 1 1.1006 1 0.0405 3 113 120570036003 1.4884 1 0.6065 3 0.1270 2 132 120570036004 1.6096 1 0.4754 3 0.0529 3 133 120570037001 1.6695 1 0.1112 3 0.0637 3 133 120570037002 2.0756 1 0.0747 3 0.0476 3 133 120570038001 1.6533 1 0.7299 3 0.2142 2 132 120570038002 1.8433 1 0.6368 3 0.0158 3 133 120570039001 1.1999 1 0.6657 3 0.0688 3 133 120570039002 1.0000 1 0.5500 3 0.1000 2 132 120570040001 1.0922 1 0.5099 3 0.2206 2 132 120570041001 1.0270 1 0.4414 3 0.1958 2 132 120570041002 1.0234 1 0.2078 3 0.1775 2 132 120570042001 0.9086 2 0.6010 3 0.0450 3 233 120570042002 1.1492 1 0.3114 3 0.1176 2 132 120570043001 0.8826 2 0.5964 3 0.2057 2 232 120570043002 0.9687 2 0.8348 2 0.3116 2 222 120570043003 0.9100 2 0.7555 3 0.2600 2 232 120570044001 0.6594 2 0.1947 3 0.0176 3 233 120570044002 0.7048 2 0.3173 3 0.0200 3 233 120570044003 0.8428 2 0.5515 3 0.0201 3 233 120570045001 0.2845 3 0.2923 3 0.0774 3 333 120570045002 0.6385 2 0.1733 3 0.0213 3 233 120570045003 0.2627 3 0.3804 3 0.0377 3 333 120570045004 0.7657 2 0.4039 3 0.0423 3 233 120570045005 0.7695 2 0.2750 3 0.3107 2 232

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131 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570046001 0.1518 3 0.9055 2 0.0133 3 323 120570046002 0.0861 3 0.9610 2 0.0844 2 322 120570046003 0.1397 3 0.7006 3 0.0420 3 333 120570047001 0.3305 3 0.8492 2 0.0998 2 322 120570047002 1.0299 1 1.6698 1 0.0506 3 113 120570047003 0.7169 2 1.2982 1 0.2274 2 212 120570047004 0.5560 3 0.8772 2 0.0948 2 322 120570048001 0.3128 3 0.0708 3 0.1117 2 332 120570048002 0.7248 2 0.0402 3 0.2583 2 232 120570048003 0.4359 3 0.0182 3 0.0261 3 333 120570048004 0.5266 3 0.1907 3 0.2403 2 332 120570048005 0.6676 2 0.2681 3 0.2297 2 232 120570048006 0.3894 3 0.3340 3 0.0892 2 332 120570049001 0.3720 3 0.5512 3 0.0868 2 332 120570049002 0.5234 3 0.7849 2 0.1172 2 322 120570049003 0.2388 3 0.8329 2 0.0680 3 323 120570049004 0.0263 3 0.8579 2 0.0585 3 323 120570049005 0.0642 3 1.0232 1 0.0870 2 312 120570050001 0.3962 3 0.6787 3 0.3620 2 332 120570050002 0.5257 3 0.6253 3 0.5785 2 332 120570050003 0.2776 3 1.1114 1 0.4413 2 312 120570051011 0.5113 3 0.1962 3 0.1419 2 332 120570051021 0.6616 2 1.0365 1 0.2925 2 212 120570051022 0.4211 3 1.2863 1 0.2702 2 312 120570053011 0.7208 2 0.8680 2 0.2834 2 222 120570053012 0.9355 2 0.8534 2 0.5934 2 222 120570053013 0.8517 2 0.8329 2 0.4369 2 222 120570053021 1.4445 1 0.0656 3 0.0785 3 133 120570053022 1.7157 1 0.0774 3 0.3185 2 132 120570054011 1.4603 1 2.2665 1 0.3795 2 112 120570054012 1.4423 1 2.4398 1 0.5276 2 112 120570054013 1.3129 1 1.9328 1 0.2000 2 112 120570054014 0.4116 3 1.4430 1 0.2830 2 312 120570054015 0.8922 2 1.8135 1 0.1179 2 212 120570054016 1.0819 1 1.7838 1 0.0407 3 113 120570055001 0.2640 3 1.2875 1 0.1324 2 312 120570055002 0.3783 3 1.1594 1 0.0506 3 313 120570055003 0.3023 3 1.0666 1 0.0825 3 313 120570055004 0.0631 3 1.0672 1 0.0750 3 313 120570057001 0.1124 3 0.6331 3 0.0452 3 333 120570057002 0.5027 3 0.5698 3 0.0843 2 332 120570057003 0.3299 3 0.5770 3 0.0377 3 333

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132 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570057004 0.0716 3 0.6212 3 0.0604 3 333 120570057005 0.3418 3 0.8874 2 0.0529 3 323 120570058001 0.5720 2 1.3101 1 0.3743 2 212 120570058002 0.3722 3 1.0075 1 0.0655 3 313 120570058003 0.2077 3 1.2514 1 0.1708 2 312 120570058004 0.2211 3 1.6911 1 0.0995 2 312 120570058005 0.1453 3 1.8277 1 0.2237 2 312 120570058006 0.2839 3 1.9106 1 0.0416 3 313 120570059001 0.8670 2 1.7226 1 0.2632 2 212 120570059002 0.9692 2 1.6364 1 0.0844 2 212 120570059003 0.7678 2 1.8004 1 0.3122 2 212 120570059004 1.1470 1 2.3556 1 0.6793 2 112 120570059005 1.1541 1 2.5394 1 0.5560 2 112 120570059006 0.7848 2 2.2720 1 0.2626 2 212 120570060001 0.0737 3 1.0537 1 0.0651 3 313 120570060002 0.1353 3 1.0524 1 0.0465 3 313 120570060003 0.3068 3 1.2982 1 0.0360 3 313 120570060004 0.0754 3 1.3008 1 0.1024 2 312 120570060005 0.7357 2 1.5256 1 0.3693 2 212 120570060006 0.4293 3 1.5294 1 0.1351 2 312 120570060007 0.1162 3 1.6685 1 0.2460 2 312 120570061011 0.1417 3 1.3969 1 0.0794 3 313 120570061012 0.2653 3 1.4626 1 0.1925 2 312 120570061013 0.5201 3 1.4425 1 0.0152 3 313 120570061014 0.3821 3 1.5686 1 0.0355 3 313 120570061031 0.5324 3 1.6377 1 0.0588 3 313 120570061032 0.8126 2 1.7978 1 0.2495 2 212 120570061033 0.7033 2 1.8227 1 0.0414 3 213 120570061034 1.0980 1 2.0604 1 0.3041 2 112 120570061035 0.7528 2 2.2963 1 0.3093 2 212 120570061036 0.4543 3 2.0055 1 0.4235 2 312 120570062001 0.5790 2 2.0610 1 0.0881 2 212 120570062002 0.6982 2 2.0304 1 0.0293 3 213 120570062003 0.4874 3 2.3804 1 0.0877 2 312 120570062004 0.1192 3 2.0465 1 0.0413 3 313 120570063001 0.6988 2 2.3802 1 0.0157 3 213 120570063002 0.5794 2 2.2266 1 0.1944 2 212 120570063003 0.4116 3 2.2895 1 0.2264 2 312 120570063004 0.1194 3 2.1423 1 0.0229 3 313 120570064001 0.9446 2 2.5738 1 0.0608 3 213 120570064002 1.0016 1 2.7346 1 0.0292 3 113 120570064003 0.8356 2 2.6591 1 0.0298 3 213

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133 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570064004 0.7743 2 2.4899 1 0.0422 3 213 120570065011 0.6589 2 2.1978 1 0.0205 3 213 120570065012 0.4801 3 2.0518 1 0.1650 2 312 120570065013 0.4016 3 1.9890 1 0.0649 3 313 120570065014 0.2776 3 1.9213 1 0.0765 3 313 120570065021 0.9856 2 2.6219 1 0.0608 3 213 120570065022 0.8605 2 2.4856 1 0.3570 2 212 120570065023 0.8287 2 2.3782 1 0.1783 2 212 120570066001 0.0907 3 1.9040 1 0.0716 3 313 120570066002 0.2310 3 1.5040 1 0.2058 2 312 120570066003 0.2179 3 1.6391 1 0.1955 2 312 120570066004 0.1704 3 1.5492 1 0.0650 3 313 120570067001 0.1357 3 1.2036 1 0.1430 2 312 120570067002 0.7523 2 1.1154 1 0.3350 2 212 120570067003 0.3673 3 1.3887 1 0.3650 2 312 120570067004 0.5566 3 0.9871 2 0.0520 3 323 120570067005 0.2243 3 1.1781 1 0.2197 2 312 120570067006 0.0689 3 0.9797 2 0.0553 3 323 120570068011 0.0636 3 0.8861 2 0.0883 2 322 120570068012 0.1839 3 0.5942 3 0.0335 3 333 120570068013 0.0900 3 0.6034 3 0.0851 2 332 120570068014 0.3675 3 0.0432 3 0.0462 3 333 120570068015 0.0465 3 0.3536 3 0.0496 3 333 120570068021 0.9597 2 0.2850 3 0.2559 2 232 120570068022 0.5856 2 0.4366 3 0.0325 3 233 120570068023 0.7757 2 0.0922 3 0.0768 3 233 120570069001 0.8767 2 0.3604 3 0.0176 3 233 120570069002 0.4738 3 0.5393 3 0.0372 3 333 120570069003 0.7759 2 0.0576 3 0.0263 3 233 120570069004 0.1328 3 0.4807 3 0.0501 3 333 120570069005 0.2968 3 0.0471 3 0.0190 3 333 120570069006 1.0229 1 0.3052 3 0.2697 2 132 120570070011 0.3049 3 0.9414 2 0.2216 2 322 120570070012 0.2402 3 0.8223 2 0.0324 3 323 120570070013 0.0451 3 0.7563 3 0.0344 3 333 120570070014 0.1331 3 0.7743 2 0.0509 3 323 120570070021 0.3762 3 0.8221 2 0.0412 3 323 120570070022 0.5526 3 1.0511 1 0.2405 2 312 120570070023 0.5059 3 0.8829 2 0.0429 3 323 120570071021 0.7093 2 2.0828 1 0.0797 3 213 120570071022 1.0151 1 1.9955 1 0.0245 3 113 120570071023 1.1458 1 1.9955 1 0.0245 3 113

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134 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570071031 0.1882 3 1.5394 1 0.0515 3 313 120570071032 0.5603 2 1.4732 1 0.0125 3 213 120570071033 0.7882 2 1.5053 1 0.0319 3 213 120570072001 1.0117 1 1.6124 1 0.0317 3 113 120570072002 1.7265 1 2.2336 1 0.3490 2 112 120570072003 1.2210 1 1.7322 1 0.0905 2 112 120570102092 2.2424 1 8.9028 1 2.5134 1 111 120570102093 0.6497 2 9.5948 1 4.1333 1 211 120570102094 0.3980 3 8.4545 1 2.7609 1 311 120570102101 0.2247 3 8.1675 1 2.6069 1 311 120570102102 0.8516 2 7.4526 1 2.7591 1 211 120570102111 0.8306 2 5.5953 1 0.5913 2 212 120570102112 1.1095 1 5.8268 1 0.9474 2 112 120570102113 0.3280 3 4.4827 1 0.1095 2 312 120570102114 1.3664 1 5.8436 1 1.3072 1 111 120570102121 1.9397 1 6.4188 1 1.3377 1 111 120570102122 1.0915 1 6.9003 1 2.0263 1 111 120570102141 1.8469 1 11.0656 1 5.5053 1 111 120570105012 1.0614 1 0.7621 3 0.7260 2 132 120570105013 0.5224 3 0.2067 3 0.0255 3 333 120570110081 0.7729 2 2.8822 1 0.6495 2 212 120570110082 1.1048 1 3.7013 1 0.3114 2 112 120570110083 0.7513 2 2.8788 1 0.2224 2 212 120570110084 0.0899 3 1.4380 1 0.6111 2 312 120570110085 0.1612 3 2.2601 1 1.2930 1 311 120570110086 0.5370 3 1.6134 1 0.8182 2 312 120570110121 1.3250 1 5.6162 1 0.7406 2 112 120570110122 2.1916 1 6.5686 1 1.0127 1 111 120570110123 1.1677 1 5.8798 1 0.4229 2 112 120570110131 0.4095 3 5.0987 1 0.4891 2 312 120570110132 0.2988 3 5.1588 1 0.3683 2 312 120570110141 1.2569 1 6.2204 1 0.3070 2 112 120570110151 0.3567 3 1.3397 1 0.7744 2 312 120570110152 0.3418 3 1.1696 1 0.3059 2 312 120570110153 1.0044 1 2.1488 1 1.5102 1 111 120570110154 1.1463 1 0.7923 2 0.3234 2 122 120570110161 1.2995 1 3.1234 1 0.4343 2 112 120570110162 0.1944 3 2.1029 1 1.0457 1 311 120570110163 0.9781 2 2.5872 1 0.6969 2 212 120570117081 2.0649 1 1.7781 1 0.0247 3 113 120570118021 0.2209 3 0.3164 3 0.1029 2 332 120570119041 0.8596 2 0.1775 3 0.3662 2 232

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135 Proximity to Nearest Type 3 HFO Proximity to Nearest Type 2 HFO Proximity to Nearest Type 1 HFO Census Block Group ID Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Distance (Miles) Score (3,2,1) Combined Score 120570119051 0.0708 3 0.7193 3 0.0441 3 333 120570119062 0.1090 3 0.2197 3 0.0561 3 333 120570120012 0.6492 2 0.9264 2 0.2026 2 222 120570120021 2.0278 1 0.3636 3 0.1526 2 132 Median Distances 0.5576 0.7704 0.0830

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136 APPENDIX C FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: DIVERSITY OF AVAILABILITY Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570001011 Y 1 1 2 Y 1 Y 1 2 3 Y 6 N 0 6 3 233 120570001012 0 1 0 0 0 1 N 0 N 0 0 1 111 120570001021 0 1 0 0 0 1 Y 7 N 0 7 3 113 120570001022 0 1 0 0 0 1 y 2 N 0 2 2 112 120570001023 0 1 0 Y 1 1 2 Y 2 Y 1 3 3 123 120570001024 0 1 0 Y 1 1 2 Y 6 Y 1 7 3 123 120570002011 0 1 0 Y 2 2 3 Y 9 Y 1 10 3 133 120570002012 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570002021 N 0 N 0 N 0 0 1 0 Y 3 3 3 Y 7 Y 1 8 3 133 120570002022 Y 1 1 2 0 Y 2 2 3 Y 4 Y 1 5 3 233 120570002023 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570003001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570003002 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570003003 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570003004 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570003005 0 1 0 0 0 1 Y 4 Y 1 5 3 113 120570003006 0 1 0 0 0 1 Y 6 Y 1 7 3 113 120570004011 0 1 0 0 0 1 Y 3 Y 1 4 3 113 120570004012 0 1 0 0 0 1 Y 5 Y 1 6 3 113 120570004013 0 1 0 0 0 1 Y 3 N 0 3 3 113

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137 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Ava ilable? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570004021 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570004022 Y 1 1 2 0 0 0 1 Y 6 Y 1 7 3 213 120570004023 0 1 0 0 0 1 Y 6 N 0 6 3 113 120570005001 0 1 0 0 0 1 N 0 N 0 0 1 111 120570005002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570005003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570005004 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570006011 Y 1 1 2 0 Y 2 2 3 Y 2 N 0 2 2 232 120570006012 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570006013 Y 1 1 2 0 Y 3 3 3 Y 2 Y 1 3 3 233 120570006021 Y 1 1 2 0 0 0 1 Y 4 Y 2 6 3 213 120570006022 Y 1 1 2 0 0 0 1 Y 4 Y 3 7 3 213 120570007001 0 1 0 0 0 1 Y 4 Y 1 5 3 113 120570007002 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570007003 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570007004 0 1 0 0 0 1 N 0 Y 1 1 2 112 120570008001 0 1 0 0 0 1 Y 3 Y 1 4 3 113 120570008002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570009011 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570009012 0 1 0 Y 1 1 2 Y 4 Y 1 5 3 123 120570009013 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570009021 0 1 0 Y 1 1 2 Y 1 Y 1 2 2 122 120570009022 0 1 0 Y 1 1 2 Y 3 Y 1 4 3 123 120570009023 0 1 0 0 0 1 N 0 N 0 0 1 111 120570010011 0 1 0 0 0 1 Y 1 N 0 1 2 112

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138 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570010012 0 1 0 0 0 1 Y 1 Y 2 3 3 113 120570010021 0 1 Y 1 0 1 2 Y 2 N 0 2 2 122 120570010022 0 1 Y 1 0 1 2 Y 2 Y 2 4 3 123 120570010023 Y 1 1 2 Y 1 0 1 2 Y 2 N 0 2 2 222 120570010024 Y 1 1 2 Y 1 0 1 2 Y 2 Y 3 5 3 223 120570011001 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570011002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570011003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570012001 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570012002 0 1 0 Y 1 1 2 Y 2 Y 1 3 3 123 120570012003 0 1 0 Y 1 1 2 Y 4 Y 1 5 3 123 120570013001 Y 1 1 2 0 Y 3 3 3 Y 5 N 3 8 3 233 120570013002 0 1 0 Y 1 1 2 Y 4 N 1 5 3 123 120570013003 Y 1 1 2 0 0 0 1 Y 3 N 1 4 3 213 120570013004 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570013005 0 1 0 Y 1 1 2 Y 3 Y 1 4 3 123 120570014001 Y 1 1 2 Y 1 0 1 2 Y 4 Y 1 5 3 223 120570014002 0 1 Y 1 0 1 2 Y 6 N 0 6 3 123 120570014003 Y 2 2 3 0 0 0 1 Y 6 N 0 6 3 313 120570014004 Y 2 2 3 Y 1 0 1 2 Y 6 N 0 6 3 323 120570015001 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570015002 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570015003 0 1 Y 1 0 1 2 Y 2 N 0 2 2 122 120570016001 Y 1 1 2 0 Y 1 1 2 Y 3 Y 1 4 3 223 120570016002 Y 1 1 2 0 Y 1 1 2 Y 4 Y 1 5 3 223

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139 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570016003 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570017001 Y 1 1 2 0 Y 1 1 2 Y 2 Y 1 3 3 223 120570017002 0 1 0 0 0 1 N 0 N 0 0 1 111 120570017003 0 1 0 0 0 1 Y 3 Y 1 4 3 113 120570017004 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570017005 0 1 0 0 0 1 Y 3 N 1 4 3 113 120570018001 Y 1 1 2 0 0 0 1 Y 1 Y 3 4 3 213 120570018002 Y 1 1 2 0 0 0 1 Y 1 Y 2 3 3 213 120570018003 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570018004 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570018005 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570019001 0 1 0 0 0 1 Y 3 Y 3 6 3 113 120570019002 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570019003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570020001 0 1 0 0 0 1 Y 4 Y 1 5 3 113 120570020002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570020003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570021001 Y 1 1 2 0 0 0 1 Y 5 N 0 5 3 213 120570021002 Y 1 1 2 0 0 0 1 Y 2 Y 1 3 3 213 120570021003 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570022001 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570022002 Y 1 1 2 0 0 0 1 Y 2 Y 1 3 3 213 120570022003 0 1 0 0 0 1 Y 2 Y 1 3 3 113 120570023001 0 1 Y 1 0 1 2 Y 4 N 0 4 3 123 120570023002 0 1 0 0 0 1 Y 2 Y 0 2 2 112

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140 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570023003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570024001 Y 2 2 3 0 0 0 1 Y 5 N 0 5 3 313 120570024002 0 1 Y 1 0 1 2 Y 2 N 0 2 2 122 120570024003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570024004 0 1 0 Y 1 1 2 Y 3 N 0 3 3 123 120570025001 0 1 0 Y 1 1 2 Y 3 N 0 3 3 123 120570025002 Y 2 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570025003 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570025004 Y 1 1 2 0 Y 1 1 2 Y 4 N 0 4 3 223 120570026001 Y 1 1 2 0 Y 1 1 2 Y 9 Y 1 10 3 223 120570026002 Y 2 2 3 0 Y 1 1 2 Y 4 N 0 4 3 323 120570027001 Y 1 1 2 Y 1 Y 2 3 3 Y 4 Y 1 5 3 233 120570027002 Y 1 1 2 0 Y 2 2 3 Y 2 Y 1 3 3 233 120570027003 Y 1 1 2 Y 1 0 1 2 N 0 Y 1 1 2 222 120570027004 0 1 Y 1 Y 1 2 3 Y 4 Y 1 5 3 133 120570027005 0 1 Y 1 Y 1 2 3 Y 5 N 0 5 3 133 120570028001 0 1 0 0 0 1 Y 3 Y 1 4 3 113 120570028002 0 1 0 0 0 1 N 0 N 0 0 1 111 120570028003 Y 1 1 2 0 Y 1 1 2 Y 1 Y 1 2 2 222 120570028004 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570029001 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570029002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570029003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570030001 0 1 0 0 0 1 Y 7 N 0 7 3 113 120570030002 0 1 0 0 0 1 Y 1 N 0 1 2 112

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141 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570030003 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570031001 0 1 0 0 0 1 Y 7 N 0 7 3 113 120570031002 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570031003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570031004 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570032001 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570032002 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570032003 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570033001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570033002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570033003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570034001 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570034002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570034003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570035001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570035002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570035003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570035004 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570036001 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570036002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570036003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570036004 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570037001 0 1 Y 1 0 1 2 Y 12 Y 1 13 3 123 120570037002 0 1 Y 1 0 1 2 Y 3 Y 1 4 3 123 120570038001 0 1 0 0 0 1 Y 2 Y 0 2 2 112

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142 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570038002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570039001 0 1 0 0 0 1 Y 4 N 0 4 3 113 120570039002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570040001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570041001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570041002 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570042001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570042002 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570043001 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570043002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570043003 0 1 0 0 0 1 N 0 N 0 0 1 111 120570044001 0 1 Y 1 0 1 2 Y 5 N 0 5 3 123 120570044002 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570044003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570045001 Y 1 1 2 Y 1 Y 1 2 3 Y 2 N 0 2 2 232 120570045002 0 1 Y 1 0 1 2 Y 5 N 0 5 3 123 120570045003 Y 1 Y 2 3 3 0 0 0 1 Y 2 N 0 2 2 312 120570045004 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570045005 0 1 Y 1 Y 1 2 3 N 0 N 0 0 1 131 120570046001 Y 2 2 3 0 0 0 1 Y 4 N 0 4 3 313 120570046002 Y 1 Y 2 3 3 0 0 0 1 Y 3 N 0 3 3 313 120570046003 Y 1 Y 2 3 3 0 0 0 1 Y 4 N 0 4 3 313 120570047001 Y 2 2 3 0 0 0 1 Y 2 N 0 2 2 312 120570047002 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570047003 0 1 0 0 0 1 Y 3 N 0 3 3 113

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143 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570047004 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570048001 Y 2 2 3 Y 1 Y 1 2 3 Y 5 N 0 5 3 333 120570048002 0 1 Y 1 Y 1 2 3 Y 1 N 0 1 2 132 120570048003 0 1 Y 1 Y 1 2 3 Y 2 N 0 2 2 132 120570048004 0 1 Y 1 Y 1 2 3 Y 4 N 0 4 3 133 120570048005 0 1 Y 1 Y 1 2 3 Y 3 N 0 3 3 133 120570048006 Y 1 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570049001 Y 1 1 2 0 0 0 1 Y 4 N 0 4 3 213 120570049002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570049003 Y 1 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570049004 Y 2 2 3 0 0 0 1 Y 4 N 0 4 3 313 120570049005 Y 3 3 3 0 0 0 1 Y 3 N 0 3 3 313 120570050001 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570050002 Y 1 1 2 0 Y 1 1 2 N 0 N 0 0 1 221 120570050003 Y 2 2 3 0 0 0 1 Y 2 N 0 2 2 312 120570051011 Y 1 1 2 0 Y 1 1 2 Y 3 N 0 3 3 223 120570051021 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570051022 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570053011 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570053012 0 1 0 0 0 1 N 0 N 0 0 1 111 120570053013 0 1 0 0 0 1 N 0 N 0 0 1 111 120570053021 0 1 0 y 1 1 2 Y 3 N 0 3 3 123 120570053022 0 1 0 Y 1 1 2 Y 2 N 0 2 2 122 120570054011 0 1 0 0 0 1 N 0 N 0 0 1 111 120570054012 0 1 0 0 0 1 N 0 N 0 0 1 111

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144 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570054013 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570054014 Y 1 1 2 0 0 0 1 N 0 N 0 0 1 211 120570054015 0 1 0 0 0 1 Y 2 Y 0 2 2 112 120570054016 0 1 0 0 0 1 Y 2 Y 0 2 2 112 120570055001 Y 2 2 3 0 0 0 1 Y 1 N 0 1 2 312 120570055002 Y 1 1 2 0 0 0 1 Y 1 N 0 1 2 212 120570055003 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570055004 Y 1 1 2 0 0 0 1 Y 1 N 0 1 2 212 120570057001 Y 2 2 3 0 0 0 1 Y 4 N 0 4 3 313 120570057002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570057003 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570057004 Y 1 1 2 0 0 0 1 Y 4 N 0 4 3 213 120570057005 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570058001 0 1 0 0 0 1 N 0 N 0 0 1 111 120570058002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570058003 Y 2 2 3 0 0 0 1 Y 1 N 0 1 2 312 120570058004 Y 2 2 3 0 0 0 1 Y 3 N 0 3 3 313 120570058005 Y 2 2 3 0 0 0 1 Y 3 N 0 3 3 313 120570058006 Y 1 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570059001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570059002 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570059003 0 1 0 0 0 1 N 0 N 0 0 1 111 120570059004 0 1 0 0 0 1 N 0 N 0 0 1 111 120570059005 0 1 0 0 0 1 N 0 N 0 0 1 111 120570059006 0 1 0 0 0 1 Y 1 N 0 1 2 112

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145 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570060001 Y 2 2 3 0 0 0 1 Y 3 N 0 3 3 313 120570060002 Y 2 2 3 0 0 0 1 Y 2 N 0 2 2 312 120570060003 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570060004 Y 2 2 3 0 0 0 1 Y 1 N 0 1 2 312 120570060005 0 1 0 0 0 1 N 0 N 0 0 1 111 120570060006 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570060007 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570061011 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570061012 Y 1 1 2 0 0 0 1 Y 1 N 0 1 2 212 120570061013 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570061014 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570061031 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570061032 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570061033 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570061034 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570061035 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570061036 0 1 0 0 0 1 N 0 N 0 0 1 111 120570062001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570062002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570062003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570062004 Y 1 1 2 0 0 0 1 Y 6 N 0 6 3 213 120570063001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570063002 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570063003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570063004 Y 1 1 2 0 0 0 1 Y 5 N 0 5 3 213

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146 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570064001 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570064002 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570064003 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570064004 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570065011 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570065012 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570065013 0 1 0 0 0 1 Y 4 Y 1 5 3 113 120570065014 Y 1 1 2 0 0 0 1 Y 4 Y 1 5 3 213 120570065021 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570065022 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570065023 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570066001 Y 1 1 2 0 0 0 1 Y 4 N 0 4 3 213 120570066002 Y 2 Y 1 3 3 0 0 0 1 Y 4 N 0 4 3 313 120570066003 Y 1 1 2 0 0 0 1 Y 2 N 0 2 2 212 120570066004 Y 1 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570067001 Y 2 Y 1 3 3 0 0 0 1 Y 6 N 0 6 3 313 120570067002 0 1 0 0 0 1 N 0 N 0 0 1 111 120570067003 0 1 0 0 0 1 N 0 N 0 0 1 111 120570067004 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570067005 Y 1 Y 1 2 3 0 0 0 1 Y 4 N 0 4 3 313 120570067006 0 1 0 0 0 1 Y 6 N 0 6 3 113 120570068011 Y 1 Y 1 2 3 0 0 0 1 Y 3 N 0 3 3 313 120570068012 Y 1 1 2 0 0 0 1 Y 6 N 0 6 3 213 120570068013 Y 1 Y 1 2 3 0 0 0 1 Y 5 N 0 5 3 313 120570068014 0 1 0 Y 1 1 2 Y 4 N 0 4 3 123

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147 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570068015 Y 1 1 2 0 0 0 1 Y 4 N 0 4 3 213 120570068021 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570068022 0 1 0 0 0 1 Y 5 N 0 5 3 113 120570068023 0 1 0 Y 1 1 2 Y 3 N 0 3 3 123 120570069001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570069002 0 1 0 0 0 1 Y 2 N 1 3 3 113 120570069003 0 1 0 Y 1 1 2 Y 2 N 0 2 2 122 120570069004 Y 1 1 2 0 0 0 1 Y 3 Y 1 4 3 213 120570069005 0 1 0 Y 1 1 2 Y 4 Y 1 5 3 123 120570069006 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570070011 Y 2 Y 1 3 3 0 0 0 1 Y 7 Y 1 8 3 313 120570070012 Y 1 1 2 0 0 0 1 Y 3 Y 1 4 3 213 120570070013 Y 1 1 2 0 0 0 1 Y 5 N 0 5 3 213 120570070014 Y 1 1 2 0 0 0 1 Y 3 Y 1 4 3 213 120570070021 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570070022 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570070023 0 1 0 0 0 1 Y 1 Y 1 2 2 112 120570071021 0 1 0 0 0 1 Y 3 N 0 3 3 113 120570071022 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570071023 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570071031 Y 1 1 2 0 0 0 1 Y 5 Y 1 6 3 213 120570071032 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570071033 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570072001 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570072002 0 1 0 0 0 1 Y 1 N 0 1 2 112

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148 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) S CORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570072003 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570102092 Y 1 N 0 N 0 1 2 0 0 0 1 N 0 N 0 0 1 211 120570102093 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102094 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102101 Y 1 N 0 N 0 1 2 0 0 0 1 N 0 N 0 0 1 211 120570102102 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102111 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102112 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102113 Y 1 N 0 N 0 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570102114 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102121 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102122 0 1 0 0 0 1 N 0 N 0 0 1 111 120570102141 0 1 0 0 0 1 N 0 N 0 0 1 111 120570105012 0 1 0 0 0 1 N 0 N 0 0 1 111 120570105013 0 1 Y 1 0 1 2 Y 2 N 0 2 2 122 120570110081 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110082 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570110083 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570110084 Y 1 N 0 N 0 1 2 0 0 0 1 N 0 N 0 0 1 211 120570110085 Y 1 N 0 N 0 1 2 0 0 0 1 N 0 N 0 0 1 211 120570110086 Y 1 N 0 N 0 1 2 0 0 0 1 N 0 N 0 0 1 211 120570110121 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110122 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110123 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110131 Y 1 N 0 N 0 1 2 0 0 0 1 Y 3 N 0 3 3 213

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149 Super markets Farmers Market Super centers Small Grocery Ethnic/Spec. Food Convenience Dollar Stores Census Block Group ID Available? (Y/N) How Many Available? (Y/N) How Many Available? (Y/N) How Many *TOTAL (n) SCORE (3,2,1) A vailable? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Available? (Y/N) How Many Available? (Y/N) How Many TOTAL (n) SCORE (3,2,1) Combined Score 120570110153 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110132 Y 1 N 0 N 0 1 2 0 0 0 1 Y 3 N 0 3 3 213 120570110141 0 1 0 0 0 1 Y 2 N 0 2 2 112 120570110151 Y 1 N 0 N 0 1 2 0 0 0 1 Y 1 N 0 1 2 212 120570110152 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570110154 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570110161 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570110162 0 1 0 0 0 1 N 0 N 0 0 1 111 120570110163 0 1 0 0 0 1 N 0 N 0 0 1 111 120570117081 0 1 0 0 0 1 Y 1 N 0 1 2 112 120570118021 Y 1 1 2 0 Y 1 1 2 Y 5 Y 1 6 3 223 120570119041 0 1 0 Y 1 1 2 Y 1 N 0 1 2 122 120570119051 Y 3 3 3 0 0 0 1 Y 8 Y 1 9 3 313 120570119062 Y 1 1 2 0 Y 1 1 2 Y 4 N 0 4 3 223 120570120012 0 1 0 0 0 1 Y 2 Y 1 3 3 113 120570120021 0 1 0 0 0 1 Y 2 Y 1 3 3 113 1 1 3 3 = Totals and densities were calculated by counting the number of food outlets within the census block group boundaries and within 1/4 buffer of the boundary

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1 50 APPENDIX D FOOD ACCESS ASSESSMENT AUDIT INSTRUMENT: MOBILITY Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570001011 26.84% 1 1310 59 4.50% 3 133 10.15% 1 131 120570001012 0.00% 1 417 20 4.80% 3 226 54.20% 2 132 120570001021 9.31% 1 379 21 5.54% 3 297 78.36% 3 133 120570001022 0.00% 1 466 163 34.98% 2 394 84.55% 3 123 120570001023 0.00% 1 461 268 58.13% 2 197 42.73% 2 122 120570001024 0.00% 1 806 163 20.22% 3 285 35.36% 2 132 120570002011 13.54% 1 838 378 45.11% 2 639 76.25% 3 123 120570002012 0.00% 1 403 41 10.17% 3 298 73.95% 3 133 120570002021 7.59% 1 457 34 7.44% 3 457 100.00% 3 133 120570002022 8.26% 1 722 148 20.50% 3 437 60.53% 2 132 120570002023 0.00% 1 508 148 29.13% 3 201 39.57% 2 132 120570003001 0.00% 1 461 122 26.46% 3 393 85.25% 3 133 120570003002 0.00% 1 320 20 6.25% 3 320 100.00% 3 133 120570003003 0.00% 1 420 42 10.00% 3 420 100.00% 3 133 120570003004 0.00% 1 440 154 35.00% 2 334 75.91% 3 123 120570003005 0.00% 1 305 42 13.77% 3 305 100.00% 3 133 120570003006 0.00% 1 391 25 6.39% 3 391 100.00% 3 133 120570004011 0.00% 1 255 24 9.41% 3 189 74.12% 3 133 120570004012 0.00% 1 295 38 12.88% 3 191 64.75% 2 132 120570004013 0.00% 1 281 16 5.69% 3 281 100.00% 3 133 120570004021 0.00% 1 547 77 14.08% 3 440 80.44% 3 133 120570004022 0.00% 1 282 90 31.91% 3 50 17.73% 1 131 120570004023 0.00% 1 246 9 3.66% 3 245 99.59% 3 133 120570005001 0.00% 1 354 12 3.39% 3 238 67.23% 3 133

PAGE 151

151 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570005002 0.00% 1 356 0 0.00% 3 356 100.00% 3 133 120570005003 0.00% 1 391 19 4.86% 3 391 100.00% 3 133 120570005004 0.00% 1 490 7 1.43% 3 490 100.00% 3 133 120570006011 0.00% 1 435 170 39.08% 2 257 59.08% 2 122 120570006012 0.00% 1 597 170 28.48% 3 597 100.00% 3 133 120570006013 0.00% 1 538 170 31.60% 3 122 22.68% 1 131 120570006021 0.00% 1 551 58 10.53% 3 543 98.55% 3 133 120570006022 0.00% 1 423 69 16.31% 3 422 99.76% 3 133 120570007001 0.00% 1 570 159 27.89% 3 570 100.00% 3 133 120570007002 0.00% 1 361 128 35.46% 2 361 100.00% 3 123 120570007003 0.00% 1 383 104 27.15% 3 383 100.00% 3 133 120570007004 0.00% 1 549 44 8.01% 3 549 100.00% 3 133 120570008001 0.00% 1 588 83 14.12% 3 558 94.90% 3 133 120570008002 0.00% 1 549 20 3.64% 3 549 100.00% 3 133 120570009011 0.00% 1 392 291 74.23% 1 67 17.09% 1 111 120570009012 0.00% 1 368 291 79.08% 1 317 86.14% 3 113 120570009013 0.00% 1 223 199 89.24% 1 223 100.00% 3 113 120570009021 0.00% 1 1116 291 26.08% 3 174 15.59% 1 131 120570009022 0.00% 1 486 291 59.88% 2 54 11.11% 1 121 120570009023 0.00% 1 593 199 33.56% 3 332 55.99% 2 132 120570010011 0.00% 1 732 197 26.91% 3 143 19.54% 1 131 120570010012 0.00% 1 459 94 20.48% 3 155 33.77% 1 131 120570010021 0.00% 1 571 17 2.98% 3 571 100.00% 3 133 120570010022 0.00% 1 332 94 28.31% 3 332 100.00% 3 133 120570010023 0.00% 1 384 61 15.89% 3 319 83.07% 3 133 120570010024 0.00% 1 538 105 19.52% 3 380 70.63% 3 133 120570011001 0.00% 1 291 21 7.22% 3 291 100.00% 3 133 120570011002 0.00% 1 270 0 0.00% 3 270 100.00% 3 133

PAGE 152

152 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570011003 0.00% 1 361 35 9.70% 3 361 100.00% 3 133 120570012001 0.00% 1 222 7 3.15% 3 222 100.00% 3 133 120570012002 0.00% 1 539 272 50.46% 2 311 57.70% 2 122 120570012003 0.00% 1 351 69 19.66% 3 334 95.16% 3 133 120570013001 0.00% 1 851 343 40.31% 2 434 51.00% 2 122 120570013002 0.00% 1 515 8 1.55% 3 515 100.00% 3 133 120570013003 0.00% 1 242 0 0.00% 3 242 100.00% 3 133 120570013004 0.00% 1 242 35 14.46% 3 242 100.00% 3 133 120570013005 0.00% 1 407 32 7.86% 3 407 100.00% 3 133 120570014001 0.00% 1 679 65 9.57% 3 549 80.85% 3 133 120570014002 0.00% 1 437 59 13.50% 3 350 80.09% 3 133 120570014003 0.00% 1 344 55 15.99% 3 334 97.09% 3 133 120570014004 22.52% 1 210 35 16.67% 3 208 99.05% 3 133 120570015001 0.00% 1 296 13 4.39% 3 296 100.00% 3 133 120570015002 0.00% 1 354 19 5.37% 3 354 100.00% 3 133 120570015003 5.72% 1 430 18 4.19% 3 430 100.00% 3 133 120570016001 0.00% 1 205 12 5.85% 3 205 100.00% 3 133 120570016002 0.00% 1 451 69 15.30% 3 409 90.69% 3 133 120570016003 5.03% 1 225 19 8.44% 3 209 92.89% 3 133 120570017001 0.00% 1 381 32 8.40% 3 381 100.00% 3 133 120570017002 0.00% 1 300 42 14.00% 3 300 100.00% 3 133 120570017003 0.00% 1 301 24 7.97% 3 301 100.00% 3 133 120570017004 0.00% 1 428 24 5.61% 3 405 94.63% 3 133 120570017005 0.00% 1 290 15 5.17% 3 290 100.00% 3 133 120570018001 0.00% 1 249 55 22.09% 3 249 100.00% 3 133 120570018002 0.00% 1 524 103 19.66% 3 123 23.47% 1 131 120570018003 0.00% 1 275 55 20.00% 3 275 100.00% 3 133 120570018004 0.00% 1 185 24 12.97% 3 210 113.51% 3 133

PAGE 153

153 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570018005 0.00% 1 238 54 22.69% 3 238 100.00% 3 133 120570019001 0.00% 1 277 52 18.77% 3 277 100.00% 3 133 120570019002 0.00% 1 383 84 21.93% 3 383 100.00% 3 133 120570019003 0.00% 1 267 50 18.73% 3 312 116.85% 3 133 120570020001 0.00% 1 320 59 18.44% 3 320 100.00% 3 133 120570020002 0.00% 1 273 30 10.99% 3 273 100.00% 3 133 120570020003 0.00% 1 258 16 6.20% 3 284 110.08% 3 133 120570021001 7.23% 1 317 27 8.52% 3 329 103.79% 3 133 120570021002 4.81% 1 278 17 6.12% 3 278 100.00% 3 133 120570021003 6.52% 1 420 78 18.57% 3 420 100.00% 3 133 120570022001 13.68% 1 299 19 6.35% 3 279 93.31% 3 133 120570022002 4.89% 1 209 22 10.53% 3 192 91.87% 3 133 120570022003 6.17% 1 176 0 0.00% 3 176 100.00% 3 133 120570023001 10.53% 1 569 50 8.79% 3 477 83.83% 3 133 120570023002 6.03% 1 276 30 10.87% 3 276 100.00% 3 133 120570023003 4.62% 1 446 27 6.05% 3 318 71.30% 3 133 120570024001 8.57% 1 225 18 8.00% 3 230 102.22% 3 133 120570024002 10.02% 1 540 208 38.52% 2 360 66.67% 2 122 120570024003 0.00% 1 634 208 32.81% 3 172 27.13% 1 131 120570024004 0.00% 1 248 12 4.84% 3 248 100.00% 3 133 120570025001 6.80% 1 1080 192 17.78% 3 352 32.59% 1 131 120570025002 21.00% 1 432 192 44.44% 2 214 49.54% 2 122 120570025003 10.97% 1 693 73 10.53% 3 171 24.68% 1 131 120570025004 16.55% 1 648 73 11.27% 3 395 60.96% 2 132 120570026001 17.31% 1 255 78 30.59% 3 77 30.20% 1 131 120570026002 20.00% 1 400 24 6.00% 3 185 46.25% 2 132 120570027001 0.00% 1 627 116 18.50% 3 541 86.28% 3 133 120570027002 0.00% 1 487 117 24.02% 3 378 77.62% 3 133

PAGE 154

154 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570027003 0.00% 1 411 29 7.06% 3 409 99.51% 3 133 120570027004 0.00% 1 627 136 21.69% 3 610 97.29% 3 133 120570027005 0.00% 1 520 66 12.69% 3 499 95.96% 3 133 120570028001 0.00% 1 286 0 0.00% 3 286 100.00% 3 133 120570028002 0.00% 1 308 32 10.39% 3 308 100.00% 3 133 120570028003 0.00% 1 299 12 4.01% 3 299 100.00% 3 133 120570028004 0.00% 1 364 18 4.95% 3 364 100.00% 3 133 120570029001 5.48% 1 250 16 6.40% 3 250 100.00% 3 133 120570029002 8.78% 1 319 48 15.05% 3 298 93.42% 3 133 120570029003 4.75% 1 205 28 13.66% 3 191 93.17% 3 133 120570030001 12.80% 1 249 93 37.35% 2 249 100.00% 3 123 120570030002 0.00% 1 398 219 55.03% 2 3 0.75% 1 121 120570030003 0.00% 1 301 88 29.24% 3 301 100.00% 3 133 120570031001 13.00% 1 229 48 20.96% 3 229 100.00% 3 133 120570031002 0.00% 1 255 39 15.29% 3 255 100.00% 3 133 120570031003 0.00% 1 230 12 5.22% 3 138 60.00% 2 132 120570031004 0.00% 1 252 67 26.59% 3 89 35.32% 2 132 120570032001 5.61% 1 439 84 19.13% 3 283 64.46% 2 132 120570032002 10.13% 1 296 113 38.18% 2 246 83.11% 3 123 120570032003 0.00% 1 194 61 31.44% 3 194 100.00% 3 133 120570033001 0.00% 1 265 56 21.13% 3 256 96.60% 3 133 120570033002 0.00% 1 375 130 34.67% 2 189 50.40% 2 122 120570033003 0.00% 1 143 130 90.91% 1 143 100.00% 3 113 120570034001 0.00% 1 272 81 29.78% 3 156 57.35% 2 132 120570034002 0.00% 1 328 150 45.73% 2 264 80.49% 3 123 120570034003 0.00% 1 371 132 35.58% 2 371 100.00% 3 123 120570035001 0.00% 1 175 55 31.43% 3 175 100.00% 3 133 120570035002 0.00% 1 210 73 34.76% 2 210 100.00% 3 123

PAGE 155

155 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570035003 0.00% 1 225 39 17.33% 3 225 100.00% 3 133 120570035004 0.00% 1 217 30 13.82% 3 217 100.00% 3 133 120570036001 15.55% 1 333 55 16.52% 3 323 97.00% 3 133 120570036002 17.34% 1 377 26 6.90% 3 252 66.84% 2 132 120570036003 10.40% 1 378 32 8.47% 3 292 77.25% 3 133 120570036004 0.00% 1 268 35 13.06% 3 255 95.15% 3 133 120570037001 18.20% 1 236 31 13.14% 3 174 73.73% 3 133 120570037002 12.95% 1 154 29 18.83% 3 98 63.64% 2 132 120570038001 0.00% 1 189 81 42.86% 2 189 100.00% 3 123 120570038002 0.00% 1 211 64 30.33% 3 211 100.00% 3 133 120570039001 1.20% 1 829 201 24.25% 3 752 90.71% 3 133 120570039002 14.28% 1 329 0 0.00% 3 0 0.00% 1 131 120570040001 6.73% 1 34 56 164.71% 1 19 55.88% 2 112 120570041001 0.00% 1 227 84 37.00% 2 131 57.71% 2 122 120570041002 2.36% 1 512 176 34.38% 2 46 8.98% 1 121 120570042001 0.00% 1 209 31 14.83% 3 209 100.00% 3 133 120570042002 8.74% 1 163 41 25.15% 3 154 94.48% 3 133 120570043001 0.00% 1 381 238 62.47% 2 9 2.36% 1 121 120570043002 0.00% 1 381 238 62.47% 2 14 3.67% 1 121 120570043003 0.00% 1 381 239 62.73% 2 0 0.00% 1 121 120570044001 0.00% 1 226 113 50.00% 2 221 97.79% 3 123 120570044002 0.00% 1 267 55 20.60% 3 267 100.00% 3 133 120570044003 2.31% 1 242 113 46.69% 2 242 100.00% 3 123 120570045001 0.00% 1 306 34 11.11% 3 306 100.00% 3 133 120570045002 0.00% 1 342 124 36.26% 2 234 68.42% 3 123 120570045003 0.00% 1 271 17 6.27% 3 271 100.00% 3 133 120570045004 0.00% 1 294 56 19.05% 3 272 92.52% 3 133 120570045005 1.58% 1 248 57 22.98% 3 237 95.56% 3 133

PAGE 156

156 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570046001 3.27% 1 307 26 8.47% 3 307 100.00% 3 133 120570046002 13.33% 1 741 62 8.37% 3 550 74.22% 3 133 120570046003 7.59% 1 380 85 22.37% 3 60 15.79% 1 131 120570047001 0.00% 1 203 27 13.30% 3 203 100.00% 3 133 120570047002 0.00% 1 316 79 25.00% 3 262 82.91% 3 133 120570047003 0.00% 1 299 37 12.37% 3 177 59.20% 2 132 120570047004 0.00% 1 318 46 14.47% 3 293 92.14% 3 133 120570048001 0.00% 1 295 26 8.81% 3 295 100.00% 3 133 120570048002 0.00% 1 274 10 3.65% 3 274 100.00% 3 133 120570048003 0.00% 1 263 11 4.18% 3 263 100.00% 3 133 120570048004 0.00% 1 254 13 5.12% 3 237 93.31% 3 133 120570048005 0.00% 1 267 10 3.75% 3 255 95.51% 3 133 120570048006 0.00% 1 245 42 17.14% 3 217 88.57% 3 133 120570049001 0.00% 1 333 107 32.13% 3 296 88.89% 3 133 120570049002 0.93% 1 170 37 21.76% 3 170 100.00% 3 133 120570049003 0.00% 1 185 53 28.65% 3 164 88.65% 3 133 120570049004 1.30% 1 690 6 0.87% 3 611 88.55% 3 133 120570049005 0.00% 1 741 50 6.75% 3 241 32.52% 1 131 120570050001 0.00% 1 573 83 14.49% 3 294 51.31% 2 132 120570050002 0.00% 1 262 43 16.41% 3 71 27.10% 1 131 120570050003 0.00% 1 345 44 12.75% 3 58 16.81% 1 131 120570051011 8.22% 1 832 0 0.00% 3 579 69.59% 3 133 120570051021 0.00% 1 884 0 0.00% 3 100 11.31% 1 131 120570051022 0.00% 1 1158 9 0.78% 3 841 72.63% 3 133 120570053011 0.00% 1 174 49 28.16% 3 174 100.00% 3 133 120570053012 7.02% 1 695 49 7.05% 3 695 100.00% 3 133 120570053013 0.00% 1 316 49 15.51% 3 316 100.00% 3 133 120570053021 0.02% 1 385 18 4.68% 3 385 100.00% 3 133

PAGE 157

157 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570053022 0.00% 1 283 42 14.84% 3 263 92.93% 3 133 120570054011 0.00% 1 380 15 3.95% 3 136 35.79% 2 132 120570054012 0.00% 1 347 0 0.00% 3 12 3.46% 1 131 120570054013 0.00% 1 345 9 2.61% 3 312 90.43% 3 133 120570054014 0.00% 1 655 18 2.75% 3 357 54.50% 2 132 120570054015 0.00% 1 462 14 3.03% 3 369 79.87% 3 133 120570054016 0.00% 1 383 26 6.79% 3 159 41.51% 2 132 120570055001 0.00% 1 358 20 5.59% 3 352 98.32% 3 133 120570055002 0.00% 1 233 20 8.58% 3 197 84.55% 3 133 120570055003 0.00% 1 165 92 55.76% 2 104 63.03% 2 122 120570055004 0.00% 1 306 92 30.07% 3 306 100.00% 3 133 120570057001 0.00% 1 330 39 11.82% 3 310 93.94% 3 133 120570057002 0.00% 1 543 56 10.31% 3 543 100.00% 3 133 120570057003 0.00% 1 595 197 33.11% 3 264 44.37% 2 132 120570057004 1.34% 1 328 62 18.90% 3 256 78.05% 3 133 120570057005 0.00% 1 322 39 12.11% 3 175 54.35% 2 132 120570058001 0.00% 1 311 30 9.65% 3 264 84.89% 3 133 120570058002 0.00% 1 369 0 0.00% 3 361 97.83% 3 133 120570058003 0.00% 1 453 19 4.19% 3 453 100.00% 3 133 120570058004 0.00% 1 268 9 3.36% 3 268 100.00% 3 133 120570058005 0.00% 1 253 0 0.00% 3 233 92.09% 3 133 120570058006 0.00% 1 304 0 0.00% 3 304 100.00% 3 133 120570059001 0.00% 1 476 7 1.47% 3 235 49.37% 2 132 120570059002 0.00% 1 570 0 0.00% 3 437 76.67% 3 133 120570059003 0.00% 1 484 0 0.00% 3 290 59.92% 2 132 120570059004 0.00% 1 260 14 5.38% 3 0 0.00% 1 131 120570059005 0.00% 1 248 14 5.65% 3 12 4.84% 1 131 120570059006 0.00% 1 257 14 5.45% 3 255 99.22% 3 133

PAGE 158

158 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570060001 0.00% 1 236 8 3.39% 3 227 96.19% 3 133 120570060002 0.00% 1 368 18 4.89% 3 296 80.43% 3 133 120570060003 0.00% 1 348 19 5.46% 3 347 99.71% 3 133 120570060004 0.00% 1 231 0 0.00% 3 231 100.00% 3 133 120570060005 0.00% 1 385 32 8.31% 3 337 87.53% 3 133 120570060006 0.00% 1 369 32 8.67% 3 165 44.72% 2 132 120570060007 0.00% 1 264 9 3.41% 3 238 90.15% 3 133 120570061011 0.00% 1 322 9 2.80% 3 259 80.43% 3 133 120570061012 0.00% 1 412 37 8.98% 3 370 89.81% 3 133 120570061013 0.00% 1 234 0 0.00% 3 226 96.58% 3 133 120570061014 0.00% 1 579 46 7.94% 3 430 74.27% 3 133 120570061031 0.00% 1 256 52 20.31% 3 178 69.53% 3 133 120570061032 0.00% 1 340 97 28.53% 3 340 100.00% 3 133 120570061033 0.00% 1 395 52 13.16% 3 395 100.00% 3 133 120570061034 0.00% 1 303 97 32.01% 3 31 10.23% 1 131 120570061035 0.00% 1 519 85 16.38% 3 457 88.05% 3 133 120570061036 0.00% 1 464 85 18.32% 3 344 74.14% 3 133 120570062001 0.00% 1 347 14 4.03% 3 331 95.39% 3 133 120570062002 0.00% 1 468 23 4.91% 3 396 84.62% 3 133 120570062003 0.00% 1 407 26 6.39% 3 407 100.00% 3 133 120570062004 0.00% 1 333 9 2.70% 3 333 100.00% 3 133 120570063001 0.00% 1 365 10 2.74% 3 365 100.00% 3 133 120570063002 0.00% 1 371 11 2.96% 3 371 100.00% 3 133 120570063003 0.00% 1 347 0 0.00% 3 347 100.00% 3 133 120570063004 0.00% 1 416 7 1.68% 3 416 100.00% 3 133 120570064001 0.00% 1 284 5 1.76% 3 176 61.97% 2 132 120570064002 0.00% 1 359 20 5.57% 3 151 42.06% 2 132 120570064003 0.00% 1 343 12 3.50% 3 343 100.00% 3 133

PAGE 159

159 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570064004 0.00% 1 478 0 0.00% 3 462 96.65% 3 133 120570065011 8.88% 1 605 202 33.39% 3 363 60.00% 2 132 120570065012 5.16% 1 250 8 3.20% 3 250 100.00% 3 133 120570065013 0.00% 1 470 107 22.77% 3 233 49.57% 2 132 120570065014 14.93% 1 275 54 19.64% 3 168 61.09% 2 132 120570065021 0.00% 1 706 107 15.16% 3 115 16.29% 1 131 120570065022 0.00% 1 0 107 100.00% 1 0 0.00% 1 111 120570065023 20.75% 1 484 107 22.11% 3 254 52.48% 2 132 120570066001 5.42% 1 392 78 19.90% 3 292 74.49% 3 133 120570066002 4.35% 1 458 23 5.02% 3 445 97.16% 3 133 120570066003 0.00% 1 629 78 12.40% 3 629 100.00% 3 133 120570066004 18.30% 1 433 105 24.25% 3 409 94.46% 3 133 120570067001 15.67% 1 555 81 14.59% 3 555 100.00% 3 133 120570067002 0.00% 1 510 18 3.53% 3 510 100.00% 3 133 120570067003 5.51% 1 342 0 0.00% 3 342 100.00% 3 133 120570067004 0.00% 1 364 11 3.02% 3 315 86.54% 3 133 120570067005 6.22% 1 251 0 0.00% 3 251 100.00% 3 133 120570067006 5.90% 1 307 0 0.00% 3 307 100.00% 3 133 120570068011 0.00% 1 359 54 15.04% 3 25 6.96% 1 131 120570068012 0.00% 1 337 68 20.18% 3 337 100.00% 3 133 120570068013 0.00% 1 335 54 16.12% 3 328 97.91% 3 133 120570068014 0.00% 1 691 9 1.30% 3 691 100.00% 3 133 120570068015 0.00% 1 366 72 19.67% 3 262 71.58% 3 133 120570068021 0.00% 1 534 127 23.78% 3 133 24.91% 1 131 120570068022 0.00% 1 567 15 2.65% 3 567 100.00% 3 133 120570068023 0.00% 1 389 32 8.23% 3 367 94.34% 3 133 120570069001 0.00% 1 594 30 5.05% 3 594 100.00% 3 133 120570069002 0.00% 1 317 24 7.57% 3 317 100.00% 3 133

PAGE 160

160 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570069003 0.00% 1 330 30 9.09% 3 146 44.24% 2 132 120570069004 0.00% 1 393 37 9.41% 3 178 45.29% 2 132 120570069005 0.00% 1 323 37 11.46% 3 323 100.00% 3 133 120570069006 0.00% 1 282 30 10.64% 3 227 80.50% 3 133 120570070011 0.00% 1 355 49 13.80% 3 57 16.06% 1 131 120570070012 0.00% 1 367 49 13.35% 3 347 94.55% 3 133 120570070013 0.00% 1 462 44 9.52% 3 458 99.13% 3 133 120570070014 0.00% 1 289 11 3.81% 3 289 100.00% 3 133 120570070021 0.00% 1 423 122 28.84% 3 221 52.25% 2 132 120570070022 0.00% 1 541 122 22.55% 3 100 18.48% 1 131 120570070023 0.00% 1 304 122 40.13% 2 36 11.84% 1 121 120570071021 25.13% 1 174 128 73.56% 1 137 78.74% 3 113 120570071022 0.00% 1 193 128 66.32% 2 193 100.00% 3 123 120570071023 0.00% 1 1001 128 12.79% 3 17 1.70% 1 131 120570071031 0.00% 1 447 35 7.83% 3 441 98.66% 3 133 120570071032 0.00% 1 597 0 0.00% 3 597 100.00% 3 133 120570071033 0.00% 1 459 26 5.66% 3 404 88.02% 3 133 120570072001 0.00% 1 655 53 8.09% 3 428 65.34% 2 132 120570072002 0.00% 1 436 11 2.52% 3 436 100.00% 3 133 120570072003 0.00% 1 508 24 4.72% 3 469 92.32% 3 133 120570102092 7.27% 1 1284 37 2.88% 3 0 0.00% 1 131 120570102093 0.00% 1 332 37 11.14% 3 0 0.00% 1 131 120570102094 27.26% 1 177 37 20.90% 3 0 0.00% 1 131 120570102101 49.21% 2 917 37 4.03% 3 0 0.00% 1 231 120570102102 0.00% 1 453 37 8.17% 3 0 0.00% 1 131 120570102111 8.34% 1 296 5 1.69% 3 0 0.00% 1 131 120570102112 0.00% 1 379 0 0.00% 3 0 0.00% 1 131 120570102113 0.00% 1 803 25 3.11% 3 0 0.00% 1 131

PAGE 161

161 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570102114 0.00% 1 441 25 5.67% 3 0 0.00% 1 131 120570102121 0.00% 1 820 25 3.05% 3 0 0.00% 1 131 120570102122 0.00% 1 868 25 2.88% 3 0 0.00% 1 131 120570102141 0.00% 1 120 0 0.00% 3 0 0.00% 1 131 120570105012 0.00% 1 806 33 4.09% 3 332 41.19% 2 132 120570105013 0.00% 1 676 77 11.39% 3 136 20.12% 1 131 120570110081 0.00% 1 345 0 0.00% 3 0 0.00% 1 131 120570110082 0.00% 1 484 0 0.00% 3 30 6.20% 1 131 120570110083 13.05% 1 853 0 0.00% 3 248 29.07% 1 131 120570110084 0.26% 1 382 0 0.00% 3 0 0.00% 1 131 120570110085 16.32% 1 259 0 0.00% 3 0 0.00% 1 131 120570110086 0.00% 1 305 10 3.28% 3 0 0.00% 1 131 120570110121 0.00% 1 1018 9 0.88% 3 74 7.27% 1 131 120570110122 0.00% 1 266 9 3.38% 3 68 25.56% 1 131 120570110123 0.00% 1 608 9 1.48% 3 0 0.00% 1 131 120570110131 8.63% 1 837 9 1.08% 3 0 0.00% 1 131 120570110132 10.79% 1 526 9 1.71% 3 0 0.00% 1 131 120570110141 48.57% 2 351 14 3.99% 3 0 0.00% 1 231 120570110151 0.00% 1 582 111 19.07% 3 0 0.00% 1 131 120570110152 0.00% 1 1035 111 10.72% 3 0 0.00% 1 131 120570110153 0.00% 1 310 0 0.00% 3 0 0.00% 1 131 120570110154 0.00% 1 915 111 12.13% 3 2 0.22% 1 131 120570110161 0.00% 1 334 0 0.00% 3 131 39.22% 2 132 120570110162 22.76% 1 467 111 23.77% 3 0 0.00% 1 131 120570110163 0.00% 1 425 0 0.00% 3 240 56.47% 2 132 120570117081 18.76% 1 1479 26 1.76% 3 379 25.63% 1 131 120570118021 0.00% 1 365 22 6.03% 3 50 13.70% 1 131 120570119041 0.00% 1 465 206 44.30% 2 1 0.22% 1 121

PAGE 162

162 Bicycle Facilities Private Vehicle Availability Transit Census Block Group ID % of Roads w/ Bicycle Facilities Score (3,2,1) Tot HH Total HH w/ No Veh % of HH w/ No Veh Score (3,2,1) HH w/in 1/2mi of Bus % HH 0 1/2mi from transit stop Score (3,2,1) Combined Score 120570119051 0.00% 1 529 206 38.94% 2 134 25.33% 1 121 120570119062 0.00% 1 582 206 35.40% 2 170 29.21% 1 121 120570120012 0.00% 1 592 58 9.80% 3 269 45.44% 2 132 120570120021 0.00% 1 641 59 9.20% 3 152 23.71% 1 131

PAGE 163

163 APPENDIX E FOOD ACCESS AUDIT INSTRUMENT: DATA SUMMARY SHEET Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570001011 232 3 233 3 131 3 0.0847 0.0115 0.0450 $20,694 6 1 Good 120570001012 132 2 111 1 132 3 0.1942 0.0935 0.0480 $36,346 3 6 Highly Insufficient 120570001021 133 2 113 1 133 3 0.2269 0.0739 0.0554 $24,260 3 6 Highly Insufficient 120570001022 133 2 112 1 123 2 0.2082 0.0880 0.3498 $46,250 2 8 Poor 120570001023 132 2 123 1 122 2 0.1171 0.0868 0.5813 $15,250 2 8 Poor 120570001024 132 2 123 1 132 3 0.3238 0.0471 0.2022 $28,355 3 6 Highly Insufficient 120570002011 232 3 133 2 123 2 0.1360 0.0310 0.4511 $31,364 4 4 Insufficient 120570002012 133 2 112 1 133 3 0.2680 0.0571 0.1017 $41,864 3 6 Highly Insufficient 120570002021 232 3 133 2 133 3 0.2319 0.1335 0.0744 $30,980 5 3 Sufficient 120570002022 332 4 233 3 132 3 0.3130 0.0748 0.2050 $25,654 7 1 Very Good 120570002023 332 4 113 1 132 3 0.2618 0.0787 0.2913 $25,654 5 3 Sufficient 120570003001 233 3 112 1 133 3 0.3774 0.0390 0.2646 $20,441 4 3 Insufficient 120570003002 233 3 112 1 133 3 0.3000 0.0719 0.0625 $38,667 4 3 Insufficient 120570003003 232 3 113 1 133 3 0.2286 0.1333 0.1000 $62,237 4 3 Insufficient 120570003004 213 2 113 1 123 2 0.2773 0.0455 0.3500 $51,379 2 1 Poor 120570003005 222 3 113 1 133 3 0.3115 0.0689 0.1377 $94,206 4 3 Insufficient 120570003006 133 2 113 1 133 3 0.2123 0.0716 0.0639 $71,875 3 6 Highly Insufficient 120570004011 132 2 113 1 133 3 0.2078 0.1412 0.0941 $75,583 3 6 Highly Insufficient 120570004012 233 3 113 1 132 3 0.2610 0.0881 0.1288 $114,079 4 3 Insufficient 120570004013 232 3 113 1 133 3 0.1993 0.1139 0.0569 $100,980 4 3 Insufficient

PAGE 164

164 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570004021 312 4 213 2 133 3 0.1426 0.1097 0.1408 $67,784 6 3 Good 120570004022 222 3 213 2 131 3 0.1525 0.0745 0.3191 $32,458 5 3 Sufficient 120570004023 323 4 113 1 133 3 0.2195 0.0732 0.0366 $66,528 5 3 Sufficient 120570005001 112 1 111 1 133 3 0.1384 0.1412 0.0339 $26,420 2 2 Poor 120570005002 222 3 112 1 133 3 0.1461 0.1376 0.0000 $21,977 4 3 Insufficient 120570005003 232 3 112 1 133 3 0.1176 0.1176 0.0486 $23,580 4 3 Insufficient 120570005004 333 4 112 1 133 3 0.1633 0.1388 0.0143 $21,892 5 3 Sufficient 120570006011 333 4 232 3 122 2 0.2506 0.0621 0.3908 $24,352 6 2 Good 120570006012 333 4 113 1 133 3 0.2044 0.1039 0.2848 $29,015 5 3 Sufficient 120570006013 332 4 233 3 131 3 0.1896 0.0279 0.3160 $21,250 7 1 Very Good 120570006021 323 4 213 2 133 3 0.1869 0.0744 0.1053 $20,905 6 3 Good 120570006022 313 4 213 2 133 3 0.2790 0.0709 0.1631 $20,435 6 3 Good 120570007001 313 4 113 1 133 3 0.3772 0.0930 0.2789 $11,223 5 3 Sufficient 120570007002 223 3 112 1 123 2 0.3352 0.0997 0.3546 $38,846 3 4 Highly Insufficient 120570007003 222 3 112 1 133 3 0.3838 0.0679 0.2715 $25,089 4 3 Insufficient 120570007004 232 3 112 1 133 3 0.2787 0.0601 0.0801 $35,625 4 3 Insufficient 120570008001 113 1 113 1 133 3 0.2925 0.0884 0.1412 $14,327 2 6 Poor 120570008002 233 3 113 1 133 3 0.2714 0.1075 0.0364 $18,158 4 3 Insufficient 120570009011 133 2 113 1 111 1 0.2117 0.0204 0.7423 $15,682 1 8 Very Poor 120570009012 133 2 123 1 113 1 0.2717 0.0815 0.7908 $12,863 1 8 Very Poor 120570009013 132 2 112 1 113 1 0.2422 0.1211 0.8924 $27,153 1 8 Very Poor 120570009021 132 2 122 1 131 3 0.2151 0.0125 0.2608 $26,450 3 6 Highly Insufficient 120570009022 132 2 123 1 121 2 0.2078 0.0226 0.5988 $26,450 2 8 Poor 120570009023 132 2 111 1 132 3 0.0978 0.0708 0.3356 $34,919 3 6 Highly Insufficient 120570010011 122 1 112 1 131 3 0.3265 0.0943 0.2691 $30,500 2 6 Poor

PAGE 165

165 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570010012 113 1 113 1 131 3 0.4553 0.0566 0.2048 $21,780 2 6 Poor 120570010021 333 4 122 1 133 3 0.3205 0.2154 0.0298 $28,684 5 3 Sufficient 120570010022 333 4 123 1 133 3 0.3404 0.1898 0.2831 $31,300 5 3 Sufficient 120570010023 333 4 222 3 133 3 0.3255 0.2057 0.1589 $41,346 7 1 Very Good 120570010024 333 4 223 3 133 3 0.4294 0.1320 0.1952 $26,250 7 1 Very Good 120570011001 232 3 122 1 133 3 0.1649 0.0722 0.0722 $30,391 4 3 Insufficient 120570011002 232 3 112 1 133 3 0.1333 0.0741 0.0000 $38,295 4 3 Insufficient 120570011003 122 1 112 1 133 3 0.2244 0.1330 0.0970 $20,441 2 6 Poor 120570012001 232 3 113 1 133 3 0.1532 0.0766 0.0315 $20,893 4 3 Insufficient 120570012002 233 3 123 1 122 2 0.3451 0.0761 0.5046 $31,941 3 4 Highly Insufficient 120570012003 233 3 123 1 133 3 0.2336 0.0598 0.1966 $29,357 4 3 Insufficient 120570013001 333 4 233 3 122 2 0.1128 0.1022 0.4031 $12,357 6 2 Good 120570013002 332 4 123 1 133 3 0.1612 0.1184 0.0155 $47,500 5 3 Sufficient 120570013003 333 4 213 2 133 3 0.1653 0.1694 0.0000 $30,918 6 3 Good 120570013004 332 4 122 1 133 3 0.1529 0.1322 0.1446 $18,333 5 3 Sufficient 120570013005 333 4 123 1 133 3 0.1843 0.1229 0.0786 $11,205 5 3 Sufficient 120570014001 333 4 223 3 133 3 0.2312 0.1237 0.0957 $32,946 7 1 Very Good 120570014002 332 4 123 1 133 3 0.2311 0.1167 0.1350 $23,523 5 3 Sufficient 120570014003 333 4 313 4 133 3 0.1860 0.1279 0.1599 $9,461 8 1 Excellent 120570014004 333 4 323 4 133 3 0.2000 0.1238 0.1667 $42,216 8 1 Excellent 120570015001 233 3 113 1 133 3 0.1014 0.1115 0.0439 $41,346 4 3 Insufficient 120570015002 333 4 112 1 133 3 0.1384 0.0819 0.0537 $17,071 5 3 Sufficient 120570015003 332 4 122 1 133 3 0.1186 0.0651 0.0419 $41,892 5 3 Sufficient 120570016001 333 4 223 3 133 3 0.1561 0.0439 0.0585 $30,391 7 1 Very Good 120570016002 333 4 223 3 133 3 0.1397 0.0355 0.1530 $38,295 7 1 Very Good

PAGE 166

166 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570016003 333 4 213 2 133 3 0.0933 0.0533 0.0844 $35,313 6 3 Good 120570017001 333 4 223 3 133 3 0.1601 0.0682 0.0840 $34,250 7 1 Very Good 120570017002 332 4 111 1 133 3 0.1700 0.0900 0.1400 $34,205 5 3 Sufficient 120570017003 222 3 113 1 133 3 0.2757 0.1196 0.0797 $40,547 4 3 Insufficient 120570017004 333 4 213 2 133 3 0.1215 0.0491 0.0561 $38,333 6 3 Good 120570017005 322 4 113 1 133 3 0.1966 0.1103 0.0517 $26,042 5 3 Sufficient 120570018001 333 4 213 2 133 3 0.3855 0.2088 0.2209 $23,946 6 3 Good 120570018002 333 4 213 2 131 3 0.4695 0.0782 0.1966 $33,750 6 3 Good 120570018003 332 4 112 1 133 3 0.4473 0.1418 0.2000 $58,125 5 3 Sufficient 120570018004 333 4 112 1 133 3 0.3946 0.1676 0.1297 $14,000 5 3 Sufficient 120570018005 223 3 112 1 133 3 0.3697 0.1345 0.2269 $22,857 4 3 Insufficient 120570019001 233 3 113 1 133 3 0.3538 0.1986 0.1877 $28,393 4 3 Insufficient 120570019002 232 3 112 1 133 3 0.4569 0.1332 0.2193 $38,182 4 3 Insufficient 120570019003 233 3 113 1 133 3 0.3483 0.1124 0.1873 $18,625 4 3 Insufficient 120570020001 313 4 113 1 133 3 0.3219 0.1000 0.1844 $24,352 5 3 Sufficient 120570020002 323 4 112 1 133 3 0.4066 0.1026 0.1099 $128,428 5 3 Sufficient 120570020003 333 4 113 1 133 3 0.3295 0.1434 0.0620 $27,752 5 3 Sufficient 120570021001 323 4 213 2 133 3 0.1483 0.0915 0.0852 $26,420 6 3 Good 120570021002 323 4 213 2 133 3 0.1942 0.0899 0.0612 $31,250 6 3 Good 120570021003 333 4 213 2 133 3 0.2571 0.1143 0.1857 $21,977 6 3 Good 120570022001 333 4 213 2 133 3 0.1204 0.0836 0.0635 $43,083 6 3 Good 120570022002 333 4 213 2 133 3 0.1196 0.0478 0.1053 $27,130 6 3 Good 120570022003 323 4 113 1 133 3 0.1364 0.1080 0.0000 $33,182 5 3 Sufficient 120570023001 233 3 123 1 133 3 0.1441 0.0457 0.0879 $31,953 4 3 Insufficient 120570023002 332 4 112 1 133 3 0.1812 0.1159 0.1087 $36,771 5 3 Sufficient

PAGE 167

167 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570023003 323 4 113 1 133 3 0.1659 0.0381 0.0605 $32,760 5 3 Sufficient 120570024001 333 4 313 4 133 3 0.1822 0.1867 0.0800 $35,313 8 1 Excellent 120570024002 332 4 122 1 122 2 0.1833 0.1019 0.3852 $43,083 4 4 Insufficient 120570024003 232 3 112 1 131 3 0.1609 0.1057 0.3281 $33,636 4 3 Insufficient 120570024004 333 4 123 1 133 3 0.1613 0.1976 0.0484 $30,547 5 3 Sufficient 120570025001 333 4 123 1 131 3 0.1694 0.0694 0.1778 $32,813 5 3 Sufficient 120570025002 333 4 113 1 122 2 0.2014 0.0949 0.4444 $31,953 4 4 Insufficient 120570025003 332 4 113 1 131 3 0.1284 0.0996 0.1053 $38,026 5 3 Sufficient 120570025004 333 4 223 3 132 3 0.1821 0.1358 0.1127 $31,797 7 1 Very Good 120570026001 333 4 223 3 131 3 0.1333 0.0549 0.3059 $22,589 7 1 Very Good 120570026002 223 3 323 4 132 3 0.1200 0.0550 0.0600 $24,709 7 1 Very Good 120570027001 333 4 233 3 133 3 0.0941 0.1691 0.1850 $35,556 7 1 Very Good 120570027002 333 4 233 3 133 3 0.2279 0.1807 0.2402 $9,114 7 1 Very Good 120570027003 332 4 222 3 133 3 0.1752 0.2068 0.0706 $52,331 7 1 Very Good 120570027004 333 4 133 2 133 3 0.1563 0.1914 0.2169 $31,033 6 3 Good 120570027005 333 4 133 2 133 3 0.1981 0.2173 0.1269 $41,595 6 3 Good 120570028001 333 4 113 1 133 3 0.1049 0.1259 0.0000 $42,216 5 3 Sufficient 120570028002 332 4 111 1 133 3 0.0812 0.1201 0.1039 $38,026 5 3 Sufficient 120570028003 332 4 222 3 133 3 0.1137 0.1070 0.0401 $31,719 7 1 Very Good 120570028004 333 4 112 1 133 3 0.1676 0.0934 0.0495 $27,321 5 3 Sufficient 120570029001 313 4 113 1 133 3 0.1880 0.0720 0.0640 $25,995 5 3 Sufficient 120570029002 322 4 112 1 133 3 0.2038 0.0596 0.1505 $42,404 5 3 Sufficient 120570029003 233 3 112 1 133 3 0.2780 0.1073 0.1366 $25,521 4 3 Insufficient 120570030001 323 4 113 1 123 2 0.2691 0.0763 0.3735 $24,276 4 4 Insufficient 120570030002 312 4 112 1 121 2 0.6658 0.0201 0.5503 $18,393 4 4 Insufficient

PAGE 168

168 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570030003 332 4 113 1 133 3 0.2392 0.0731 0.2924 $27,106 5 3 Sufficient 120570031001 323 4 113 1 133 3 0.2533 0.1572 0.2096 $27,250 5 3 Sufficient 120570031002 333 4 113 1 133 3 0.2784 0.1333 0.1529 $27,031 5 3 Sufficient 120570031003 233 3 113 1 132 3 0.3913 0.1565 0.0522 $21,364 4 3 Insufficient 120570031004 233 3 112 1 132 3 0.5317 0.0992 0.2659 $36,736 4 3 Insufficient 120570032001 333 4 113 1 132 3 0.2141 0.1435 0.1913 $52,969 5 3 Sufficient 120570032002 223 3 113 1 123 2 0.2230 0.0980 0.3818 $13,831 3 4 Highly Insufficient 120570032003 233 3 112 1 133 3 0.2526 0.1082 0.3144 $27,500 4 3 Insufficient 120570033001 233 3 112 1 133 3 0.3547 0.1170 0.2113 $25,213 4 3 Insufficient 120570033002 132 2 112 1 122 2 0.3547 0.1173 0.3467 $35,054 2 8 Poor 120570033003 132 2 112 1 113 1 0.2517 0.0769 0.9091 $35,156 1 8 Very Poor 120570034001 133 2 113 1 132 3 0.5662 0.0956 0.2978 $31,927 3 6 Highly Insufficient 120570034002 133 2 113 1 123 2 0.4177 0.1341 0.4573 $27,262 2 8 Poor 120570034003 133 2 113 1 123 2 0.3908 0.1698 0.3558 $47,778 2 8 Poor 120570035001 132 2 112 1 133 3 0.3314 0.0800 0.3143 $25,089 3 6 Highly Insufficient 120570035002 133 2 113 1 123 2 0.3714 0.1333 0.3476 $17,875 2 8 Poor 120570035003 133 2 113 1 133 3 0.3956 0.1600 0.1733 $30,066 3 6 Highly Insufficient 120570035004 132 2 112 1 133 3 0.3733 0.2074 0.1382 $26,739 3 6 Highly Insufficient 120570036001 133 2 113 1 133 3 0.3453 0.1772 0.1652 $25,224 3 6 Highly Insufficient 120570036002 113 1 112 1 132 3 0.4377 0.0504 0.0690 $16,250 2 6 Poor 120570036003 132 2 113 1 133 3 0.4101 0.0926 0.0847 $20,446 3 6 Highly Insufficient 120570036004 133 2 113 1 133 3 0.3470 0.2239 0.1306 $19,135 3 6 Highly Insufficient 120570037001 133 2 123 1 133 3 0.2288 0.0720 0.1314 $37,232 3 6 Highly Insufficient 120570037002 133 2 123 1 132 3 0.2532 0.0519 0.1883 $31,016 3 6 Highly Insufficient 120570038001 132 2 112 1 123 2 0.1640 0.0582 0.4286 $14,306 2 8 Poor

PAGE 169

169 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570038002 133 2 113 1 133 3 0.2512 0.1469 0.3033 $21,480 3 6 Highly Insufficient 120570039001 133 2 113 1 133 3 0.0410 0.0386 0.2425 $26,039 3 6 Highly Insufficient 120570039002 132 2 112 1 131 3 0.3982 0.0395 0.0000 $45,385 3 6 Highly Insufficient 120570040001 132 2 112 1 112 1 0.1471 0.1471 1.6471 $32,607 1 8 Very Poor 120570041001 132 2 112 1 122 2 0.3700 0.0396 0.3700 $32,824 2 8 Poor 120570041002 132 2 122 1 121 2 0.2539 0.0625 0.3438 $26,042 2 8 Poor 120570042001 233 3 112 1 133 3 0.1770 0.1053 0.1483 $41,892 4 3 Insufficient 120570042002 132 2 122 1 133 3 0.2393 0.1656 0.2515 $28,935 3 6 Highly Insufficient 120570043001 232 3 122 1 121 2 0.5833 0.0533 0.6247 $59,792 3 4 Highly Insufficient 120570043002 222 3 112 1 121 2 0.2768 0.0969 0.6247 $76,633 3 4 Highly Insufficient 120570043003 232 3 111 1 121 2 0.5702 0.0203 0.6273 $33,462 3 4 Highly Insufficient 120570044001 233 3 123 1 123 2 0.3274 0.1283 0.5000 $43,833 3 4 Highly Insufficient 120570044002 233 3 113 1 133 3 0.3146 0.2022 0.2060 $44,338 4 3 Insufficient 120570044003 233 3 113 1 123 2 0.3471 0.1074 0.4669 $126,723 3 4 Highly Insufficient 120570045001 333 4 232 3 133 3 0.1993 0.2124 0.1111 $28,512 7 1 Very Good 120570045002 233 3 123 1 123 2 0.1637 0.1696 0.3626 $34,615 3 4 Highly Insufficient 120570045003 333 4 312 4 133 3 0.2030 0.2103 0.0627 $31,149 8 1 Excellent 120570045004 233 3 113 1 133 3 0.2313 0.1803 0.1905 $25,291 4 3 Insufficient 120570045005 232 3 131 2 133 3 0.2540 0.1250 0.2298 $26,354 5 3 Sufficient 120570046001 323 4 313 4 133 3 0.2671 0.1889 0.0847 $94,057 8 1 Excellent 120570046002 322 4 313 4 133 3 0.1619 0.1134 0.0837 $58,456 8 1 Excellent 120570046003 333 4 313 4 131 3 0.1750 0.0571 0.2237 $54,185 8 1 Excellent 120570047001 322 4 312 4 133 3 0.1823 0.2315 0.1330 $50,625 8 1 Excellent 120570047002 113 1 112 1 133 3 0.0728 0.0918 0.2500 $42,404 2 6 Poor 120570047003 212 2 113 1 132 3 0.1003 0.0870 0.1237 $35,166 3 6 Highly Insufficient

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170 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570047004 322 4 113 1 133 3 0.1415 0.1195 0.1447 $35,880 5 3 Sufficient 120570048001 332 4 333 4 133 3 0.1390 0.1525 0.0881 $38,622 8 1 Excellent 120570048002 232 3 132 2 133 3 0.1971 0.1460 0.0365 $27,159 5 3 Sufficient 120570048003 333 4 132 2 133 3 0.1521 0.2548 0.0418 $33,462 6 3 Good 120570048004 332 4 133 2 133 3 0.1575 0.1260 0.0512 $34,615 6 3 Good 120570048005 232 3 133 2 133 3 0.1498 0.1685 0.0375 $41,881 5 3 Sufficient 120570048006 332 4 213 2 133 3 0.1510 0.1020 0.1714 $31,149 6 3 Good 120570049001 332 4 213 2 133 3 0.2432 0.1021 0.3213 $27,031 6 3 Good 120570049002 322 4 112 1 133 3 0.2647 0.1412 0.2176 $21,364 5 3 Sufficient 120570049003 323 4 213 2 133 3 0.1892 0.1351 0.2865 $26,354 6 3 Good 120570049004 323 4 313 4 133 3 0.0174 0.0029 0.0087 $40,313 8 1 Excellent 120570049005 312 4 313 4 131 3 0.0351 0.0148 0.0675 $44,191 8 1 Excellent 120570050001 332 4 212 2 132 3 0.1536 0.0803 0.1449 $22,500 6 3 Good 120570050002 332 4 221 3 131 3 0.4618 0.0611 0.1641 $22,589 7 1 Very Good 120570050003 312 4 312 4 131 3 0.0174 0.0754 0.1275 $21,838 8 1 Excellent 120570051011 332 4 223 3 133 3 0.0168 0.0192 0.0000 $14,086 7 1 Very Good 120570051021 212 2 112 1 131 3 0.0633 0.0566 0.0000 $128,408 3 6 Highly Insufficient 120570051022 312 4 112 1 133 3 0.0276 0.0363 0.0078 $68,464 5 3 Sufficient 120570053011 222 3 112 1 133 3 0.0345 0.0345 0.2816 $30,078 4 3 Insufficient 120570053012 222 3 111 1 133 3 0.0331 0.0058 0.0705 $10,172 4 3 Insufficient 120570053013 222 3 111 1 133 3 0.0158 0.0127 0.1551 $55,500 4 3 Insufficient 120570053021 133 2 123 1 133 3 0.1325 0.1065 0.0468 $26,739 3 6 Highly Insufficient 120570053022 132 2 122 1 133 3 0.1661 0.1661 0.1484 $21,597 3 6 Highly Insufficient 120570054011 112 1 111 1 132 3 0.0789 0.1263 0.0395 $29,688 2 6 Poor 120570054012 112 1 111 1 131 3 0.0605 0.1931 0.0000 $141,553 2 6 Poor

PAGE 171

171 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570054013 112 1 112 1 133 3 0.1014 0.1391 0.0261 $70,156 2 6 Poor 120570054014 312 4 211 2 132 3 0.0336 0.0824 0.0275 $57,206 6 3 Good 120570054015 212 2 112 1 133 3 0.0844 0.0931 0.0303 $52,222 3 6 Highly Insufficient 120570054016 113 1 112 1 132 3 0.0836 0.0418 0.0679 $37,059 2 6 Poor 120570055001 312 4 312 4 133 3 0.0670 0.0307 0.0559 $51,625 8 1 Excellent 120570055002 313 4 212 2 133 3 0.0644 0.0172 0.0858 $33,973 6 3 Good 120570055003 313 4 212 2 122 2 0.0606 0.0242 0.5576 $82,497 5 4 Sufficient 120570055004 313 4 212 2 133 3 0.0327 0.0425 0.3007 $31,719 6 3 Good 120570057001 333 4 313 4 133 3 0.0764 0.0731 0.1182 $40,104 8 1 Excellent 120570057002 332 4 113 1 133 3 0.0718 0.0405 0.1031 $36,779 5 3 Sufficient 120570057003 333 4 112 1 132 3 0.0471 0.0353 0.3311 $46,964 5 3 Sufficient 120570057004 333 4 213 2 133 3 0.0518 0.0091 0.1890 $33,750 6 3 Good 120570057005 323 4 112 1 132 3 0.0683 0.0124 0.1211 $34,637 5 3 Sufficient 120570058001 212 2 111 1 133 3 0.0740 0.0772 0.0965 $34,250 3 6 Highly Insufficient 120570058002 313 4 112 1 133 3 0.0759 0.0623 0.0000 $24,338 5 3 Sufficient 120570058003 312 4 312 4 133 3 0.0905 0.1369 0.0419 $23,750 8 1 Excellent 120570058004 312 4 313 4 133 3 0.1567 0.1604 0.0336 $7,486 8 1 Excellent 120570058005 312 4 313 4 133 3 0.1265 0.0909 0.0000 $23,542 8 1 Excellent 120570058006 313 4 213 2 133 3 0.1316 0.1184 0.0000 $21,328 6 3 Good 120570059001 212 2 112 1 132 3 0.0630 0.1723 0.0147 $27,426 3 6 Highly Insufficient 120570059002 212 2 112 1 133 3 0.0596 0.0526 0.0000 $15,179 3 6 Highly Insufficient 120570059003 212 2 111 1 132 3 0.0579 0.1694 0.0000 $109,681 3 6 Highly Insufficient 120570059004 112 1 111 1 131 3 0.0500 0.2692 0.0538 $40,114 2 6 Poor 120570059005 112 1 111 1 131 3 0.0726 0.1613 0.0565 $128,428 2 6 Poor 120570059006 212 2 112 1 133 3 0.0739 0.2374 0.0545 $128,428 3 6 Highly Insufficient

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172 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570060001 313 4 313 4 133 3 0.0466 0.1186 0.0339 $112,605 8 1 Excellent 120570060002 313 4 312 4 133 3 0.0625 0.0598 0.0489 $44,318 8 1 Excellent 120570060003 313 4 212 2 133 3 0.0718 0.1092 0.0546 $52,465 6 3 Good 120570060004 312 4 312 4 133 3 0.0476 0.2381 0.0000 $126,723 8 1 Excellent 120570060005 212 2 111 1 133 3 0.0597 0.0286 0.0831 $51,379 3 6 Highly Insufficient 120570060006 312 4 112 1 132 3 0.0569 0.0136 0.0867 $35,294 5 3 Sufficient 120570060007 312 4 212 2 133 3 0.0795 0.1174 0.0341 $47,500 6 3 Good 120570061011 313 4 212 2 133 3 0.0466 0.0497 0.0280 $23,523 6 3 Good 120570061012 312 4 212 2 133 3 0.0388 0.0850 0.0898 $32,946 6 3 Good 120570061013 313 4 112 1 133 3 0.0513 0.1068 0.0000 $39,813 5 3 Sufficient 120570061014 313 4 112 1 133 3 0.0345 0.0449 0.0794 $28,750 5 3 Sufficient 120570061031 313 4 112 1 133 3 0.0547 0.0117 0.2031 $49,351 5 3 Sufficient 120570061032 212 2 112 1 133 3 0.0382 0.1235 0.2853 $35,556 3 6 Highly Insufficient 120570061033 213 2 112 1 133 3 0.0253 0.1519 0.1316 $37,935 3 6 Highly Insufficient 120570061034 112 1 112 1 131 3 0.0132 0.0297 0.3201 $33,375 2 6 Poor 120570061035 212 2 112 1 133 3 0.0578 0.1464 0.1638 $46,350 3 6 Highly Insufficient 120570061036 312 4 111 1 133 3 0.0884 0.1530 0.1832 $46,350 5 3 Sufficient 120570062001 212 2 112 1 133 3 0.0749 0.0375 0.0403 $71,875 3 6 Highly Insufficient 120570062002 213 2 112 1 133 3 0.0833 0.0192 0.0491 $59,792 3 6 Highly Insufficient 120570062003 312 4 113 1 133 3 0.0934 0.0590 0.0639 $39,621 5 3 Sufficient 120570062004 313 4 213 2 133 3 0.1081 0.0901 0.0270 $111,606 6 3 Good 120570063001 213 2 112 1 133 3 0.1068 0.0658 0.0274 $75,583 3 6 Highly Insufficient 120570063002 212 2 112 1 133 3 0.0755 0.1024 0.0296 $105,121 3 6 Highly Insufficient 120570063003 312 4 112 1 133 3 0.1239 0.1037 0.0000 $57,083 5 3 Sufficient 120570063004 313 4 213 2 133 3 0.1130 0.0649 0.0168 $63,864 6 3 Good

PAGE 173

173 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570064001 213 2 112 1 132 3 0.0563 0.1444 0.0176 $47,292 3 6 Highly Insufficient 120570064002 113 1 113 1 132 3 0.0864 0.1448 0.0557 $19,615 2 6 Poor 120570064003 213 2 113 1 133 3 0.0845 0.1137 0.0350 $73,750 3 6 Highly Insufficient 120570064004 213 2 113 1 133 3 0.1318 0.1109 0.0000 $40,104 3 6 Highly Insufficient 120570065011 213 2 112 1 132 3 0.0711 0.1074 0.3339 $39,773 3 6 Highly Insufficient 120570065012 312 4 112 1 133 3 0.1240 0.1080 0.0320 $44,191 5 3 Sufficient 120570065013 313 4 113 1 132 3 0.0723 0.0723 0.2277 $36,009 5 3 Sufficient 120570065014 313 4 213 2 132 3 0.2945 0.1055 0.1964 $42,500 6 3 Good 120570065021 213 2 112 1 131 3 0.0524 0.0255 0.1516 $34,674 3 6 Highly Insufficient 120570065022 212 2 113 1 111 1 0.0000 0.0000 1.0000 $18,179 1 8 Very Poor 120570065023 212 2 113 1 132 3 0.0351 0.2025 0.2211 $34,674 3 6 Highly Insufficient 120570066001 313 4 213 2 133 3 0.1378 0.0969 0.1990 $22,857 6 3 Good 120570066002 312 4 313 4 133 3 0.1288 0.1419 0.0502 $18,625 8 1 Excellent 120570066003 312 4 212 2 133 3 0.0827 0.0588 0.1240 $32,697 6 3 Good 120570066004 313 4 213 2 133 3 0.1109 0.0762 0.2425 $28,393 6 3 Good 120570067001 312 4 313 4 133 3 0.0793 0.0486 0.1459 $12,357 8 1 Excellent 120570067002 212 2 111 1 133 3 0.0451 0.1137 0.0353 $31,771 3 6 Highly Insufficient 120570067003 312 4 111 1 133 3 0.0643 0.1023 0.0000 $52,222 5 3 Sufficient 120570067004 323 4 113 1 133 3 0.0604 0.1291 0.0302 $89,243 5 3 Sufficient 120570067005 312 4 313 4 133 3 0.0677 0.1434 0.0000 $38,929 8 1 Excellent 120570067006 323 4 113 1 133 3 0.0749 0.0814 0.0000 $50,625 5 3 Sufficient 120570068011 322 4 313 4 131 3 0.1560 0.0724 0.1504 $38,622 8 1 Excellent 120570068012 333 4 213 2 133 3 0.1395 0.0682 0.2018 $33,973 6 3 Good 120570068013 332 4 313 4 133 3 0.1433 0.1343 0.1612 $37,143 8 1 Excellent 120570068014 333 4 123 1 133 3 0.1056 0.0868 0.0130 $40,101 5 3 Sufficient

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174 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570068015 333 4 213 2 133 3 0.1667 0.0792 0.1967 $37,148 6 3 Good 120570068021 232 3 112 1 131 3 0.0300 0.1536 0.2378 $73,750 4 3 Insufficient 120570068022 233 3 113 1 133 3 0.1041 0.1323 0.0265 $42,031 4 3 Insufficient 120570068023 233 3 123 1 133 3 0.1028 0.0566 0.0823 $107,379 4 3 Insufficient 120570069001 233 3 112 1 133 3 0.0926 0.0741 0.0505 $46,657 4 3 Insufficient 120570069002 333 4 113 1 133 3 0.1325 0.0820 0.0757 $32,458 5 3 Sufficient 120570069003 233 3 122 1 132 3 0.0879 0.0545 0.0909 $52,465 4 3 Insufficient 120570069004 333 4 213 2 132 3 0.1501 0.1628 0.0941 $30,000 6 3 Good 120570069005 333 4 123 1 133 3 0.1455 0.0712 0.1146 $38,641 5 3 Sufficient 120570069006 132 2 112 1 133 3 0.0993 0.0957 0.1064 $27,159 3 6 Highly Insufficient 120570070011 322 4 313 4 131 3 0.1070 0.0394 0.1380 $41,553 8 1 Excellent 120570070012 323 4 213 2 133 3 0.1635 0.1444 0.1335 $95,977 6 3 Good 120570070013 333 4 213 2 133 3 0.1602 0.1429 0.0952 $77,230 6 3 Good 120570070014 323 4 213 2 133 3 0.1730 0.1730 0.0381 $55,156 6 3 Good 120570070021 323 4 112 1 132 3 0.3333 0.0827 0.2884 $21,121 5 3 Sufficient 120570070022 312 4 112 1 131 3 0.3364 0.0259 0.2255 $21,121 5 3 Sufficient 120570070023 323 4 112 1 121 2 0.1513 0.0000 0.4013 $35,300 4 4 Insufficient 120570071021 213 2 113 1 113 1 0.1034 0.0172 0.7356 $49,279 1 8 Very Poor 120570071022 113 1 112 1 123 2 0.0881 0.0622 0.6632 $35,417 1 8 Very Poor 120570071023 113 1 112 1 131 3 0.0979 0.0100 0.1279 $35,417 2 6 Poor 120570071031 313 4 213 2 133 3 0.1230 0.1119 0.0783 $44,297 6 3 Good 120570071032 213 2 112 1 133 3 0.1692 0.1005 0.0000 $49,706 3 6 Highly Insufficient 120570071033 213 2 112 1 133 3 0.2026 0.1460 0.0566 $8,750 3 6 Highly Insufficient 120570072001 113 1 112 1 132 3 0.1756 0.0641 0.0809 $38,667 2 6 Poor 120570072002 112 1 112 1 133 3 0.1674 0.1055 0.0252 $20,938 2 6 Poor

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175 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570072003 112 1 112 1 133 3 0.1417 0.0807 0.0472 $27,113 2 6 Poor 120570102092 111 1 211 2 131 3 0.1472 0.0553 0.0288 $38,056 3 6 Highly Insufficient 120570102093 211 2 111 1 131 3 0.0813 0.0181 0.1114 $32,472 3 6 Highly Insufficient 120570102094 311 4 111 1 131 3 0.1017 0.0678 0.2090 $20,446 5 3 Sufficient 120570102101 311 4 211 2 231 4 0.2236 0.0284 0.0403 $22,734 7 3 Very Good 120570102102 211 2 111 1 131 3 0.0552 0.0839 0.0817 $12,350 3 6 Highly Insufficient 120570102111 212 2 111 1 131 3 0.1318 0.0338 0.0169 $22,750 3 6 Highly Insufficient 120570102112 112 1 111 1 131 3 0.0475 0.1187 0.0000 $30,208 2 6 Poor 120570102113 312 4 213 2 131 3 0.2291 0.0785 0.0311 $25,071 6 3 Good 120570102114 111 1 111 1 131 3 0.0862 0.1315 0.0567 $30,375 2 6 Poor 120570102121 111 1 111 1 131 3 0.1195 0.0671 0.0305 $17,875 2 6 Poor 120570102122 111 1 111 1 131 3 0.0622 0.1025 0.0288 $27,941 2 6 Poor 120570102141 111 1 111 1 131 3 0.0750 0.1750 0.0000 $40,313 2 6 Poor 120570105012 132 2 111 1 132 3 0.2705 0.1129 0.0409 $128,408 3 6 Highly Insufficient 120570105013 333 4 122 1 131 3 0.3624 0.0947 0.1139 $24,333 5 3 Sufficient 120570110081 212 2 111 1 131 3 0.0493 0.0754 0.0000 $21,023 3 6 Highly Insufficient 120570110082 112 1 112 1 131 3 0.1157 0.0207 0.0000 $16,250 2 6 Poor 120570110083 212 2 112 1 131 3 0.0926 0.0246 0.0000 $32,500 3 6 Highly Insufficient 120570110084 312 4 211 2 131 3 0.0838 0.1257 0.0000 $95,977 6 3 Good 120570110085 311 4 211 2 131 3 0.1042 0.0965 0.0000 $109,681 6 3 Good 120570110086 312 4 211 2 131 3 0.1246 0.0230 0.0328 $41,027 6 3 Good 120570110121 112 1 111 1 131 3 0.1120 0.0413 0.0088 $56,250 2 6 Poor 120570110122 111 1 111 1 131 3 0.0677 0.0338 0.0338 $48,906 2 6 Poor 120570110123 112 1 111 1 131 3 0.1760 0.0510 0.0148 $66,292 2 6 Poor 120570110131 312 4 213 2 131 3 0.0872 0.0155 0.0108 $57,083 6 3 Good

PAGE 176

176 Census Block Group ID Proximity Reclassified Proximity Diversity in Availability Reclassified Diversity Mobility Reclassified Mobility Percent of Single Female Householders with Families Percent of Householders 65 and over Percent of Households with Zero Cars Median Household Income (2000 Census) Sum of Reclassified values Typology Food_Access_Rating 120570110132 312 4 213 2 131 3 0.1160 0.0076 0.0171 $66,832 6 3 Good 120570110141 112 1 112 1 231 4 0.1994 0.0997 0.0399 $58,281 3 6 Highly Insufficient 120570110151 312 4 212 2 131 3 0.1907 0.1065 0.1907 $68,646 6 3 Good 120570110152 312 4 112 1 131 3 0.0193 0.0386 0.1072 $46,203 5 3 Sufficient 120570110153 111 1 111 1 131 3 0.3742 0.0258 0.0000 $53,822 2 6 Poor 120570110154 122 1 112 1 131 3 0.0350 0.0219 0.1213 $46,203 2 6 Poor 120570110161 112 1 112 1 132 3 0.2006 0.0659 0.0000 $75,285 2 6 Poor 120570110162 311 4 111 1 131 3 0.0857 0.0557 0.2377 $46,203 5 3 Sufficient 120570110163 212 2 111 1 132 3 0.0871 0.1200 0.0000 $75,457 3 6 Highly Insufficient 120570117081 113 1 112 1 131 3 0.0609 0.0622 0.0176 $71,167 2 6 Poor 120570118021 332 4 223 3 131 3 0.3781 0.4438 0.0603 $54,821 7 1 Very Good 120570119041 232 3 122 1 121 2 0.1871 0.0172 0.4430 $26,811 3 4 Highly Insufficient 120570119051 333 4 313 4 121 2 0.1720 0.1059 0.3894 $28,545 7 2 Very Good 120570119062 333 4 223 3 121 2 0.1770 0.0859 0.3540 $41,250 6 2 Good 120570120012 222 3 113 1 132 3 0.2432 0.2111 0.0980 $36,378 4 3 Insufficient 120570120021 132 2 113 1 131 3 0.2995 0.0998 0.0920 $58,158 3 6 Highly Insufficient

PAGE 177

177 REFERENCES Adler, M., American Documentary, I., Hirschberg, G., Kenner, R., Magnolia Home Entertainment, Magnolia Pictures, et al. (2009). Food, Inc. [video recording] (Widescreen ed.). Los Angeles, C A: Magnolia Home Entertainment. Algert, S. J., Agrawal, A., & Lewis, D. S. (2006). Disparities in access to fresh produce in low income neighborhoods in Los A ngeles. American Journal of Preventive Medicine, 30(5), 365370. American Planning Association. (2007). Policy guid e on community and regional food planning. American Planning Association. ASLA. (2011). Designing for active living. Retrieved 7/1, 2011, from http://vimeo.com/21666466 Bedore, M. (2010). Just urban food systems: A new direction for food access and urban social justice. Geography Compass, 4(9), 14181432. Beebout, H. S. (2006). Nutrition, food security, and obesity. Gender Issues, 23(3), 54 64. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&db= aph&AN=24565372&site= ehost live Bhattacharya, J., Currie, J., & Haider, S. (2004). Poverty, food insecurity, and nutritional outcomes in children and adults. Journal of Health Economics, 23(4), 839 862. Bishaw, A., & Macartney, S. (2010). Poverty: 2008 and 2009 (American Community Survey Brief No. A CSBR/09 1). Washington, D.C.: U .S. Census Bureau. Block, J. P., Scribner, R. A., & DeSalvo, K. B. (2004). Fast food, race/ethnicity, and income: A geographic analysis. American Journal of Preventive Medicine, 27(3), 211217. Block, D., & Kouba, J. (2006). A comparison of the availability and affordability of a market basket in two communities in the Chicago area. Public Health Nutrition, 9 (07), 837. Bodor, J. N., Rice, J. C., Farley, T. A., Swalm, C. M., & Rose, D. (2010). Disparities in food access: Does aggregate availability of key foods from other stores offset the relative lack of supermarkets in A frican A merican neighborhoods. Unpublished manuscript. Bolen, E., & Hecht, K. (2003). Neighborhood groceries: New access to healthy food in low income communi ties California Food Policy Advocates.

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186 BIOGRAPHICAL SKETCH Meredith Leigh is a life long Gator who, with the completion of this dissertation, has capped a diverse educational background. After earning her Bachelor of Arts in history in 2001 and a minor in art h istory at the University of Florida, she joined the United States Air Force for a brief stint. In 2009, she earned her Master of Landscape Architecture and completed her doctorate in d esign construction and planning in 2012. Me redith now lives in Gainesville with her cat and faithful companion, Scout and holds onto hope that she will one day be able to buy a new car and pay back her student loans.