Citation
Examining the Relationships between Fbcs and Active Built Environment

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Title:
Examining the Relationships between Fbcs and Active Built Environment
Creator:
Noh, Soowoong
Publisher:
University of Florida
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Design, Construction, and Planning
Design, Construction and Planning
Committee Chair:
BEJLERI,ILIR
Committee Co-Chair:
ZWICK,PAUL D
Committee Members:
PRUGH,PETER E
HARMAN,JEFFREY SCOTT

Subjects

Subjects / Keywords:
fbcs
gis
zoning

Notes

General Note:
Recently, Form-based codes (FBCs) have emerged as a strategy to resolve contemporary urban issues that have been caused by urban sprawl in the U.S., especially issues involving physical inactivity. This paper examines whether FBCs are conducive to creating active built environments, urban form that encourages more opportunities for physical activity. In order to answer the research question, I operationalized environmental variables in terms of how they affect physical activity in a Geographic Information System (GIS). Second, I developed GIS suitability modeling with the operationalized variables. Eventually, I used GIS modeling to compute an active built environment index and applied this index to compare the urban form that have resulted from FBCs, conventional zoning, and historic places. This study finds that there is a statistically significant difference among the above groups as determined by a Welch ANOVA test. Because the assumption of the homogeneity of the variances is violated, I utilized a Welch ANOVA test instead of the standard one-way ANOVA. The scores were statistically different for the comparison groups, with Welch's F(2, 12.857) = 6.370 and p < .012. Additionally, the Games-Howell post hoc analysis revealed that there is a difference between the scores of 3.6 +- 1.6 in the FBCs group and 2.1 +- 0.4 in the conventional zoning group, a difference of 1.5 (95% CI, 0.1 to 3.0), which is statistically significant (p = .038). These results suggest that FBCs create more urban form that are conducive to physical activity than conventional zoning does. Using cases in Florida, this study investigates the active living potential of FBCs and confirms that FBCs can create opportunities to support active built environments. Moreover, the GIS-based visualization method provides an expanded set of tools to help urban planners and public health professionals understand the relationships between urban form and active built environments. These map-based visualized results are useful not only in identifying inactive built environment areas, but also in providing valuable information that can help health and urban policymakers and professionals work together to address mutual challenges.

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UFRGP
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All applicable rights reserved by the source institution and holding location.
Embargo Date:
12/31/2017

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EXAMINING THE RELATIONSHIPS BETWEEN FBCS AND ACTIV E BUILT ENVIRONMENT By SO O WOONG NOH 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 2015

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2015 Soowoong Noh

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To Moonkyoung

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4 ACKNOWLEDGEMENT S This undertaking would not have been possible without excellent advice from my Committee and lo ve and support from my family. Special thanks go to Dr. Ilir Bejleri, my Committee chair, for all his guidance and encouragement throughout this journey as well as for providing me with the opportunity tion and urban planning issues. I also want to thank for my committee members : Dr. Paul Zwick who guides GIS modeling process Professor Peter Prugh, and Dr. Jeffrey Harm a n for sharing their valuable time and expertise, which helped me improve my research I also want to express thanks to the other professor s in the Department of Urban and Regional Planning, especially, Dr. Joseli Macedo and Dr. Ruth Steiner for their valuable advice on my journey Finally, most heartfelt acknowledgement must go to my wi fe, Moonkyoung Choi and my little child Caleb Juan Noh Especially, the compass for my journey to a Ph.D. Along with her love, my son made me smile all the time. I sincerely dedicate this dissertation to my wife and my child

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5 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ................................ ................................ ............................... 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ........................... 13 ABSTRACT ................................ ................................ ................................ ................... 14 CHAPTER 1 INTROD UCTION ................................ ................................ ................................ .... 16 Background ................................ ................................ ................................ ............. 16 Research Question ................................ ................................ ................................ 18 Research Objectives ................................ ................................ ............................... 19 2 LITERATURE REVIEW ................................ ................................ .......................... 21 Active Built Environment ................................ ................................ ......................... 21 Physical Activi ty ................................ ................................ ................................ 21 Built Environment and Physical Activity ................................ ............................ 22 Walkability ................................ ................................ ................................ ........ 26 Bui lt Environment and Health ................................ ................................ ........... 29 Limitations of Existing Research ................................ ................................ ...... 32 Summary of Previous Studies ................................ ................................ .......... 35 Form based Codes ................................ ................................ ................................ 36 Historical Context ................................ ................................ ............................. 36 Current Status ................................ ................................ ................................ .. 40 Components of FBCs ................................ ................................ ....................... 41 Previous Studies ................................ ................................ .............................. 42 Summary of Literature Review ................................ ................................ ................ 43 3 METHODOLOGY ................................ ................................ ................................ ... 67 Study Areas ................................ ................................ ................................ ............ 67 Criteria for Selecting the Study Areas ................................ .............................. 67 FBCs Cases ................................ ................................ ................................ ..... 68 Conventional Zoning Areas ................................ ................................ .............. 68 Historic Cities ................................ ................................ ................................ ... 69 Suitability Modeling to Assess Urban Form ................................ ............................ 71 Suitability Modeling ................................ ................................ .......................... 71 Suitability Modeling for Measu ring Active Built environment ............................ 76

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6 Comparative Statistical Analysis ................................ ................................ ............. 83 Statistical Analysis ................................ ................................ ............................ 83 One Way ANOVA ................................ ................................ ............................. 84 4 RESULTS ................................ ................................ ................................ ............. 107 Suitability Modeling Results ................................ ................................ .................. 107 FBCs Cases ................................ ................................ ................................ ... 107 Historic Cities ................................ ................................ ................................ 113 Conventional Zoning Areas ................................ ................................ ............ 118 Statistical Comparative Analysis Results ................................ .............................. 126 Descriptive Statistics ................................ ................................ ...................... 126 One Way ANOVA Test Results ................................ ................................ ...... 126 5 CONCLUSIONS ................................ ................................ ................................ ... 201 Summary of Study ................................ ................................ ................................ 201 Discussion ................................ ................................ ................................ ............ 203 Health Promoting Urban Design ................................ ................................ ........... 205 Study Limitations and Directions of Future Research ................................ ........... 210 LIST OF REFERENCES ................................ ................................ ............................. 215 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 228

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7 LIST OF TABLES Table page 2 1 Physi cal Activity Guideline by Age ................................ ................................ ..... 45 2 2 The Relationships between Mixed use Development and Physical Activity ....... 46 2 3 The Relationshi ............. 46 2 4 The Relationships between Distance to Recreational Spaces and Physical Activity ................................ ................................ ................................ ................ 47 2 5 The Relationships between Size of Open Spaces and Physical Activity ............ 49 2 6 The Relationships between Facilities of Recreational Spaces and Physical Activity ................................ ................................ ................................ ................ 50 2 7 The Relationships between Public Transit and Physical Activity ........................ 52 2 8 The Relationships between Street C onnectivity and Physical Activity ................ 53 2 9 The Relationships between Walkability and Obesity ................................ .......... 55 2 10 The Relationships between Walkability and Health Outcomes ........................... 57 2 11 The Relationships between Grocery Stores and Health Outcomes .................... 59 2 12 FBCs by State and Scale in U.S. as of January 2015 ................................ ....... 60 2 13 The Required Components of FBCs ................................ ................................ .. 62 2 14 Conventional zoning code vs. FBCs ................................ ................................ .. 63 3 1 Form based Codes Study Areas ................................ ................................ ........ 86 3 2 Conventional Zoning Study Areas ................................ ................................ ..... 86 3 3 Study Variables by Category ................................ ................................ .............. 87 3 4 GIS Data and Sources ................................ ................................ ........................ 87 3 5 Operationalization of Variables ................................ ................................ ........... 88 3 6 Compre hensive Active Built environment Scores Table by Group ..................... 89 3 7 Standard ANOVA table ................................ ................................ ....................... 89

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8 LIST OF FIGURES Figure page 1 1 Percent of Obese in U.S. Adults between 1990 and 2010 ................................ .. 20 2 1 Social Determinants of Health and Environmental Health Promotion ................. 64 2 2 Tran sect Zone ................................ ................................ ................................ .... 64 2 3 Number of FBCs by State ................................ ................................ ................... 65 2 4 Number of Adopted FBCs by Year ................................ ................................ ..... 66 3 1 Case Study Areas ................................ ................................ ............................... 90 3 2 The Number of Municipalities by Year of Incorporation ................................ ...... 91 3 3 Conceptual GIS Su itability Modeling ................................ ................................ .. 92 3 4 Formula to Calculate Entropy Index ................................ ................................ ... 92 3 5 Conceptual Diagram for Creating Walking Catchment Area by Distance ........... 93 3 6 Conceptual Diagram for Creating Walking Catchment Area by Exercise ........... 93 3 7 Creating Suita bility Layer for Entropy Index (A01) ................................ .............. 94 3 8 Creating Suitability Layer for Grocery Store (A02) ................................ .............. 95 3 9 Creating Suitability Layer f or Public Facility (A03) ................................ .............. 96 3 10 Creating Suitability Layer for School (A04) ................................ ......................... 97 3 11 Creating Suitability Layer for Park (B01) ................................ ............................ 98 3 12 Creating Suitability Layer for Public Space (B02) ................................ ............... 99 3 13 Creating Suitability Layer for Recreational Facility (B03) ................................ .. 100 3 14 Creating Suitability Layer for Public Transit (C01) ................................ ............ 101 3 15 Creating Suitability Layer for Pedestrian Path (C02) ................................ ........ 102 3 16 Creating Suitability Layer for Bikeways (C03) ................................ ................... 103 3 17 Creating Suitability Layer for each Category ................................ .................... 104 3 18 Creating Comprehensive Active Built environment Score Map ........................ 105

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9 3 19 Suitability Modeling Process ................................ ................................ ............. 106 4 1 GIS Modeling Results of University Heights (Gainesville) ................................ 129 4 2 Scores by Parcel and Hot Spot Analysis of University Heights (Gainesville) .... 130 4 3 GIS Modeling Results of Traditional Neighborhood Development District (Jacksonville) ................................ ................................ ................................ .... 131 4 4 Scores by Parcel and Hot Spot Analysis of Traditional Neighborhood Devel opment District (Jacksonville) ................................ ................................ .. 132 4 5 GIS Modeling Results of Fort Myers Beach ................................ ...................... 133 4 6 Scores by Parcel and Hot Spot Analysis of Fort Myers Beach ......................... 134 4 7 GIS Modeling Results of Downtown Kendall ................................ .................... 135 4 8 Scores by Parcel and Hot Spot Analysis of Downtown Kenda ll ........................ 136 4 9 GIS Modeling Results of Naranja Urban Center ................................ ............... 137 4 10 Scores by Parcel and Hot Spot Analysis of Naranja Urban Center .................. 138 4 11 GIS Modeling Results of Baldwin Park (Orlando) ................................ ............. 139 4 12 Scores by Parcel and Hot Spot Analysis of Baldwin Park (Orlando) ................ 140 4 13 GIS Modeling Results of Parramore Heritage District (Orlando) ....................... 141 4 14 Scores by Parcel and Hot Spot Analysis of Parramore Heritage District (Orlando) ................................ ................................ ................................ .......... 142 4 15 GIS Modeling Results of Winter Springs Town Center ................................ ..... 143 4 16 Scores by Parcel and Hot Spot Ana lysis of Winter Springs Town Center ........ 144 4 17 GIS Modeling Results of Seaside ................................ ................................ ..... 145 4 18 Scores by Parcel and Hot Spot Analysis of Se aside ................................ ........ 146 4 19 GIS Modeling Results of Rosemary Beach ................................ ...................... 147 4 20 Scores by Parcel and Hot Spot Analysis of Rosemary Beach .......................... 148 4 21 GIS Modeling Results of Micanopy Historic District ................................ .......... 149 4 22 Scores by Parcel and Hot Spot Analysis of Micanopy Historic District ............. 150

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10 4 23 GIS Modeling Results of Pensacola Historic District ................................ ........ 151 4 24 Scores by Parcel and Hot Spot Analysis of Pensacola Historic D istrict ............ 152 4 25 GIS Modeling Results of Apalachicola Historic District ................................ ..... 153 4 26 Scores by Parcel and Hot Spot Analysis of Apalach icola Historic District ........ 154 4 27 GIS Modeling Results of Quincy Historic District ................................ .............. 155 4 28 Scores by Parcel and Hot Spot Analysis of Quincy Historic District ................. 156 4 29 GIS Modeling Results of Marianna Historic District ................................ .......... 157 4 30 Scores by Parcel and Hot Spot Ana lysis of Marianna Historic District .............. 158 4 31 GIS Modeling Results of Tallahassee Park Avenue Historic District ................ 159 4 32 Scores by Parcel and Hot Spot Analysis of Tallahassee Park Avenue Historic District ................................ ................................ ................................ .............. 160 4 33 GIS Modeling Results of Key West Historic District ................................ .......... 161 4 34 Scores by Parcel and Hot Spot Analysis of Key West Historic District ............. 162 4 35 GIS Modeling Results of Fernandina Beach Historic District ............................ 163 4 36 Scores by Parcel and Hot Spot Analysis of Fernandina Beach Historic District 164 4 37 GIS Modeling Results of Milton Historic District ................................ ................ 165 4 38 Scores by Parcel and Hot Spot Analysis of Milton Historic District ................... 166 4 39 GIS Modeling Results of St. Augustine Historic District ................................ .... 167 4 40 Scores by Parcel and Hot Spot Analysis of St. Augustine Historic District ....... 168 4 41 GIS Modeling Results of Malabar ................................ ................................ ..... 169 4 42 Scores by Parcel and Hot Spot Analysis of Malabar ................................ ........ 170 4 43 GIS Modeling Results of Palm Bay ................................ ................................ ... 171 4 44 Scores by Parcel and Hot Spot Analysis of Palm Bay ................................ ...... 172 4 45 GIS Modeling Results of Miramar ................................ ................................ ..... 173 4 46 Scores by Parcel and Hot Spot Analysis of Miramar ................................ ........ 174 4 47 GIS Modeling Results of Port Charlotte ................................ ............................ 175

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11 4 48 Scores by Parcel and Hot Spot Ana lysis of Port Charlotte ............................... 176 4 49 GIS Modeling Results of Citrus Springs ................................ ........................... 177 4 50 Scores by Parcel and Hot Spot Analysis of Citrus Springs ............................... 178 4 51 GIS Modeling Results of Pine Ridge ................................ ................................ 179 4 52 Scores by Parcel and Hot Spot Analysis of Pine Ridge ................................ .... 180 4 53 GIS Modeling Results of Cape Coral ................................ ................................ 181 4 54 Scores by Parcel and Hot Spot Analysis of Cape Coral ................................ ... 182 4 55 GIS Modeling Results of Lehigh Acres ................................ ............................. 183 4 56 Scores by Parcel and Hot Spot Analysis of Lehigh Acres ................................ 184 4 57 GIS Modeling Results of Key Biscayne ................................ ............................ 185 4 58 Scores by Parcel and Hot Spot Analysis of Key Biscayne ................................ 186 4 59 GIS Mode ling Results of Palm Beach Gardens ................................ ................ 187 4 60 Scores by Parcel and Hot Spot Analysis of Palm Beach Gardens ................... 188 4 61 GIS Modeling Res ults of St. Augustine Shores ................................ ................ 189 4 62 Scores by Parcel and Hot Spot Analysis of St. Augustine Shores .................... 190 4 63 GIS Modeling Results of Port St. Lucie ................................ ............................ 191 4 64 Scores by Parcel and Hot Spot Analysis of Port St. Lucie ................................ 192 4 65 GIS Modeling Results of Deltona ................................ ................................ ...... 1 93 4 66 Scores by Parcel and Hot Spot Analysis of Deltona ................................ ......... 194 4 67 Descriptive Statistics of FBCs, Historic, and Zoning Group .............................. 195 4 68 Histogram of FBCs Group Scores ................................ ................................ .... 195 4 69 Histogram of Historic Group Scores ................................ ................................ 196 4 70 Histogram of Zoning Group Scores ................................ ................................ .. 197 4 71 Box Plots of three Groups ................................ ................................ ................ 198 4 72 Test of Normality with Raw Scores ................................ ................................ ... 198

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12 4 73 Test of Normality with Transformed Scores ................................ ...................... 199 4 74 Test of Homogeneity of Variances ................................ ................................ .... 199 4 75 Standard ANOVA table ................................ ................................ ..................... 199 4 76 Robust Tests of Equality of Means ................................ ................................ ... 199 4 77 Post Hoc Tests ................................ ................................ ................................ 200 5 1 Facilities Types by Walking Distance ................................ ............................... 213 5 2 Conceptual Diagram for Comparing Entropy Index ................................ .......... 214

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13 LIST OF ABBREVIATIONS ANOVA Analysis of Variance APA American Planning Association BMI Body Mass Index CDC Center for Disease Control and Prevention FBCI Form Based Codes Institute FBCs Form based Codes FDOT Florida Department of Transportation FGDL Florida Geographic Data Library FLC Florida League of Cities FTIS Florida Transit Information System GIS Geographic Information System HHS U.S. Department of Health and Human Service LUCIS Land Use Conflict Identification Strategy NHTSA Nationa l Highway Traffic Safety Administration NPS National Park Service PAGAC Physical Activity Guidelines Advisory Committee RCI Road Characteristics Inventory SZZEA The Standard State Zoning Enabling Act T zones Transect zones ULI Urban Land Institute WHO World Health Organization

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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 EXAMINING THE RELATIONSHIPS BET WEEN FBCS AND ACTIV E BUILT ENVIRONMENT By Soowoong Noh December 2015 Chair: Ilir Bejleri Major: Design, Construction, and Planning Recently, Form based codes (FBCs) have emerged as a s trategy to resolve contemporary urban issues that have been caused b y urban sprawl in the U.S ., especially issues involving physical inactivity This paper examines whether FBCs are conducive to creating active built environment s, urban form that encourage s more opportunities for physical activity. In order to answer the r esearch question, I operationalized environmental variables in terms of how they affect physical activity in a Geographic Information System (GIS). Second, I developed GIS suitability modeling with the operationaliz ed variables Eventually, I us ed GIS mode ling to compute an active built environment index and applied this index to compare the urban form that have resulted from FBCs, c onventional z oning, and h istoric places in Florida Th is study finds that there is a statistically significant difference among the above groups as determined by a Welch ANOVA test The scores we re statistically different for the comparison groups, with Welch's F (2, 12.857) = 6.370 and p < .012. Because the assumption of the homogeneity of the variances is violated, I utilized a Welch ANOVA test instead of the standard one way ANOVA. Additionally, the Games Howell post hoc

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15 analysis revealed that there is a difference between the scores of 3.6 1.6 in the FBCs group and 2.1 0.4 in the conventional zoning group, a difference of 1.5 (95% CI, 0.1 to 3.0), which is statistically significant ( p = .038). Th ese results suggest that FBCs create more urban form that are conducive to physical activity than conventional zoning does Using cases in Florida, t his study investigates the active living pot ential of FBCs and confirms that FBCs can create opportunities to support active built environments. Moreover, the GIS based visualization method provides an expanded set of tools to help urban planners and public health professionals understand the relati onship s between urban form and active built environment s These map based visualized results are useful not only in identify ing inactive built environment areas but also in provid ing valuable information t hat can help health and urban policymakers and pro fessionals work together to address mutual challenges

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16 CHAPTER 1 INT R ODUCTION Background Since the beginning of the Industrial Revolution, addressing public health issues has been an essential element of urban planners (Benevolo, 1980) However, th e early stages of urban planning focused on public hygiene and its urban infrastructure because of the necessity of infectious disease control In order to address this need, urban planners and leaders started to establish plans to construct urban infrastr uctures. After the basic functions were set up, including roads, drainage systems, sewer collection system s drinking water suppl ies and electric power line s cities started to grow according to regulated master plan s such as zoning and comprehensive plan However, the desire for better living led to an undesirable phenomenon: urban sprawl Urban sprawl is a pattern of urban growth that reflect s low density, automobile dependent, and exclusionary new development s on the fringe of existing urban fabric (Sq uires 2002 p. 2) This pattern has caused a number of urban problems including dependence on automobiles and profusion of health problems the invasion of preservation areas and finally climate change ( Duany, Plater Zyberk, & Spec k, 2010; Resnik, 2010; Sloane, 2006 ). Moreover, a decrease in physical activity is the most pressing problem of suburban residents in North America (Lopez & Hynes, 2012) According to the definition o f the Physical Activity Guidelines Advisory Committee ( PAGAC) physical activity is that increases energy expenditure above a basal level everyone should attain a certain level of physical activity based on his or her age and body condit ion ( PAGAC 2008, p. c 1)

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17 A lack of physical activity can lead to serious health problems such as obesity and type 2 diabetes. A statistic of the Center for Disease Control and Prevention (CDC) reveals that one consequence of physical inactivity obesity has increased dramatically in the United States over the last two decades (Figure 1 1) Needless to say, physical activity is an essential factor in weight maintenance and the reduction of partially implies that the average le vel of physical activity has continued to decrease at an alarming pace since 1990 With regard to physical activity and built environments, the findings from previous studies suggest that built environment s and physical activity have positive associations (Aytur, Rodriguez, Evenson, Catellier, & Rosamond, 2007; Frank, Kerr, Chapman, & Sallis, 2007; Cohen et al., 2007; Handy, Xinyu, & Mokhtarian, 2008 ; Kaczynski, Potwarka, & Saelens, 2008). In particular, Frank and Engelke (200 1) assert that creating active built environments is the most effective way to help people achieve their recommended physical activity as a byproduct of urban form which is the output of planning regulations (Talen, 2012). Unfortunately current planning regulations that operate based on functional zoning have resulted in non active built environment. In order to address this and other issues that have resulted from functional zoning, urban planners have developed new approaches in the urban planning and urban design fields, such as sm art growth, new urbanism, and sustainable developments. These approaches involve efforts to address public health problems, including physical inactivity. In particular, F orm b ased C odes (FBCs) ha ve emerged in recent years as an alternative to conventional planning approaches. T he Form Based Codes Institute

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18 ( FBCI ) states that, FBCs are regulations adopted into city or county and foster predictable built results and a high quality public realm by using physical form After the city of Seaside, Florida was developed in 1981 many municipal ities started to adopt FBC s instead of using conventional zoning regulations (Dancy, 2007, p. 2) This is because many municipalities now believe that FBCs offer numerous advantages and can create better places to live, wor k, and play (Cable, 2009; Coyle, 2010; Hendon & Adams, 2010) These advantage s also enable FBCs to be a possible solution to the problems resulting from urban sprawl (Ross, 2009 ; Spilowski, 2010; Tombari, 2009) Research Question Based on these conditions, this research strives to answer the question of whether FBCs can create active built environment s. M y ultimate research goal is to determine if the products of FBCs can improve public health especially through increased physical activity Therefore, I ai m to answer the more specific question of whether FBCs lead to urban form s that provide more opportunities for physical activity than the urban forms created by conventional zoning and historic cities. As Rodriguez, Khattak, and Everson (2006) assert, peop le who live in urban forms that support physical activity are more likely to be involved in physical activ ity in their neighborhood s (p. 52) They argue that there are positive associations between built environment s and physical activity. Although positiv e correlations between built environment s and physical activity may not imply causation, research suggests that physical urban form s or pattern s are essential to building and maintaining a healthy society (City of New York, 2010, p. 13). Consequently, by a ssessing urban form suitability to support physical activity, we can compare the planning approaches that

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19 have produced different types of built environment s in the absence of physical activity measurements Research Objectives In order to answer the rese arch query, several objectives have been established. First, I identify elements of urban form s that have been seen to be related to active built environment s in previous studies. This part of the study provides the variables that are utilized in the resea rch method Second I create a comparative analysis of chosen urban form sites that were developed from three planning approaches: f orm based c odes, c onventional z oning, and h istoric c ities. Florida was selected as the study area because Florida played a p recursory role in using FBCs in the U nited States and has gone through the evolution of various phases of planning approaches T hird, I buil d a Geographic Information System ( GIS ) suitability model to assess the three urban form groups in terms of their ab ilities to support physical activity. The output of the suitability modeling for each site is a suitability map that is ranked by an active built environment index. Fourth I perform a statistical comparative analysis of the three urban form groups. The co mparative analysis uses the mean s of the active built environment index in each comparison group as response variables and uses each comparison group as explanatory variables The analysis of variance (ANOVA) statistical test analyze s whether the differenc es observed among the groups could have reasonably occurred by chance (H 0 : the three groups have identical means). The P value show s whether H 0 can be rejected or not. The results of this analysis can help to determine which group, FBCs, conventional zonin g, or historic cities, has a more significant ability to create urban forms that are conducive to physical activity

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20 Figure 1 1 Percent of Obese (BMI >30) in U.S. Adults between 1990 and 2010 ( CDC, 2013)

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21 CHAPTER 2 LITERATURE REVIEW This chapter is organized in two sections. The first section examines previous efforts to determine the link between the built environment and health, particularly exploring the relationship as it relates to physical activity. Because the variables which show the correla tion between built environment and physical activity are vast and complex, the first section is divided into four categories: Land Use, Recreational Space, Transportation, and Street. The second section examines the historical context and trends of FBCs, t he components of FBCs, and the relevant research efforts related to FBCs Active Built Environment Since the active built environment is an urban form that encourages physical activity, this section looks at physical activity and the relationship between physical activity and built environments Physical Activity As aforementioned, physical activity is b odily movement s that use skeletal muscles. P hysical activity can be categorized by mode, intensity, and purpose (PAGAC 2008, p. C 1 ). Mode is the type of activity or exercise that is being performed (b iking, walking, rowing, and weight lifting are all examples of different modes of activity ). Intensity refers to how much work is performed or the magnitude of the effort required to perform an activity or ex ercise Purpose can be identified as occupational, leisure time or recreational, household, self care, and transportation or commuting activities (PAGAC 2008, p p C 1 C 3, C 4)

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22 However the recommended physical activity levels vary by age and health con dition. The World Health Organization (WHO) established three age categories: 5 17 years old, 18 64 years old, and 65 years old and above (WHO 2010) whereas the CDC uses four categories: children (6 17 years of age) adults (18 64 years of age), old adults ( 65 years of age or older) and Healthy Pregnant or Postpartum Women ( CDC, 2015 b ) The U.S. Department of Health and Human Service (HHS) uses the same categories as the CDC but also includes a dults w it h d isabilities and p eople w ith c hronic m edical c onditions (HHS, 2008) Table 2 1 summarizes physical activity types by modes and intensity. According to the table, children need more physical activity because they are in a growth period. Although older adu lts have the same guidelines for physical activity as adults, they should understand their functional limitations can make some activities risky and should select types of physical activities accordingly. Despite all kinds of theoretical categorization reg arding physical activity recommendation the CDC (2015 c ) encourages adults to partake in a 10 minute brisk walk, 3 times a day, 5 days a week Essentially, if adults walk a total of 30 minutes per endation Built E nvironment and P hysical A ctivity The relationships between built environment and human behavior, especially physical activity vary by contexts and have several aspects However Brownson, Hoehner, Day, Forsyth, and Sallis (2009) tried to identify urban form variables to affect physical activity from about 50 archival research, which are used GIS in their analysis. They found 5 common variables such as density, land use mixed level, accessibility to destinations, and street pattern for asse ssing urban form. In addition to physical urban

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23 forms, Schulz, Williams, Israel, and Lempert (2002) identified that race based residential segregation is a critical factor of racial disparities in health. Their findings indicates that health outcomes are c aused by both socio economic factors and physical built environment. F igure 2 1 demonstrates that physical activity is influenced by a variety of factors Although this Social D eterminants of H ealth and Environmental H ealth P romotion model includes possi ble relationships between urban forms and physical activity, m any studies have tried to find factors or variable s of active built environment within this complex model Despite the challenges i n order to determine relevant variables of active built envir onment, this section explore s previous efforts and find s appropriate variables from existing studies Thus in this study, to clearly summarize relevant studies which show the relationships between physical activity and built environment I have divided t hem into four parts: Land Use, Recreational Space, Transportation, and Street. Land Use Since land use directly defines urban forms, numerous studies have tried to identify the relationships between physical activity and land use. Mixed Use. Several studie s show how m ixed use development has a strong and positive relationship with physical activity (Table 2 2) Some researchers report ed that people are more likely to walk if they live in mixed used neighborhoods with parks, schools, and commercial destinati on s nearby ( Aytur et al. 2007 ; Frank et al. 2007 ; Mumford, Contant, Weissman, Wolf, & Glanz, 2011 ) Grocery Stores. Some studies show that the full size grocery stores in neighborhood s positively correlate with healthier diet and weight among residents (Morland, Wing, and Diez Roux, 2002 ; Sallis & Galnz, 2009 ). Conversely, a high

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24 concentration of fast food increase s the risk of obesity among neighborhood residents ( Larson Story & Nelson 2009; McCormack Giles Corti & Bulsara 2008; Moudon et al. 20 06). Children s Play Area Regarding these studies have suggested that environments with well maintained s chool facilities and w ell made street s environment have a ( Boarne t, Anderson, Day, McMillan, & Alfonzo, 2005 ; Dunton, Intille, Wolch, & Pentz, 2012 ) Also travel distance to school is a significant variable to the enhance ment of activity. McDonald (2008 ) support ed this argument by report ing that youth who li ve within a half mile of a school had a greater likelihood of walking o r biking to school (Table 2 3) Recreational Space In addition to land use, recreational spaces have been analyzed and researchers have argued for the significance of recreational space s with regard to active living. Studies found that users of open space have a heightened chance of achiev ing recommended physical activity level s Most of these studies have utilized explanatory variables which are related with urban form elements (e.g. tr avel distance to open space, open space size, and installed facilities in open space ) and response variable ( number of users ) Distance to Space The travel distance to an open space is a critical variable that has a strong correlation with the number of park users (Table 2 4) The optimal distance var ies but people who live closer to an open space are more likely to visit parks and exercise more often ( Babey, Hastert, Yu & Brown, 2008 ; Cohen et al., 2007;

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25 Coutts, 2008; Giles Corti et al. 2005; Moore, D iez Roux, Evenson, McGinn, & Brines, 2008; Pierce, Denison, Arif, & Rohrer, 2006 ) Size of Space S ome studies show ed that large open space s are not only more attractive to people but also ha ve more park users (Cohen et al., 2009; Farley, Meriwether, B aker, Rice, & Webber, 2008 ) Table 2 5 explains detailed data and findings regarding the relationships between size of open spaces and physical ac tivity. Facilities for Activity. Even though open space s are available if the open spaces do not have proper facilities, the open space may not encourage physical activity (Table 2 6) Some studies report ed ap propriate elements of open space such as trails, paths, playgrounds, drinking fountains, picnic areas, restrooms, and aesthetic features lead park users to exercise more (Brink et al. 2010; Floyd, Spengler, Maddock, Gobster, & Suau, 2008 ; Kaczynski et al. 2008 ; Mobley et al., 2006; Roemmich et al., 2010; Shores & West, 2008 ) Transportation In addition to land use and recreational spaces, transportation has played a significant role in providing more physical activity opportunities. Three transportation modes have been commonly analyzed: public trans it (Table 2 7) walking, and biking. Public Transit. P ublic transit is an important variable that encourages physical activity ( Besser & Dannenberg, 2005 ; Saelens & Handy, 2008 ) Rundle et al. (2007 ) found a reverse relationship between the density of bus and subway stops and obesity among residents. Also, transit stops with well connected streets are used more h eavily than less connected streets (Lund, Wilson, & Cervero, 2006). Pedestrian Pathway. Th is variable is discussed under the street category.

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26 Bikeways. Pucher and Buehler (2008) showed that obesity rate s of frequent bicycle users are lower in countri es that have good bicycle infrastructure Also, well connected bikeways with public transit can provide more opportunities for physical activity Street Street walkability, defined as the extent to which the built environment, is walk er friendly ( Abley 2 005, p.3) is a key variable to physical activity. As Jacobs (1961) asserted, the street is the main frame which serves man y purpose s besides accommodating pedestrians (Ehrenfeucht & Loukaitou Sideris, 2010) and a number of urban designers and architects h ave stressed the significance of the street connectivity and consistency Sidewalk Connectivity. Table 2 8 presents that a reas with well maintained and well connected sidewalk s that offer resident an opportunity to walk in their neighborhoods are more li kely to meet physical activity guideline s ( Boarnet, Forsyth, Day, & Oakes, 2011; Chin, Van, Giles Corti, & Knuiman, 2008; Coogan et al., 2011; Dill, 2009; Frank, Schmid, Sallis, Chapman, & Saelens, 2005 ; Gordon Larsen, Nelson, Page, & Popkin, 2006 ; Handy, Xinyu, & Mokhtarian, 2008 ; King, 2008; Lopez Zetina, Lee, & Friis, 2006 ; Reed, Wilson, Ainsworth, Bowles, & Mixon, 2006 ) Sidewalk Consistency. It is essential to make sidewalks appropriate width for walking ( Ce vero & Kockelman 1997 ; Eyler, Brownson, Ba cak, & Houseman, 2003). Walkability Despite among numerous theoretical discussions how to achieve daily physical activity recommendation walking is the most affordable way. The CDC recommendation that adults take a 10 minute brisk walk, 3 times a day, 5 days a week

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27 is widely accepted. That is, if walking trips are collectively 30 minutes, the walking activity meets the minimum per day suggested aerobic activity for adults. Urban Morphology and Walkability As previously mentioned, the street is an esse ntial feature to affect walkability. Thus many studies have investigated the relationships between walkability and urban form, including the street, with morphological dimension which includes land use, building structures, street patterns, enclosure, and movement pattern. Land Use S ince everyone depended upon ready access by foot to jobs and the marketplace w alkability was essential in cities before the automobile era Recent studies also support the fact that people are more likely to walk if they live in mixed used neighborhoods with parks, schools, and commercial destinations nearby ( Aytur et al. 2007 ; Frank et al. 2007 ; Mumford et al. 2011 ) Building Structure Since buildings had long, narrow plot patterns and faced their front side into street these structures support social interaction on the public spaces and streets ( Carmona, Heath, Oc, & Tiesdell 2010, p. 7 8) Street Patterns In urban design, the street is the most important urban features since it provides the opportunity for movement as well as social interaction. To provide various movement options, fine urban grain, which has integrated and connected small delicately meshed streets, is encouraged ( Jacobs, 1961 ) Additionally, r ecent research argued the significance of well connected sid ewalks because they provide more chances for pedestrian movement in modern cities. Several studies showed that areas with well maintained and well connected sidewalks offer residents an opportunity to walk in their neighborhood (Dill, 2009; Frank et al. 2 005 ; Gordon Larsen et al. 2006 ; Handy et al. 2008 ; King, 2008; Lopez Zetina et al. 2006 ; Reed et al 2006 )

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28 Movement Patterns. Since p edestrian movement can create trip which means movement from point A to point B as well as social interaction this m oving pattern is an essential factor for walking Compared to automobile movement, b ecause of walking speed, it is possible to make interaction during the movem ent. Other Physical Features e e Ds: Density, Divers and argued that sidewalk width consistency is critical for pedestrian safety. Frank et al. (2010) developed walkability index to measure walkability using net residential density, retail floor area ratio, intersection density measured the connectivity of the street network, and land use mix index A dditionally, trees and other landscape elements contribute to more appealing sidewalks and can be used to help separate pedestrians from vehicular traffic (Larsen et al., 2009) The Other Factors to Affect Walkability Even though physical urban form such as destination proximity, density and connectivity make an impact on walkability ( Moudon et al. 2006) walkability is a multi faceted concept that inclu des qualitative factors ( Adkins, Dill, Luhr, & Neal, 2012 ; Giles Corti, Kelty, Zubrick, & Villanueva, 2009). Southworth (2005) organized six attributes to affect walkability: connectivity linkages to other modes fine grained and varied land use patterns safety quality of path and path context (e.g. visual interest, landscaping, spatial definition, etc.). This indicates walkability studies have tried to find qualitative variables such as safety, quality, and perceptual context. Alfonzo's (2005) work sug gested the basic needs of feasibility (an individual's ability) and accessibility (somewhere to go) to measure walkability as well as qualitative characteristics (safety, comfort, and pleasurability) of walkability. Similarly, Agrawal, Schlossberg, and Irv in (2008) reported that commuters walking to transit stations chose routes based on safety

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29 and aesthetic characteristics Alfonzo, Boarnet, Day, McMillan, and Anderson (2008) reported urban design features related to both accessibility and safety are assoc iated with the amount of walking done in a specific environment The enclosure as one of the important visual aesthetic characteristics of urban spaces, is also one of the factor s that can encourage walkability. Alexander, Ishikawa, and Silverstein (1977) stated that An outdoor space is positive when it has a distinct and definite shape, as definite as the shape of a room, and when its shape is as important as the shapes of the buildings which surround it (p. 518) Jacobs (1993) also has presented the id ea that a positive space is defined by proper horizontal and vertical elements Ewing and Handy (2009) proposed a conceptual framework to explain how built environment elements which cause user reactions (e.g. sense of safety, comfort and level of interest ) contribute to an overall perception of walkability and, ultimately, walking behavior. As they discussed, though these five qualities ( imageability, enclosure, human scale, transparency, and complexity ) cannot fully explain the walkability, the study is v aluable because this effort tried to explain qualitative features as quantitative variables regarding urban design. Alt hough Cervera and Kockelman (1997) asserted these qualitative design elements are too micro to change travel behavior patterns (p. 220), the micro scale elements still have an influence on travel behavior (Saelens & Handy, 2008). Built Environment and Health As seen Figure 2 1 the relationships between built environment and health outcomes are complex. However, several researchers have ar gued that there is a positive correlation between certain built environment elements and health outcomes. In this section, I examine the evidence that shows the relationships between walkability and health outcomes as well as urban form and health outcomes

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30 Walkability and Health Outcome s S everal studies showed people were more likely to be overweight or obese if they lived in less walkable environments (Table 2 9) Giles Corti, Macintyre, Clarkson, Pikora, and Donovan (2003) studied associations between en vironmental and lifestyle factors and being overweight or obese They suggested factors that influence excess weight and obesity appear to differ, but aspects of the physical environment may be important. Ewing, Schmid, Killingsworth, Zlot, and Raubenbush (2003) tried to determine the relationship between urban sprawl, health, and health related behaviors. Saelens, Sallis, Black, and Chen (2003) evaluated a neighborhood environment survey and compared the physical activity and weight status of the residents in 2 neighborhoods. They concluded neighborhood environment was associated with physical activity and overweight prevalence. Frank, Andresen, and Schmid (2004) place of resi dence and self reported travel patterns (walking and t ime in a car), BMI, and obesity for specific gender and ethnicity classifications. Frank et al., (2006) examined single use, low density land development and disconnected street networks and concluded t hat they were positively associated with auto dependence and negatively associated with walking and transit use. Rosenberg Sallis, Conway, Cain, and Mckenzie (200 6 ) found active commuting (walking, biking, or skateboarding) to school may contribute to pre venting excessive weight gain and leaner children may walk or cycle to school. Furthermore several studies reported the relationships between urban sprawl and health outcomes. This is because this type of study regards urban sprawl as increased reliance o n automobile transportation and decreased ability to walk to

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31 destinations, decreased neighborhood cohesion, and environmental degradation (Lopez, 2004, p. 1594). Lopez (2004) showed a positive association between urban sprawl and the risk of being overweig ht or obese among U. S. adults. Doyle, Kelly Schwartz, Schlossberg, and Stockard (2006) argued that living in more walkable counties is associated with more walking and lower body mass indices. Kelly Schwartz, Stockard, Doyle, and Schlossberg (2004) found that residents who have more walkable environments tend to walk. Additionally they found that subjects reported better health, and were rated by physicians as having better health, when they lived in Metropolitan Statistical Areas that w ere more walkable Sturm and Cohen (2004) showed sprawl in metropolitan areas is directly related to the prevalence of chronic diseases. Finally Table 2 10 reveals that walking has also been linked to various health benefits and improved cardiovascular health in adults (Man son et al., 2002). Among older adults, research show s links between walking and improved longevity (Hakim et al., 1998), cognitive function (Weuve et al., 2004) and quality of life (Strawbridge Cohen, Shema, & Kaplan 1996; Leveille Guralink, Ferrucci, & Langlois 1999) Urban Forms and Health Outcome s B ased on previous studies, one can better recognize the relationships between the elements of the built environments such as mixed land use, proximity to recreational spaces and physical activity. However, some elements of built environment have more solid evidence regarding health outcomes. Recent research reported that several features of the built environment such as grocery stores, recreational facilities, and sidewalks have positive associations with desirable health outputs. First, table 2 11 show s that full size grocery stores in neighborhoods positively correlate with healthier diet among residents (Morland et al. 2002 ; Sallis & Gl a nz,

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32 2009 ). Conversely, a high concentration of fast food increase s risk s of obesity among neighborhood residents ( Larson et al. 2009). Second, access to recreational facilities is key in prevent ing residents from becoming overweight (Gordon Larsen et al. 2006; Mobley et al., 2006 ). Third, areas with well connected sid ewalk s that offer residents an opportunity to walk in their neighborhoods are more likely to meet physical activity guideline s ( Lopez Zetina et al. 2006 ) Limitations of Existing Research Even though the relationships between several elements of built env ironments and health have been documented, it is almost impossible to explain all aspects (social context, stressors, health behaviors, built environment, and etc.) that affect health outcome or well being in one single model. The following reveals the lim itations of previous research regarding active built environment Study Variables Cities are complex communities of heterogeneous individuals, and multiple variables may be important determinants of health outcome. In addition to this complexity, previou s studies have had limitations when explaining the relationships among various variables that affect health outcomes. Urban study scholars or public health professionals have used correlations and association ecological analys e s to consider the association between factors at the group or aggregate level (Galea & Schulz, 2006, p. 282). These methods are useful to test hypotheses regarding the urban features that may affect active living But it is not possible to answer how these elements may be associated w ith health. In the public health discipline, the variables such as stress factors, healthy behaviors, and social context have been shown to be empirically related to individual health (Israel, Farquhar, Schulz, James, & Parker, 2002;

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33 Lantz et al. 1998; Ye n & Syme, 1999). However, the above efforts may have limitations because those studies have been conducted in isolation. Thus, to overcome the limitations, Schulz and Northridge (2004) developed the l Health Promotion addresses for the built environment and social context (intermediate factors) that influence stressors, health behaviors, and social relationships (proxi mate factors) that ultimately result in individual and population (p. 458). However despite the ir efforts Schulz and Northridge (2004) recognized the limitations of presenting social processes and environmental effects as a series o f boxes and arrows as they are far more complex (p. 458). In conclusion, it is difficult to evaluate health outcome s with only one discipline such as medical or urban planning perspectives Although previous efforts indicate that the relationship between s everal elements of built environment s and health can be demonstrated, it is impossible to explain all aspects that affect health outcome and their relationships. In order to overcome this limitation, Galea and Schulz (2006) suggested quantitative methods t o study urban health. Though th e approach of Galea and Schulz is useful in utilizing multiple variables simultaneously, it has other issues including: variable specification issues complex casual pathways and nonlinear association Study Durations. In pre vious studies to identify correlation between physical activity and elements of built environments, t hese studies have utilized cross sectional methods (Galea & Schulz, 2006). However, because this type of study relies on cross

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34 sectional data, these result s should be interpreted as associations, not causative factors. Thus longitudinal studies are needed to advance thoughts about how urban characteristics may cause different health behaviors and outcomes (Galea & Schulz, 2006, p.284). Recently, researchers have included longitudinal components to address the issue of causality. Krizek (2003) found that when households moved to neighborhoods with different urban form, the likelihood of using active transportation remained unaltered. Also, Rodriguez et al. (2 006) investigated if neighborhood design can support or undermine active lifestyles using suburban neighborhood s and new urbanist neighborhood s They found residents of the new urbanist neighborhood were more likely to be physically active than were reside nts of suburbs In addition to duration issue s sample size (in public health stud ies this refers to the number of people) gives another limitation for research. Because lots of cross sectional studies have used self reported data or observational data, t here are limited sample numbers. Both time and people are critical variables for measuring health output and current study methodology cannot fully cover the all variables. Therefore, in order to fill the gaps, further research conducted in the public heal th discipline and by urban study is necessary to build a solid health model Location Choice and Life Style Since p eople choose to live in an area for many reasons that do not necessarily relate to healthy lifestyle opportunities it is also necessary to review how people to select their living location.

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35 Generally, the residential location choice model is able to quantify the trade offs between transport, amenities and other factors (Kim, Pagliara; & Preston, 2005), but in reality, the choice of where to live is determined by market imperfections combined with preferences. Kleit and Galvez (2011 information available through close social relationships may play an important role in determining l ocation 375). For example, a previous experience of living in a city increases the probability of returning to that city ( Feijten, Hooimeijer, & Mulder, 2008 ) and having lived in a rural area increases the probability of choosing a rural home location ( Van Dam, H eins, & Elbersen, 2002 ). However, i ndividuals or households with preferences towards certain health behaviors (like physically activity) may choose built environments that support those behaviors (Rodriguez et al. 2006, p. 47). As mentioned previously th is type of research needs further longitudinal research methods. It is important to note that the criteria to select locations for housing are as vast as the complex relationship between built environment and health outcome Summary of Previous Studies Des pite complexit ies inherent in urban form s and physical activity several studies show positive relationships between physical activities and some urban design elements, such as open space, school s and transportation facilit ies The key response variable o f previous research is walking or biking, which is considered everyday physical activity. T h us to encourage everyday physical activity, well made and maintained built environment s are critical. H o wever there are still some limitations of the previous stud ies. F irst, b ecause most studies utilized self reported or observational data, there is a data reliability issue.

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36 This is because participants who self report ed data may have remembered incorrectly, or deliberately misreported S econd, case studies might n ot show unique r egional context of each study T hough most studies showed common results, the results are limited to people in the case study area s This is b ecause every study has different regional context, though using same criteria, the result may vary (Rodr i guez et al. 2006 ). T hird, e ven though this type of research which analyzes relationships between the built environment and public health should be conducted across interdisciplinary boundaries, most studies were conducted in individual fields. Ho wever, the cooperation informed by science between public health and urban planning is helpful to make substantive progress through intellectual collaboration (Northridge & Freeman, 2011; Northridge, Sclar, & Biswas, 2003). This coordinated approach among academic disciplines is fundamental to answer not only how urban living affects public health but also to research methodological development ( Galea & Schulz 2006). Form b ased Codes FBCs a good solution to conventional zoning regulation issues. Furthermore, this section establishes foundational information about FBCs by exploring the following topics: the definition, the basic components and previous studies on the topic Historical C ontext As T alen (2009) states, many urban places are the result of explicit rules (p. 144) and t he present urban spaces result from specific rules such as zoning regulations I n this section I analyze how these specific rules influence the history of urb an planning

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37 Early Land Use Controls T he initial measures of regulation in the late nineteen th century and early twentieth century were based on the authority of cities to exercise their political power ( Parolek Parolek, & Crawford 2008, p. 6). It was na tural that there were a number of conflicts between individual s and administrative bodies before zoning regulations. In the beginning stage of urban planning the Supreme Court was focused on civil right s ( Yick Wo v. Hopkins, 118 US 356 1 Supreme Court 18 86 ) But, as city populations increased, cities authority began to grow. In order to sustain the quality of urban area s cities developed regulations regarding land use control. Several Supreme Court decision s reflect the time of early 20 th century, the C ourt upheld the regulations of cities ( Welch v. Swasey, 214 US 91 2 Supreme Court 1909 and Hadacheck v. Sebastian, 239 US 394 3 Supreme Court 1915 ). However, most of those regulations were passive tactics because they only regulated noxious land use or b uilding height. New York City in 1916. After the mid nineteenth century there was a population boom in New York City because an influx of immigrants Around the same time, the height of buildings increases because of developing technology. As a result of these drastic changes, the city faced two problems: fire hazards and decreasing light because of the shadows cast by tall buildings. Because of these new situations, the first American zoning ordinance was enacted in New York City in 1916, with the aim of imposing minimum standards of light and air for streets (Barnett, 1982, p. 61). Barnett (1982) also noted that the ordinance sought to separate activities that were viewed as 1 Supreme Court invalidate d S an F rancisco O rdinance 2 Supreme Court uph e ld height restrictions in Boston 3 Supreme Court uph e ld ban on brickyards in L os Angeles.

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38 incompatible such as the factors of the garment center and the fashionable sho ps and homes along the Fifth avenue (p. 61). The ordinance had three districts : Residential Business a nd Unrestricted ( City of New York, 1916, ARTICLE 2 ). Though this ordinance had some disadvantages like land use segregation and monotonous building hei ghts in same the zone, it ha s been expanded out to other municipalities The Heyday of Euclidean Zoning The legal rational e for zoning is the so called police power of the s t ate to make regulations to protect public health, safety, and general welfare (Bar nett, 1982, p. 61). This kind of power was affirmed by following two events in the U.S. urban planning history : The Standard State Zoning Enabling Act and Village of Euclid v. Ambler Realty Co., 272 US 365 The Standard State Zoning Enabling Act (SSZEA ) Herbert Hoover who was the Commerce Secretary create d an advisory committee to draft model state zoning enabling acts in 1921 The act de legated power to zone ; e stablished procedures for amendments, special exceptions, variances ; and c reated the board of zoning appeals After the SSZEA was issued, B y the 1930 s 35 states adopted legislation based on the SSZEA (Meck, 1996, p. 2) Village of Euclid v. Ambler Realty Co., 272 US 365 Supreme Court 1926 The Supreme Court upheld the zoning law of Euclid a s constitutional because it contribute s to the general welfare of the public. After this decision, a number of municipalities adopted zoning legislation. By 1977, 97% of local governments had utilized zoning as the primary regulation tool (Haar & Kayden 19 89, p. 185)

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39 New Attempts The adverse impacts of early zoning regulations were not fully realized until the 1950s ( Parolek et al. 2008, p. 8) However, a s the prob lem s of conventional zoning ( e.g. land use segregation, travel cost increase air pollution) became more apparent over time, there have been various attempts to fix those issues. Growth Management This approach basically regard ed land as a limited resource. Its main objectives clearly show that perspective : first, r educe consumption of land ; sec ond, make development more compact ; third, e stablish minimum standards of competence for local planning and land use control ; fourth, v ertical and horizontal integration In order to achieve original purposes of growth management there have been several t echniques developed : u rban growth areas ; p riority funding areas ; p ermit allocation systems ; adequate public facilities ordinances ; i mpact fees ; s tate review of plans, and regulations Growth management efficiently protect s natural recourses improve s susta inability in the development process, and create s opportunities for providing appropriate public facilities (Nelson & Duncan, 1995). New Urbanism. Though Growth Management is conducive to relieving the problems from urbanization, it was just a temporary to ol in terms of urban design. The consequences from modified conventional zoning have been almost the same since zoning regulations were adopted. While public agency planners were beginning to streamline conventional zoning codes in the 1980s, a group of ur ban professionals dedicated to promoting walkable and mixed use places as described in the principal of Smart Growth and the Chapter of the New Urbanism worked collaborat ive ly to formulate and refine an alternative to conventional zoning ( Parolek et al. 2 008, p. 9).

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40 Form Based Codes Th e first example of this new approach was seen in Seaside, Florida. T he Development Code for Seaside of Florida, drafted by Duany Plater Zyberk in 1981, was one of the first modern day applications of Form Based Coding ( Parol ek et al. 2008, p. 9). This alternative approach neighborhood use and live/work codes, transit oriented development ordinances, transit area codes, transect based codes smart growth codes, sustainable codes, transit supportive codes, urbanist codes, and green building codes of various stripes (Talen, 2013, p 178). B ut in 2001, Chicago consultant Carol Wyant coined the term Form Based Codes, which has been the common name ( Parolek et al. 2008, p. 10). T he distinct difference between FBCs and conventional zoning is that FBCs allow common understanding by relat ing development characteristics to p l aces within the urban fabric (Local Government Commission, p3). Figure 2 2 shows six Transect zones (T zones), which have a prototypical rural to urban transect as well as provide unique characteristics of each zone A l so, FBCs proponents assert that the understanding of context is conducive to creating walkable, mixed use, and compact development which lead acti ve living. Current Status According to The Codes Study by Borys and Talen (2015 a ) a s of January 2015 researchers have tracked 584 codes that meet criteria established by the FBC I (FBCI, 2015) as well as an additional 16 form based guidelines. Table 2 1 2 shows that currently 550 municipalities in all 50 states except Alaska, South Dako t a, and North Dako t a have adopted FBCs F lorida has the highest amount at 66 codes followed by California ( 57 codes). The t op 10 states, which are Florida

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41 California, T exas, Virginia, Georgia, North Carolina Michigan, South Carolina Illinois, Louisiana, and New Jersey have 330 codes (6 0 % of total ). That means these states have exerted a lot of effort to establish FBCs as their regulation tools Figure 2 4 shows the tot al n umber of FBCs by state level However, based on table 2 12, a bout 57% ( 314 codes) of the total number of codes are neighborhood scale, and 1 81 codes are applied to a citywide scale. This implies that small area s can more easily apply FBCs rather than l arge ( regional or statewide ) area s In addition to the number of municipals, f igure 2 4 shows the number of adopted FBCs by year. As shown after 200 3 the number of municipal ities that have adopted FBCs ha s increased significantly indicating that many mu nicipalities have recognized FBCs as an alternative to conventional zoning regulations. Components of FBCs I n order to create urban form, FBCI developed a structure to include a set of minimum components (Table 2 1 3) that may also accommodate a variety of optional ones ( Parolek et al. 2008, p. 1 5 ) The required components of FBCs are: a regulating plan, public space standards, building form standards, an administration, and a glossary. Additional c omponents consist of the following standards ( these compone nts may be included depending on the needs of the community ) : Block Standards, Building Type Standards, Architectural Standards, Green Building Standards, Landscape Standards ( Parolek et al. 2008, p. 16) In addition to regulating physical urban form a s Parolek et al. (2008) asserted, FBCs emphasize harmony with local contexts. Although supporters of FBCs argue that components are helpful to create active built environment, some architects are concerned about losing their design freedom (Berg, 2010) becau se of specific standards to define building forms

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42 Previous Studies While many municipalities have adopt ed FBC as a method of settling their unsolved urban issues few studies have been conducted regarding FBC s As a part of legal aspect, Barry (2008) clai ms that FBCs are superior to conventional zoning regulations regardless FBCs are mandatory or optional. Sitkowski and Ohm (2006) summarized legal perspective of FBCs. Rangwala (2012) argued FBCs have certain advantages in terms of civic participation due t o the visualized process of FBCs. Several studies reached that FBCs are helpful to create mixed land use and walkable places. Hansen de Chapman (2008) showed the value of FBCs in the view of walkability and asserted th at communities developed from FBCs ar e superior to other communities. Laakso (2011) urged that FBCs are conducive to create dense and mixed land use. Polikov (2008) added this dense and mixed land use eventually has economic benefit s. Talen ( 2013) asserted FBCs are helpful to mitigate urban s prawl issue. Senbel, van der Laan, Kellett, Girling, and Stuart (2013) concluded FBCs are helpful to r educe greenhouse gas due to compact development characteristics of FBCs Another study by Talen (2009) presented the differences between FBCs and convent ional zoning regulations in the view of history and defined F BCs as physical planning Other non peer reviewed articles mainly mentioned the regulat ory aspects (Broberg, 2010; Katz, 2004; Purdy, 2006; Ross, 2009 ; Spilowski, 2010; Tombari, 2009) or design p erspectives (Berg, 2010; Cable, 2009; Coyle, 2010; Hendon & Adams, 2010 ; Rangwala, 2010) of FBC Some articles defined FBCs as a physical planning compared to conventional zoning ( Madden & Spikowski, 2006; Mammoser, 2011; Rangwala, 2005 ) physical planning aspect, they concluded FBCs make a direct

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43 impact on urban form s. However, most of non peer reviewed papers lauded FBC without academic verification This is a critical point since zoning also spread without questions. In order to avoid th e same mistake, scholars should pay attention to see if FBCs follow the same trajectory Most FBCs efforts started in order to demonstrate that FBCs are a possible solution to solve existing zoning problem s The comparison between c onventional zoning code s and FBCs shows why many municipalities have move d from convention zoning to FBC s (see Table 2 1 4 ) E ven though FBCs have more opportunities to create mixed use, compact, and walkable spaces than traditional zoning there are possible pitfall s of FBC imp lementation If FBC s work well, it is good for both urban space and people. However, if FBC s do not work properly, then urban space will be irrevocable. We already have experienced undesirable results from zoning regulation : urban sprawl, climate change, a nd name a few (Duany et al., 2010) Before reaching a quick decision to adopt FBCs as an alternative of conventional zoning regulations we need to verify the various effects of FBCs. In fact, urban planners and designers have created new methods such as n ew urbanism, smart growth, and sustainable development that could possibl y cure contemporary urban issues But there have always been gaps between the theory and its implementation in practice (Grant, 2009) Summary of Literature Review The literature sho w s that certain urban form elements such as open space, school s and transportation facilit ies can affect the creation of active built environment A s one possible solution, FBCs have become a leading method of solv ing contemporary

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44 urban issues created by conventional zoning and can lead to development of more active built environments

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45 Table 2 1 Physical A ctivity G uideline by A ge ( HHS, 2008) Age g roup Type of a ctivity Aerobic activity Muscle strengthening activity Bone strengthening activity Children Moderate or vigorous intensity: the 60 or more minutes a day (should include vigorous intensity at least 3 days a week) At least 3 days of the week. At least 3 days of the week. Adults Moderate intensity: at least 150 minutes a week (or) 75 min utes a week of vigorous intensity Involve all major muscle groups on 2 or more days a week Older Adults Moderate intensity: at least 150 minutes a week (or) 75 minutes a week of vigorous intensity Involve all major muscle groups on 2 or more days a week

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46 Table 2 2. The Relationships between Mixed use D evelopment and Physical Activity Citation Explanatory v ariable(s) Response v ariable(s) Data Key f indings Aytur et al. (2007) Active Community Environment Mode (Walking, Biking) 6,694 residents in 67 N orth Carolina counties People living in counties with Comm (ACE) scores were more than twice as likely to walk and bike for transportation as residents in counties with the lowest ACE scores Frank et al. (2007) Land u se type Children with Walking 2001 2002 from 3,161 Atlanta children 5 to 18 year olds were more likely to walk for transportation if they lived in mixed used neighborhoods with parks, schools, and commercial destinations nea rby Mumford et al. (2011) Befor e and after moving to a mixed use development Self reported physical activity and travel behaviors 101 adult residents of Atalantic station There were significant increases in walking for 54%; p<0.05) and walking for transportation (44% 84%; p<0.001) after moving Table 2 3. The Relationships between Children s Play Area and Physical Activity Citation Explanatory v a riable(s) Response v ariable(s) Data Key f indings Boarnet et al. (2005) improvements in sidewalks and street crossings, as well as traffic calming increase the number of students who walk to school Recent evaluations of Safe Routes to Schools projects in C alifornia I mprovements in sidewalks and street crossings, as well as traffic calming, increase the number of students who walk to school Mcdonald (2008) Distance from school Mode (Walking, Biking) low income and minority youth (N=14,553) using data from t he 2001 National Household Travel Survey Y outh who live within a half mile of school had a greater likelihood of walking of biking to school, even after controlling for socioeconomic stat us and neighborhood covariates

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47 Table 2 4. The Relationships between Distance to Recreational Space s and Physical Activity Citation Explanatory v ariable(s) Response v ariable(s) Data Key f indings Babey et al. (2008) Urban areas, Neighborhood perceived as unsafe, Income, Ethnic Regular Physical Activity Regular Physical Inac tivity 2003 California Health Interview Survey (N=4010) A dolescents are more likely to engage in regular physical activity and/or less likely to be inactive when they have access to safe parks Cohen et al. (2007) Distance lived from park % visiting park or exercising weekly 1318 residents of predominantly low income Los Angeles P eople who live closer to a park are more likely to visit parks and exercise more often Adults living within a half mile of a park visit parks and exercise more often Coutt s (200 8) Population density, land use mixture physical activity along greenways Greenways have higher levels of physical activity when: A park or wooded area is nearby, The trail intersects areas with greater land use mixture, Trail segments connect both green settings and areas with greater land use mixture Giles Corti et al. (2005) Accessibility, Size Walking time (Physical Activity Level) 1773 adults (Perth, Australia) People with very good access to attractive and large Public Open Space ( POS ) were 50% mo re likely to achieve high levels of walking, totaling 180 minutes or more per week People who use any POS, regardless of attractiveness or size, were nearly 3 times more likely to achieve recommended physical activity levels of 150 minutes or more per week

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48 Table 2 4. Continued. Citation Explanatory v ariable(s) Response variable(s) Data Key findings Moore et al. (2008) Distance to recreational resource Physical Activity 2,723 adults living in New York, Baltimore, and North Carolina Adults were 28% mo re likely to participate in recreational activities if there were more recreational resources within five miles of their homes. The relationship between physical activity and proximity to recreational resources was significantly greater among African Ameri cans and Hispanics. Pierce et al. (2006) Destinations within walking distance % meeting physical activity guidelines by walking People were more likely to walk 30 minutes 5 times/week if they lived near walkable destinations

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49 Table 2 5 The Relationsh ips between S ize of Open Spaces and Physical Activity Citation Explanatory variable(s) Response variable(s) Data Key findings Cohen et al. (2009) Park improvement Physical Activity 10 urban parks located in predominantly Latino, African American, and low income communities in southern California and self reports from 2867 residents Although perceptions of safety increased significantly after park improvements were implemented, this was not associated with park use or exercise More parks users observed in l arger parks Park use is weakly related to the number of scheduled programs and organized activities Most used parks had many activities or unique features Farely et al. (2008) Play Area Features (Field, Basketball, Equipped Concrete, Installed Play Struc ture) Number of Active Children the 2nd through 8th grades over two years Observations of children in the 2nd through 8th grades over two years in an inner city New Orleans schoolyard* show that children are more likely to be very active in play areas with installed play structures than those with an open field

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50 Table 2 6. The Relationships between Facilities of Recreational Spaces and Physical Activity Citation Explanatory variable(s) Response variable(s) Data Key findings Brink et al. (2010) Renovated (or not) Physical Activity 9 public elementary schools in Denver, Colorado C hildren had significantly higher rates of physical activity in schoolyards renovated by Learning Landscapes than in schoolyards that were not renovated Floyd et al. (2008) Activi ty Type Park Users White, African American, and Hispanic park users in 10 Tampa parks (N=7043) and 18 Chicago parks (N=2413) S ignificantly more users engaged in sedentary behavior than in vigorous or walking activity. Based on direct observation. P eople u sing tennis, racquetball, and basketball courts burned more energy than people using other park areas Kaczynski et al. (2008) Facilities included trails, paths, playgrounds, and courts. Amenities included drinking fountains, picnic areas, restrooms, and aesthetic features. Physical Activity 380 adults and 33 neighborhood parks in Ontario, Canada P arks with more facilities and amenities were more likely to be used for some physical activity Mobley et al. (2006) fitness facility per 1000 residents BMI 2692 low income women from WISEWOMAN study W omen who lived in areas containing a ratio of one fitness facility per 1000 residents had on average a BMI that was 1.39 kg/m2 lower than the BMI of women living in areas with fewer fitness facilities

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51 T able 2 6. Continued. Citation Explanatory variable(s) Response variable(s) Data Key findings Roemmich et al. (2010) Park elements, Age, Gender Physical Activity children aged 8 to 16 (N=106) in Buffalo, NY Younger children use play equipment more than old er children, while older children are more likely to use open natural areas. Findings suggest play areas should incorporate diverse features to encourage physical activity among different age groups of youth. Shores & West (2008) Park elements (Shelter, G reen space, Courts, Path, Playground) Physical Activity SOPARC (System for Observing Play and Recreation in Communities) observations of four suburban parks in the southeastern US found that park visitors are more likely to engage in vigorous physical act ivity when using courts, paths, and playgrounds

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52 Table 2 7 The Relationships between Public Transit and Physical Activity Citation Data Key findings Besser & Dannenberg (2005) 3312 transit users among 105,942 respondents (2001 National Household Trave l survey) activity a day solely by walking to and from transit rail users, minorities, people in households earning <$15,000 a year, and people in high density urban areas were more likely S aelens & Handy (2008) original studies published in 2005 to 2006 W alking for transportation is most strongly related to living in neighborhoods with high residential density, mixed land use, and short distances to destinations.

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53 Table 2 8 The Relations hips between Street C onnectivity and Physical Activity Citation Explanatory variable(s) Response variable(s) Data Key findings Besser & Dannernberg (2005) Public Transit Users Percent walked 30+minutes/day 2001 National Household Travel Survey (N=3,312) 2 9% of public transit users achieve the recommendation of 30 minutes or more of physical activity a day while walking to and from transit. Racial/ethnic minorities reported even greater percentages of achieving the recommended level of act ivity. Dill (2009) Importance of factors in bicycle route choice Mean Score adult cyclists in Portland, OR (N=166) Well connected network of low traffic neighborhood streets Bike lanes should be networked with paths and bike boulevards Zoning standards th at support mixed land use Frank et al. (2005) Urban Form Physical Activity 357 Atlanta adults People who live in walkable neighborhoods are more likely to meet recommended daily levels of physical activity Gorden Larsen et al. (2006) Physical Activity F acility Physical Activity, Weight US adolescents (N=20,745) A greater number of physical activity facilities is directly related to increased physical activity and inversely related to risk of overweight

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54 Table 2 8. Continued. Citation Explanatory vari able(s) Response variable(s) Data Key findings Handy et al. (2008) Neighborhood Characteristic Physical Activity residents (N=1,497) in northern California After controlling for sociodemographic and attitudinal variables, certain neighborhood characterist ics are significantly associated with physical activity frequency within the neighborhood King (2008) Walkability, Safety, Social cohesion Physical Activity seniors in Denver, CO (N=190) Total physical activity and community based activity were highest in neighborhoods with fewer walkability variables but higher respondent perceptions of safety and social cohesion (p<.01) Lopez Zetina et al. (2006) Vehicle Miles traveled Obesity 33 California cities Adults who drove the most had obesity rates (27%) that were three times higher than those who drove the least (9.5%). Reed et al. (2006) Sidewalk Physical Activity 1,148 adults living in the southeastern US The number of adults who met physical activity guidelines was 15% higher in neighborhoods with sidewal ks.

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55 Table 2 9 The Relationships between Walkability and Obesity Citation Data and methods Key findings Ewing et al. (2003) Measures: Sprawl indices, derived with principal components analysis from census and other data (independent variables). Self r eported behavior and health status from BRFSS (dependent variables). Subjects: Adults (n=206,992) from pooled 1998, 1999, and 2000 BRFSS. After controlling for demographic and behavioral covariates, significant associations with minutes walked, obesity, BM I, and hypertension. Residents of sprawling counties were likely to walk less during leisure time, weigh more, and have greater prevalence of hypertension than residents of compact counties. Frank et al. (2004) 10,878 participants (Atlanta, Georgia region ): BMI, minutes spent in a car, kilometers walked, age, income, educational attainment, and gender. Objective measures of land use mix, net residential density, and street connectivity (within a 1 kilometer network distance of Each additional hour spent in a car per day was associated with a 6% increase in the likelihood of obesity. Each additional kilometer walked per day was associated with a 4.8% reduction in the likelihood of obesity. Frank et al. (2006) The association bet ween a single index of walkability that incorporated land use mix, street connectivity, net residential density, and retail floor area ratios, with health related outcomes (King County, Washington). 5% increase in walkability to be associated with a per ca pita 32.1% increase in time spent in physically active travel, a 0.23 point reduction in BMI. Giles Corti et al. (2003) Measures: Four lifestyle factors, one social environmental factor, and five physical environment factors (three objectively measured). Data: Healthy sedentary workers and homemakers aged 18 to 59 years (n = 1803) living in areas within the top and bottom quintiles of social disadvantage. Overweight was associated with living on a highway or streets with no sidewalks or sidewalks on one side only and perceiving no paths within walking distance. Also, poor access to four or more recreational facilities and sidewalks and perceiving no shop within walking distance were associated with obesity. Conversely, access to a motor vehicle all the ti me was negatively associated with obesity.

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56 Table 2 9. Continued. Citation Data and methods Key findings Rosenberg et al. (2006) grade ( N= 1083) and 5th grade ( N = 924). Participants were classified as active (walkin g, biking, or skateboarding) or non active commuters to school. Accelerometers were used to measure physical activity. Height, weight, and skinfolds were objectively assessed. Boys who actively commuted to school had lower BMI ( p < 0.01) and skinfolds ( p< 0 .05) than non active commuters to school in the fourth grade. Saelens et al. (2003) On 2 occasions, 107 adults from neighborhoods with different walkability. Physical activity was assessed by self report and by accelerometer, height and weight were asses sed by self report. Residents of high walkability had more than 70 more minutes of physical activity and had lower obesity prevalence than did residents of low walkability neighborhoods.

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57 Table 2 10. The Relationships between Walkability and Health Out comes Citation Methods and data Key findings Hakim et al. (1998) 707 nonsmoking retired men (61 81) The distance walked (miles per day) was recorded at a base line examination (1980 1982) Data on overall mortality (from any cause) were collected over a 12 year period of follow up. After adjustment for age, the mortality rate among the men who walked less than 1 mile per day was nearly twice that among those who walked more than 2 miles per day. The cumulative incidence of death after 12 years for the m ost active walkers was reached in less than 7 years among the men who were least active. Leveille et al. (1999) Estimate the prevalence of having no disability in the year prior to death in very old age and to examine factors associated with this outcome. Participants were men and women aged 65 years and older who were followed prospectively between 1981 and 1991 from three communities. Physical activity was a key factor predicting nondisability before death. There was nearly a twofold increased likeliho od of dying without disability among the most physically active group compared with sedentary. Manson et al. (2002) 73,743 postmenopausal women (50 79 & they were free of diagnosed cardiovascular disease and cancer) Completed detailed questionnaires about physical activity. An increasing physical activity score had a strong, graded, inverse association with the risk of both coronary events and total cardiovascular events. Walking and vigorous exercise were associated with similar risk reductions, and the results did not vary substantially according to race, age, or body mass index.

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58 Table 2 10. Continued. Citation Methods and data Key findings Strawbridge et al. (1996) 356 Alameda County men & women (65 95) measured prospectively in 1984 and followed to 1990. Successful aging: needing no assistance nor having difficulty on any of 13 activity/mobility measures. Cross sectional comparisons at follow up revealed significantly higher community involvement, physical activity, and mental healt h for those aging successfully. Weuve et al. (2004) Women reported participation in leisure time physical activities beginning in 1986. Assessed long term activity in baseline cognitive assessments (1995 to 2001). Linear regre ssion to estimate adjusted mean differences in cognitive performance. Higher levels of activity were associated with better cognitive performance. Compared with women in the lowest physical activity quintile, we found a 20% lower risk of cognitive impairme nt for women in the highest quintile of activity.

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59 Table 2 11 The Relationships between Grocery Stores and Health Outcomes Citation Methods and data Key findings Larson et al. (2009) A snowball strategy was used to identify relevant research studies ( n=54) completed in the U.S. and published between 1985 and April 2008. Neighborhood residents who have better access to supermarkets and limited access to convenience stores tend to have healthier diets and lower levels of obesity. Residents with limited a ccess to fast food restaurants have healthier diets and lower levels of obesity. Morland et al. (2002) Recommended intakes of foods and nutrients for 10,623 Atherosclerosis Risk in Communities participants were estimated from food frequency questionnaire s. Supermarkets, grocery stores, and full service and fast food restaurants were geocoded to census tracts. vegetable intake increased by 32% for each additional supermarket in the census tract vegetab le intake increased by 11% with the presence of 1 or more supermarket. Sallis & Gl a nz (2009) Summarizes and synthesizes recent reviews and provides examples of representative studies. Residents of communities with ready access to healthy foods also tend t o have more healthful diets.

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60 Table 2 1 2 FBCs by State and Scale in U.S. as of January 2015 ( Borys & Talen, 2015a ) State Largest Scale Total n umbers Neighborhood City Region State Alabama 9 4 1 14 Arkansas 2 1 3 Arizona 3 5 8 California 33 21 2 1 57 Colorado 5 6 11 Connecticut 7 7 Delaware 1 1 Florida 31 25 8 2 66 Georgia 13 5 3 1 22 Hawaii 1 1 2 4 Iowa 2 1 3 Idaho 1 1 1 3 Illinois 11 6 1 18 Indiana 3 1 4 Kansas 1 3 4 Kentucky 7 1 8 Louisiana 6 11 1 18 Mass achusetts 4 3 1 8 Maryland 4 2 2 8 Maine 8 1 9 Michigan 14 5 1 1 21 Minnesota 3 1 4 Missouri 4 4 8 Mississippi 4 10 2 1 17 Montana 1 1 North Carolina 12 10 22 Nebraska 2 1 3 New Hampshire 7 2 9 New Jersey 16 1 1 18 New Mexico 3 2 1 6

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61 Table 2 1 2 Continued. State Largest Scale Total n umbers Neighborhood City Region State Nevada 1 1 2 New York 5 5 1 11 Ohio 3 2 5 Oklahoma 1 1 Oregon 4 1 5 Pennsylvania 5 4 1 10 Rhode Island 2 2 South Carolina 12 5 3 20 Tennessee 7 4 1 12 Texas 25 16 2 43 Utah 6 1 7 Virginia 17 1 7 25 Vermont 5 2 1 8 Washington 6 2 8 Wisconsin 1 1 1 3 West Virginia 1 1 Wyoming 1 1 2 Grand Total 314 181 48 7 550

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62 Table 2 1 3 The Required Components of FBCs ( Paro lek et al., 2008, p p 15 18, 28 38, 41 54) Components Sub Components A regulating Plan Administrative Direct Regulation Planning Public Space Standards Thoroughfares : Movement Type, Design Speed, Pedestrian Crossing T i me, Transect Zone, Right of Way Wid th, Curb Face to Curb Face Width, Traffic Lanes, Bicycle Lanes, Parking Lanes, Curb Type, Planter Type, Landscape Type, Walkway Type, Lighting, Curb Radius, Distance between Intersections Civic Space: Acreage, Location, Size, Allowable Transect Zones, Acti vity Type, General Character Building Form standards Overview of the zone Building Placement regulations: Build to Line (BTL), Setback, Maximum Lot Width, Minimum Lot Width Building Form regulations: Minimum Building Height, Maximum Building Height, Grou nd Floor Finished Level Height, Minimum Ground Floor Ceiling Height, Minimum Upper Floor(s) Ceiling Height, Maximum Building Width, Maximum Building Depth, Maximum Ancillary Building Size Parking regulations: Required Spaces Location Allowed use types and detailed use table Allowed Frontage types Allowed Encroachments Allowed Building Types Administration Glossary

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63 Table 2 1 4 Conventional zoning code vs. FBC s ( Parolek et al., 2008, p. 13 ) Conventional zoning code FBC s Auto oriented, segregated lan d use planning principles Mixed use, walkable, compact development oriented principles Organized around single use zones Based on spatial organizing principles that identify and reinforce an urban hierarchy, such as the rural to urban transect Use is pri mary Physical form and character are primary, with secondary attention to use Reactive to individual development proposals Proactive community visioning Proscriptive regulations, regulating what is not permitted, as well as unpredictable numeric paramete rs, like density and FAR Prescriptive regulations, describing what is required, such as build to lines and combined min/max building heights Regulates to create buildings Regulate to create places

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64 Figure 2 1 Social D eterminants of H ealth and Environ mental H ealth P romotion ( Northridge et al. 2003 p. 559 ) Figure 2 2 Transect Zone ( Retrieved from http://www.transect.org/transect.html )

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65 Figure 2 3 Number of FBCs by State ( Borys & Talen, 201 5b )

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66 Figure 2 4 Number of Adopted FBCs by Year ( Borys & Talen, 2015c)

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67 CHAPTER 3 METHO D OLOGY This chapter is organized into three parts: the selection of the study area and urban form groups, the suitability modeling for assessing urban form s, and a c o mparative analysis of the urban form types I n order to conduct the comparative study, the first part of this chapter explains the differences between FBCs, conventional zoning, and historic cities and establishes the specific sites to be studied T he seco nd part examines the selected urban form groups based on variables related to physical activity using GIS suitability model ing Finally the last part compares the urban form groups using a one way ANOVA F test Study Area s Criteria for S electing the S tudy A reas In order to analyze built environments in terms of their ability to support physical activity, this study required FBCs sites and other urban form sites to be compared. It is appropriate to compare conventional zoning areas to FBCs because many FBC supporters argue that FBCs are superior to conventional zoning in terms of prompting active living In addition historic cities are an other suitable compa rison group because s ome researchers have pointed out that FBC s are about returning to traditional u rban form because they embrace the compact urban form s of historic cities (Talen, 2009) Accordingly, in order to conduct the comparative analysis, this study utilizes three groups: f orm based c odes sites c onventional z oning sites and h istoric c ities. T he f ollowing sections describe the selection c riteria for each group

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68 F BCs Cases This research selected Florida as the study area. According to Borys and Talen (2015b), F lorida has 66 cases of f orm based c odes being used, which is the highest amount of any state in the U.S. In addition, Florida has played a precursory role in terms of FBCs in the U.S. Finally, Florida has an abundant availability of GIS data, which is needed in order to perform this analysis. Although Florida has many cases of FBCs it is h ard to verify the effectiveness of FBCs that were adopted too recently That is, time is a very critical element in creating urban space based on certain land regulations In the U.S., 83 % of FBCs were adopted after 2003 This shows that, although FBCs hav e gained popularity most are still in their incipient stages. Therefore this study utilizes only area s that adopted FBCs between 1981 and 2003. Ten cases were selected ( T able 3 1 and Figur e 3 1 ) : University Heights (Gainesville), Jacksonville Traditional Neighborhood Development District Fort Myers Beach Downtown Kendal l, Naranja Urban Center Baldwin Park (Orlando), Parramore Heritage District (Orlando), Winter Springs Town Center District Seaside and Rosemary Beach Conventional Zoning Area s After F lorida was incorporated in to the U.S., t ransportation improvement s, includ ing enhancements to automobile s and train s, enabled people who were interested in vacation home s during the winter to move to Flori da ; this occurred primarily in the 1920s This was the starting point so called second great land boom ( Mormino 2005, p 45). During this period, urban planning was ignored and did not play a major role in controlling land development. However, this development boom did not last long. In the l ate 1920s, hurricanes added to the woe of those who had moved to

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69 Florida, and the national depression solidified the collapse ( RuBino & Starnes p 91). Though zoning had prevailing power after 1930, World War II changed urban planning perspe ctives. During this war, a lot of developments were made for military purposes, and urban planning was neglected ( RuBino & Starnes 2008, p 125). However the Federal Aid Highway Act of 1956 led to an unf oreseen spread of population and economic activity in U.S. cities ( RuBino & Starnes 2008, p 104). In addition technolog ical development s the rising numbers of senior citizens, and political and leisure revolutions shaped the development of modern Florida ( Mormino 2005, p 45). These complex circumstan ces led to Florida third boom. One of the negative development al results of this process was suburbanization which led to sprawl ; this urban development pattern was ruled by conventional zoning regulation s Thus, in selecting its conventional zoning sit es this study focuse d on areas that were develop ed between 1950 and 1980. Drawing from both RuBino and Starnes (2008) and Mormino (2005) the following cases are selected ( T able 3 2 and F igure 3 1 ) : Malabar, Palm Bay, Miramar, Port Charlotte, Citrus Sprin gs, Pine Ridge, Cape Coral, Lehigh Acres, Key Biscayne, Palm Beach Gardens, St. Augustine Shores, Port St. Lucie, and Deltona Historic Cities This study also adds traditional urban forms to the comparative analysis between FBCs and conventional zoning cas es. As Talen (2009) assert s The constraints which include transportation construction methods, and the need for defense, identity and proximity to agricultural land created urban form that today s FBCs emulate in many ways (p. 158) F BCs embody certain elements from traditional cities : a pedestrian

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70 oriented scale public space location s small block size s and mixed land use. Although it is hard for places to maintain their historic urban form s without chang ing at all, older cities tend to sustain relat ively traditional urban form s Before selecting historic cases in Florida for this study it wa s necess ary to understand the regulatory systems of early colonial settlements and how those rules have affected urban forms. At the beginning of U.S. history, p art of North America was a battlefield in which England, France, and Spain fought to gain control of the continent. Their governments took different approaches to ruling colonized lands, and these differences deeply affected the settlement of the U.S. Flo rida was mainly controlled by Spain before it became a U.S. territory ; therefore, Spanish regulations made the largest impact on its newly developed towns In particular, the Laws of the Indies that the Spanish Empire enacted in its colonies delineated str eet or plaza configurations and the location s of important buildings. Since these laws provide d generic rules for new colonial towns, it is most likely that early Florid ian towns were developed with them in mind. The details of the laws regarding urban pla nning techniques were as follows : First, they provided criteria for the selection of appropriate settlement locations ; s econd, they allocated populations by geographic scale such as neighborhoods and towns ; f pat terns, including the placement of plazas, plots, and streets. These laws especially emphasized the significance and functionality of the plaza. Also, they proposed the shapes of street networks ( Mundigo & Crouch, 1977) In summary many Florid ian cities we re built according to Spanish colonial laws that detailed the placement of specific urban features such as streets, plazas, and plots.

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71 T hese main controlling features have several commonalities with the central characteristics of FBCs in terms of how they regulate physical urban forms and harmonize with local contexts. Thus, along with conventional zoning, I have added historic cases as one of the comparison groups in this study. In order to select the historic cities to be examined, this study used Florid ian As F igure 3 2 presents, b efore Florida became the 27 th state in 1845 s everal human settlements were established. Additionally, most cities that were established before 1845 have historic districts (Florida Divis ion of Historical Resources), meaning that they have maintained some traditional urban forms. T hus the following 1 0 cities were selected for this group : Pensacola, St. Augustine, Fernandina Marianna Tallahassee Key West Quincy Apalachicola Micanopy and Milton (Figure 3 1) Suitability Modeling to A ss ess Urban Form Suitability Model ing Introduction to Suitability Modeling A suitability model is a method for finding the most suitable locations based on a given set of criteria. Since Charles Eliot and Warren Manning used the sunprints technique, which overlays multiple site characteristics in order to analyze a current project site, the methods of synthesizing multiple layers have been well developed in GIS application (Carr & Zwick, 2007, p. 46). Figur e 3 3 presents the theoretical suitability modeling process. Basically, a suitability model works with multipart hierarchical structures, which consist of goals and objectives. As Carr and Zwick (2007) t of statements that first define what is to be accomplished and second, define how each accomplishment is to be

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72 (p. 82). In a suitability model, the sequence of hierarchical combinations is repeated until a rating is achieved that includes all relevant factors (Hopkins, 1977, p. 396). However, this modeling process has been challenged by its complexity, which is one reason to utilize GIS for suitability modeling. In the early stage s of GIS development GIS had certain limitations. As Malczewski (1999) explains, although GIS was useful for data storage, management, manipulation, and analysis, it still lacked the capacity to solve complex spatial issues. However, the improvement of computer technology has overcome those issues. For instance, the La nd Use Conflict Identification Strategy (LUCIS) model uses a suitability model to aid land use decision making process es Based on a given definition of land use suitability and its defined criteria and factors, GIS can allow researchers to answer the fol lowing questions: Which of a group of locations is the best one? Which is the most typical in the suitability model? In this that influence flow. The results of this t ype of analysis can be used to answer questions like: Where should new factories be built? Which areas are safe from floods? Where should we build a new public facility? GIS also allows researchers to explore alternate scenarios by changing the criteria an d parameters that they specify The Connection between Suitability Modeling and this Study research question could be modified to include: Where are the most and least suit able locations for physical activity among the chosen sites? This question does not simply entail naming the best or the worst locations, but will also allow them to be evaluated.

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73 This study will look at various characteristics of a set of locations in ord er to determine how suitable they are for physical activity. The various characteristics will be represented by layers of information. These include not only the characteristics of the locations themselves (e.g., levels of mixed use), but also of the surro unding or nearby suitability modeling, I can not only identify areas in which residents will most likely become involved in physical activity, but also pinpoint other a reas that need to be When applying built environment variables that are related to physical activity, suitability modeling can provide several advantages. First it may overcome one of the limitations of previous research. For example, many of the p revious studies that examined the relationship between built environments and physical activity failed to investigate the regional contexts. Using GIS suitability modeling, it is possib le to determine geographical patterns of urban form that offer more opportunities for physical activity. Second a suitability model can handle a large data size and high level of complexity better. The built environment variables that are related to peopl activity are complex However, these variables can be converted to GIS layers, which can be controlled in the modeling process. Third, the model has the ability to derive new information from the data and also to identify spatial relationships among the combined layers Conceptual Suitability Modeling Process for this Study T his research follows the LUCIS modeling process that was established by Carr and Zwick (2007): D efine the question, define the criteria, collect the source layers, create the suitability layers, and combine the layers.

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74 Define the Q uestion Based on my research question, I established this question for the suitability model: Where in this study area are the locations that are the most and the least conducive to physical acti vity? Define the Criteria I looked at the literature to determine the specific criteria that make a location more or less suitable for physical activity. The literature claims that built environments that support physical activity include a reas with mix ed use sidewalks and certain land uses such as grocery stores public facilities schools recreational spaces, public transit, and bike paths Each criterion has a corresponding source data layer. In order to create a model to rate the suitable location s, I assign ed a relative suitability value to each value on a source layer Collect the Source Layers In order to build a suitability model the first task is to determine the relevant variables. According to the findings from the literature review, the f ollowing are the built environment variables most associated with the potential for ph ysical activity: access to grocery stores, schools, public facilities, parks, public spaces, streets, bikeways, and public transit. In order to get the most detailed scal e, this study mostly use d property parcel level data. This is because parcel data c ould provide t he necessary variables in a fine grain resolution in GIS format. In addition to parcel data, street network d ata was utilized to examine the sidewalk network. Create the Suitability Layers Before combining the source layers, it is necessary to assign relative suitability values to each one thus creating corresponding suitability layers (Mitchell, 2012, p. 102). In order to convert the source layers to suitabil ity layers I evaluate d each source layer and group ed its values into class es

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75 In terms of physical activity for adults between 18 and 64, the minimum adult aerobic activity guideline s are met if walking trips take 30 minutes per day Thus I chose pedestrian proximity as the level of measurement for each source layer except for mixed use and bikeways. Th e walking distance is measured along the actual street network rather than in straight lines or Euclidian distance. In order to gauge the mixed use level s I utilize d the entropy index, which is common ly used as a measure of land use diversity. After reclassifying the source layers, the next step wa s to decide on a suitability scale. In this study, s uitability values are assigned on an int erval scale, which measures relative value s For example, on a scale of 1 to 5, a location with a value of 5 is more suitable than a location with a value of 1 ; however, this does not mean that the location with the value of 5 it is five times as suitable as the location with a value of 1. Combine the Layers After creating the suitability layers, I combined them into a single layer to assign an overall suitability value to each cell. In suitability modeling, some criteria might be more important than other s. In that case, performing a weighted overlay allows the researcher to assign weights to the suitability layers in order to specify the degree to which suitability is dependent on each criterion (Mitchell, 2012, p. 115). The final suitability maps for phy sical activity show location maps and include tabular data. T he final suitability layer can be used to derive statistics and other summary information

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76 Suitability M odeling for M easuring A ctive B uilt environment Study Variables In build ing a suitability mo del the first task is to determine the relevant variables. According to the findings from the literature review, the following are built environment variables that are associated with the potential for physical activity (Table 3 3) Land Use. The m ost si gnificant and frequently appear ing variables are m ixed u se, g rocery s tore l ocation s s chool locations and p ublic f acility location s That is, in terms of land use, attracti ve or magnet points are required in order for the residents to achieve a certain le vel of average physical activity. Recreational Space. Open space is essential for promoting physical activity. This category includes park s public space s and recreational facilit ies Transportation. An active transportation system which includes stree t walking and bicycling is one of the agenda items for healthy cit ies In addition the availability of public transit encourages more physical activity and less dependency on cars Data and Analysis Unit Since this study uses GIS suitability modeling, i t was critical to obtain appropriate GIS data for measuring active built environments. Table 3 4 gives detailed descriptions of each dataset. Analysis Boundary. In order to identify each case, obtaining a proper analysis boundary is required. As listed in the section on the areas being studied, some sites matched city or census place boundaries. In these cases, I used city or census place boundaries from the Florida Geographic Digital Library (FGDL). In addition to defined municipality boundaries, since the National Park Service (NPS) provides historic place boundaries in Filegeodatabase format, I obtained data on historic places from the NPS

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77 website. However, GIS data was not available for some FBCs sites and historic places. In this case, I digitized bound aries for the sites using the available non digital information. Entropy Index. Two data layers are required to calculate the entropy index: land use and census boundaries. I used land use data from the Florida Department of Transportation (FDOT), since th is department maintains generalized land use derived from parcels. For census boundaries, I used 2010 census block boundaries from the U.S. Census Bureau. Land Use and Recreational Spaces. I used parcel data for parks, schools, and public facilities. FGDL maintains county level data, which includes tax information and specific land uses. Transportation. This category includes three datasets: public transit, bikeways, and sidewalks. To find public transit locations, I used public transit information from th e Florida Transit Information System (FTIS). For bikeways, I utilized bike land features from the Road Characteristics Inventory (RCI) of FDOT. Regarding sidewalks, I used NAVTEQ street data, since this contains detailed street networks and a functional cl assification of roads. Analysis Unit. Suitability modeling uses raster based data, wherein each cell provides geolocation as well as quantified information. Determining the proper analysis cell size is critical because cell size affects the resolution of the results, the model performance and the disk storage requirements. This study uses a 5 meter cell size in order to capture the details of parcel level data

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78 Creating Suitability Layer s by Types of Measurement In order to convert the source data to sui tability layers it was necessary to p rocess the raw GIS data by suitability values Table 3 5 shows the values assigned to each data layer. As discussed in the previous chapter, walking is the most effective way to achieve a high physical activity level i n everyday life Thus pedestrian proximity determines the level of measurement for most of the variable s above, except for bikeways and mixed use Based on the nature of the variables, the types of measurements can be categorized into three groups: the en tropy index, walking catchment areas, and buffer areas from transportation modes. Entropy Index. Although there are many methods to measure mixed land use, I chose the entropy index since this measurement is one of the most common methods in geography and urban planning (Manaugh & Kreider, 2013). Figure 3 4 shows the entropy index equation. As the expression shows, the entropy index is set at 1 w hen land use is maximally diverse and set at 0 when land use is maximally homogeneous. This measurement has been used in a study that found a positive correlation between the entropy index and increased levels of physical activity (Frank et al., 2010; Kockelman, 1997). Land use and census data is required in order to calculate the entropy index. Although many researc hers have used census tract data, I used census block data because it provides finer geographic units. In addition, I used land use data that contained 15 generalized classes of land use. However, since there are no tools to calculate this index in ArcGIS, I developed a geoprocessing tool and programmed a Python script (see Appendix). The general algorithm of the script is as follows: First, it intersected land use with census block groups; next, it selected all of the land uses

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79 associated with each census block group; it then found the amount of unique land use in each census block group and found the total area in acres for all of the land uses in each census block group; next, for each unique land use, it found the total area and calculated the portion of current land use area over the total land use area; finally, it calculated the entropy index using a portion of the land use and the unique land use count. Figure 3 7 presents the input data, the process, and an output example for the whole modeling proce dure, applied to the Jacksonville site as example. Network Walking Catchment Areas. Th is measurement is used to determine pedestrian proximity to v ariables such as grocery stores, public facilities, schools, parks public spaces, and recreational facilitie s. T he mechanism of this measurement is almost identical for all of these variables. This is the process for developing measurements for the grocery store variable : Obtain grocery dataset (A 02) convert it into point s (A02_p) and create a network dataset generat ing network distance buffers by 5 minutes walking distance from grocery store points (A 02_srv). However, there are two potential methods to create walking catchment areas: one possibility involves residents walking the smallest distance possible, a nd the other involves residents walking the necessary distance as an opportunity to exercise. The following paragraphs show how to develop suitability values for each method, assuming that people will walk a maximum of 1/2 mile to a bus stop. First, there are w alking c atchment a reas based on d istance In order to create a suitability layer using walking distance, I reclassif ied the distance from the bus stop point layer into two classes: areas within 1/2 mile of bus stop s and areas over 1/2 mile from bus st op s. After reclassifying the source layers, I set up the suitability scale with

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80 areas within 1/2 mile receiving a value of 3 and the remaining areas receiving a value of 1. In Figure 3 5 the blue area has a suitability value of 3 and the white area has a suitability value of 1 within the study boundar ies Also the left graph shows the relationship between distance and suitability values with walking proximity g enerated by Euclidean distance However, a s previously discussed, th is distance suitability simply indicates 1/2 1/2 from a bus stop It may be difficult to define th is specific range as most suitable because some pedestrians who live within 0.7 mile s of the bus stop may still walk to it That is this distance based analysis might miss in determining distance suitability. Second there are w alking c atchment a reas based on exercise. (ArcGIS Spatial Analyst T ools Overlay Fuzzy Membership) is a possible option for overcoming the limitations of the previous method. This is because the f uzzy m embership tool allows researchers to specify the likelihood that a given value is a member of a set rather than merely specifying whether the value is eith er in side or out side of the set (Mitchell, 2012, p. 129). That is, f uzzy m embership can display the possibilities for the option In essence, The Fuzzy Membership tool reclassifies or transforms the input data to a 0 to 1 scale based on the pos sibility of being a member of a specified set. 0 is assigned to those locations that are definitely not a member of the specified set and 1 is assigned to those values that are definitely a member of the specified set (ESRI 2014 ) However, e ach specific m embership function varies in its equation and application. In order to identify the exercise possibility, I chose the f uzzy s mall member s of the set In other words, a s the di stance from a bus stop decreases, it is

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81 more likely that someone would walk to it so it is more likely that the locations will be members of the favorable and suitable set. Using this function, source layer values are assigned corresponding values on this continuous scale according to the possibility that they may be members of the set. Thus a new layer is created with corresponding fuzzy membership values rating from 1 to 0 accordingly. The left diagram of Figure 3 6 shows the locations from which peopl e are most likely (dark blue) and least likely (light blue) to walk to bus stops The graph in Figure 3 6 allows us to visualize the relationship between observed values (distance, in this example) and fuzzy membership values. Another benefit to using fuzz y membership is that the output value is in the same scale as the entropy index, which ranges from 0 to 1. This common scale simplifies the overlay process and helps avoid the need to reclassify the data. Thus, this study utilizes fuzzy membership to crea te suitable layers for walking catchment areas Figure s 3 8, 3 9, 3 10, 3 11, 3 12 and 3 13 present the input data, the process, and output examples of each suitability layer in this analysis category, using Jacksonville as an example Buffer from T ranspo rtation Options The last measurement is used to determine proximity based on whatever transportation options available, such as bus routes, pedestrian pathways, and bikeways. Both public transportation routes and pedestrian pathways are used for asses sing potential walkability and bikeways are used for finding potential physical activities via bike usage. Though the data on bus routes is different from the data on pedestrian pathways, the measurement method is identical for both variables.

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82 This is the pr ocess for the bus routes variable : O btain bus routes dataset ( C01 ) and create 5 minute walking distance buffers from routes (C01_bfr) Figures 3 14 and 3 15 present the input data, process, and output examples (using the Jacksonville case) regarding public transportation and pedestrian paths. In assessing bikeways, the process is as follows: O btain bikeways dataset ( C03 ) and create 0.5 mile and 1 mile distance buffers from routes (C03_bfr) At the end of each process, a suitability layer is created using fu zzy membership. Figure 3 16 presents the input data, the process, and some output examples (using Jacksonville) of the bikeways variable Comprehensive Active Built Environment Scores The last step in this model is to combine the outputs from the previous individual scoring process. The cells with combined high scores represent locations with a higher suitability for physical activity. First, Figure 3 17 shows the procedure of creating the composite layer by category and outputs. The final step of this mode ling process is to combine three layers into three categories (Figure 3 18). There are four typical rules for combining the spatial data: 1 ) e numeration, 2 ) d ominance, 3 ) c ontributory, and 4 ) i nteraction ( Carr & Zwick, 2007, p. 47 ) This study uses the LUC IS model rule which is i nteraction. Generally, during the composite scoring process, it is possible to weight the datasets differently based on their importance In this study, all of the variables are considered to have equal weights. Since this model us es 10 variables and the maximum score of each variable is 1, 10 would be a perfect score. Figure 3 1 9 presents the entire model structure for generating the physical activity suitability map Spatial Statistics for A nalyzing P attern s In addition to the ac tive built environment index, it is possible to use the output of previous modeling for another type of spatial analysis: Hot Spot Analysis. Although Hot

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83 Spot Analysis in GIS uses vector data to create output, since the cell size of the active built enviro nment index map is 5 meters, it is not hard to apply this cell to each parcel. I assigned each parcel the mean of the active built environment index values of all of the cells that belong to that parcel using the Zonal Statistics function of GIS. After tha t, I ran a Hot Spot Analysis in GIS. This parcel level Hot Spot Analysis provides additional valuable information because it statistically identifies which parcels have higher or lower active built environment indexes as well as clustering patterns. This i s valuable because the Hot Spot Analysis results can indicate if a spatial pattern is random, implying that there is no evidence of underlying causes Comparative Statistical Analysis The outcomes of the suitability modeling assign a measure of opportuniti es for physical activity to locations. The output cells have two types of information: spatial and quantitative. While the spatial information indicates locations that provide a higher level of opportunities for physical activity, the quantitative informat ion enabled me to utilize statistical analysis tools to compare the three different groups of urban form. Below is a descriptive statistical analysis of composite scoring results and a discussion of the one way ANOVA that compared the three groups Statist ical Analysis To conduct a comparative statistical analysis, each case must be represented by a single numeric value. Table 3 6 shows the results from the previous section (the suitability model) in terms of both the quantitative response variable and the categorical explanatory variable. These two types of variable s are essential for comparing the means of several groups related to the association between quantitative response variables and categorical explanatory variables ( Agresti & Finlay 2009, p. 369)

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84 Based on the results from the suitability model, each case was given both a suitability map for physical activity and a distribution of scores. In order to generalize the score results, this study used a mean value for each case: the physical activity o pportunity scores in Table 3 6. In this table, the quantitative response variable is the active built environment index, and the categorical explanatory variable represents each group One W ay ANOVA Table 3 6 summarizes the statistical results of each grou explanatory variable is each group, and the response variable is measured as the active built environment index mean for each case. Accordingly, in order to compare the three groups, it is reasonable to use the analysis of variance (ANOVA) t est because ANOVA is an assessment of the independence between the quantitative response variable and the categorical explanatory variable ( Agresti & Finlay 2009, p. 370). Hypotheses The initial research question of this study was: Do FBCs contain eleme nts of urban form that provide more opportunities for residents to engage in physical activities? Based on this question, it was hypothesized that the FBCs cases in this study would obtain higher average scores than the other cases. In order to verify this hypothesis, the statistical test examined whether the three populations had equal means. Accordingly, the null hypothesis was that each group had an identical mean Therefore, ANOVA is an F test for: H 0 : F = Z = H ( where F is the population mean for the F BCs area s Z is the population mean for conventional zoning area s and H is the population mean for historic cities) H a : A t least two of the population means are unequal.

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85 The test analyze d whether, if H 0 were true, the differences observed among the sam ple means could have reasonably occurred by chance ( Agresti & Finlay 2009, p. 370) Test statistic. For testing H 0 : F = Z = H the statistic uses the analysis of variance F statistics (ANOVA F statistics). Using SPSS software, the test results can be p resented as in Table 3 7. In this table, if H 0 is true, we can expect the values of F to be near 1.0. Additionally, the significance (P value) uses F distribution ( Agresti & Finlay 2009, p. 373). The P value shows whether we can reject H 0 or not. If we re ject H 0 it means that there are differences among the three groups that are being compared

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86 Table 3 1 Form based Codes Study Area s Title Adoption y ear Seaside Form Based Code 1981 Jacksonville Traditional Neighborhood Development District Ordinance 1987 Orlando: Parramore Heritage District 1994 Rosemary Beach 1995 Winter Springs Town Center District 1996 Kendall: Downtown Kendall Master Plan and Code 1998 Fort Myers Beach 1999 Naranja Urban Center, Miami Dade County 1999 Gainesville: Univers ity Heights 2000 Orlando: Baldwin Park Form Based Code 2001 Table 3 2 Conventional Zoning Study Area s ( RuBino & Starnes, 2008, pp. 101 178 ; Mormino, 2005, pp. 44 60) County Name Census u nit Brevard Malabar Town Brevard Palm Bay City Broward Mirama r City Charlotte Port Charlotte Census Designated Place Citrus Citrus Springs Census Designated Place Citrus Pine Ridge Census Designated Place Lee Cape Coral City Lee Lehigh Acres Census Designated Place Miami Dade Key Biscayne village Palm Beach P alm Beach Gardens City St. Johns St. Augustine Shores Census Designated Place St. Lucie Port St. Lucie City Volusia Deltona City

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87 Table 3 3 Study Variables by C ategory Category Variables Description Land Use Mixed use Mixed use level by entropy in dex Grocery Store Community shopping f or fresh produce Public facility L ibrary, theater, or auditorium School P ublic and private school Recreational Space Park Recreational park Public space Outdoor recreational space Recreational facility A ny space for physical activity Transportation Public Transit Bus routes Pedestrian pathways S idewalk s Bikeways Bike routes Table 3 4 GIS D ata and Sources Layer Name Year Originator Description City Limits 2011 University of Florida GeoPlan Center C it Census Places 2010 U.S. Census Bureau T he U S Census Bureau 2010 Census Places for the State of Florida Historic Places 2014 National Park Service A comprehensive inventory of all cultural resources that are listed i n the National Register of Historic Places Census Blocks 2011 U.S. Census Bureau T he U S Census Bureau 2010 Census Blocks for the State of Florida Land Use 2014 University of Florida GeoPlan Center G eneralized land use derived from parcel specific l and use for FDOT Parcels 2010 Florida Department of Revenue P arcel boundaries for each county in Florida, with each parcel's associated tax information from the Florida Department of Revenue's tax database Streets (Sidewalks) 2010 NAVTEQ T he functional c lassifications of roads (sidewalk s highway s etc.) detailed street network Bike Lanes 2015 Florida Department of Transportation B ike l ane features from FDOT Roads Characteristics inventory (RCI) dataset Public Transit Routes 2008 Florida Transit Informa tion System Florida's public transit routes

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88 Table 3 5 Operationalization of V ariables Variables Data and Process Coding Mixed use Entropy Index by Python Script M ost diversity: 1 Poor diversity: 0 Grocery Store Select p arcel center point ( converted f rom parcel ) Network analysis (using sidewalk network ) Create walking catchment areas by walking time Less than 5 min: 12 Between 5 10 min: 11 Between 10 15 min: 10 Between 15 20 min: 9 Between 20 25 min: 8 Between 25 30 min: 7 Between 30 35 min: 6 Betw een 35 40 min: 5 Between 40 45 min: 4 Between 45 50 min: 3 Between 50 55 min: 2 Between 55 60 min: 1 More than 60 min: 0 Public Facility Select p arcel center point ( converted from parcel ) Network analysis (using sidewalk network ) Create walking catchm ent areas by walking time School Select p arcel center point ( converted from parcel ) Network analysis (using sidewalk network ) Create walking catchment areas by walking time Park Select p arcel center point ( converted from parcel ) Network analysis (using sidewalk network ) Create walking catchment areas by walking time P ublic Space Select p arcel center point ( converted from parcel ) Network analysis (using sidewalk network ) Create walking catchment areas by walking time Recreational facilit y Select p arcel center point ( converted from parcel ) Network analysis (using sidewalk network ) Create walking catchment areas by walking time Public Transit Transit routes Buffer Create buffer areas Less than 400 meters: 2 More than 400 meters: 1 Pedestrian pathways Sidewalk network Buffer Create buffer areas Less than 400 meters: 2 More than 400 meters: 1 Bikeways Bike routes Buffer Create buffer areas Less than 800 meters: 3 From 600 800 meters: 2 More than 1600 meters: 1

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89 Table 3 6 Comp rehensive Active Built environment Scores T able by Group Group CABS M Sample Size * Mean S tandard Deviation FBCs F 1 F 2 F 3 F 4 S F M F S D F Zoning Z 1 Z 2 Z 3 Z 4 S Z M Z S D z Historic H 1 H 2 H 3 H 4 S H M H S D H Note: CABS M is a mean of each case a ctive built environment index ** Sample size is the number of cases Table 3 7 Standard ANOVA table Source of Variation Sum of Squares Degrees of Freedom Mean Square F Significance Between Group SST* t 1 MST=SST/(t 1) F=MST/MSE P value With in Group SSE** N t MSE=SSE/(N t) Total TSS N 1 N o te: *SST: Between Group (Sample) Variation **SSE: Within Group (Sample) Variation

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90 Figure 3 1 Case Study Areas

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91 Figure 3 2 The Number of Municipalities by Year of Incorporation ( FLC, 2015 )

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92 Figure 3 3 Conceptual GIS Suitability Modeling ( Zwick, 2009) Figure 3 4 Formula to C alculate Entropy Index (Manaugh & Kreider, 2013, p. 64)

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93 Figure 3 5 Conceptual D iagram for Creating Walking Catchment Area by Distance Fig ure 3 6 Conceptual D iagram for Creating Walking Catchment Area by Exercise

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94 Figure 3 7 Creating Suitability Layer for Entropy Index (A01)

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95 Figure 3 8 Creating Suitability Layer for Grocery Store (A02)

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96 Figure 3 9 Creating Suitability Layer for Public Facility ( A0 3)

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97 Figure 3 10 Creating Suitability Layer for School ( A0 4)

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98 Figure 3 11 Creating Suitability Layer for Park (B01)

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99 Figure 3 12 Creating Suitability Layer for Public Space (B02)

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100 Figure 3 13 Creating Suitability Layer for Rec reational Facility (B03)

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101 Figure 3 14 Creating Suitability Layer for Public Transit (C01)

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102 Figure 3 15 Creating Suitability Layer for Pedestrian Path (C02)

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103 Figure 3 16 Creating Suitability Layer for Bikeways (C03)

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104 Figure 3 17 Creating Suita bility Layer for each Category

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105 Figure 3 18 Creating Comprehensive Active Built environment Score Map

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106 Figure 3 1 9 Suitability Modeling P rocess

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107 CHAPTER 4 RESULTS Suitability M odeling R esults The following sections presents the active built enviro nment index along with the maps for each case. In order to identify each case difference through the maps, I used a legend with the same color range and scale (e.g. active built environment index with 1 10 scale ). Along with the active built environment in dex results, Hot Spot Analysis results are shown to analyze the scoring pattern. FBCs Cases University Height s (Gainesville) Figure 4 1 presents the GIS suitability modeling results. The map shows the scores, which are mainly between 3 and 6, as well as t heir spatial distributions. In descriptive statistics, this case range s from 4.74 to 6.26 (M = 5.61, SD = 0.38). Scores of 5.35, 5.64, and 5.83 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to the left since the median is greater than mean Figure 4 2 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics Figure 4 2 B shows the hot and cold spots of spatial clustering of parcels cal culated using Hot Spot Analysis based on the mean active built environment index values The number of residential units were calculated in hot and cold spots with 90 % 95%, and 99% confidence level For this site 2,393 of 2,435 total residential units ar e located in hot spots. Th is indicates that 98.3 % of residential units have relatively good potential to be involve d in physical activity. Traditional Neighborhood Development District (Jacksonville) Figure 4 3 presents GIS suitability modeling results. The map shows the scores, which are mainly

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108 consist of between 5 and 7 as well as their spatial distributions In descriptive statistics, this case range s from 1.04 to 8.99 (M = 6.02 SD = 1.16 ) Scores of 5.41, 6.15 and 6.81 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 4 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Fi gure 4 4 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 742 of 61,455 total residential units are located in cold spots. This indicates that 1.21 % of residential units have less potential to be involve d in physical activity. Fort Myers Beach Figure 4 5 presents GIS suitability modeling results. The map shows the scores which are mainly consist of between 2 and 3 as well as their spatial distributions In descriptive statistics, this case rang es from 0.01 to 5.81 (M = 2.82 SD = 0.67 ) Scores of 2.40, 2.84, and 3.34 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left since median is greater than mean. Figure 4 6 A shows the mean active built environment index values for each parcel calculated using Z onal Statistics. Figure 4 6 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots wit h 90%, 95%, and 99% confidence level. For this site, 1,122 of

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109 3,192 total residential units are located in cold spots. This indicates that 35.15 % of residential units have less potential to be involve d in physical activity. Downtown Kendall Figure 4 7 pre sents GIS suitability modeling results. The map shows the scores which are mainly consist of between 2 and 3 as well as their spatial distributions In descriptive statistics, this case rang e s from 2.01 to 3.87 (M = 2.84 SD = 0.47 ) Scores of 2.51, 2.52 and 3.42 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 8 A shows the mean active built environment index values for each par cel calculated using Zonal Statistics. Figure 4 8 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site there is no residential units since this area is located in downtown. Naranja Urban Center Figure 4 9 presents GIS suitability modeling results. The map shows the scores which are mainly consist of between 2 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 1.01 to 7.71 (M = 2.36 SD = 0.42 ) Scores of 2.01, 2.35 and 2.73 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 10 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 10 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean

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110 active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 750 of 818 total residential units are located in cold spots. This indicates that 91.69 % of residential units have less potential to be involve d in physical activity. Orlando Baldwin Park Figure 4 11 presents GIS suitability modeling results. The map sh ows the scores, which are mainly consist of between 3 and 5 as well as their spatial distributions In descriptive statistics, this case rang e s from 0.17 to 5.92 (M = 3.88 SD = 0.99 ) Scores of 3.30, 3.95, and 4.67 represent the 25th, 50th, and 75th perc entiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 12 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 12 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confide nce level. For this site 922 of 1,565 total residential units are located in cold spots. This indicates that 58.91 % of residential units have less potential to be involve d in physical activity. Orlando Parramore Heritage District Figure 4 13 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 5 and 6 as well as their spatial distributions In descriptive statistics, this case range s from 3.86 to 7.44 (M = 5.94 SD = 0.60 ) Scores of 5.49, 6.02 and 6.3 0 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean.

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111 Figure 4 14 A shows the mean active built environment index values for each parcel calcu lated using Zonal Statistics. Figure 4 14 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 1,733 of 1,909 total residential units are located in hot spots. This indicates that 90.78 % of residential units have relatively good potential to be involve d in physical activity. Winter S prings Town Center Figure 4 15 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 0.18 to 3.97 (M = 2.5 6 SD = 0.83 ) Scores of 1.98, 2.61 and 3.22 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left since median is greater than mean. Figure 4 16 A shows the mean active built en vironment index values for each parcel calculated using Zonal Statistics. Figure 4 16 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of r esidential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 12 of 268 total residential units are located in cold spots. This indicates that 4.48 % of residential units have less potential to be involve d in physical activity. Seaside Figure 4 17 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 1.01 to 4.51 (M = 2.11 SD =

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112 0.76 ) Scores of 1.44, 1.64 and 3.51 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 18 A shows the mean activ e built environment index values for each parcel calculated using Zonal Statistics. Figure 4 18 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The n umber of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 232 of 354 total residential units are located in cold spots. This indicates that 65.54 % of residential units have less potential to be involve d in physical activity. Rosemary Beach Figure 4 19 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics, this case range s from 1.01 to 3.52 (M = 1.39 SD = 0.36 ) Scores of 1.01, 1.47 and 1.56 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left since median is greater than mean. Figure 4 20 A sh ows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 20 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment i ndex values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 144 of 290 total residential units are located in cold spots. This indicates that 49.66 % of residential units have le ss potential to be involve d in physical activity.

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113 Historic Cities Micanopy Historic District Figure 4 21 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 2 and 3 as well as their spatial distributio ns In descriptive statistics, this case range s from 1.50 to 2.92 (M = 2.38 SD = 0.37 ) Scores of 2.00, 2.43 and 2.75 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sinc e med ian is greater than mean. Figure 4 22 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 22 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysi s based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 37 of 173 total residential units are located in cold spots. This indic ates that 21.39 % of residential units have less potential to be involve d in physical activity. Pensacola Historic District Figure 4 23 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 4 and 6 as wel l as their spatial distributions In descriptive statistics, this case range s from 2.95 to 6.47 (M = 5.13 SD = 0.64 ) Scores of 4.90 5.26 and 5.62 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 24 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 24 B shows the hot and cold spots of spatial clustering of parcels calc ulated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in

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114 hot and cold spots with 90%, 95%, and 99% confidence level. For this site 168 of 168 total residential units are loc ated in hot spots. This indicates that all residential units have relatively good potential to be involve d in physical activity. Apalachicola Historic District Figure 4 25 presents GIS suitability modeling results. The map shows the scores, which are main ly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 1.40 to 4.94 (M = 1.97 SD = 0.21 ) Scores of 1.86, 2.00 and 2.41 represent the 25th, 50th, and 75th percentiles respectively. The dist ributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 3 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 3 B shows the hot and cold spots o f spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site 135 of 556 total residential units are located in cold spots. This indicates that 24.28 % of residential units have less potential to be involve d in physical activity. Quincy Historic District Figure 4 27 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics, this case range s from 1.04 to 2.05 (M = 1.56 SD = 0.39 ) Scores of 1.05, 1.65 and 1.87 represent the 25th, 50th, and 75th percenti les respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean.

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115 Figure 4 28 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 28 B sh ows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 75 of 170 total residential units are located in cold spots. This indicates that 44.12 % of residential units have less potential to be involve d in physical activity. Marianna Historic District Figure 4 29 presents GIS suitability mod eling results. The map shows the scores, which are mainly consist of between 3 and 4 as well as their spatial distributions In descriptive statistics, this case range s from 2.33 to 4.06 (M = 3.31 SD = 0.33 ) Scores of 3.03 3.35 and 3.48 represent the 2 5th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left since median is greater than mean Figure 4 30 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 30 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 9 0%, 95%, and 99% confidence level. For this site, 4 of 309 total residential units are located in cold spots. This indicates that 1.29 % of residential units have less potential to be involve d in physical activity. Tallahassee Park Avenue Historic District Figure 4 31 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 6 and 7 as well as their spatial distributions In descriptive statistics, this case range s

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116 from 5.50 to 7.03 (M = 6.48 SD = 0.33 ) Scor es of 6.03, 6.55 and 6.78 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 32 A shows the mean active built environment index val ues for each parcel calculated using Zonal Statistics. Figure 4 32 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units we re calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 137 of 138 total residential units are located in hot spots. This indicates except one residential units, all residential units have relatively good potential to b e involve d in physical activity. Key West Historic District Figure 4 33 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 4 and 5 as well as their spatial distributions In descriptive statistics, th is case range s from 2.02 to 5.00 (M = 4.36 SD = 0.38 ) Scores of 4.07, 4.46, and 4.68 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left since median is greater than mean. Fi gure 4 34 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 34 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 93 of 3,722

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117 total residential units are located in cold spots. This indicates that 2.50 % of residential units have less potential to be involve d in physical activity. Fernandina Beach Historic District Figure 4 35 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 3 and 4 as well as their spatial distri butions In descriptive statistics, this case range s from 1.86 to 4.15 (M = 3.24 SD = 0.41 ) Scores of 1.86, 3.26 and 3.58 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 36 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 36 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot An alysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 84 of 236 total residential units are located in cold spots. This indicates that 35.59 % of residential units have less potential to be involve d in physical activity. Milton Historic District Figure 4 37 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 2 and 3 as w ell as their spatial distributions In descriptive statistics, this case range s from 1.51 to 6.72 (M = 2.67 SD = 0.32 ) Scores of 2.46, 2.74 and 2.92 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environme nt index are skewed to left sin ce median is greater than mean. Figure 4 38 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 38 B shows the hot and cold spots of

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118 spatial clustering of parcels c alculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 94 of 110 total residential units are l ocated in hot spots. This indicates that 85.45 % of residential units have relatively good potential to be involve d in physical activity. St. Augustine Historic District Figure 4 39 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics, this case range s from 1.04 to 2.53 (M = 1.92 SD = 0.28 ) Scores of 1.71, 1.90 and 2.17 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right sin ce mean is greater than median. Figure 4 40 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 40 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 128 of 143 total residential units are located in cold spots. This indicates that 89.51 % of residential units have less potential to be involve d in physical activity. Conventional Zoning Areas Malaba Figure 4 41 presents GIS suitability modeling res ults. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics this case range s from 0.01 to 3.85 (M = 1.79 SD = 0.56 ) Scores of 1.60, 1.70 and 1.99 represent the 25th, 50t h, and 75th percentiles

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119 respectively. The distributions of active built environment index are skewed to right since mean is gr eater than median. Figure 4 42 A shows the mean active built environment index values for each parcel calculated using Zonal Statis tics. Figure 4 42 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95% and 99% confidence level. For this site, 529 of 1045 total residential units are located in cold spots. This indicates that 50.62 % of residential units have less potential to be involve d in physical activity. Palm Bay Figure 4 43 presents GIS suitabilit y modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 0.01 to 6.77 (M = 2.05 SD = 1.38 ) Scores of 0.94, 2.03 and 2.97 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 44 A shows the mean active built environment index values for each parcel calculated usin g Zonal Statistics. Figure 4 44 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 16,824 of 50,260 total residential units are located in cold spots. This indicates that 33.47 % of residential units have less potential to be involve d in physical activity.

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120 Miramar Figure 4 45 prese nts GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 0.03 to 4.98 (M = 2.40 SD = 0.96 ) Scores of 1.71, 2.33 and 3.00 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median Figure 4 46 A shows the mean active built environment index values for each parc el calculated using Zonal Statistics. Figure 4 46 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 9,218 of 32,414 total residential units are located in cold spots. This indicates that 28.44 % of residential units have less potential to be involved in physical activity. Port Char lotte Figure 4 47 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics, this case range s from 0.01 to 4.96 (M = 1.84 SD = 0.80 ) Scores of 1.17, 1.68 and 2.37 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 48 A shows the mean active built environment in dex values for each parcel calculated using Zonal Statistics. Figure 4 48 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential u nits were calculated in

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121 hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 10,397 of 26,032 total residential units are located in cold spots. This indicates that 39.94 % of residential units have less potential to be involved in phy sical activity. Citrus Springs Figure 4 49 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 2 as well as their spatial distributions In descriptive statistics, this case range s from 0.60 to 3 .80 (M = 1.98 SD = 0.50 ) Scores of 1.63, 1.99 and 2.33 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 50 A shows the mean act ive built environment index values for each parcel calculated using Zonal Statistics. Figure 4 50 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 926 of 3,874 total residential units are located in cold spots. This indicates that 23.90 % of residential units have less potential t o be involved in physical activity. Pine Ridge Figure 4 51 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case rang e s from 0.06 to 4.84 (M = 2.12 SD = 0.84 ) Scores of 1.47, 2.02 and 2.64 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median.

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122 Figure 4 52 A s hows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 52 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 1,670 of 4,114 total residential units are located in cold spots. This indicates that 40.59 % of residential units ha ve less potential to be involved in physical activity. Cape Coral Figure 4 53 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statisti cs, this case range from 0.01 to 6.59 (M = 2.32 SD = 1.10 ) Scores of 1.57, 2.31 and 2.98 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than medi an. Figure 4 54 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 54 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 26,296 of 69,329 total residential units are located in cold spots. This indicates that 37.93 % of residential units have less potential to be involved in physical activity. Lehigh Acres Figure 4 55 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions I n descriptive statistics this case range s from 0.02 to 5.67 (M = 1.90 SD =

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123 0.84 ) Scores of 1.16, 1.85 and 2.21 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mea n is greater than median. Figure 4 56 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 56 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis bas ed on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 10,282 of 34,476 total residential units are located in cold spots. This ind icates that 29.82 % of residential units have less potential to be involved in physical activity. Key Biscayne Figure 4 57 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 2 and 3 as well as their sp atial distributions In descriptive statistics, this case range s from 1.01 to 6.96 (M = 2.45 SD = 0.59 ) Scores of 2.02, 2.26 and 2.86 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are sk ewed to right since mean is greater than median. Figure 4 58 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 58 B shows the hot and cold spots of spatial clustering of parcels calculated usin g Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 835 of 1,320 total residential units are located in c old spots. This indicates that 63.26 % of residential units have less potential to be involved in physical activity.

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124 Palm Beach Gardens Figure 4 59 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 0 a nd 2 as well as their spatial distributions In descriptive statistics, this case range s from 0.01 to 5.90 (M = 1.27 SD = 1.42 ) Scores of 0.03, 0.66 and 2.02 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median. Figure 4 60 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 60 B shows the hot and cold spots of spatial clustering o f parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 7,433 of 24,367 total reside ntial units are located in cold spots. This indicates that 30.50 % of residential units have less potential to be involved in physical activity. St. Augustine Shores Figure 4 61 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range s from 0.66 to 3.99 (M = 2.55 SD = 0.70 ) Scores of 1.96, 2.46 and 3.01 represent the 25th, 50th, and 75th percentiles respectively. The distributions of active built environment index are skewed to right sin ce mean is greater than median. Figure 4 62 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 62 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in

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125 hot and cold spots with 90%, 95%, and 99% confidence level. For this site 819 of 2,386 total residential units are located in cold spots. This indicates that 34.33 % of residential units have less potential to be involved in physical activity. Port St. Lucie Figure 4 63 presents GIS suitability modeling results. The map shows the scores, which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics this case range s from 0.01 to 7.89 (M = 2.31 SD = 1.54 ) Scores of 1.06, 2.38 and 3.35 represent the 25th, 50th, and 75th percentil es respectively. The distributions of active built environment index are skewed to left sin ce median is greater than mean. Figure 4 64 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 64 B sho ws the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence l evel. For this site, 23,787 of 63,949 total residential units are located in cold spots. This indicates that 37.20 % of residential units have less potential to be involved in physical activity. Deltona Figure 4 65 presents GIS suitability modeling results The map shows the scores which are mainly consist of between 1 and 3 as well as their spatial distributions In descriptive statistics, this case range from 0.01 to 5.77 (M = 2.43 SD = 1.00 ) Scores of 1.71, 2.22 and 3.13 represent the 25th, 50th, an d 75th percentiles respectively. The distributions of active built environment index are skewed to right since mean is greater than median.

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126 Figure 4 66 A shows the mean active built environment index values for each parcel calculated using Zonal Statistics. Figure 4 66 B shows the hot and cold spots of spatial clustering of parcels calculated using Hot Spot Analysis based on the mean active built environment index values. The number of residential units were calculated in hot and cold spots with 90%, 95%, and 99% confidence level. For this site, 13,754 of 31,602 total residential units are located in cold spots. This indicates that 43.52 % of residential units have less potential to be involved in physical activity. Statistical C omparative A nalysis Results Desc riptive S tatistics Figure 4 67 presents descriptive statistics for the three comparison groups. First, the average scores of the FBCs group ranged from 1.81 to 6.02 (M = 3.63, SD = 1.62). The scores had an asymmetrical distribution, with a skewness of 0.70 (SE = 0.69) and kurtosis of 1.39 (SE = 1.33). As seen in Figure 4 68, the mass of the distribution is concentrated on the left side of the histogram. Second, t he average scores of the historic group ranged from 1.47 to 6.43 (M = 3.30, SD = 1.61). The sco res had an asymmetrical distribution, with a skewness of 0.89 (SE = 0.69) and kurtosis of 0.07 (SE = 1.33). As seen in Figure 4 69, the mass of the distribution is concentrated on the left side of the histogram. Finally, t he average scores of the zoning g roup ranged from 1.27 to 2.55 (M = 2.11, SD = 0.36). The scores had an asymmetrical distribution, with a skewness of 0.99 (SE = 0.61) and kurtosis of 1.13 (SE = 1.19). As seen in Figure 4 70, the mass of the distribution is concentrated on the right side of the histogram One Way ANOVA T est Results In order to perform a one way ANOVA test, several assumptions were considered. First, it was necessary to determine if each group had outliers. There were

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127 no outliers in the data, as assessed by an inspection of a boxplot for values greater than 1.5 box lengths from the edge of the box (Figure 4 71). Second, I needed to determine scores were normally distributed for the historic and zoning groups, as assessed by Shapiro Wilk's test ( p > .05). However, the scores of the FBCs group were not normally distributed (Figure 4 72) Since the using the square root. After the transformation distributed (Figure 4 73), as assessed by Shapiro Wilk's test ( p > .05). Third, it was necessary to determine if the population variances of the dependent variable were equal for the three groups of the independent variable. However, the assumption of the homogeneity of the variances was violated, as assessed by Levene's test (Figure 4 74) for equality of variances ( p = .001). Since the assumption of the homogeneity of the variances was violated, the standard one w ay ANOVA test (Figure 4 75) was not applicable to the data. Thus, I used a Welch ANOVA test instead. The scores were statistically different for the comparison groups, with Welch's F (2, 12.857) = 6.370 and p < .012 (Figure 4 76) Because the test was stati stically significant, I could use the results of the Games Howell post hoc test to identify where the differences were located (Figure 4 77). A Games Howell post hoc analysis revealed a difference between the score s of 3.6 1.6 in the FBCs group and 2.1 0.4 in the conventional zoning group a difference of 1.5 (95% CI, 0.1 to 3. 0), which is statistically significant ( p = .0 38 )

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128 Test Hypotheses. The group means were discovered to be statistically significantly different ( p < .05) T herefore, the null hyp othesis could be rejected, and the alternative hypothesis could be accepted

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129 Figure 4 1 GIS Modeling Results of Univers ity Height s (Gainesville) A) Aerial Photo. B) GIS Suitability Modeling Results.

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130 Figure 4 2 Score s by Parcel and Hot Spot Analysis of University Height s (Gainesville) A) Zonal Statistics. B) Hot Spot Analysis.

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131 Figure 4 3 GIS Modeling Results of Traditional Neighborhood Development District ( Jacksonville ) A) Aerial Photo. B) GIS Suitability Mode ling Results.

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132 Figure 4 4 Scores by Parcel and Hot Spot Analysis of Traditional Neighborhood Development District (Jacksonville) A) Zonal Statistics. B) Hot Spot Analysis.

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133 Figure 4 5 GIS Modeling Results of Fort Myers Beach A) Aerial Photo. B) GIS Suitability Modeling Results.

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134 Figure 4 6 Scores by Parcel and Hot Spot Analysis of Fort Myers Beach A) Zonal Statistics. B) Hot Spot Analysis.

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135 Figure 4 7 GIS Modeling Results of Downtown Kendall A) Aerial Photo. B) GIS Suitability Modeling Resul ts.

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136 Figure 4 8 Scores by Parcel and Hot Spot Analysis of Downtown Kendall A) Zonal Statistics. B) Hot Spot Analysis.

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137 Figure 4 9 GIS Modeling Results of Naranja Urban Center A) Aerial Photo. B) GIS Suitability Modeling Results.

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138 Figure 4 10 Sco res by Parcel and Hot Spot Analysis of Naranja Urban Center A) Zonal Statistics. B) Hot Spot Analysis.

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139 Figure 4 11 GIS Modeling Results of Baldwin Park (Orlando) A) Aerial Photo. B) GIS Suitability Modeling Results.

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140 Figure 4 12 Scores by Parcel and Hot Spot Analysis of Baldwin Park (Orlando) A) Zonal Statistics. B) Hot Spot Analysis.

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141 Figure 4 13 GIS Modeling Results of Parramore Heritage District ( Orlando ) A) Aerial Photo. B) GIS Suitability Modeling Results.

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142 Figure 4 14 Scores by Parce l and Hot Spot Analysis of Parramore Heritage District ( Orlando ) A) Zonal Statistics. B) Hot Spot Analysis.

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143 Figure 4 15 GIS Modeling Results of Winter Springs Town Center A) Aerial Photo. B) GIS Suitability Modeling Results.

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144 Figure 4 16 Scores by Parcel and Hot Spot Analysis of Winter Springs Town Center A) Zonal Statistics. B) Hot Spot Analysis.

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145 Figure 4 17 GIS Modeling Results of Seaside A) Aerial Photo. B) GIS Suitability Modeling Results.

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146 Figure 4 18 Scores by Parcel and Hot Spot Ana lysis of Seaside A) Zonal Statistics. B) Hot Spot Analysis.

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147 Figure 4 19 GIS Modeling Results of Rosemary Beach A) Aerial Photo. B) GIS Suitability Modeling Results.

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148 Figure 4 20 Scores by Parcel and Hot Spot Analysis of Rosemary Beach A) Zonal St atistics. B) Hot Spot Analysis.

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149 Figure 4 21 GIS Modeling Results of Micanopy Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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150 Figure 4 22 Scores by Parcel and Hot Spot Analysis of Micanopy Historic District A) Zonal Statist ics. B) Hot Spot Analysis.

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151 Figure 4 23 GIS Modeling Results of Pensacola Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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152 Figure 4 24 Scores by Parcel and Hot Spot Analysis of Pensacola Historic District A) Zonal Statistics B) Hot Spot Analysis.

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153 Figure 4 25 GIS Modeling Results of Apalachicola Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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154 Figure 4 26 Scores by Parcel and Hot Spot Analysis of Apalachicola Historic District A) Zonal Statist ics. B) Hot Spot Analysis.

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155 Figure 4 27. GIS Modeling Results of Quincy Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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156 Figure 4 28 Scores by Parcel and Hot Spot Analysis of Quincy Historic District A) Zonal Statistics. B) H ot Spot Analysis.

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157 Figure 4 29 GIS Modeling Results of Marianna Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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158 Figure 4 30 Scores by Parcel and Hot Spot Analysis of Marianna Historic District A) Zonal Statistics. B) Hot Sp ot Analysis.

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159 Figure 4 31 GIS Modeling Results of Tallahassee Park Avenue Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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160 Figure 4 32 Scores by Parcel and Hot Spot Analysis of Tallahassee Park Avenue Historic District A) Zo nal Statistics. B) Hot Spot Analysis.

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161 Figure 4 33 GIS Modeling Results of Key West Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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162 Figure 4 34 Scores by Parcel and Hot Spot Analysis of Key West Historic District A) Zonal S tatistics. B) Hot Spot Analysis.

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163 Figure 4 35 GIS Modeling Results of Fernandina Beach Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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164 Figure 4 36 Scores by Parcel and Hot Spot Analysis of Fernandina Beach Historic District A) Zonal Statistics. B) Hot Spot Analysis.

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165 Figure 4 37 GIS Modeling Results of Milton Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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166 Figure 4 38 Scores by Parcel and Hot Spot Analysis of Milton Historic District A) Zonal Statistics. B) Hot Spot Analysis.

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167 Figure 4 39 GIS Modeling Results of St. Augustine Historic District A) Aerial Photo. B) GIS Suitability Modeling Results.

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168 Figure 4 40 Scores by Parcel and Hot Spot Analysis of St. Augustine Historic District A) Zonal Statistics. B) Hot Spot Analysis.

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169 Figure 4 41 GIS Modeling Results of Malaba r A) Aerial Photo. B) GIS Suitability Modeling Results.

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170 Figure 4 42 Scores by Parcel and Hot Spot Analysis of Malaba r. A) Zonal Statistics. B) Hot Spot Analysis.

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171 Figure 4 43 GIS Modeling Results of Palm Bay A) Aerial Photo. B) GIS Suitability Modeling Results.

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172 Figure 4 44 Scores by Parcel and Hot Spot Analysis of Palm Bay A) Zonal Statistics. B) Hot Spot Analysis.

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173 Figure 4 45 GIS Modeling Results of Mira ma r A) Aerial Photo. B) GIS Suitability Modeling Results.

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174 Figure 4 46 Scores by Parcel and Hot Spot Analysis of Mirama r A) Zonal Statistics. B) Hot Spot Analysis.

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175 Figure 4 47 GIS Modeling Results of Port Charlotte A) Aerial Photo. B) GIS Suitabi lity Modeling Results.

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176 Figure 4 48 Scores by Parcel and Hot Spot Analysis of Port Charlotte A) Zonal Statistics. B) Hot Spot Analysis.

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177 Figure 4 49 GIS Modeling Results of Citrus Springs A) Aerial Photo. B) GIS Suitability Modeling Results.

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178 Fig ure 4 50 Scores by Parcel and Hot Spot Analysis of Citrus Springs A) Zonal Statistics. B) Hot Spot Analysis.

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179 Figure 4 51 GIS Modeling Results of Pine Ridge A) Aerial Photo. B) GIS Suitability Modeling Results.

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180 Figure 4 52 Scores by Parcel and Ho t Spot Analysis of Pine Ridge A) Zonal Statistics. B) Hot Spot Analysis.

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181 Figure 4 53 GIS Modeling Results of Cape Coral A) Aerial Photo. B) GIS Suitability Modeling Results.

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182 Figure 4 54 Scores by Parcel and Hot Spot Analysis of Cape Coral A) Zon al Statistics. B) Hot Spot Analysis.

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183 Figure 4 55 GIS Modeling Results of Lehigh Acres A) Aerial Photo. B) GIS Suitability Modeling Results.

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184 Figure 4 56 Scores by Parcel and Hot Spot Analysis of Lehigh Acres A) Zonal Statistics. B) Hot Spot Analys is.

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185 Figure 4 57 GIS Modeling Results of Key Biscayne A) Aerial Photo. B) GIS Suitability Modeling Results.

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186 Figure 4 58 Scores by Parcel and Hot Spot Analysis of Key Biscayne A) Zonal Statistics. B) Hot Spot Analysis.

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187 Figure 4 59 GIS Modeling Results of Palm Beach Gardens A) Aerial Photo. B) GIS Suitability Modeling Results.

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188 Figure 4 60 Scores by Parcel and Hot Spot Analysis of Palm Beach Gardens A) Zonal Statistics. B) Hot Spot Analysis.

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189 Figure 4 61 GIS Modeling Results of St. August ine Shores A) Aerial Photo. B) GIS Suitability Modeling Results.

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190 Figure 4 62 Scores by Parcel and Hot Spot Analysis of St. Augustine Shores A) Zonal Statistics. B) Hot Spot Analysis.

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191 Figure 4 63 GIS Modeling Results of Port St. Lucie A) Aerial P hoto. B) GIS Suitability Modeling Results.

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192 Figure 4 64 Scores by Parcel and Hot Spot Analysis of Port St. Lucie A) Zonal Statistics. B) Hot Spot Analysis.

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193 Figure 4 65 GIS Modeling Results of Deltona A) Aerial Photo. B) GIS Suitability Modeling Re sults.

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194 Figure 4 66 Scores by Parcel and Hot Spot Analysis of Deltona A) Zonal Statistics. B) Hot Spot Analysis.

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195 Figure 4 67 Descriptive Statistics of FBCs, Historic, and Zoning Group (SPSS) Figure 4 6 8 Histogram of FBCs Group Scores (SPSS)

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196 Figure 4 6 9 Histogram of Historic Group Scores (SPSS)

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197 Figure 4 70 Histogram of Zoning Group Scores (SPSS)

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198 Figure 4 71 Box Plots of three G roups (SPSS) Figure 4 72 Test of Normality with R aw S cores (SPSS)

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199 Figure 4 73 Test of Normality w ith Transformed S cores (SPSS) Figure 4 7 4 Test of Homogeneity of Variances (SPSS) Figure 4 7 5 Standard ANOVA table (SPSS) Figure 4 7 6 Robust Tests of Equality of Means (SPSS)

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200 Figure 4 7 7 Post Hoc Tests (SPSS)

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201 CHAPTER 5 CONCLUSION S Summary of Study Most FBCs were introduced as a method of addressing problems that were created by conventional zoning, such as a lack of support for physical activity. However, evidence is required to support this capability of FBCs As such, t his paper e xamines whether FBCs are conducive to creating active built environments In order to answer the research question, the following objectives were established: first, to determine and operationalize the built environment variables that affect physical activ ity in a GIS and second, to employ GIS suitability modeling to develop an index of active built environments that c ould be used to compare th e urban forms of FBCs, c onventional z oning, and h istoric c ities. The literature reveals that several urban form el ements such as open spaces, grocery stores, schools, and transportation facilities can affect the physical activity Previous studies on this topic also disclose the fact that FBCs have become a leading alternative for creatin g active built environments and mitigating contemporary urban issues that have been caused by conventional zoning. The methodology of this study consists of four parts. For the first part, developing a comparative analysis, this study selected different s ites within each of the three groups: f orm b ased c odes, c onventional z oning, and h istoric c ities. Florida was chosen as the area to be studied because Florida has played a precursory role in the development of FBCs in the U.S. and has experienced the evolu tion of various phases of planning approaches. Second, based on prior studies, this study categorized and aggregated any GIS data that was relevant to active built environments in Florida Third,

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202 this study built a GIS suitability model to assess the three urban form groups. Suitability modeling was chosen for its ability to overcome the limitations of previous efforts regarding the assessment of active built environments as well as the analysis of complex urban form data. Since each GIS modeling process yi elds a suitability map ranked by the active built environment index, the GIS modeling results not only reveal the spatial distribution of the scores, but also provide cell based quantified output. Fourth, the study performed a statistical comparative analy sis among the three groups. An ANOVA test was used to determine if the differences observed among the groups could have reasonably occurred by chance. The results of this analysis c ould reveal whether or not FBCs have a significant ability to create urban forms that are more conducive to physical activity than those developed by c onventional z oning or h istoric c ities. Because the assumption of the homogeneity of the variances was violated, a standard one way ANOVA test was not be applicable to this study; t herefore, the study used a Welch ANOVA test instead. In terms of the results, a statistically significant difference was found between the scores of the comparison groups, with Welch's F (2, 12.857) = 6.370 and p < .012. However, this result does not reveal which groups were different. In order to identify specific group differences, I performed a Games Howell post hoc analysis. The Games Howell post hoc analysis revealed a difference between the scores of 3.6 1.6 in the FBCs group and 2.1 0.4 in the c on ventional z oning group, a difference of 1.5 (95% CI, 0.1 to 3.0); this is statistically significant ( p = .038). These results indicate that, in Florida, urban forms created based on FBCs are more

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203 likely to lead to active living than urban forms created as a result of c onventional z oning regulations Discussion As mentioned in the literature review, previous research efforts have describe d how built environments are related to physical activity. However, since each study has been conducted separately, it is difficult to find urban planners and policymakers with comprehensive perspectives on the full impacts that urban form can have on health. This study begins to overcome this kind of limitation. I n order to do this, I used GIS based methods to assess three d ifferent urban forms by active built environment criteria (characterized by mixed use, proximity to open space, public facilities, and active transportation options). Additionally, the use of the same GIS based index in 33 cases allowed me to compare the o utcomes of three different regulation s As a result, I found that urban forms created based on FBCs are more likely to lead to active living than urban forms created by conventional zoning regulations. Also, I initially expected that historic cities would score higher than traditional zoning cases, since the urban forms of traditional cities are typically more compact and designed for walkability Al though the scores of the historic cases were higher than those of the conventional zoning cases and were clos e to the scores of the FBCs cases the ANOVA test did not show a statistical difference between the historic cities and conventional zoning cases More research may be needed to explore this result further. During the case selection process, I assumed that cases consist ed of mutually exclusive sub groups and used judgement to select them. This process, however, revealed some case selection issues in the study design. Because FBCs, conventional zoning, and historic cases have developed during different timel ines, I chose cases

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204 based on fundamental events that occurred in Florida planning history. For cases involving FBCs, I found 10 sites that adopted FBCs between 1981, the first year that FBCs were utilized in Florida and 2003; this latter date was chosen because the FBCs adopted after 2003 are still in their incipient stages. Next, for historic cases, I found 10 sites that have historic districts and were established before 1845, since these cities tend to maintain relatively traditional urban form s The urban forms of historic cities that adopted conventional zoning or FBCs after 1845 might have be en affected by those rules however For zoning cases, I tried to find conventional cities that were built between 1950 and 1980 because many Florida citi es were developed by zoning regulation after World War II. Based on data from the F lorida L eague of C ities (2015), 107 cities were applicable I n this study, I selected the 13 cases that were presented by Ru B ino and Starnes (2008) This non random selectio n was based on the assumption that RuBino and Starnes had chosen these cases to represent the most typical examples of conventional zoning in Florida for that period. This study used physical distance as the measurement of walkability However, as mentione d in the literature review, there are additional factors that affect walkability, su ch as the socioeconomic status of the residents (their races and incomes) or qualitative variables (the sense of enclosure and pleasurability) I did not include these vari ables due to data unavailability. However, if data on these variables become available, future research will be able to identify a more de t ailed relationship between walkability and variou s factors, including urban form socioeconomic status, and qualitati ve variables.

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205 An entropy index was used to measure the land use mix. Although the entropy index is a useful tool to assess land use diversity, it also has one limitation. As Brown et al. (2009) note, the entropy formula (Figure 3 1) sometimes does not pres ent land use diversity. In Figure 5 2, both indexes are the same (with a value of 1 for both); however, while the right diagram only has two land uses (multifamily and single family), the left diagram shows six different land uses (multifamily, single fami ly, office, retail, education, and entertainment). However, in all of my 33 cases, the entropy index results did not reveal this theoretical issue. Although there are several potential problems with this modeling process, the GIS based visualization method provides an expanded set of tools that can help urban planners and public health professionals to understand the relationship between urban form and active built environment s In addition to visualizing the results, the modeling process has the potential to present changes over time (e.g., before and after); this is because the GIS modeling process can not only show existing active built environment conditions, but can also show what future active built environment conditions could be. The capability of GI S to visualize future settings can be used for alternative future land use scenarios, and it is relatively easy to add or change variables in the modeling sequence (Carr & Zwick, 2007, p. 200). Also, although this study employed a static model, increasing the GIS capacity could support a more dynamic model that would be more responsive to variable changes over time Health Promoting Urban Design Recently, a number of urban planners and institutions have recognized that the most successful and innovative so lutions of contemporary urban issues, such as physical inactivity, are created when multiple professionals collaborate. For example,

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206 the Planning and Community Health Center of the American Planning Association (APA) has provided practical tools and polici es to prompt public health through active living, healthy eating, and health living in all planning policies. Additionally, the Urban Land Institute (ULI) has initiated collaboration with health care, architecture, planning, and development professionals t o improve public health and increase active living (The Building Healthy Places Initiative). In addition to these interdisciplinary efforts to resolve current urban problems, though the GIS modeling yielded meaningful results, I recognized the necessity o f a more comprehensive framework for analysis. This is because, as Northridge et al. ( 2003 ) argued, the relationships between study variables and physical activity is complex. Thus before discussing future research, I summarize the findings of collaborati on efforts for active living and to see if there is a comprehensive analysis framework that will embrace complex variables and methods of active built environment analysis As Badland and Schofiel d ( 2005 ) argued, the limitations of built environments influ ence on physical activity, suitable built environments is essential to sustaining physical activity behaviors (p. 177). The m ost common suggestion s for health y communities are active living and heathy eating. Within this context, ULI introduced ten p rincip les for b uilding h ealthy p laces (ULI, 2013 pp. 10 29 ): Put People First: Individuals are more likely to be active in a community designed around their needs Recognize the Economic Value : Healthy places can create enhanced economic value for both the priv ate and public sectors Empower Champions for Health : Every movement needs its champions.

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207 Energize Shared Spaces : Public gathering places have a direct, positive impact on human health Make Healthy Choices Easy : Communities should make the healthy choice the one that is SAFE safe, accessible, fun, and easy. Ensure Equitable Access : Many segments of the population would benefit from better access to services, amenities, and opportunities. Mix It Up : A variety of land uses, building types, and public spaces can be used to improve physical and social activity. Embrace Unique Character : Places that are different, unusual, or unique can be helpful in promoting physical activity. Promote Access to Healthy Food : Because diet affects human health, access to healthy food should be a considered as part of any development proposal. Make It Active : Urban design can be employed to create an active community. Despite the great potential to create healthy places, the above recommendations cannot be accomplish ed by one dis cipline Even so, as an urban designer, I would like to explore s everal urban design strategies to achieve above principles. In urban design, o ne common and strong method to prompt physical activity is walking, since walking is the most effective ways to a chieve the daily physical activity recommendation of adults and can be i ncorporated into everyday life (Gehl, 2010, p. 111 ) Below I descr ibe the know n approaches to enhance walkability. Destinations First of all, walkability requires destinations For thi s study, destinations such as grocery stores, parks, and public facilities were used as variables By walking to destinations people include physical activity in their daily lives without noticing However, during the analysis process, I recognized each d estination has a different hierarchy. Figure 5 1 describes possible amenities by distance and implies the difference of walking intensity by amenit y Thus, when designing for those amenities urban designers should consider their locations as well as their suggested walking distance to maximize walking

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208 Street D esign for W alking As mentioned previously, s treets not only provide space for the movement of people but also offer a place for social interaction. Well known street design concepts for walking inclu de: provide well designed transit stops for encouraging transit use and walking on the streets; place public plazas along with streets since pedestrian networks that alternate street and squares can make the psychological impact on making walking distance shorter ( Gehl, 1987 ); and since traffic calming also affects walkability, incorporate street additions such as curb extensions, med ians, and raised speed reducers Including aforementioned concepts, the City of New York (2010) listed the following design s trategies : Provide seating, drinking fountains, restrooms, and other infrastructure that support increased frequency and duration of walking. Provide lights on sidewalks and active play areas to extend opportunities for physical activity into the evening Make sidewalks wide enough to comfortably accommodate pedestrians, including those with disabilities. Incorporate traffic calming street additions such as curb extensions, medians, and raised speed reducers. Create a buffer to separate pedestrians fro m moving vehicles using street furniture, trees, and other sidewalk infrastructure. Create paths with auditory crossing signals, adequate crossing times, clear signage, visible access ramps, and connections to walking. Different Design by U sers Furtherm ore, it is also crucial for design strategies to accommodate the variations of healthy physical activity by age or health conditions. Here I discuss the literature findings specific to designing for vulnerable populations such as school children and senior citizens. For the literature shows the significance of amenities on the street. In order to support their physical activity, as Boarnet et al. (2005)

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209 reported, well made sidewalks and street environment s allow children to wal k safe ly. made associated with higher rates of walking to school among children. Also, Lockett, Willis, and Edwards (2005) showed that benches and restrooms would support childr en in walking more, while traffic hazards were a deterrent. Compared to children identified as key components to encouraging their activity. Traffic calming measures are important for the elderly, who are more vulnerable to pedestrian accidents due to slower reaction time and limited mobility. Statistics from t he National Highway Traffic Safety Administration (NHTSA) indicated that f or older people, 63 percent of pedestrian fatalities in 2010 occur red at non intersection locations That means sidewalks for seniors need more proper street calming additions such as medians and raised speed reducers. In addition to traffic calming, frequent seating places make streets more accessible for elders as well as handicapped people who have difficulty standing for extended periods. D esign Regulation Although design regulations do not ipso facto create high quality places, regulations are the primary method to create urban spaces ( Carmona et al., 2010 p. 319 ). Thus, well established urban design regulations are helpful to create healthy places. From the regulation s perspective, FBCs have several advantages that can help build the places that both designers and citizens desire. First, FBCs can embrace the aforem entioned active urban design strategies since FBCs have the capacity to include detailed urban design elements using public space standards and building form standards.

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210 Second, FBCs can fill the gaps between initial design and the outputs. Despite all good features in urban design, if the reality is different from what people expected, they become skeptical. However, FBCs might alleviate such concerns since FBCs have administrative power as well as visualization process. Parolek et al. (2008) argued that : The codes work best when they are developed in draft form during the multiday charrette. Presenting the proposed ordinance alongside the increased confidence that what is drawn might actually be built. Furthermore, by riding the wav e of enthusiasm that often accompanies the charrette process, the FBCs can be written into law much more quickly, thus minimizing the inevitable watering down process that can severely comprise a worthy development plan. ( Parolek et al. 2008, p. 14 ) That is, FBCs are a possible answer for questions regarding gaps between initial renderings and final results, and they are easily translated into written regulations based on the envisioned process. Study Limitations and Directions of Future Research This stud y opens up several opportunities for future research. First, since I successfully converted an active built environment index into parcel level scores and analyzed which areas showed statistically significant spatial clustering researchers can recognize t he areas that have potential issues in terms of active living. That is, the visual outputs of this analysis provide information on sites for future study by researchers, urban designers, community planners, and public health professionals. Second, although I used composite GIS layers and their scores for a comparison study, since the suitability model of this study consists of multiple layers, researchers can use the analysis to determine the elements of built environments that are statistically different.

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211 Nevertheless, there are several limitations to this study. First, I utilized only quantified data for the analysis. Although several studies suggest that well established urban forms are positively correlated with physical activity, there are a number of f actors that cannot be easily quantified. As several researchers have argued, qualitative values such as safety, comfort, and pleasurability might make an impact on physical activity. In future research, these qualitative variables should be considered in t he modeling process. also critical factors in encouraging physical activity (Heath et al., 2006), this study only the FBCs cases actually involved walkable urban forms that included both 3 D (vertical) and 2 D (horizontal) factors, this limitation might explain why some FBCs areas showed a lower suitability than others. Future research needs to include these 3 D varia bles in the modeling process. Third, some data was unavailable or only available for different time periods. The transportation data particularly varied in terms of its date of issue, since it was aggregated from different sources. Bus stop data were not a vailable at the time of the analysis, so I utilized bus routes to create walking catchment areas instead; this expanded the data on access to transit more than is normally possible through bus stop data alone. Fourth, I used the same weights for the combin ed layers. Typically, suitability modeling uses weighted layers to allow each variable to impact the model differently.

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212 Thus, future research may consider establishing an appropriate weight for each variable. Finally, I only assessed how active living pote ntial was affected by physical urban forms. There have been efforts to identify the connection between built environment and behavioral patterns using accelerometer data (Kang, Moudon, Hurbitz Reichley, & Saelens, 2013; Saelens et al., 2003). However, as Badland and Schofield (2005) note, these devices may not detect certain types of body movements (such as cycling), and the cost of the devices is an issue (p. 192). Due to recent smartphone and smart watch technology developments, though, this data has bec ome more reliable and easy to use. Therefore, in future research, tracking the movement patterns of residents via smart wearable devices will enable actual measures of physical activity and health outcomes. This data would tremendously enhance the methodol ogy adopted in this study

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213 Figure 5 1 Facilities Types by Walking Distance ( Carmona et al,. 2010, p. 237)

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214 Figure 5 2 Conceptual Diagram for Comparing Entropy Index ( Brown et al., 2009 p. 1132)

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215 L IST OF REFERENCES Abley, S. (2005). Walka bility scoping paper New Zealand. Adkins, A., Dill, J., Luhr, G., & Neal, M. (2012). Unpacking walkability: Testing the influence of urban design features on perceptions of walking environment attractiveness. Journal of Urban Design, 17 (4), 499 510. Agra wal, A. W., Schlossberg, M., & Irvin, K. (2008). How far, by which route and why? A spatial analysis of pedestrian preference. Journal of Urban Design, 13 (1), 81 98. doi:10.1080/13574800701804074 Agresti, A., & Finlay, B. (2009). Statistical methods for t he social sciences (4th ed. ). Upper Saddle River, N.J: Pearson Prentice Hall. Alexander, C., Ishikawa, S., & Silverstein, M. (1977). A pattern language: Towns, buildings, construction New York: Oxford University Press. Alfonzo, M. A. (2005). To walk or not to walk? the hierarchy of walking needs. Environment and Behavior, 37 (6), 808 836. doi:10.1177/0013916504274016 Alfonzo, M., Boarnet, M. G., Day, K., McMillan, T., & Anderson, C. L. (2008). The relationship of neighborhood built environment features a nd adult parents' walking. Journal of Urban Design, 13 (1), 29 51. doi:10.1080/13574800701803456 Aytur, S. A., Rodriguez, D. A., Evenson, K. R., Catellier, D. J., & Rosamond, W. D. (2007). Promoting active community environments through land use and transp ortation planning. American Journal of Health Promotion, 21(4S), 397 407. Babey, S. H., Hastert, T. A., Yu, H., & Brown, E. R. (2008). Physical activity among adolescents: When do parks matter? American Journal of Preventive Medicine, 34 (4), 345 348. doi: 10.1016/j.amepre.2008.01.020 Badland, H., & Schofield, G. (2005). Transport, urban design, and physical activity: An evidence based update. Transportation Research Part D, 10 (3), 177 196. doi:10.1016/j.trd.2004.12.001 Barnett, J. (1982). An introduction to urban design (1st ed.). New York: Harper & Row. Barry, J. M. (2008). Form based codes: Measured success through both mandatory and optional implementation. Connecticut Law Review, 41 (1), 305 337. Benevolo, L. (1980). The origins of modern town plannin g (MIT Press paperback ed. ). Cambridge, Mass: M.I.T. Press. Berg, N. (2010, July). Brave new codes zoning, planning, urban development. Architecture 50 53.

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216 Besser, L. M., & Dannenberg, A. L. (2005). Walking to public transit: Steps to help meet physic al activity recommendations. American Journal of Preventive Medicine, 29 (4), 273 280. doi:10.1016/j.amepre.2005.06.010 Boarnet, M. G., Anderson, C. L., Day, K., McMillan, T., & Alfonzo, M. (2005). Evaluation of the California safe routes to school legisla tion: Urban form changes and children s active transportation to school. American Journal of Preventive Medicine, 28 (S2), 134 140. doi:10.1016/j.amepre.2004.10.026 Boarnet, M. G., Forsyth, A., Day, K., & Oakes, J. M. (2011). The street level built environ ment and physical activity and walking: Results of a predictive validity study for the Irvine Minnesota inventory. Environment and Behavior, 43 (6), 735 775. doi:10.1177/0013916510379760 Borys, H., & Talen, E. (2015a). The full codes study. Retrieved from http://www.placemakers.com/wp content/uploads/2012/08/CodesStudy_Jan2015_web.htm Borys, H., & Talen, E. (2015b). Codes study: Smart c odes and other form ba sed codes. Retrieved from http://www.placemakers.com/wp content/uploads/2015/01/CodesStudy_Jan2015_bar.jpg Borys, H., & Talen, E. (2015c). Form based codes adopted 1981 2014 + in process 2015. Retrieved from http://www.placemakers.com/wp content/uploads/2015/01/CodesStudy_Jan2015_time.jpg Brink, L. A., Nig g, C. R., Lampe, S. M. R., Kingston, B. A., Mootz, A. L., & Vliet, W. v. (2010). Influence of schoolyard renovations on children's physical activity: The learning landscapes program. American Journal of Public Health, 100 (9), 1672 1678. doi:10.2105/AJPH.20 09.178939 Broberg, B. (2010, Winter ). A new kind of zoning : Communities of all sizes are adopting form based codes. On Common Ground 40 45. Brown, B. B., Yamada, I., Smith, K. R., Zick, C. D., Kowaleski Jones, L., & Fan, J. X. (2009). Mixed land use and walkability: Variations in land use measures and relationships with BMI, overweight, and obesity. Health & Place, 15 (4), 1130 1141. doi:http://dx.doi.org/10.1016/j.healthplace.2009.06.008 Brownson, R. C., Hoehner, C. M., Day, K., Forsyth, A., & Sallis, J F. (2009). Measuring the built environment for physical activity: State of the science. American Journal of Preventive Medicine, 36 (4S), S99 S123. doi:10.1016/j.amepre.2009.01.005 Cable, F. (2009, July). Design first codify second. Planning 24 27. Ca rmona, M., Heath, T., Oc, T., & Tiesdell, S. (2010). Public places urban spaces: The dimensions of urban design (2nd ed.) Elsevier Science.

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217 Carr, M. H., & Zwick, P. D. (2007). Smart land use analysis: The LUCIS model land use conflict identification strat egy (1st ed.). Redlands, Calif: ESRI Press. Centers for Disease Control and Prevention. (2013). obesity_trends_2010. Retrieved from http://www.cdc.gov/obesity/downloads/obesity _trends_2010.ppt Centers for Disease Control and Prevention. (2015a). Body mass index (BMI). Retrieved from http://www.cdc.gov/healthyweight/assessing/bmi/ Centers for Disease Control an d Prevention. (2015b). Physical activity basics. Retrieved from http://www.cdc.gov/physicalactivity/basics/index.htm Centers for Disease Control and Prevention. (2015c). How much phys ical activity do adults need? Retrieved from http://www.cdc.gov/physicalactivity/basics/adults/index.htm Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density diversity, and design. Transportation Research Part D, 2 (3), 199 219. Chin, G. K. W., Van Niel, K. P., Giles Corti, B., & Knuiman, M. (2008). Accessibility and connectivity in physical activity studies: The impact of missing pedestrian data. Preventive Medicine, 46 (1), 41 45. City of New York. ( 1916 ). Building zone resolution City of New York. (2010). Active design guidelines: Promoting physical activity and health in design New York, NY: City of New York. Cohen, D. A., Golinelli, D., Williamson, S., Sehgal, A., Marsh, T., & McKenzie, T. L. (2009). Effects of park improvements on park use and physical activity: Policy and programming implications. American Journal of Preventive Medicine, 37 (6), 475 480. doi:10.1016/j.amepre.2009.07.017 Cohen, D. A., Sehgal, A., Williamson, S., Golinelli, D., Lurie, N., & McKenzie, T. L. (2007). Contribution of public parks to physical activity. American Journal of Public Health, 97 (3), 509 514. doi:10.2105/AJPH.2005.072447 Coogan, P. F., White, L. F., Evans, S. R., A dler, T. J., Hathaway, K. M., Palmer, J. R., & Rosenberg, L. (2011). Longitudinal assessment of urban form and weight gain in african american women. American Journal of Preventive Medicine, 40 (4), 411 418. doi:10.1016/j.amepre.2010.12.013 Coutts, C. (200 8). Greenway accessibility and physical activity behavior. Environment and Planning B: Planning and Design, 35(3), 552 563. doi:10.1068/b3406 Coyle, C. (2010, Winter). Community planning & form based codes: Greater Seminole heights A livable and sustainab le urban village. Florida Planning Magazine 12 13.

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218 Dancy, S. L. (2007). A case study examination of form based code use in N orth Carolina Dill, J. (2009). Bicycling for transportation and health: The role of infrastructure. Journal of Public Health Policy, 30 (S1), S95 S110. doi:10.1057/jphp.2008.56 Doyle, S., Kelly Schwartz, A., Schlossberg, M., & Stockard, J. (2006). Active community environments and health. Journal of the American Planning Association, 72 (1), 19 31. Duany, A., Plat er Zyberk, E., & Speck, J. (2010). Suburban nation: The rise of sprawl and the decline of the American dream (10th anniversary ed.). New York: North Point Press. Dunton, G. F., Intille, S. S., Wolch, J., & Pentz, M. A. (2012). Investigating the impact of a smart growth community on the contexts of children's physical activity using ecological momentary assessment. Health & Place, 18 (1), 76 84. doi:10.1016/j.healthplace.2011.07.007 Ehrenfeucht, R., & Loukaitou Sideris, A. (2010). Planning urban sidewalks: Infrastructure, daily life and destinations. Journal of Urban Design, 15 (4), 459 471. doi:10.1080/13574809.2010.502333 Environmental Systems Research Institute. (2014). How fuzzy membership works. Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., & Raub enbush, S. (2003). Relationship between urban sprawl and physical activity, obesity, and morbidity. American Journal of Health Promotion, 18 (1), 47 57. Ewing, R., & Handy, S. (2009). Measuring the unmeasurable: Urban design qualities related to walkabilit y. Journal of Urban Design, 14 (1), 65 84. doi:10.1080/13574800802451155 Eyler, A. A., Brownson, R. C., Bacak, S. J., & Housemann, R. A. (2003). The epidemiology of walking for physical activity in the united states. Medicine and Science in Sports and Exer cise, 35 (9), 1529 1536. Farley, T. A., Meriwether, R. A., Baker, E. T., Rice, J. C., & Webber, L. S. (2008). Where do the children play? the influence of playground equipment on physical activity of children in free play. Journal of Physical Activity & He alth, 5 (2), 319 331. Feijten, P., Hooimeijer, P., & Mulder, C. H. (2008). Residential experience and residential environment choice over the life course. Urban Studies, 45 (1), 141 162. doi:10.1177/0042098007085105 Florida League of Cities. (2015). Comple te municipal directory. Retrieved from http://www.floridaleagueofcities.com/Assets/Files/FLC%20Directory.pdf

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219 Floyd, M. F., Spengler, J. O., Maddock, J. E., Gobster, P H., & Suau, L. J. (2008). Park based physical activity in diverse communities of two U.S. cities: An observational study. American Journal of Preventive Medicine, 34 (4), 299 305. doi:10.1016/j.amepre.2008.01.009 Form Based Codes Institute. (2015). Ident ifying & evaluating form based codes. Retrieved from http://formbasedcodes.org/identifying evaluating Frank, L. D., Andresen, M. A., & Schmid, T. L. (2004). Obesity relationships with com munity design, physical activity, and time spent in cars. American Journal of Preventive Medicine, 27 (2), 87 96. doi:http://dx.doi.org/10.1016/j.amepre.2004.04.011 Frank, L. D., & Engelke, P. O. (2001). The built environment and human activity patterns: E xploring the impacts of urban form on public health. Journal of Planning Literature, 16 (2), 202 218. doi:10.1177/08854120122093339 Frank, L. D., Sallis, J. F., Conway, T. L., Chapman, J. E., Saelens, B. E., & Bachman, W. (2006). Many pathways from land us e to health. Journal of the American Planning Association, 72 (1), 75 87. Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J., & Saelens, B. E. (2005). Linking objectively measured physical activity with objectively measured urban form: Findings from S MARTRAQ. American Journal of Preventive Medicine, 28 (S2), 117 125. doi:10.1016/j.amepre.2004.11.001 Frank, L., Kerr, J., Chapman, J., & Sallis, J. (2007). Urban form relationships with walk trip frequency and distance among youth. American Journal of Heal th Promotion, 21 (4S), 305 311. Frank, L., Sallis, J., Saelens, B., Leary, L., Cain, K., Conway, T., & Hess, P. (2010). The development of a walkability index: Application to the neighborhood quality of life study. British Journal of Sports Medicine, 44 (13 ), 924 933. doi:10.1136/bjsm.2009.058701 Galea, S., & Schulz, A. (2006). Methodological considerations in the study of urban health: How do we best assess how cities affect health? In N. Freudenberg, S. Galea & D. Vlahov (Eds.), Cities and the health of t he public (1st ed. pp. 277 293). Nashville Tenn: Vanderbilt University Press. Gehl, J. (1987). Life between buildings: Using public space New York: Van Nostrand Reinhold. Gehl, J. (2010). Cities for people Washington, DC: Island Press. Giles Corti, B ., Broomhall, M. H., Knuiman, M., Collins, C., Douglas, K., Ng, K., . Donovan, R. J. (2005). Increasing walking: How important is distance to,

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220 attractiveness, and size of public open space? American Journal of Preventive Medicine, 28 (S2), 169 176. doi: 10.1016/j.amepre.2004.10.018 Giles Corti, B., Kelty, S. F., Zubrick, S. R., & Villanueva, K. P. (2009). Encouraging walking for transport and physical activity in children and adolescents how important is the built environment? Sports Medicine, 39 (12), 99 5 1009. Giles Corti, B., Macintyre, S., Clarkson, J. P., Pikora, T., & Donovan, R. J. (2003). Environmental and lifestyle factors associated with overweight and obesity in Perth Australia American Journal of Health Promotion, 18 (1), 93 102. Gordon Lars en, P., Nelson, M. C., Page, P., & Popkin, B. M. (2006). Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics, 117 (2), 417 424. doi:10.1542/peds.2005 0058 Grant, J. L. (2009). Theory and practic e in planning the suburbs: Challenges to implementing new urbanism, smart growth, and sustainability principles. Planning Theory & Practice, 10 (1), 11 33. doi:10.1080/14649350802661683 Haar, C. M., & Kayden, J. S. (1989). Zoning and the American dream: Pr omises still to keep Chicago, Ill: Planners Press, American Planning Association in association with the Lincoln Institute of Land Policy. Hakim, A. A., Petrovitch, H., Burchfiel, C. M., Ross, G. W., Rodriguez, B. L., White, L. R., . Abbott, R. D. (1 998). Effects of walking on mortality among nonsmoking retired men. N Engl J Med, 338 (2), 94 99. doi:10.1056/NEJM199801083380204 Handy, S. L., Xinyu, C., & Mokhtarian, P. L. (2008). The causal influence of neighborhood design on physical activity within t he neighborhood: Evidence from northern california. American Journal of Health Promotion, 22 (5), 350 358. Hansen de Chapman, G. M (2008). Design codes for healthy communities: The potential of form based codes to create walkable urban streets (Doctoral d issertation). Heath, G. W., Brownson, R. C., Kruger, J., Miles, R., Powell, K. E., & Ramsey, L. T. (2006). The effectiveness of urban design and land use and transport policies and practices to increase physical activity: A systematic review. Journal of P hysical Activity & Health, 3 (S1), S55 S76. Hendon, D., & Adams, A. (2010, Winter). Miami 21: The blueprint for future. Florida Planning Magazine 4 6. Hopkins, L. D. (1977). Methods for generating land suitability maps: A comparative evaluation. Journal of the American Institute of Planners, 43 (4), 386 400. doi:10.1080/01944367708977903

PAGE 221

221 Israel, B. A., Farquhar, S. A., Schulz, A. J., James, S. A., & Parker, E. A. (2002). The relationship between social support, stress, and health among women on De Health Education & Behavior, 29 (3), 342 360. doi:10.1177/109019810202900306 Jacobs, A. B. (1993). Great streets Cambridge, Mass: MIT Press. Jacobs, J. (1961). The death and life of great American cities New York: Random House. Kaczy nski, A. T., Potwarka, L. R., & Saelens, B. E. (2008). Association of park size, distance, and features with physical activity in neighborhood parks. American Journal of Public Health, 98 (8), 1451 1456. doi:0.2105/AJPH.2007.129064 K ang B., M oudon A. V., H urvitz P. M., R eichley L., & S aelens B. E. (2013). Walking objectively measured: Classifying accelerometer data with GPS and travel diaries. Medicine & Science in Sports & Exercise, 45 (7), 1419 1428. doi:10.1249/MSS.0b013e318285f202 Katz, P. (2004 ). F orm first: The new urbanist alternative to conventional zoning. Planning Kelly Schwartz, A. C., Stockard, J., Doyle, S., & Schlossberg, M. (2004). Is sprawl unhealthy?: A multilevel analysis of the relationship of metropolitan sprawl to the health of ind ividuals. Journal of Planning Education and Research, 24 (2), 184 196. doi:10.1177/0739456X04267713 Kim, J. H., Pagliara, F., & Preston, J. (2005). The intention to move and residential location choice behavior Urban Studies, 42 (9), 1621 1636. doi:10.1080 /00420980500185611 King, D. (2008). Neighborhood and individual factors in activity in older adults: Results from the neighborhood and senior health study. Journal of Aging & Physical Activity, 16 (2), 144 170. Kleit, R. G., & Galvez, M. (2011). The locat ion choices of public housing residents displaced by redevelopment: Market constraints, personal preferences, or social information? Journal of Urban Affairs, 33 (4), 375 407. doi:10.1111/j.1467 9906.2011.00557.x Kockelman, K. (1997). Travel behavior as fu nction of accessibility, land use mixing, and land use balance: Evidence from S an Francisco bay area. Transportation Research Record, 1607 (1607), 116 25. doi:10.3141/1607 16 Krizek, K. J. (2003). Residential relocation and changes in urban travel: Does ne ighborhood scale urban form matter? Journal of the American Planning Association, 69 (3), 265 281.

PAGE 222

222 Laakso, S. (2011). A comparison of sustainability in g reenfield development under form based codes and Euclidean zoning regulations a case study of St. Lucie county, Florida Lantz, P. M., House, J. S., Lepkowski, J. M., Williams, D. R., Mero, R. P., & Chen, J. (1998). Socioeconomic factors, health behaviors, and mortality: Results from a nationally representative prospective study of us adu lts. Journal of American Medical Association, 279 (21), 1703 1708. doi:10.1001/jama.279.21.1703 Larsen, K., Gilliland, J., Hess, P., Tucker, P., Irwin, J., & He, M. (2009). The influence of the physical environment and sociodemographic characteristics on c hildren's mode of travel to and from school. American Journal of Public Health, 99 (3), 520 6. Larson, N. I., Story, M. T., & Nelson, M. C. (2009). Neighborhood environments disparities in access to healthy foods in the US. American Journal of Preventive M edicine, 36 (1), 74 81. Leveille, S. G., Guralnik, J. M., Ferrucci, L., & Langlois, J. A. (1999). Aging successfully until death in old age: Opportunities for increasing active life expectancy. American Journal of Epidemiology, 149 (7), 654 664. Litman, T. (2002). The costs of automobile dependency and the benefits of balanced transportation Victoria, BC: Victoria Transport Policy Institute. Lockett, D., Willis, A., & Edwards, N. (2005 09 01). Through seniors' eyes: An exploratory qualitative study to ide ntify environmental barriers to and facilitators of walking. Canadian Journal of Nursing Research, 37 (3), 48 65. Lopez, R. (2004). Urban sprawl and risk for being overweight or obese. American Journal of Public Health, 94 (9), 1574 1579. Lopez, R. P., & H ynes, H. P. (2006). Obesity, physical activity, and the urban environment: Public health research needs. Environmental Health, 5 (25) doi:10.1186/1476 069X 5 25 Lopez Zetina, J., Lee, H., & Friis, R. (2006). The link between obesity and the built environme nt: evidence from an ecological analysis of obesity and vehicle miles of travel in California Health & Place, 12 (4), 656 664. doi:10.1016/j.healthplace.2005.09.001 Lund, H., Willson, R. W., & Cervero, R. (2006). A re evaluation of travel behavior in Cali fornia TODs Journal of Architectural and Planning Research, 23 (3), 247 263. Madden, M., & Spikowski, B. (2006, September ). Place making with form based codes. Urban Land 174 178.

PAGE 223

223 Malczewski, J. (1999). GIS and multicriteria decision analysis New York: J. Wiley & Sons. Mammoser, A. (2011, Winter). The frontier of form based codes. The Regional and Intergovernmental Planning 1 21. Retrieved from http ://intergovernmental.homestead.com/newslettters/APARIPD_Winter2011_Ne wsletter.pdf Manaugh, K., & Kreider, T. (2013). What is mixed use? presenting an interaction method for measuring land use mix. Journal of Transport and Land use, 6 (1), 63 72. doi:10.51 98/jtlu.v6i1.291 Manson, J. E., Greenland, P., LaCroix, A. Z., Stefanick, M. L., Mouton, C. P., Oberman, A., . Siscovick, D. S. (2002). Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N Engl J Med, 347 (10) 716 725. doi:10.1056/NEJMoa021067 McCormack, G. R., Giles Corti, B., & Bulsara, M. (2008). The relationship between destination proximity, destination mix and physical activity behaviors. Preventive Medicine, 46 (1), 33 40. McDonald, N. C.,. (2008). Cri tical factors for active transportation to school among low income and minority students: Evidence from the 2001 national household travel survey. American Journal of Preventive Medicine, 34 (4), 341 344. doi:10.1016/j.amepre.2008.01.004 Meck, S. (1996). M odel planning and zoning enabling legislation: A short history Washington, DC: American Planning Association. Mitchell, A. (2012). In Environmental Systems Research Institute (Ed.), Modeling suitability, movement, and interaction Redlands, CA: ESRI Press Mobley, L. R., Root, E. D., Finkelstein, E. A., Khavjou, O., Farris, R. P., & Will, J. C. (2006). Environment, obesity, and cardiovascular disease risk in low income women. American Journal of Preventive Medicine, 30 (4), 327 332.e1. doi:10.1016/j.amepre .2005.12.001 Moore, L. V., Diez Roux, A. V., Evenson, K. R., McGinn, A. P., & Brines, S. J. (2008). Availability of recreational resources in minority and low socioeconomic status areas. American Journal of Preventive Medicine, 34 (1), 16 22. doi:10.1016/j .amepre.2007.09.021 Morland, K., Wing, S., & Diez Roux, A. (2002). The contextual effect of the local food environment on residents' diets: The atherosclerosis risk in communities study. American Journal of Public Health, 92 (11), 1761 1767. Mormino, G. R (2005). Land of sunshine, state of dreams: A social history of modern Florida Gainesville: University Press of Florida.

PAGE 224

224 Moudon, A. V., Lee, C., Cheadle, A. O., Garvin, C., Johnson, D., Schmid, T. L., . Lin, L. (2006). Operational definitions of wal kable neighborhood: Theoretical and empirical insights. Journal of Physical Activity & Health, 3 S99 S117. Mumford, K. G., Contant, C. K., Weissman, J., Wolf, J., & Glanz, K. (2011). Changes in physical activity and travel behaviors in residents of a mix ed use development. American Journal of Preventive Medicine, 41 (5), 504 507. doi:10.1016/j.amepre.2011.07.016 Mundigo, A. I., & Crouch, D. P.. (1977). The City Planning Ordinances of the Laws of the Indies Revisited. Part I: Their Philosophy and Implicatio ns. The Town Planning Review 48 (3), 247 268. Retrieved from http://www.jstor.org/stable/40103542 National Highway Traffic Safety Administration. (2012). TRAFFIC SAFETY FACTS 2010 data. Retrieved fro m http://www nrd.nhtsa.dot.gov/Pubs/811640.pdf Nelson, A. C., & Duncan, J. B. (1995). Growth management principles and practices Chicago, Ill. ; Washington D.C: Planners Press, American Plan ning Association. Northridge, M. E., & Freeman, L. (2011). Urban planning and health equity. Journal of Urban Health, 88 (3), 582 597. doi:10.1007/s11524 011 9558 5 Northridge, M. E., Sclar, E., & Biswas, P. (2003). Sorting out the connections between the built environment and health: A conceptual framework for navigating pathways and planning healthy cities. Journal of Urban Health, 80 (4), 556 568. doi:10.1093/jurban/jtg064 Oleru, N., & Roof, K. (2008). Public health: Seattle and king county's push for t he built environment. Journal of Environmental Health, 71 (1), 24 27. Parolek, D. G., Parolek, K., & Crawford, P. C. (2008). Form based codes: A guide for planners, urban designers, municipalities, and developers (1st ed.) Wiley. Physical Activity Guideli nes Advisory Committee. (2008). Physical activity guidelines advisory committee report Washington D.C.: U.S. Department of Health and Human Service. Pierce, J., Denison, A., Arif, A., & Rohrer, J. (2006). Living near a trail is associated with increased odds of walking among patients using community clinics. Journal of Community Health, 31 (4), 289 302. doi:10.1007/s10900 006 9014 8 Polikov, S. (2008 ). The new economics of place. Chamber Executive, 35 7 16. Pucher, J., & Buehler, R. (2008). Making cycl ing irresistible: Lessons from the Netherlands Denmark and Germany Transport Reviews, 28 (4), 495 528.

PAGE 225

225 Purdy, J. R. (2006 ) Form based codes new approach to zoning Smart Growth Tactics, 28 1 8. Retrieved from http://www.planningmi.org/downloads/issue_28_formbased_codes.pdf Rangwala, K. (2005). Retooling planners. Places, 17 (1), 84 85. Rangwala, K. (2010 ). Why design guidelines, on their own, don work. New Urban Netwo rk Retrieved from http://newurbannetwork.com/news opinion/blogs/kaizer rangwala/13778/why design guidelines their own don%E2%80%99t work Rangwala, K. (2012). Form based codes. Economic Development Journal, 11 (3), 35 40. Raterman, D. (2007, December). In the tropical zone. Planning, 34 37. Reed, J. A., Wilson, D. K., Ainsworth, B. E., Bowles, H., & Mixon, G. (2006). Perceptions of neighborhood sidewalks on walking and physical activity patterns in a southeastern community in the US. Journal of Physical Activity & Health, 3 (2), 243 253. Resnik, D. B. (2010). Urban sprawl, smart growth, and deliberative democracy. Amer ican Journal of Public Health, 100 (10), 1852 1856. doi:10.2105/AJPH.2009.182501 Rodr i guez, D. A., Khattak, A. J., & Evenson, K. R. (2006). Can new urbanism encourage physical activity ? Journal of the American Planning Association, 72 (1), 43 54. Roemmich, J., Raja, S., Yin, L., Epstein, L., Lobarinas, C., Baek, S., . Paluch, R. (2010). Children's choices of park elements for physical activity Paper presented at the Retrieved from http:// www.activelivingresearch.org/node/11972 Rosenberg, D. E., Sallis, J. F., Conway, T. L., Cain, K. L., & McKenzie, T. L. (2006). Active transportation to school over 2 years in relation to weight status and physical activity. Obesity, 14 (10), 1771 1776. do i:10.1038/oby.2006.204 Ross, J. (2009, Spring). The advent of form based codes: A critical time to ensure mixed income communities. The Housing News Network 13 16. RuBino, R. G., & Starnes, E. M. (2008). Lessons learned? The history of planning in Flori da Tallahassee, Fla: Sentry Press. Rundle, A., Diez Roux, A. V., Free, L. M., Miller, D., Neckerman, K. M., & Weiss, C. C. (2007). The urban built environment and obesity in new york city: A multilevel analysis. American Journal of Health Promotion, 21 (4 S), 326 334. Saelens, B. E., & Handy, S. L. (2008). Built environment correlates of walking: A review. Medicine and Science in Sports and Exercise, 40 (7S), S550 S566. doi:10.1249/MSS.0b013e31817c67a4

PAGE 226

226 Saelens, B. E., Sallis, J. F., Black, J. B., & Chen, D (2003). Neighborhood based differences in physical activity: An environment scale evaluation. American Journal of Public Health, 93 (9), 1552 1558. Sallis, J. F., & Glanz, K. (2009). Physical activity and food environments: Solutions to the obesity epide mic. The Milbank Quarterly, 87 (1), 123 154. Schulz, A. J., Williams, D. R., Israel, B. A., & Lempert, L. B. (2002). Racial and spatial relations as fundamental determinants of health in Detroit The Milbank Quarterly, 80 (4), 677 707. Schulz, A., & Northr idge, M. E. (2004). Social determinants of health: Implications for environmental health promotion. Health Education & Behavior, 31 (4), 455 471. doi:10.1177/1090198104265598 Senbel, M., van, d. L., Kellett, R., Girling, C., & Stuart, J. (2013). Can form b ased code help reduce municipal greenhouse gas emissions in small towns? the case of revelstoke, british columbia. Canadian Journal of Urban Research, 22 72 92. Shores, K. A., & West, S. T. (2008). The relationship between built park environments and phy sical activity in four park locations. Journal of Public Health Management and Practice, 14 (3), e9 e16. doi:10.1097/01.PHH.0000316495.01153.b0 Sitkowski, R., & Ohm, B. (2006). Form based land development regulations. The Urban Lawyer 28(1), 163 172. Slo ane, D. C. (2006). From congestion to sprawl. Journal of the American Planning Association, 72 (1), 10 18. Smart Growth America. What is "smart growth?" Retrieved from http://www.sma rtgrowthamerica.org/what is smart growth Southworth, M. (2005). Designing the walkable city. Journal of Urban Planning and Development, 131 (4), 246 257. doi:10.1061/(ASCE)0733 9488(2005)131:4(246) Spilowski, B. (2010, Winter). Form based codes. Florida Planning Magazine 9 11. Squires, G. D. (2002). In Squires G. D. (Ed.), Urban sprawl: Causes, consequences, & policy responses Washington, D.C: Urban Institute Press. Strawbridge, W. J., Cohen, R. D., Shema, S. J., & Kaplan, G. A. (1996). Successful agi ng: Predictors and associated activities. American Journal of Epidemiology, 144 (2), 135 141. Sturm, R., & Cohen, D. A. (2004). Suburban sprawl and physical and mental health. Public Health, 118 (7), 488 496. doi:http://dx.doi.org/10.1016/j.puhe.2004.02.007

PAGE 227

227 Talen, E. (2009). Design by the rules: The historical underpinnings of form based codes. Journal of the American Planning Association, 75 (2), 144 160. doi:10.1080/01944360802686662 Talen, E. (2012). City rules: How regulations affect urban form Washing ton, DC: Island Press. Talen, E. (2013). Zoning for and against sprawl: The case for form based codes. Journal of Urban Design, 18 (2), 175 200. doi:10.1080/13574809.2013.772883 The Center for Applied Transect Studies. The transect Retrieved from http:// transect.org/transect.html Tombari, E. (2009 ). The future of zoning? Land Development, 22 22 26. U.S. Department of Health and Human Service. (2008). 2008 physical act ivity guidelines for Americans Washington D.C.: U.S. Department of Health and Human Service. Urban Land Institute. (2013). Ten principles for building healthy places Retrieved from http://uli.org/wp content/uploads/ULI Documen ts/10 Principles for Building Healthy Places.pdf Van Dam, F., Heins, S., & Elbersen, B. S. (2002). Lay discourses of the rural and stated and revealed preferences for rural living : some evidence of the existence of a rural idyll in the Netherlands Journ al of Rural Studies, 18 (4), 461 476. doi:http://dx.doi.org/10.1016/S0743 0167(02)00035 9 Weuve, J., Kang, J. H., Manson, J. E., Manson, J. E., Ware, J. H., & Grodstein, F. (2004). P h ysical activity, including walking, and cognitive function in older women The Journal of the American Medical Association, 292 (12), 1454 1461. doi: 10.1001/jama.292.12.1454 World Health Organization. (2010). Global recommendations on physical activity for health ( No. QT 255). Switzerland: WHO Press. Yen, I. H., & Syme, S. L. (1999). The social environment and health: A discussion of the epidemiologic literature. Annual Review of Public Health, 20 (1), 287 308. doi:10.1146/annurev.publhealth.20.1.287 Zwick, P. D. (2009). Why LUCIS? Unpublished manuscript.

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228 BIOGRAPHICAL SK ETCH Soowoong Noh received his Bachelor of Engineering degree ( u rban e ngineering) from the Hongik University (South Korea) in 2002, and was awarded the Excellence Master of La ndscape Architecture degree ( u rban d esign concentration) at the Graduate School of Environmental Studies (Seoul National University, South Korea) in 2005. As a registered urban engineer since 2004, his professional work includes: urban design, downtown red evelopment, campus master plan, industrial park, urban infrastructure, amusement park international competition, and national research project. After he entered the University of Florida as a Ph.D. student in 2010, he joined several GIS related research pr ojects and has taught 3D visualization techniques and urban design theories in his classes. Recently he was awarded the Carl Feiss Urban and Environmental Design Award from Urban and Regional Planning Department at University of Florida.