Citation
Setting the Parameters for Florida's Shrinking Cities

Material Information

Title:
Setting the Parameters for Florida's Shrinking Cities
Creator:
Corales-Cuadrado, Alvimarie
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (120 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.U.R.P)
Degree Grantor:
University of Florida
Degree Disciplines:
Urban and Regional Planning
Committee Chair:
FRANK,KATHRYN I
Committee Co-Chair:
ZWICK,PAUL D

Subjects

Subjects / Keywords:
agriculture -- cities -- decline -- economy -- florida -- population -- shrinking -- stagnation -- suburbanization -- tourism
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Urban and Regional Planning thesis, M.U.R.P

Notes

Abstract:
Florida, USA is a fast-growing state with a population of 18.84 million as of 2010 Census and it is expected to grow by 2030 to 26 million. If Florida population is growing, why are there cities in Florida that are decreasing in population? Where are these cities located? This research focused on methods to find and analyze shrinking cities in Florida. Shrinking cities is a phenomenon where urban clusters and urbanized areas experience a negative population growth over 40 years due to natural and man-made hazards, political, economic, and social events. The first step of this project was to analyze multiple case studies in order to decompose and categorize the events previously mentioned by cause of shrinkage. Secondly, Florida cities population data was used to calculate population growth. Those cities with a negative population growth were categorized as shrinking. Florida had 40 cities that were declining in population. Out of these 40 cities, seven were selected by size and location in order to conduct a thorough analysis on causes of shrinkage. After performing these analyzes, three causes of shrinkage were found for Florida's cities. The first cause of shrinkage was tourism where it is affecting most cities located on the coast since most homes are bought and used as vacation rentals. The second cause was agriculture and it has been affecting most inland cities because most lands are being used for agriculture. The third cause was sub-urbanization and it can be found mostly in cities located in the panhandle. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.U.R.P)--University of Florida, 2017.
Local:
Adviser: FRANK,KATHRYN I.
Local:
Co-adviser: ZWICK,PAUL D.
Statement of Responsibility:
by Alvimarie Corales-Cuadrado.

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Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
LD1780 2017 ( lcc )

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By ALVIMARIE CORALES CUADRADO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTE R OF URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2017

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2017 Alvimarie Corales Cuadrado

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To my loving husband, grandparents, and parents

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4 ACKNOWLEDGMENTS Firstly, I would like to thank God for ev ery opportunity and blessings given to me. Secondly, I would like to express my sincere gratitude to my advisor Dr. Kathryn Frank for the continuous hesis study, for her patience, motivation, and immense knowledge. Her guidance help ed me in all the time of research and writing of this thesis. In addition, I would like to thank Dr. Paul Zwick and Stanley Latimer for their support and feedback. I would also like to express gratitude to my family, whom from Puerto Rico supported my ever y decision. They went through tough times during hurricane Maria but they never ceased to support and encourage me throughout the end of my academic endeavors. Finally, I would like to thank my husband who has been with me throughout this adventure. He is my road trip buddy, companion, and biggest support.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .............. 4 LIST OF TABLES ................................ ................................ ................................ ......................... 8 LIST OF FIGURES ................................ ................................ ................................ ....................... 9 LIST OF ABBREVIATIONS ................................ ................................ ................................ ...... 11 ABSTRACT ................................ ................................ ................................ ................................ 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ................. 14 Cities ................................ ................................ ................................ ................................ ..... 15 Shrinking Cities ................................ ................................ ................................ .................... 15 Methodology ................................ ................................ ................................ ........................ 16 Thesis Outli ne ................................ ................................ ................................ ...................... 17 2 LITERATURE REVIEW ................................ ................................ ................................ ...... 19 Defining Shrinking Cities ................................ ................................ ................................ .... 19 Shrinking ................................ ................................ .. 20 Effects of Shrinkage ................................ ................................ ................................ ............ 21 Interventions of Shrinkage ................................ ................................ ................................ 22 Summary of Literature Review ................................ ................................ .......................... 22 3 METHODOLOGY ................................ ................................ ................................ ................ 26 Conceptual Model and Hypotheses ................................ ................................ ................. 26 Research Design and Techniques ................................ ................................ ................... 28 Case Studies Analysis Tools ................................ ................................ ............................. 30 Site Selection and An alysis Tools ................................ ................................ .................... 31 Limitations of the Research Design and Tools ................................ ............................... 35 4 DATA COLLECTION AND RESULTS ................................ ................................ ............. 38 Conceptualizing Shrinking Cities ................................ ................................ ...................... 38 Shrinkage ...................................................................................................................... 38 Demographic actors .................................................................................................. 38 Selective igration ............................................................................................... 38 Ageing opulation and irth/eath ates ........................ ................................ 39 Socio-ultura l actors .............................................................................................. .. 40 Suburban izati on .................................................................................................... 40

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6 Women and regnanc y ....................................................................................... 40 Economic actor .......................................................................................................... 41 Financial risis, e-ndustriali zation, co nomic tagnatio n ......................... 41 Global rade .......................................................................................................... 42 Political actors ........................................................................................................... 43 Environmental Factors ................................................................................................ 44 Other Factors ................................................................................................................ 45 War s ........................................................................................................................ 45 Epidemics ............................................................................................................... 45 ........................................................ 4 ................................ ................................ ........................ 46 ................................ ................................ ................................ ... 58 Purposive Sample Selection ................................ ................................ ............................. 59 se Studies, and Observational Data ............. 60 Results ................................ ................................ ................................ ................................ .. 61 ................................ ................................ ........................... 61 Selective Migration and Ageing Population ................................ ............................. 61 Women Lifestyle ................................ ................................ ................................ ........... 64 Financial Crisis and Economic Stagnation ................................ .............................. 64 De Industrialization ................................ ................................ ................................ ...... 65 Export/Import ................................ ................................ ................................ ................ 65 Policies and Legislations ................................ ................................ ............................ 66 Natural Disasters ................................ ................................ ................................ ......... 66 War ................................ ................................ ................................ ................................ 67 Epidemic ................................ ................................ ................................ ........................ 67 Correlations and Other Factors ................................ ................................ ................. 67 5 SHRINK ING CITIES PHENOMENON IN FLORIDA ..................................................... 6 Why Are Cities in Flori da Losing Population? What are the Factor? ......................... 69 Demographic Factors ................................ ................................ ................................ .. 69 Economic Factors ................................ ................................ ................................ ........ 71 Political Factors ................................ ................................ ................................ ............ 71 Environmental Factors ................................................................................................ 7 Where Are These Cities Located? ................................ ................................ ................... 72 ................................ ................................ ... 72 APPENDIX A MAP OF SHRINKING CITIES ................................ ................................ ........................... 78 B SPSS V ARIABLE AND DATA VIEW SCREENSHOTS ................................ ................ 80 C

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7 D SUBURBANIZATION MAPS ................................ ................................ ........................... 105 E MAPS OF POPULATION CHANGE IN FLORIDA AND SHRINKING CITIES ........ 112 LIST OF REFERENCES ................................ ................................ ................................ ......... 116 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ..... 120

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8 LIST OF TABLES Table page 2 1 Negative and Po sitive Eff ects of Shrinkage ................................ ............................... 24 3 1 Formulas Used for Florida's Population Data Analysis ................................ ............ 32 4 1 List of Factors, Data Acquisition, and Anal ysis Tool ................................ ................. 46 4 2 Florida's Population Data in 40 years by city. ................................ ............................ 46 4 3 Florida's Shrinking Cities by Urban Clusters, Urbanized A reas, and Mini Cities ................................ ................................ ................................ ................................ 59 4 4 Women Lifestyle Changes ................................ ................................ ............................ 64 4 5 Correlation betwe in Florida ................................ ................................ ................................ .......................... 68

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9 LIST OF FIGURES Figure page 2 1 Interventions of Shrink age ................................ ................................ ............................ 25 3 1 Shrinking Cities Conceptual Framework ................................ ................................ .... 26 3 2 Cities Selection Criteria ................................ ................................ ................................ 33 3 3 Coastal Counties and Panhandle Counties ... ........................................ 33 3 4 Methodology Map ................................ ................................ ................................ ........... 37 4 1 Sample cities selected by location ................................ ................................ ............... 60 4 2 1990 Population by Percentage of Age Group for the Shrinking Cities ................ 62 4 3 2000 Population by Percentage of Age Group in Florida and its Shrink ing Cities ................................ ................................ ................................ ................................ 62 4 4 2010 Population by Percentage of Age Group in Florida and its Shrinking Cities ................................ ................................ ................................ ................................ 63 4 5 Cost of Living Index for the Nation, Florida and the Shrinking Cities ..................... 64 4 6 4 7 Natural Disaster Count by City ................................ ................................ ..................... 67 4 8 Correlations, Significance, and Null Hypothesis ................................ ....................... 68 A 1 Map of USA Population Change ................................ ................................ .................. 78 A 2 B 1 SPSS Variable View of variables 1 to 31. ................................ ................................ .. 80 B 2 SPSS Variable View of variables 32 to 61. ................................ ................................ 81 B 3 SPSS Data View Part I ................................ ................................ ................................ .. 81 B 4 SPSS Data View Part II ................................ ................................ ................................ 81 B 5 SPSS Data View Part III ................................ ................................ ................................ 82 B 6 SPSS Data View Part IV ................................ ................................ ............................... 82 B 7 SPSS Data View Part V ................................ ................................ ................................ 82

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10 C 1 Holmes Beach ................................ ................................ ................................ ................. 83 C 2 Miami Beach ................................ ................................ ................................ ................... 87 C 3 DeFuniak Springs ................................ ................................ ................................ ........... 90 C 4 Perry ................................ ................................ ................................ ................................ 93 C 5 Pahokee ................................ ................................ ................................ ........................... 96 C 6 Pensacola ................................ ................................ ................................ ........................ 99 C 7 Fort Walton Beach ................................ ................................ ................................ ....... 102 D 1 Holmes Beach Suburbanization Map ................................ ................................ ........ 105 D 2 Miami Beach Suburbanization Map ................................ ................................ ........... 106 D 3 DeFuniak Springs Suburbanization Map ................................ ................................ .. 107 D 4 Perry Suburbanization Map ................................ ................................ ........................ 108 D 5 Miami Beach Suburbanization Map ................................ ................................ ........... 109 D 6 Pensacola Suburbanization Map ................................ ................................ ............... 110 D 7 Miami Beach Suburbanization Map ................................ ................................ ........... 111 E 1 Holmes Beach Population Change 1990, 2000, 2010. ................................ .......... 112 E 2 Miami Beach Population Change 1990, 2000, 2010 ................................ .............. 112 E Perry Population Change Map 1990 2000, 2010 ................................ ................... 113 E DeFuniak Spring Population Change 1990, 2000, 2010 ................................ ....... 113 E 5 Pahokee Population Change Map 1990, 2000, 2010 ................................ ............. 114 E 6 Pe nsacola Population Change Map 1990, 2000, 2010 ................................ .......... 114 E 7 Fort Walton Beach Population Change Map 1990, 2000, 2010 ........................... 115

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11 LIST OF ABBREVIATIONS ArcGIS geographic information system program GIS geographic information system MC mini city SPSS statistical analysis program UA urbanized areas UC urban clusters

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Urban and Regional Planning By Alvimarie Corales-Cuadrado December 201 7 Chair: Kathryn Frank Cochair: Paul Zwick Major: Urban and Regional Planning Florida, USA is a fast-growing state with a population of 18.84 million as of 2010 growing, why are there cities in Florida that are decreasing in population? Where are these cities located? This research focused on methods to find and analy ze cities in Florida. Shrinking cities is a phenomenon where urban clusters and urbanized areas experience a negative population growth over 40 years due to natural and hazards, political, economic, and social events. The first step of this project was to analy ze multiple case studies in order to decompose and categorize the events previously mentioned by cause of shrinkage. Those cities with a negative population growth were categorized as shrinking. Florida had 40 cities that were declining in population. Out of these 40 cities, seven were selected by size and location in order to conduct a thorough analysis on causes of shrinkage.

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13

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14 CHAPTER 1 INTRODUCT ION Florida, USA i s a fast-growing state with a population of 18.84 million a s of 2010 Census (United State s Censu s Bureau, 2010) 26 million by 2030 (Roberts, n.d.). Florida grew 1.84 percent in 2015 (Florida Trend, 2016) decreasing in population? The purpose of thi s research i s to compare the cause s of population decline in citie s around the United State s with citie s in Florida, to find similaritie s and differences between them. Florida ha s 282 citie s where some of them ha ve increased population o ver the year s but other s ha ve been decreasing in population for the past years. There are about 40 known shrinking citie s in the United States. Some e xamples of these cities are, Flint, Michigan; Buffalo, New York; and Cleveland, Ohio (Stansel, 2010) (See Figure A-1 for a map of USA shrinking cities). The closure of their cities main manufacturing company i s what these citie s ha ve in common (Pallagst & Aber, 2009) These companie s pro vided emplo yment to thousand s of people. When these people became unemplo yed they resorted to lea ving the city and finding job s in other citie s and towns. Furthermore, there are different case studie s pertaining to shrinking citie s all around the United States. Thi s project will lightly touch on some of these cities but it will mainly focu s on researching the shrinking citie s phenomenon a s discussed by other author s and listing the factor s of shrinkage. i s growing, but why are there citie s in Florida decreasing in population? Which citie s are shrinking? It i s important to know thi s because if shrinkage i s not addressed at an early stage, citie s can become

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15 deserted, housing taxes increases, businesses clos es, among other (Pallagst & Aber, 2009) This research provides a list of parameters of shrinking cities to help in further research of the topic, as well as a list of the cities in Florida that have lost significant populat ion and categorizing them as shrinking cities. Not to mention, finding unique throughout the years. Cities Cities are densely populated areas where people can live in, g o to work and section of this research project. Shrinking Cities made by various aut city phenomenon is a multidimensional process, comprising cities, parts of cities, or entire metropolitan areas that have experienced dramatic decline in their economic and social bases (Pallagst & Aber, 2009) Pallagst has been an influential figure in finding an accurate definition for Shrinking cities. In one of her many articles about the topic, she defined shrinking cities as a metropolitan area that exper iences significant population loss in a short period of time (Pallagst K. 2009) Based on these previous definitions and a series of information gathered throughout this project, I define shrinking cities as a phenomenon where urban clusters and urbanized areas experience significant population decline due to natural and manmade hazards, economic, and social events.

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16 Methodology This research project will have a mixed method research design. For the qualitative research m ethod, case studies will be used to research shrinking cities causes in the United States. The type of case study design that will be used is a collective case study approach. As defined by Stake (1995), a collective case study is the study of several case s in order to inquire into a particular phenomenon. As for the quantitative research method, census data and physical data will be collected to find if we wish to understand sh rinkage in a specific location, we need to integrate theoretical explanations with historical trajectories, as well as to combine these with a study of the specific impacts caused by shrinkage and to analyze the policy environment in which these processes For this reason, I developed a serious of steps that guides my thesis through the causes Shrinking cities. After gathering the data from case studies and analyzing it, a set of factors of shrinkage will be listed. The second set of data will be gathered using the population data acquired from the Census Bureau and applying the information to GIS to find cities in Florida with population decline. The project will be looking at peak population year, current population, decline from peak, and perce ntage in decline from peak to current year. They will be categorized by size: Urban Cluster, Urbanized Areas, or Mini Cities; and then by location: Coastal, Panhandle, In land, or Coastal/Panhandle Cities. After finding the cities in Florida, a purposive s ample will be selected. Two of

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17 each location category will then be selected, visited, and analyzed individually. Furthermore, they will be compared with the key factors found in the case studies. Nonetheless, Florida may have its own factors that contribut e to population decline in their cities. A framework had to be created in order to recognize and analyze shrinking cities but it had to be flexible enough to observe the phenomena in Florida. Thesis Outline The introduction contains the problem that this p roject will be focused on, some background information on shrinking cities, current situations, and the research questions that will be addressed. The literature review will be divided into two parts. This section is the first part of the literature review and contains brief historical aspects, definitions, effects, and interventions of Shrinking cities. The second part of the literature review can be found in Chapter 4, Conceptualizing Shrinking Cities. The methodology chapter contains a conceptual model a nd hypotheses, based on the framework used for both literature reviews. It also contains the research design and techniques that will be used. In addition, how the sites will be selected and the tools that will be used to analyze the information. The limit ations of each of the methods, tools, and techniques will also be discussed. The data collection and analysis chapter has the second part of the literature section contains the causes of shrinkage. In addition, it covers the population data of site visit notes.

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18 This discussion chapter will have an interpretation of the findings, and its significance. It will focus on answering the research questions. In the conclusion chapter, you will find a synthesis of the thesis, limitations of the project, future project directions, and how this data can be used to address shrinking cities in Florida as well as other cities around the United States.

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19 CHAPTER 2 LITERATURE REVIEW This first part of the literature review contains how authors define shrink ing cities and their different approaches to the topic based on numbers of population loss and location. A brief historical perspective containing some initial causes, is also included. Furthermore, a list of effects of shrinking cities and some interventi ons of shrinkage will be provided in order to have an overall understanding of what shrinkage is, its causes and effects, as well as different ways other cities have addressed the issue. Defining Shrinking Cities Oswalt and Rienits in their book, Atlas of Shrinking Cities, they use numbers and that have temporarily or permanently lost a significant number of their inhabitants. Population losses are considered to be s ignificant if they amount to a total of at least (Oswalt & Rienits, 2006, p. 156) This definition focuses on the percentage of population loss. s International Research population of 10,000 residents that has faced population losses in large parts for more than two years and is undergoing economic transformations w ith some symptoms of a an urban area, and the minimum population as a minimum of 10,000 residents. While an urban area as defined by the Census can be composed of 2, 500 residents or more and can be divided into two different categories, here it focuses on an overall idea that the minimum population size of 10,000 is what defines a city.

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20 To take an overall representation of these definitions, I define shrinking cities as a phenomenon where urban clusters and urbanized areas experience a negative population growth over 40 years due to natural and manmade hazards, political, economic, and social events. Going a few centuries b ack, epidemics and plagues were the main cause of population loss all around the world. Not to mention that warfare also brought a lot of death and destruction to cities, thinning out the population. As medicine, technology, and even diplomatic affairs kep t evolving and updating many of these hazards have been contained. Natural hazards, like hurricanes, are impossible to control but with new technology it can be predicted, monitored, and emergency steps can be taken to prevent population and infrastructure loss. But there are other natural hazards that cannot be managed or prevented, like earthquakes. Kabisch et al. (n.d.) mention that epidemic plagues. A recent phase of sh rinkage began after 1945. Since then, urban The Industrial Revolution that ended over 200 years ago brought with it changes in transportation, infrastructure, and new techno logy. Cities became the right option in terms of employment, amusement, and living which led to an increase in city growth. But after the second World War some USA cities began shrinking including New York due to migration (Schett 2012) By the 1990s the process started to stabilize but shrinkage did not end. Up until now many cities around the world have experienced shrinkage and may in fact continue to experience it for decades. Now demographic changes are occurring in the hum an population. People are

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21 getting older and are li ving longer; there are declining birth rate s and more planned birth s in de veloped countrie s that also lead to shrinking cities. Furthermore, there are economi c and social change s like, higher unemplo yment rates, income disparit y, and gentrification that are making people lea ve the cities. People are now forced to accept to commute between home and work, the city lose s attraction because there are certain commoditie s that a city cannot bring to a growing fam ily (Schett, 2012). Effects of Shrinkage The cause s of shrinkage are the focu s of thi s research but once the cause s are known, the effects must also be understood in order to take the necessary steps for preventing shrinkage or accepting it. On Table 2-1 to note that not these effects may occur in a single place but they can occur at different shrinkage, and one cause can lead to another and another subsequently. If shrinkage is not caught in time more negative effects can keep on happening in a city. Schett (2012) mention s that the effect s of shrinkage can be categori zed into four; aspects; economic, structural, political, and a combined category of all three. As can be seen on Table 2-1, the negative impacts outweigh the positive ones. The positive impacts are centered towards the environment and revitalization using green infrastructure. For this reason, shrinkage needs to be caught at the right moment before the next domino falls. Ho llander, et al. (2009) mention that a s industrie s lea ve and populations declines, nature begin s to take o ver. They also mention green space i s usually an amenity in cities, but these ambiguou s unmanaged landscape s contribute to an xiet y,

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22 reduced property va Hollander, et al. (2009) e xplain that the vacant land s e ventually can be a good way to impro ve air quality and reduce urban heat-island effect. In addition, large-scale depopulation allow s for remo val of building s and pa vement from floodplains. Interventions of Shrinkage Each city responds to shrinkage depending on the circumstances they face. Figure 2-1, lists some of the interventions that cities around the world have implemented. To better understand these, Detroit, Michigan can be used as an example. Detroit has implemented various of the interventions listed; first it has gone through a process of right-sizing where they are relocating people to densify neighborhoods (McGreal, 2010). They have also implemented community gardens and art to its empty lots and properties making them more attractive and appealing (Herscher, 2013). permeate more deeply into urban neighborhoods, returning surplus and derelict land to productive use. Cleveland, for example, now has a large-scale farm (the Blue Pike to de-densifying the city by dispersing vacancy and not developing areas and vacating others (Hollander, Pallagst, Schwarz, & Popper, 2009). Summary of Literature Review The term shrinking cities has multiple definitions and has been studied in different aspects as well as in different parts of the world. It is a term for population loss and urban decline but it also functions as a warning to state and local governments.

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23 Cities have been losing population for centuries from diseases and plagues, to technological advancements and lower de ath rates. As stated by Hollander, et al. inevitable trajectory, from something to nothing. If not caught on time, shrinkage can cause significant damage to a city, do we catch it on time? As mentioned in the introduction, Haase et al. (2014, p. 1) if we wish to understand shrinkage in a specific location, we need to integrate theoretical explanations with historical trajectories, as well as to combine these with a study of the specific impacts caused by shrinkage and to analyze the policy

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24 Negative Effe cts Positive Effects Abandoned buildings Carbon and Carbon Dioxide sequestration Collapsed government Green infrastructure Decrease in life expectancy Reduction in urban heat island effect Decrease in public transportation Revitalization Decrease in w ater supply networks Decrease numbers of school enrolment Decreasing tax revenues Economic decline, poverty Housing vacancies Malnutrition Poor health care Property values lower Social gap Table 2-1. Negative and ositive ffects of hrinkage (Haase, Rink, Grossmann, Bernt, & Mykhnenko, 2014) (Hollander, Pallagst, Schwarz, & Popper, 2009) (Schett, 2012)

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25 Right sizing Demolition, stabilization and relocation of residents Creative Economy More life style options to attract people Community Gardens To reduce the look of empty lots by adding gardens "Planned" Shrinkage Resuscitation and revival through tourism Land Banks Demolition and rehabilitation of a b andoned and tax delinquent properties Art Artistically developing properties with mosaics and murals Figure 2-1. Interventions of hrinkage (Source: The Future of Shrinking Cities: Problems, Patterns and Strategie s of Urban Transformation in a Global Conte xt)

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26 SHRINKAGE Demographic Factors Selective Migration Ageing Birth Rates vs Death Rates Socio Cultural Factors Suburb anizatio n Women Lifestyl e Economic Factors Financi al Crisis De industria lization Economic Stagnation Global Exportat ion/Imp ortation Political Factors Conflicting policies and legislations Environ mental Factors Natural Disaster Other Factors Epidemic s Wars CHAPTER 3 METHODOLOGY Conceptual Model and Hypotheses The conceptual model found on Figure 3-1 based on the theoretical framework flow of the notice that a conceptual model is a simplification of 1994). The rev iew of ex amples of urban shrinkage and necessarily provide the validity to generaliz e the findings Florida, USA. Ev ery case of urban shrinkage is unique. Specific the actual contex t, causes and effects of shrinkage. For this chosen city was analy zed indiv idually and then compared.

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27 The conceptual model holds all the factors found in the first literature review and it was then broadened by key causes found in other shrinking citie s around the United States. The causes were explained in the second literature review found on Chapter 4. In this research, the causes of shrinkage are reviewed and listed in order to relate them hrinkage, the locations of literature review, three questions were answered and their respective hypotheses were tested. 1. What are the factors and causes of population loss in shrinking cities? a) Demographic Factors i) There are lower death and birth rates. ii) Retirees are moving into the cities. iii) Young people are moving out of the cities. b) Socio Cultural Factors i) People prefer to raise their families in the safe and quiet environment of t he suburbs. ii) People prefer to live in single family homes outside of the city. iii) Women are prolonging the time to get pregnant. c) Economic Factors i) Living in cities have higher costs. ii) De industrialization is causing higher unemployment rates. iii) Poverty rates are g oing to be higher. iv) Stagnation of regional economy. v) Regional trade is being affected. d) Political Factors

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28 i) New policies are causing people to move out of the area. e) Environmental Factors i) Natural disasters have caused families to lose their homes and they must r elocate. f) Other Factors i) Florida will have other unique factors that contribute to city shrinkage that are not related to wars or epidemics. 2. 3. What specific factors affect Florida? Research Design and Techniques T his research project has a mixed method research design. Based on Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2002, p. 18 20), in a mixed method approach the researcher bases knowledge claims on pragmatic grou nds by collecting both quantitative and qualitative either simultaneously or sequentially to best understand the research problem. For the qualitative research method, case studies were used to research shrinking cities causes. The type of case study desig n that was used was a collective case study approach. concurrent procedure where they were joined to provide a comprehensive analysis of the research problem. To explain, c oncurrent procedures are those where the investigator collects both forms of data at the same time during the study and then integrates the information in the interpretation of the overall results (Cresswell, 2002, p. 16 ) Quantitative research approach is the analysis of numerical data using

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29 techniques that include (1) simply describing the phenomenon of interest or (2) looking for significant differences between groups or among variables (Teddlie & Tashakkori, 2009, p. 5) Florida population data was collected in a span of 40 years in order to find peak population, decline from peak, and annual growth percentages. The annual growth percentage was the main factor in finding shrinking c ities, while peak population and decline from peak was used as reference in the analysis section. These facilitated the a comprehensive analysis. Qualitative researc h approach is a collection of descriptive data recorded from researchers. It uses descriptive observation, case studies, and personal interviews. Ray Rist (1977) mentioned that this technique is a way of approaching the empirical world. Numerous shrinking cities case studies from the United States were used to find the causes of shrinkage. This case study design is called collective case study. As mentioned in the introduction, a collective case study is the study of several cases in order to inquire into a particular phenomenon (Stake, 1995). This method is designed to support the use of multiple data sources. Collective case study design provides a structure to gain insight into the issue of interest across settings as it allows comparison within and betw een cases (Stake, 1995) Case studies are scientific and evidence based so they are a reliable tool to find valid information. These case studies contained the information on causes and examples of shrinking cities that helped find patterns in the data, documenting or listing research on each city selected in order to have some historical facts and background

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30 information on them. A side from the case studies, site visit s were conducted to do casual obser vation s of the area and anal yze the situation. Obser ving the area did not require con versation with people but it did require time. During thi s time photo s were taken to support any obser vation s and field note s gathered during the visit. There are no prearranged categorie s or scoring system for thi s obser vation method. Furthermore, the site visit s added an e xtra perspecti ve to the project in a way that it can help understand and complement the quantitati ve data a s well a s the data found in the case studies. By using thi s mi xed method s research design, data wa s collected, anal yzed, and findings w ere integrated and inferred to form thi s research project. Case Studies Analysis Tools The case studie s pla yed an important role since they contained the factor s of shrinkage from citie s around the United States. They were anal yzed using a technique ca lled qualitati ve content anal y sis. According to Titscher et al. (2000, p.55), content anal ysi s i s "the longest established method of te xt anal ysi s among the set of empirical method s of social in vestigation". Qualitati v e Content Anal ysi s use s variou s central point s to preser ve the ad v antage s of it and at the same time appl ying a more qualitati ve te xt interpretation (Kohlbacher, 2006). into categorie s which are carefully founded and re vised within the proce ss of anal ysis (Kohlbacher, 2006). A s you may recall, the categorie s of shrinkage are organi zed into 6 factors: demographic, economic, environmental, political, social-cultural, and others. (See Figure 3-1

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31 Site Selection and Analysis Tools There are over 19,000 incorporated places in the United States (Census, 2010). Incorporated places are usually a city, town, village, or borough but are subject to the laws of their respective states. It does not regard population it is mostly a place where governmental functions are established. Cities were the only incorporated places that this project was focusing on. Cities can be divided into two categories urbanized area (UA) and urban cluster (UC). An urbanized area has a population of 50,000 or more while an urban cluster must have a population between 2,500 and 49,999 (Census, 2010). ation and urban clusters, where people congregate, live, work, and have leisure. With this devised definition, the project will focus on cities with a population of 2,500 or more. Shrinking cities were divided into urbanized areas, urban clusters, or mini cities. But before they can be classified as UA, UC, or MC, they need to be found. This research utilized three tools: Excel, ArcGIS, and SPSS. Excel was used to keep records of all cities in Florida, population from 1970-2010, peak population, percent of annual growth, and percent of decline from peak. Based on these records, cities that have a negative percent of annual growth were categorized as shrinking. The formulas used to calculate these can be seen on Table 3-1

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32 the parameters set for this project. Nonetheless, they are still shrinking and were carefully mentioned in the project as mini cities (MC). Table 3-1. Formulas sed for lorida's opulation ata nalysis T y pe of Formula Formula Population Growth Rate Decline from Peak Percent of Decline from Peaks ArcG IS pro vided a location-based anal ysi s i n the form of maps. The data gathered in E xcel wa s joined into the citie s shapefile in order to create a more complete anal ysi s of the location s of the cities. After pin-pointing the location of the cities, the citie s were di vided into group s an d a purposi ve sample of the citie s wa s selected. A purposi ve sample i s not a random sample, citie s were selected based on a set of criteria because these can provide the best information to achieve the objectives of the project. These criteria can be found on Figure 3-2. The criteria used for the site selection s where; ha ving a negati ve population growth, the city si ze had to be either urbani zed area s or urban clusters, and they had to be located in specific areas. Two cities were selected by each location category (Go to Figure 3-3

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33 Subsequently, the sample cities were then visited, photographed, and observatio nal notes were taken. The data was then transcribed onto SPSS a long with Negative population growth rate Shrinking Urbanized Areas (50,000 and up) Urban Cluster (2,500-49,999) Cities Panhandle Coastal In-land (anything that isn't coastal or panhandle) Combination of two Pan-Coast or Pan-Land Location Figure 3-3. oastal ounties and anhandle ounties Figure Error!Not extofspecifiedstyleindocument. 2 :Methodology MapFigure3 3:CoastalCounties Figure 3-2. Cities election riteria

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34 the case study data, and the population data located in Ex cel, but more data had to be compiled in order to run a correlation. SPSS is a statistics software that helps in the collection and organiz ation of data, creates statistical analy sis and reports. The software helped create detailed analy sis such as frequencies and correlations of the shrinking cities. After acquiring the data from the case studies, census data, and site visits the factors of shrinkage were listed and categoriz ed. The two sets of data, Florida and ov erall shrinking cities, were compared and contrasted. Comparison and contrasting is used to find the similarities and differences among the v ariables by utiliz ing a subjectby-subject organiz ation method. In this paper, each city was analy zed indiv idually by subject and was organiz ed by the six factors prev iously mentioned. If for some reason a city does not hav e attributing points to a factor it was noted and the analy sis moved forward to the nex t factor. They were organiz ed in a table and div ided by factors and then the full analy sis was made. Some cities had unique factors that could not be categoriz ed into any other factor. These new factors will be discussed on Chapter 5. Then the missing data, along with the new factors, were added to SPSS for a correlation analy sis. See Figure 3-4 for a map of methods used. In SPSS, the causes listed in the conceptual model were key ed in and added as variables onto the variable view of the program. The data acquired from the case Appendix B for SPSS V D V S ). Afterwards, a correlation was performed between percent of population decline and the factors of shrinkage, individually.

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35 Limitations of the Research Design and Tools A mixed method study design has some chall enges. It can take time to organize and analyze both quantitative and qualitative data. Qualitative data in the form of case studies can be tricky because you need to treat each case study as a single entity. The other qualitative method used was observ ations. Some disadvantages of using observations is that it is time consuming especially when the cities visited were farther apart in terms of location and driving time. Reliability is another problem with observations. In addition, purposive samples are a great way to choose suitable cities that can have a lot of information but it can also lead to having badly biased samples hence its validity could be reduced if not careful. Sommer & Sommer (2012, p. 242) Qualitative content analysis when used as a way to formulate and organize categories can be of great use. Kohlabacher (2006) explains the following limitation of qualitative content analysis: The procedures of qualitative co ntent analysis seem less appropriate, if the research question is highly open ended, explorative, variable and working with categories would be a restriction, or if a more holistic, not step by step ongoing of analysis is planned (MAYRING, 2000b, p.474, 20 00a, [27]). In fact, MAYRING (2002) recommends his qualitative content analysis in the case of theory guided text analysis but rather not in the case of merely explorative interpretive interpretation of the material (p.121) There are countless of researc hes that mention that Excel is not the right tool for statistics. For this reason, Excel was used as a data organizer and SPSS was used as the statistics program. As for ArcGIS, there is an operating system limitation. It needs to run on a Windows PC so th ere is the constant change between using a MacBook for redacting the thesis and the PC for adding data to the map which can result in loss of

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36 data. In doing this process there must be an extensive attention to detail capability. Lastly, a limitation to com paring and contrasting is that in the end you will end up with a table full of points and data with little to no explanation. For this reason, an analysis section will follow the table in order to tie all the different points together.

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37 Setting the Parameters for Florida's Shrinking cities Introduction Problem Background Research Question Literature Review History Defining Shrinking cities Effect Interventions Methodology Conceptual Model Hypotheses Research Design and Techniques Site Selections Tools Limitations of the Methods Data Data Collection Quantitative Data Population Data Annual Growth Peak Population Decline from Peak Qualitative Data Case Studies Site Visits Casual Observation Photographing Data Analysis Quantitative Data Population Data GIS SPSS Qualitative Data Excel Listing Causes Conclusion Limitations Recommendations Figure 3-4. Methodology ap

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38 CHAPTER 4 DATA C OLLECTION AND RESULTS Conceptuali zing Shrinking Cities Thi s section contain s the second part of the literature re view. It i s a compilation of information gathered for e very factor and cause listed in the conceptual model. Shrinkage Author s Haase, Rink, Grossmann, Bernt and M ykhnenko describe shrinkage as Before the mid-2000s, there were other term s used in place of shrinkage, for e xample: decline, deca y and abandonment. These author s e xplain in their article, le ss ob viou s and do not follow uni versal patterns. Shrinkage ha s variou s cause s and effects, so it cannot be defined by them. It can only be defined by demographi c and geographi c terms. For thi s reason, shrinking cities ha s been defined in thi s thesi s a s a phenomenon where urban cluster s and urbani zed area s e xperience significant population decline o v er time. While taking into consideration factors, like demographic, socio-cultural, economic, political, en vironmental and man-made disaster, a s a binding determinant that triggers shrinkage. Demographic Factors Selective igration S elective migration is when a group of people decide to move to a more livable community, a place where they can enjoy themselves and work. In a report of the Netherlands Interdisciplinary Demographic Institute (NIDI), Van Nimwegen & Heering

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39 familie s with children looking for a li vable neighborhood and a decent primary school, young people looking f the population p yramid (Nimwegen & Heering, 2009, pp. 67) In many occasions, these t ype s of migration can take a negati ve turn and the migrants can face socio-economic problems like unemployment and segregation. Ageing opulation and irth/eath ates C hange s in the economy and demography can lead to change s in birth rate s and ha ve con (Population Reference Bureau, n.d.) In de veloped countries, high ad vancement s in medicine and technology ha ve prolonged life e xpectancy by 71.4% (Sirotin, 2016). These ad vancement s create a more resilient elderly communit y, lowering the death rates. Citie s ha ve been allocating it s elderly and retired citi zen s into smaller communities, which bring s forward e xtra costs. Pallagst (2007) mention s that such processe s ha ve drained essential resources from many urban areas, lea ving the citie s with a diminishing fiscal base. The Population Reference Bureau (PRB) e xplain s that since the 1950s, birth rate s ha ve continued their decline, while death rate s declined into the 1960 s but ha ve been slowly increasing since. European countries, declining birth rate s and an increase in death rate s are contributing to declining population si ze, causing urban shrinkage. Hollander et al. (2009) also mentioned thi s same corr elation, where many European countrie s ha ve low birth rates, which magnify shrinkage issues a s in German y. Lower birth rate s mean that there will be le ss children in each cit y. These same children are

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40 the one s that e ventually will need a school to attend to. If birth rate s lower, school s will not ha ve the necessary number of pupil s to keep some of them open. Thus, decreasing the con venience of ha ving a school within a reasonable radius. Which then lead s to an out-migration of the population. Socio-Cultural Factors Suburbani zation Suburbani zation i s not a t ypical American phenomenon. It i s a sign of prosperity since people can manage the time and cost of commuting a s well a s a home farther Peter Larkham (1999) said that for him suburb s meant the edge of a town but also communit y, with other s of hi s age, with landscape, familiarit y. Suburbani zation can better be understood a s a phenomenon of a growing middle cla ss with the asset s to afford a house with a garden outside the dense city centers (Oswalt & Rienits, 2006). Not to mention, that people tend to mo ve to more secure and le ss dense area s when ha ving a famil y. South and Crowder (1997) state th le vel s of violent crime and unemplo yment in citie s relati ve to suburb s also tend to spur city-t o-suburb mobility or inhibit suburb-t oWomen and regnancy Another major lifest yle change i s the fact that women are holding off to ha ving familie s until they finish their career s and/or they reach economi c stabilit y. The a verage age of U.S. first time mother s ha s risen to 26.3 a s of 2014 (National Center for Health Statistics). Dela ying initial childbearing affect s the number of children a t ypical woman might ha ve in her lifetime, family si ze, and for the o verall population change in the United State s (Mathew s & Hamilton, 2016). Women who delay initial childbearing ha ve

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41 late life change s while women who ha ve children before they are 30, ha ve early life changes. Economic Factor Financial risis, e-ndustrialization, conomic tagnation F inancial crisis, de-industriali zation, and economi c stagnation are some of the main economi c factor s that lead citie s to shrinkage. Koen El zerman (2009) mentions that in the former industrial citie s the relocation of economi c acti vity result s in a general decline of emplo yment, combined with a mismatch between a rising demand for higheducated and white-collar emplo yee s and a supply of lower-educated blue-collar workers. Declining emplo yment lead s to an ine vitable out-migration of the working force. The Industrial Re volution, which too k place from the late 1 8 th century to the early 19 th centur y, brought an era of economi c vitality for many state s in the USA but like -industriali zation had a disastrou s effect on industrial region s which speciali zed in traditional industrie s like mining, shi p-building, steel, and te xtile. Dependency on one-sided industrial economie s increase s the effect of the decline of that particular industry (At zema et al. 2002: 125 126; Boschma et al. 2002: 99 (El zerman, 2009). Some e xample s of these industrial region s are Ohio and Michigan, located in the Rust Belt. To remediate the deindustriali zation proce ss that began in the 2 0 th centur y, some re vitali zation efforts, referred to a s Renaissance II, which aimed to find a more di verse and strong econ (Pallagst K. 2009). Some of these strategie s were based on high-tech industries, education, health care, culture, and tourism (Pallagst K. 2009). Oswalt and Rieniet s deduced that there i s a correlation

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42 between social factor s and economi c factor s when it come s to population decline. They noticed that depri vation and po verty arise from de-industriali zation related phenomena like unemplo yment, but pay little attention to the processe s of social polari zation which occur because of unemplo yment, po verty and urban depri vation (Oswalt & Rienits, 2006) emplo yment result s in economi c stagnation, which can cause a decline in the number of If the city i s not economically stable, then it i s not economically viable for a person to li ve there. Cost of li ving goe s up in citie s where they are not economically stable. A costof -li ving index can be used to measure s difference s in the price of goods and ser vice s (US Bureau of Labor Statistics, 2016). Based on thi s it can be determined how cities costs measure out to the Nation and the State. Global rade The World Trade Report 2013 highlight s that demographi c change affect s trade tage and in import demand. It also participation in the labor force will all play a role in year s to come, a s will the continuing emergence of a global middle class. The report continue s to mention that an ageing population can diminish an economy due to a la ck of a younger workforce. It also mention s that immigrant s are particularly important in agriculture and information technolog y, sector s where the United State s i s an e xport powerhouse (The World Trade Report 2013). Without immigrants, e xportation s of agricultural good s decrease and with it the econom y. Population decline can negati vely affect the supply of products/good s and

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43 services if there are no demands for such goods in each city In basic economic course, they teach the law of supply and demand. This law refers to the amount of a product or service and the desires of the buyers. If the number of buyers decreases, this will cause a reduction in the equilibrium price and quantity of a good, ending in a decrease of supply (Edgmand, Moowaw, & Olson, 1996). Political Factors The economic factors hav e been a conflicting issue between policies and legislativ e issues. In the housing market, renters and homeowners are been displaced by a combination of economic and political factors such as affordable housing policies, Liv e crisis in US cities is not just about buying homes, it also focuses on rents. She continues mentioning that rents hav e increased since the Great Recession but those same increases hav e put coastal and hot cities like Denver and Austin out of reach for many in the middle class, between $50-$125,000 (Dreyfuss, 2016). Dreyfuss ends her article with the following statement: With immigration reform and climate change, housing affordability is something that states and cities can tackle on their own. In 2017, this trend toward decentralized power will continue that is, if cities make retaining middle-class residents a priority. That means relaxing the zoning laws to permit more housing stock to enter the market. A s mentioned before, in order for citie s to gain economi c growth they used multiple revitalization efforts like education and tourism to increase their population and reach economic stability. Zoning laws have changed in high tourism areas where there

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44 Joseph city staff hav e studied short-term rentals in the past year, and in Waite Park, short-term rentals are covered under rental licenses. Sartell is the first of the St. Cloud metro cities to outright ban short(Berg, 2017). Homeowners can no longer put a room for rent or hav e a Bed-and-Breakfast in Sartell. Environmental Factors Natural disasters are uncontrollable natural processes of the Earth. They bring destruction of infrastructure, diseases, and disruption of Katrina did in New Orleans. The destruction of housing displaced hundreds of thousands of residents for varying lengths of time, often permanently (Fussell, 2015). Seltzer and Nobles (2017) state that, large-scale climate events can hav e enduring effects on population siz e and composition. shortage of disaster safety supplies such as shelter, food, water, and medical resources, can leav e a city and its citizens with many environmental and social burdens which can quickly lead to more serious medical, mental health, and social protective (Burney Simmonds, & Queeley 2007). These burdens lead to population decline due to fear, safety concerns, and unwanted displacement. John A.Cross (2014) looked at 92 US communities that suffered disasters (mainly from hurricanes, river flooding, and tornadoes) between 1992 and 2008, found that locations that were already experiencing declining populations before their disaster were most likely to experience large post

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45 Other Factors Wars Wars are manmade hazards that result in the destruction of cities and lives. Elzerman mentions that there are two forms of population decline due to wars, the direct and the indirect decrease. The indirect is a form of destruction that causes many deaths and other human sorrow, which crudely means a direct decrease of the population. While the indirect urban shrinkage due to wars does for instance occur when cities are conquered or isolated by a new ruler (Elzerman, 2009). leave the city, especially when the new ruler refuses to maintain essential urban (Elzerman, 2009). Epidemics Epidemics had been the cause of population decline in historical times, where examples is that growth and shrinkage cannot be seen independently. Cities rise and (Elzerman, 2009). Elzerman (2009) states that in the pre-industrial era, the causes of urban shrinkage were mainly destructive: wars, city fires, natural disasters and epidemics. The bubonic plague was centuries ago but that does not mean that Dengue or as current as substance abuse. Table 4-1

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46 in Chapter 2: Literature Review. The factors were measured using specific data sources and analy zed using GIS, SPSS, and/or Content Analysis. Table 4-1. List of actors, ata cquisition, and naly sis ool Factor Data Source Analysis Tool Migration Census Blocks from 1990, 2000, 2010 GIS Ageing Median Population Age SPSS Suburbanization Observation/Single Family Homes from 1980, 1990, 2000, 2010 Content Analysis and GIS Women Lifestyle Birth Rate, Avg. First Time Mothers SPSS Financial Crisis Cost of Living Index and Poverty Rate SPSS De -Industrialization Observation, articles, and Manufacturing Industry Percentage Content Analysis and SPSS Ec onomic Stagnation Unemployment and Poverty Rate SPSS Exportation/Importation Retail and Wholesale Trade Industries SPSS Policies and Legislation Researching News art i cles Content Analysis and SPSS Natural Disaster Natural Disaster Count SPSS War War Count SPSS Epidemic Outbreak Count SPSS Table 4-2. Florida's opulation ata in 40 years by city. Cities 1980 1990 2000 2010 Pop. GR Peak Pop Decli ne Percent Decline 40 yrs. from peak from peak Alachua 3,561 4,529 6,098 9,059 386% 9059 Altamonte Springs 21,105 4,879 1,200 1,496 242% 41496 100% 100%

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47 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak Pop. Decli ne from peak Percent Decline from peak Anna Maria 1,537 1,744 1,814 1,503 6% 1,814 311 83% Apalachicol a 2,565 2,602 2,334 2,231 33% 2602 371 86% Apopka 6,019 13,512 26,642 41,542 1475 % 41542 100% Arcadia 6,002 6,488 6,604 7,637 68% 7637 100% Archer 1,230 1,372 1,289 1,118 23% 1372 254 81% Atlantic Beach 7,847 11,636 13,368 12,655 153% 13,368 713 95% Atlantis 1,325 1,653 2,005 2,005 128% 2005 100% Auburndale 6,501 8,858 11,032 13,507 269% 13507 100% Aventura 9,698 14,914 25,267 35,762 672% 35762 100% Avon Park 8,026 8,042 8,542 8,836 25% 8836 100% Bartow 14,780 14,716 15,340 17,298 43% 17298 100% Bay Lake 74 24 23 47 91% 74 27 64% Belleair Beach 1,643 2,070 1,751 1,560 13% 2070 510 75% Belleair Bluffs 2,522 2,128 2,243 2,031 49% 2522 491 81% Belle Glade 16,535 16,177 14,906 17,467 14% 17467 100% Belle Isle 2,848 5,272 5,531 5,988 276% 5988 100% Belleview 1,913 2,666 3,478 4,492 337% 4492 100% Blountstown 2,632 2,404 2,444 2,514 11% 2632 118 96% Boca Raton 49,447 61,492 74,764 84,392 177% 84392 100% Bonifay 2,534 2,612 2,743 2,793 26% 2793 100% Bonita Springs 5,435 13,600 32,797 43,914 1770 % 43914 100% Bowling Green 2,310 1,836 2,892 2,930 67% 2930 100% Boynton Beach 35,624 46,194 60,389 68,217 229% 68217 Bradenton 30,228 43,779 49,504 49,546 160 % 49546 100% 100% Bradenton Beach 1,603 1,657 1,482 1,171 67% 1657 486 71% Bristol 1,044 937 845 996 11% 1044 48 95%

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48 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P o p. Decli ne from peak Percent Decline fro m peak Brooker 429 312 352 338 53% 429 91 79% Brooksville 5,582 7,440 7,264 7,719 96% 7719 100% Bunnell 1,816 1,873 2,122 2,676 118% 2676 100% Bushnell 983 1,998 2,050 2,418 365% 2418 100% Callaway 7,1 54 12,253 14,233 14,405 253% 14405 100% Cape Canaveral 5,733 8,014 8,829 9,912 182% 9912 100% Cape Coral 32,103 74,991 102,28 6 154,30 5 952% 154305 100% Carrabelle 1,304 1,200 1,303 2,778 283% 2778 100% Casselberry 15,037 18,911 22,629 26,241 186% 26241 100% Cedar Key 700 668 790 702 1% 790 88 89% Center Hill 751 735 910 988 79% 988 100% Chattahooc hee 5,332 4,382 3,287 3,652 79% 5332 1,680 68% Chiefland 1,986 1,917 1,993 2,245 33% 2245 100% Chipley 3,330 3,866 3,592 3,605 21% 3866 261 93% Clearwater 85,170 98,784 108,78 7 107,68 5 66% 108,78 7 1,102 99% Clermont 5,461 6,910 9,333 28,742 1066 % 28742 100% Clewiston 5,219 6,085 6,460 7,155 93% 7155 100% Cocoa 16,096 17,722 16,412 17,140 16% 17722 582 97% Cocoa Beac h 10,926 12,123 12,482 11,231 7% 12,482 1,251 90% Coconut Creek 6,288 27,485 43,566 52,909 1854 % 52909 100% Coleman 1,022 857 647 703 78% 1022 319 69% Cooper City 10,140 20,791 27,939 28,547 454% 28547 100% Coral Gables 43,241 40,091 42,249 46,780 20% 46780 100% Coral Springs 37,349 79,443 117,54 9 1 21,09 6 561% 121096 100% Crescent City 1,722 1,859 1,776 1,577 21% 1859 282 85% Crestview 7,617 9,886 14,766 20,978 439% 20978 100%

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49 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak Crystal Rive r 2,778 4,044 3,485 3,108 30% 3108 100% Dade City 4,923 5,633 6,188 6,437 77% 6437 100% Dania Beac h 11,796 13,024 20,061 29,639 378% 29639 100% Davenport 1,509 1,529 1,924 2,888 228% 2888 100% Daytona Beach 54,176 61,921 64,112 61,005 32% 64,112 3,107 95% Daytona Beach Shores 1,324 2,335 4,299 4,247 552% 4247 100% DeBary 4,980 7,176 15,559 19,320 720% 19320 100% Deerfield Beach 39,193 46,325 64,583 75,018 229% 75018 100% DeFuniak Springs 5,563 5,120 5,089 5,177 17% 5563 386 93% DeLand 15,354 16,491 20,904 27,031 190% 27031 100% Delray Beach 34,329 47,181 60,020 60,522 191% 60522 100% Deltona 15,710 50,828 69,543 85,182 1106 % 85182 100% Destin 8,080 11,119 12,305 #DIV/ 0! 12305 100% Doral 3,126 21,130 45,704 #DIV/ 0! 45704 100% Dunedin 30,203 34,012 35,691 35,321 42% 35,691 370 99% Dunnellon 1,427 1,624 1,898 1,733 54% 1,898 165 91% Eagle Lake 1,678 1,758 2,496 2,255 86% 2,496 241 90% Edgewater 6,726 15,337 18,668 20,750 521% 20750 100% Edgewood 1,034 1,062 1,901 2,503 355% 250 3 100% Eustis 9,453 12,967 15,106 18,558 241% 18558 Everglades 524 321 479 400 59% 524 124 100% 76% Fanning Springs 314 493 737 764 358% 764 Fellsmere 1,161 2,179 3,813 5,197 869% 5197 100% 100 %

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50 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak Pop. Decli ne from peak Percent Decline from peak Fernandina Beach 7,224 8,765 10,549 11,487 148% 11487 100% Flagler Beach 2,208 3,820 4,954 4,484 2 58% 4,954 470 91% Florida City 6,174 5,806 7,843 11,245 205% 11245 100% Fort Lauderdale 153,27 9 149,37 7 152,39 7 165,52 1 20% 165521 100% Fort Meade 5,546 4,976 5,691 5,626 4% 5,691 65 99% Fort Myers 36,638 45,206 48,208 62,298 175% 62298 100% Fort Pierce 33,802 36,830 37,516 41,590 58% 41590 100% Fort Walton Beach 20,829 21,471 19,973 19,507 16% 21471 1,964 91% Freeport 669 843 1,190 1,787 418% 1787 100% Frostproof 2,995 2,808 2,975 2,992 0% 2995 3 100% Fruitland Park 2,259 2,754 3,186 4,078 201% 4078 100% Gainesville 81,771 84,770 95,447 124,35 4 130% 124354 100% Graceville 2,918 2,675 2,402 2,278 55% 2918 640 78% Gr eenacres 8,780 18,683 27,569 37,573 820% 37573 100% Green Cove Springs 4,154 4,497 5,378 6,908 166% 6908 100% Gretna 1,557 1,981 1,709 1,460 16% 1981 521 74% Groveland 1,992 2,300 2,360 8,729 846% 8729 100% Gulf Breeze 5,478 5,530 5,665 5,763 13% 5763 100% Gulfport 11,180 11,727 12,527 12,029 19% 12,527 498 96% Haines City 10,799 11,683 13,174 20,535 225% 20535 100% Hallandale Beach 36,517 30,996 34,282 37,113 4% 37113 Hampton 466 296 431 500 18% 500 Hawthorne 1,303 1,305 1,415 1,417 22% 1417 Hialeah 145,25 4 188,00 4 226,41 9 224,66 9 137% 226,41 9 1,750 100% 100% 100% 99% 7,713 19,297 21,744 1763 % 21744 100% Hialeah Gar

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51 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak High Springs 2,491 3,144 3,863 5,350 287% 5350 100% Holly Hill 9,953 11,141 12,119 11,659 43% 12,119 460 96% Hollywood 121,32 3 121,69 7 139,35 7 140,76 8 40% 140768 100% Holmes Beach 4,018 4,810 4,966 3,836 11% 4,966 1,130 77% Homestead 20,668 26,866 31,909 60,512 482% 60512 100% Indian Harbour Beach 5,967 6,933 8,152 8,225 95% 8225 100% Indian Rock s Beach 3,717 3,963 5,072 4,113 27% 5,072 959 81% Inverness 4,095 5,797 6,789 7,210 190% 7210 100% Islandia 12 13 6 18 125% 18 100% Jacksonville 540,92 0 635,23 0 735,61 7 821,78 4 1303 7% 821784 100% Jacksonville Beach 15,462 17,839 20,990 21,362 95% 21362 100% Jacob City 261 281 250 #DIV/ 0! 281 31 89% Jasper 2,093 2,099 1,780 4,546 293% 4546 100% Key Colony Beach 977 977 788 797 46% 977 180 82% Keystone Heights 1,056 1,315 1,349 1,350 70% 1350 100% Key West 24,382 24,832 25,478 24,649 3% 25,478 829 97% Kissimmee 15,487 30,050 47,814 59,682 713% 59682 100% LaBelle 2,287 2,703 4,210 4,640 257% 4640 100% Lake Alfred 3,134 3,622 3,890 5,015 150% 5015 Lake Buena Vista 98 1,776 16 10 224% 1776 1,766 Lake Butler 1,830 2,116 1,927 1,897 9% 2116 219 100% 1% 9 0% Lake City 9,257 10,005 9,980 12,046 75% 12046 Lake Helen 2,047 2,344 2,743 2,624 70% 2,743 119 100% 96%

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52 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P o p. Decli ne from peak Percent Decline from peak Lakeland 47,406 70,576 78,452 97,422 264% 97422 100% Lake Mary 2,853 5,929 11,458 13,822 961% 13822 100% Lake Wales 8,466 9,670 10,194 14,225 170% 14225 100% Lake Worth 27,048 28,564 35,133 34,910 73% 35,133 223 99% Largo 57,958 65,674 69,371 77,648 85% 77648 100% Lauderdale Lakes 25,426 27,341 31,705 32,593 70% 32593 100% Lauderhill 37,271 49,708 57,585 66,887 199% 66887 100% Laurel Hill 610 543 549 537 30% 610 73 88% Lawtey 692 676 656 250% 692 692 0% Layton 88 183 186 184 273% 186 2 99% Leesburg 13,191 14,903 15,956 20,117 131% 20117 100% Lighthouse Point 11,488 10,378 10,767 10,344 25% 11488 1,144 9 0% Live Oak 6,732 6,332 6,480 6,850 4% 6850 100% Longwood 10,029 13,316 13,745 13,657 90% 13,745 88 99% Lynn Haven 6,239 9,298 12,451 18,493 491% 18493 100% Macclenny 3,851 3,966 4,459 6,374 164% 6374 100% Madeira Beach 4,520 4,225 4,511 4,263 14% 4520 257 94% Madison 3,487 3,345 3,061 2,843 46% 3487 644 82% Maitland 8,763 9,110 12,019 15,751 199% 15751 100% Marathon 7,568 8,857 10,255 8,297 24% 10,255 1,958 81% Marco Island 4,694 9,493 14,879 16,413 624% 16413 100% Margate 35,900 42,985 53,909 53,284 121% 53,909 625 99% Marianna 7,006 6,292 6,230 6,102 904 87% 4,139 4,055 3,851 32% 7006 23% 4139 288 93% Mary Esther 3,530

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53 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak Mascotte 1,112 1,761 2,687 5,101 897% 5101 100% Melbourne 46,539 59,646 71,382 76,068 159% 76068 100% Mexico Beach 632 992 1,017 1,072 174% 1072 100% Miami 346,68 1 358,54 8 362,47 0 399,45 7 38% 399457 100% Miami Beach 96,298 92,639 87,933 87,779 22% 96298 8,519 91% Miami Gardens 8,482 7,448 100,75 8 107,16 7 2909 % 107167 100% Miami Springs 12,350 13,268 13,712 13,809 30% 13809 100% Midway 852 1,446 3,004 #DIV/ 0! 3004 100% Milton 7,206 7,216 7,045 8,826 56% 8826 100% Minneola 851 1,515 5,435 9,403 2512 % 9403 100% Miramar 32,813 40,663 72,739 122,04 1 680% 122041 100% Monticello 2,994 2,573 2,533 2,506 41% 2994 488 84% Moore Haven 1,250 1,432 1,635 1,680 86% 1680 100% Mount Dora 5,883 7,196 9,418 12,370 276% 12370 100% Mulberry 2,932 2,988 3,230 3,817 75% 3817 100% Naples 17,581 19,505 20,976 19,537 28% 20,976 1,439 93% Neptune Beach 5,248 6,816 7,270 7,037 85% 7,270 233 97% Newberry 1,826 1,644 3,316 4,950 428% 4950 100% New Port Richey 11,196 14,044 16,117 14,911 83% 16,117 1,206 93% New Smyrna Beach 13,557 16,543 20,048 22,464 164% 22464 Niceville 8,543 10,507 11,684 12,749 123% 12749 North Bay Village 4,920 5,383 6,733 7,137 113% 7137 100% 100% 100%

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54 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak North Lauderdale 18,653 26,506 32,264 41,023 300% 41023 100% North Miami 42,566 49,998 59,880 58,786 95% 59,880 1,094 98% North Miami Beach 36,553 35,359 40,786 41,523 34% 41523 100% North Port 6,205 11,973 22,797 57,357 2061 % 57357 100% Oak Hill 938 917 1,378 1,792 228% 1792 100% Oakland 658 700 936 2,538 714% 2538 100% Oakland Park 22,944 26,326 30,966 41,363 201% 41363 100% Ocala 37,170 42,045 45,943 56,315 129% 56315 100% Ocoee 7,803 12,778 24,391 35,5 79 890% 35579 100% Okeechobe e 4,225 4,943 5,376 5,621 83% 5621 100% Oldsmar 2,608 8,361 11,910 13,591 1053 % 13591 100% Opa-locka 14,460 15,283 14,951 15,219 13% 15283 64 100% Orange City 2,795 5,347 6,60 4 10,599 698% 10599 100% Orlando 128,29 1 164,69 3 185,95 1 238,30 0 214% 238300 100% Ormond Beach 21,436 29,721 36,301 38,137 195% 38137 100% Oviedo 3,074 11,114 26,316 33,342 2462 % 33342 100% Pahokee 6,346 6,822 5,985 5,649 27% 6822 1,173 83% Palatka 10,175 10,201 10,033 10,558 9% 10558 100% Palm Bay 18,560 62,632 79,413 103,19 0 1140 % 103190 100% Palm Beach Gardens 14,407 22,965 35,058 48,452 591% 48452 Palm Coast 2,837 14,287 32,732 75,180 6375 % 75180 100% 100%

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55 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak Palmetto 8,637 9,268 12,571 12,606 115% 12606 100 % Panama City 33,346 34,378 36,417 36,484 24% 36484 100% Panama City Beach 2,148 4,051 7,671 12,018 1149 % 12018 100% Parker 4,298 4,598 4,623 4,317 1% 4,623 306 93% Parkland 545 3,558 13,835 23,962 1074 2% 23962 100% Pembroke Pines 35,776 65,452 137,42 7 154,75 0 831% 154750 100% Pensacola 57,619 58,165 56,255 51,923 25% 57619 5,696 90% Perry 8,254 7,151 6,847 7,017 37% 8254 1,237 85% Pinellas Park 32,811 43,426 45,6 58 49,079 124% 49079 100% Plantation 48,653 66,692 82,934 84,955 187% 84955 100% Plant City 17,064 22,754 29,915 34,721 259% 34721 100% Pompano Beach 52,618 72,411 78,191 99,845 847% 99845 100% Port Orange 18,756 35,317 45,823 56,048 497% 56048 100% Port Richey 2,165 2,523 3,021 2,671 58% 3,021 350 88% Port St. Joe 4,027 4,044 3,644 3,445 36% 4044 599 85% Port St. Lucie 14,690 55,866 88,769 164,60 3 2551 % 164603 100% Punta Gorda 6,797 10,747 14,344 16,641 362% 16641 100% Quincy 8,591 7,444 6,982 7,972 18% 8591 619 93% Riviera Beach 26,489 27,639 29,884 32,488 57% 32488 Rockledge 11,877 16,023 20,170 24,926 275% 24926 100% 100% Safety Harbor 6,461 15,124 17,203 16,884 403% 17,203 319 98% St. Augustine 11,985 11,692 11,592 12,975 21% 12975 100%

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56 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P op. Decli ne from peak Percent Decline from peak St. Augustine Beach 1,289 3,657 4,683 6,176 948% 6176 100% St. Cloud 7,840 12,453 20,074 35,183 872% 35183 100% St. Marks 286 307 272 293 6% 307 14 95% St. Petersburg Beach 9,354 9,200 9,929 9,346 0% 9,929 583 94% St. Petersburg 238,64 7 238,62 9 248,23 2 244,76 9 6% 248,23 2 3,463 99% San Antonio 529 776 655 1,138 288% 1138 100% Sanford 23,176 32,387 38,291 53,570 328% 53570 100% Sanibel 3,363 5,468 6,064 6,469 231% 6469 100% Sarasota 48,868 50,961 52,715 51,917 16% 52,715 798 98% Satellite Beach 9,163 9,889 9,577 10,109 26% 10109 100% Sebastian 2,831 10,205 16,181 21,929 1687 % 21929 100% Sebring 8,736 8,900 9,667 10,491 50% 10491 100% Seminole 4,586 9,251 10,890 17,233 689% 17233 100% Sopchoppy 444 367 426 457 7% 457 100% South Bay 3,886 3,558 3,859 4,876 64% 4876 100% South Daytona 11,252 12,482 13,177 12,252 22% 13,177 925 93% South Miami 10,895 10,404 10,741 11,657 17% 11657 100% South Pasadena 4,188 5,644 5,778 4,964 46% 5,778 814 86% Springfield 7,220 8,715 8,810 8,903 5 8% 8903 Starke 5,306 5,226 5,593 5,449 7% 5,593 Stuart 9,467 11,936 14,633 15,593 162% 15593 Sunny Isles Beach 12,564 11,772 15,315 20,832 165% 20832 100% 144 97% 100% 100%

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57 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P o p. Decli ne from peak Percent Decline from peak Sunrise 39,681 64,407 85,779 84,439 282% 85,779 1,340 98% Sweetwater 8,067 13,909 14,226 13,499 168% 14,226 727 95% Tallahassee 81,548 124 ,77 3 150,62 4 181,37 6 306% 181376 100% Tamarac 29,376 44,822 55,588 60,427 264% 60427 100% Tampa 271,57 7 280,01 5 303,44 7 335,70 9 59% 335709 100% Tarpon Springs 13,251 17,906 21,003 23,484 193% 23484 100% Tavares 4,398 7,383 9,700 13,951 543% 13951 100% Temple Terrace 11,097 16,444 20,918 24,541 303% 24541 100% Titusville 31,910 39,394 40,670 43,761 93% 43761 100% Treasure Island 6,316 7,266 7,450 6,705 15% 7,4 50 745 90% Trenton 1,131 1,287 1,617 1,999 192% 1999 100% Umatilla 1,872 2,350 2,214 3,456 212% 3456 100% Valparaiso 6,142 4,672 6,408 5,036 45% 6,408 1,372 79% Venice 12,153 16,922 17,764 20,748 177% 20748 100% Vernon 885 778 743 687 56% 885 198 78% Vero Beach 16,176 17,350 17,705 15,220 15% 17,705 2,485 86% Waldo 993 1,017 821 1,015 6% 1017 2 100% Wauchula 3,296 3,253 4,368 5,001 129% 5001 100% Webster 856 746 805 785 21% 856 71 92% Weeki Wachee 8 53 12 12 125% 53 41 23% W est Melbourne 5,078 8,399 9,824 18,355 654% 18355 West Miami 6,076 5,727 5,863 5,965 6076 111 100% 98% Weston -9,829 49,286 65,333 5% #DIV/ 0! 65333 67,643 82,103 99,919 145% 99919 100% 100% West Palm 63,305

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58 Table 4-2. Continued Cities 1980 1990 2000 2010 Pop. GR 40 yrs. Peak P o p Decli ne from peak Percent Decline from peak West Park 9,003 10 ,347 14,225 14,156 143% 14225 69 100% Wewahitchk a 1,742 1,779 1,722 1,981 34% 1981 100% Wildwood 2,665 3,421 3,924 6,709 379% 6709 100% Williston 2,240 2,179 2,297 2,768 59% 2768 100% Wilton Manors 12,74 2 11,804 12,697 11,632 22% 12742 1,110 91% Winter Garden 6,789 9,745 14,351 34,568 1023 % 34568 100% Winter Haven 21,119 24,725 26,487 33,874 151% 33874 100% Winter Park 22,339 22,242 24,090 27,852 62% 27852 100% Winter Springs 10,475 22,151 31,666 33,282 544% 33282 100% Zephyrhills 5,742 8,220 10,833 13,288 329% 13288 100% Table 4-3 Florida's Shrinking Cities by Urban Clusters, Urbanized Areas, and Mini Cities, shows the total of shrinking cities found in Florida. This total takes into account the three city sizes previously discussed, urban cluster, urbanized areas, and mini cities. These cities were found by calculating their population growth. Those with a negative population growth are listed below in Table 4-3.

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59 Table 4-3 Florida's Shrinking Cities by Urban Clusters, Urbanized Areas, and Mini Cities Based on (-) Population Growth Total Shrinking Cities Urban Clusters Urbanized Areas Mini Cities 4 0 22 2 16 Apalachicola Miami Beach Anna Maria Belleair Bluffs Pensacola Archer Blountstown Bay Lake Chattahoochee Belleair Beach DeFuniak Springs Bradenton Beach Fort Walton Beach Bristol Graceville Brooker Holmes Beach Coleman Lighthouse Point Crescent City Madeira Beach Everglades Madison Gretna Marianna Key Colony Beach Monticello Lake Buena Vista Pahokee Laurel Hill Perry Vernon Port St. Joe Webster Quincy St. Petersburg Beach Valparaiso Vero Beach West Miami Wilton Manors Purposive Sample Selection As mentioned in Chapter 3: Methodology, a purposive sample is not a random sample rather it i s a sample that ha s been selected based on criteria. The shrink ing citie s have been selected based on si ze: urban cluster (UC), urbani zed area s (UA), or mini cities (MC); and by location: coastal, panhandle, in-land, or coastal/panhandle cities. As can be seen in Figure 4-1, Miami Beach and Holmes Beach were selected to represent Coastal Shrinking Cities, Miami Beach being the only UA in the coastal category. DeFuniak Springs and Perry represent the Panhandle Shrinking Cities while Pensacola and Fort Walton Beach

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6 0 represent the Coastal Panhandle Shrinking Cities. Pensacola is the only UA in the coastal/panhandle category Pahokee is the only shrinking In-land city for UC, for this reason it was the only one selected for the In-land cities. There are no UAs in the panhandle or the in-land cities. See Figure A-2 cities. Coastal Cities Holmes Beach Miami Beach Panhandle Cities DeFuniak Springs Perry In land Cities Pahokee Coastal/Panhandle Pensacola Fort Walton Beach Coastal Cities Belleair Bluffs Holmes Beach Lighthouse point Madeira Beach Miami Beach St. Petersburg Beach Vero Beach West Miami Wilton Manors Panhandle Cities Blountstown Chattahoochee DeFuniak Springs Graceville Madison Marianna Monticello Perry Quincy In land Cities Pahokee Coastal/Panhandle Apalachicola Fort Walton Beach Pensacola Port St. Joe Valparaiso ensus, Case Studies, and Observational Data Data was collected on the field and online in order to find the causes of shrinkage for the sev en sample cities. Each city was organiz ed into tables that can be found on Appendix B, SPSS V D V S The information included in these tables are: name, location, and siz e of the city population from 1980, 1990, 2000, and 2010, Population growth in 40 years, decline from peak, percent of decline from peak, population age, birth and death rates,

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61 information, household income, pov erty and unemploy ment rates, major industries, natural disasters, policies and legislations The tables also include a summary of the site visits and key findings found in case studies and research done for each of the sev en sample cities. These tables were a combination of data gathered from the US Census Bureau, Data USA, CityData, and Town Charts. Results The results are div ided by the key causes outlined in the conceptual model. They are mostly focused on a detailed ev aluation of the sev en sample cities along with an SPSS data analy sis where a correlation was performed to very statistical significance between population decline and all the causes outlined in the conceptual model. As can be seen on Figure 4-1 hrinking cities found in Flori da, 14 cities are located on the coast, 11 cities are in-land (4 in Central Florida), 10 are in the panhandle, and 5 are coastal-panhandle cities. Selective Migration and Ageing Population e can see that from 1990 from 2000 there was a decrease in the young population (19 to 24 y ears) in all cities including Florida as a state. Florida lost 3.6% of the young population, while Pensacola lost 1.4%, Miami Beach 1.9%, Fort Walton Beach 4.1%, DeFuniak Springs 2.3%, Holmes Beach 3.4%, Pahokee 3.4%, and Perry 2.8%. From

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62 Miami Beach lost 0.1%. From 1990 to 2010 there was a decrease in the adults, 24 to 44 years old. In Figure 4-2 1990 Population by Percentage of Age Group for the Shrinking Cities Figure 4-3 2000 Population by Percentage of Age Group in Florida and its Shrinking Cities and Figure 4-4 2010 Population by Percentage of Age Group in Florida and its Shrinking Cities, it can be seen how the population significantly changed in Holmes Beach from a 24.3 % in 1990 to a 12.3% in 2010. 6.6 6.5 4.6 6.7 7 10.5 8.5 15.5 17.1 9.5 17 18 3 8 4 .9 25.7 21.6 9.4 9.8 7.6 10.9 8 5.2 9.9 8.8 30.4 30 28.9 31.3 25.1 24.3 27 28 19.8 20.2 19.3 22.9 20.3 24.2 17.9 18.4 18.3 16.4 30.1 11.2 21.6 34 9 14.7 0 20 40 60 80 100 Florida Pensacola Miami Beach For t Walton Beach DeFuniak Springs Holmes Beach Pahokee PerryPERCENTAGEC IT IES 1990 Populat ion by Percentage of Age G roup Under 5 years 5 to 19 years 18 to 24 years 25 to 44 years 45 to 64 years 65 years and over Figure 4-3. 2000 opulation by ercentage of ge roup in Florida and its hrinking ities 5.9 6.1 5.5 5.6 9.5 7.1 19.4 21.5 1 1 4 .2 19.2 19.2 1 1 1 2. 7 .8 .2 32.7 24 5.8 8.4 5.7 6.8 5.7 6.5 6 28.5 28.8 35.9 31.3 30.5 20.1 25 26.3 22.6 21.6 21.9 23.8 23.3 31.1 18.1 21.5 17.6 13.5 21.3 13.6 15.7 33.2 8.2 15 0 20 40 60 80 100 Florida Pensacola Miami Beach F or t Walton Beach Defuniak Springs Holmes Beach Pahokee Perry PERCENTAGES C IT IES 2000 Population by Percentage of Age Group Under 5 years 5 to 19 years 20 to 24 years 25 to 44 years 45 to 64 years 65 years and over

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63 Figure 4-4. 2010 opulation by ercentage of ge roup in Florida and its hrinking ities 5.7 6.6 4.6 6.1 6.6 1.9 8 8 18.2 19.1 11.1 16.4 17.5 8.3 24.9 20 6.5 9.4 5.6 7.7 6.7 2.7 7.2 6.2 25.2 24.5 35.6 26.1 27.2 12.3 23.6 22.2 27 26.2 26 28.5 26.5 36.2 24.5 27.5 17.4 12.5 14.4 13.6 13.6 33.4 10.4 14.2 0 10 20 30 40 50 60 70 80 90 100 Florida Pensacola Miami Beach Fort Walton Beach Defuniak Springs Holmes Beach Pahokee PerryPERCENTAGESCITIES 2010 Population by Percentage of Age Group Under 5 years 5 to 19 years 20 to 24 years 25 to 44 years 45 to 64 years 65 years and over Miami Beach went from an ageing population (65 year s and o ver) of 30.1% to a 14.4%. Holme s Beach kept it s ageing population at appro ximately 34% of the total population. The other citie s had minimal percent change s in it s ageing population. Suburbanization are areas outside of the city limits that hav e been constructed from 2000 to 2010. There are clusters of suburbs on the North and South of the city In Fort Walton Beach and Pensacola, it is ev ident that there is suburbaniz ation outside of the city limits and in small towns surrounding both cities. There are clusters of suburbs to th much suburbaniz ation on the island but there are newer suburbs in the mainland, to the East and South East. Perry has suburbanization all around the city limits. In Miami Beach and Pahokee there is no suburbanization. Women Lifestyle Based on the birth rate and the av erage age of first time mother, Table 4-4 was

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64 created. Table 4-4 Women Lifesty le Changes shows that women in DeFuniak Springs, Holmes Beach, Miami Beach, and Pensacola hav e late life changes than women in Fort Walton Beach, Pahokee, and Perry By delay ing initial childbearing women hav e late life changes, while women who hav e children before they are 30, hav e early life changes. Table 4-4. Women Lifesty le Changes Cities Women Lifestyle DeFuniak Springs Late Life Changes Fort Walton Beach Early Life Changes Holmes Beach Late Life Changes Miami Beach Late Life Changes Pahokee Early Life Changes Pensacola Late Life Changes Perry Early Life Changes Financial Crisis and Economic Stagnation Based on Figure 4-5 Ho liv ing index with 141 and 124 respectfully Pensacola and Fort Walton Beach are 100 99 141 124 84 82 88 95 98 0 50 100 NationFloridaHolmes Beach Miami Beach DeFuniak Springs PerryPahokeePensacolaFort Walton Beach Cost of Living Index Cost of Living Index Figure 4-5. Cost of iving Index for the ation, Florida and the hrinking ities

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65 The current poverty rate in Florida is 14.7% and as can be seen on Figure 4-6 the sample shrinking cities are ab The city with the highest pov erty rate is Pahokee. 14.7% 16.2% 15.0% 19.9% 22.9% 34.2% 15.7% 17.5% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% Florida Holmes Beach Miami Beach DeFuniak Springs Perry Pahokee Pensacola Fort Walton Beach Poverty Rate Poverty Rate De -Industriali zation After r e v iewing case studie s on historical fact s about the se ven cities, I found that DeFunia k Spring s ha s gone through de-industriali zation. Perdue Farms, a chicken processing plant, wa s closed on April 2004 (Miami Herald, 2004). Export/Import On the data tables found in Appendix C C O D, you will find a section on major industries and in those tables, you will find boxes for retail trade and wholesale trade. Each city has its own importation and exportation system that were reviewed during the site visit. As can be seen from the boxes these trades take up some of the top industries for each city. After reviewing the

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66 cities have no issues with importation and exportation. Policies and Legislations Holmes Beach has a new zoning policy for vacation rental, implemented February implemented on March 2016Ord16.04. Miami Beach also has a new zoning policy for vacation/shortterm rentals which prohibits it in all single-family homes and in many multi-family housing buildings in certain zoning districts of Miami Beach. A side from having short term growth, DeFuniak Springs and Perry have no conflicting policies. Taxable property taxes decreased in Pahokee, lacking policies to stable it. While Fort Walton beach has gone down on a home price slump of 30.4, also lacking policies to stable it. In 2008, Escambia county, where Pensacola is located adopted a strict no smoking policy effective October 1, 2008. The mandatory drug test includes a test for tobacco usage after that date. If a person tests positive for tobacco, they will not be hired for any county position. Natural Disasters Figure 4-7. Natural Disaster Count by City disasters that occurred by city. Miami Beach had the least amount of natural disasters with only 5 disasters while Fort Walton Beach had a total of 24 natural disasters. Following Fort Walton Beach is Holmes Beach and Pahokee with 23 natural disasters. DeFuniak Springs and Pensacola have had 22 natural disaster followe by Perry with a total of 15 natural disasters.

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67 Figure 4-7. Natural isaster ount by ity War Based on the research performed and article s re viewed, none of the cities selected ha ve gone through a war in the past 40 years. Epidemic Based on the research performed and article s re viewed, there are no case s of viral epidemi c outbrea ks in any of the citie s but DeFunia k Springs, Fort Walton Beach, Holme s Beach, Miami Beach, and Pensacola ha ve gone through a substance abuse epidemic. Correlations and Other Factors shows the correlation analysis run on SPSS. Percent of population decline from peak was compared to 14 factors. On Figure 4-8. Correlations, Significance, and Null Hypothesis, there is a summarized table with the correlations and their relationship, significance and the evidence level, as well as well as whether the null hypothesis was failed or rejected. Significance was tested on a 0.01 level and a 0.05 level.

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68 **. Correlation is significant at the 0.01 level (1-tailed) *. Correlation is significant at the 0.05 level (1-tailed) Table 4 5. Co d in Florida Figure 4-8. Correlations, ignificance, and ull ypothesis

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69 Selective migration Ageing population Birth and death rates Suburbanization Women and pregnancy Financial crisis De-industrialization Economic stagnation Global trade Conflicting policies and legislations Natural Disasters Wars Epidemics After studying Florida there are two additional factors that were added to this CHAPTER 5 SHR INK ING C IT IES PHENOMENON IN FLOR IDA Thi s chapter pro v ide s a more detailed o ver view of the result s and how my site visit s influenced thi s whole project. First, it will pro vide the o ver view by factor s and then by location. Why Are Cities in Florida Losing Population? What are the Factor? Shrinking cities is a phenomenon that has been changing throughout the years. hrinkage are known. While gathering information on shrinking cities around the USA, several factors were found. These are: Ageing population is a critical factor in Holmes Beach since 34% of its population is 65 years and ov er. This percentage has remained constant since 1990. It also has approx imately 30% of its population between the ages of 45 and 64 from 1990 to 2010. Meaning that as the y ears progress there will be an increase in Holmes Beach ageing population. Based on the correlation ageing population has an inv erse relationship percent of decline from peak meaning that if one decreases the other increases

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70 vice versa. One city cannot define the rest of them, for this reason the ageing population remains a possible shrinkage factor in Florida. Mortality Rates has a strong positive relationship and a significant evidence to confirm that population decline does affect mortality rates. If the percent of population declines decreases so does mortality rates. Based on the results Florida lost 3.6% of the young population, while Pensacola lost 1.4%, Miami Beach 1.9%, Fort Walton Beach 4.1%, DeFuniak Springs 2.3%, Holmes Beach 3.4%, Pahokee 3.4%, and Perry 2.8%. Meaning that the younger population is moving out of the cities and some even out of the state. Confirming the Socio-Cultural Factors To gather the data to know if suburbanization was occurring in the city, GIS was used to find single family homes in the county that the city belonged to. These single-family homes were then categorized by year built. Everything that was boundaries, a cluster of homes within one mile, and was built from 2000 to 2010 was suburban areas. If there were significant numbers of clusters surrounding the city, it was considered suburbanized. The correlation between population decline and suburbani zation show s that there i s a wea k negati v e correlation between them, suggesting that they ha v e an inverse relationship where if one decreases the other one increases. Furthermore, the maps found in Appendix D S M

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71 Based on the birth rate and the average age of first time mother, by delaying initial childbearing women have late life changes, while women who have children before they are 30, have early life changes. Delaying childbearing negatively affects birth rates. Moreover, the correlation shows that there is also an inverse relationship between birth rates and population decline where if one decreases the other one increases. Economic Factors There is a strong positive correlation between economic stagnation and population decline, meaning that economic stagnation is a factor of shrinkage for s cities. Given that there is strong evidence to back up the hypothesis. As for industrialization, there was only one city that had gone through De-industrialization and that was DeFuniak Springs. As mentioned for ageing population in Holmes Beach, one city cannot define the entire state but that does not discard the possibility that de-industrialization can affect Florida.

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72 Environmental Factors There is a weak relationship and ev idence between population decline and natural disasters. Further ev idence must be collected to know whether this factor affects Florida. Where Are These Cities Located? Out of the 40 shrinking cities found in Florida, 14 cities are located on the coast, 11 cities are in-land (4 in Central Florida), 10 are in the panhandle, and 5 are coastal-panhandle cities. Most of the shrinking cities are located not only on the coast, and the panhandle, but also in-land. Holmes Beach is a coastal city located on the Southwest. It has lost 1,130 people in 40 years. Holmes Beach is a retiree city with a median age of 63.5 composing 33.4% of its population. It has around 4,173 houses where almost half of them are show as v acant homes in the Census. The av erage household size here is 2.3. After visiting

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73 in this research, located on the Southeastern coast of Florida. It has lost 8,519 people in 40 years. While visiting Miami Beach I saw it had hotels, retail, and restaurants. Miami Beach has a total of 68,103 homes including condominiums, with an average household size of 1.8. It had fewer single family detached homes compared to apartments. Miami Beach is a lot of congestion and traffic as well as people walking around the streets, so the city feels alive but it is still shrinking in population. Pahokee is the only in-land city that fitted the parameters that I was looking for in my research. It is in the Southeast of Florida, overlooking Lake Okeechobee. It has lost 1,173 people over 40 years. It has 2,058 homes were 236 are vacant. While visiting Pahokee I saw a lot of closed commercial spaces and abandoned homes. The most impactful was seeing a big hospital completely closed and deteriorated. Driving around rate and its 34.2% unemployment rate. The major industry is agriculture, forestry, and

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74 As can be seen on the map, people are moving outside of the city boundaries and are moving North. Fort Walton Beach is located on the coast in the panhandle. It is about an hour away east of Pensacola. It has lost 1,964 people in 40 years. This city has a total of 9,803 homes where 1,301 are vacant. Vacancy and shrinkage are not apparent in this city, seeing as these homes are being well kept. This city has a beautiful historic district, nice neighborhoods, nice houses, and a lot of local businesses. I had to rely on the data to verify the cause of shrinkage in this city. As I reviewed the data I found that suburbanization is the main cause for shrinkage, as can be seen on Fort Walton Appendix D. Most of the single-family homes developed after 1990 were located outside of the city boundaries, in other smaller towns surrounding Fort Walton Beach.

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75 Perry is located in the panhandle and more in-land. It has lost 1,237 people in 40 years. Perry has a total of 3,216 homes where 521 are vacant. The houses here are deteriorating and yet there were people still living in some of them. Their poverty level is 22.9% confirming that there are people living in poverty. This city felt like a ghost town. major industry in Perry is manufacturing where most of the outskirts of the city have a few lumber mills Appendix D.

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76 CHAPTER 6 CONCLUSION tha t ther e wer e citie s tha t wer e decreasin g i n population Sin ce shrinkin g citie s i s a phenomeno n tha t ca n occu r an ywher e i n th e worl d a n intere st i n determinin g whethe r it wa s happenin g i n Florida rose Thi s proje ct helpe d fin d th e citie s tha t wer e shrinkin g in population Fort y citie s wer e foun d an d categori ze d b y locatio n an d si ze Ou t o f these 40 7 wer e selecte d t o b e anal yze d an d visited The se wer e Holme s Beach Pahokee, Miami Beach, Perry DeFuniak Springs, Pensacola, and Fort Walton Beach. Various data was gathered to make the necessary assessments to determine the factors of shrinkage. The first part was to find in the literature, factors of shrinkage for other cities in USA. The second part consisted of locating data from the census and site

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77 factors for Florida helped adding two new cause for shrinkage. Holmes Beach and Miami Beach being the two cities with shrinkage due to tourism. Pahokee has shrinkage issues due to agriculture. If we looked at the findings by location we can see that most coastal cities had tourism as a cause for shrinkage. The Panhandle had suburbaniz ation as a main reason for shrinkage. Since Pahokee was the only in-land city studied its reason for shrinkage, agriculture, may not be the same reason for shrinkage in other in-land cities. This could lead to a study of in-land mini cities in order to find if agriculture is causing in land cities to shrink. The project needed a bigger sample siz e in order to hav e more ev idence to support the hy pothesis. Funding and more time was needed to visit additional sites. A

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7 APPENDIX A MAP OF SHRINKING CITIES Figure A 1 Map of USA Population Change The areas colored i n magenta are areas expected to decline in population by 2020. The areas colored in blue are expected to increase in population at a normal rate while those in green are expected to increase at a faster rate. Follow th is link for a full and interactive view of this map.

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Figure A-2.

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8 APPENDIX B SPSS VARIABLE AND DATA VIEW SCREENSHOTS Figure B 1. SPSS Variable View of variables 1 to 31.

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8 Figure B 2. SPSS Variable View of variables 32 to 61. Figure B-3. SPSS Data View Part I Figure B-4. SPSS Data View Part II

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8 Figure B 5. SPSS Data View Part III Figure B 6. SPSS Data View Part IV Figure B 7. SPSS Data View Part V

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8 A PPENDIX C Coastal Cities Tables Figure C-1. Holmes Beach (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Holmes Beach Location: Southwestern Coast Size: 1.7 sq. mi of land Demographic Census 1980 4,018 1990 4,810 2000 4,966 2010 3,836 Population Growth in 40 Years 6% Decline from Peak 1,130 Percent of Decline from Peak 23% Population Age and Rates Median Age 63.5 Birth Rate 2% Mortality Rate 13% per thousand Average Age of Mother 40 44 Housing Total Houses 4,173 Occupied Homes 2,113 Vacan t Homes 2,300 Owned Homes 1,611 Rented Homes 502 Ave. Household Size 2.3 Median Property Value $459,300 Econo mic Median Household Income $54,543 Unemployment Rate 11.4% Poverty Rate 16.2%

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8 Major Industries (Source: Data USA): Environm ental Natural Disasters: overall US average. Earthquake is 96% smaller than US average (2 in 2006, 5.3 and 5.9) Manatee County has 25 natural disasters, US average is 13. o 18 major disasters o 5 emergencies Natural disasters: o Hurricanes: 10 o Tropical Storms: 6 o Storms: 4 o Tornados: 2 o Floods: 5 o Freeze: 1 Figure C-1.

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8 o Wind: 1 o Fires:4 Political Policies & Legislation: Zoning for Vacation Rental, implemented February 2016 Ord 16.02 Noise Ordinance fro m 10:00 p.m. to 7:00 a.m., implemented on March 2016 Ord16.04 Site Visits Visited Holmes Beach on May 10, 2017 People were enjoying the beach and local restaurants People were riding bicycles along a road Restaurants and Surf Shops are very common in t he area A lot of homes where overlooking the ocean A lot of vacation homes for rent o Many for rent signs and realtor signs in the yards It was very rare to see and abandoned or poorly maintained home o tall Majority of the houses looked expensive Figure C-1.

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8 o Zillow listed the cheapest at $280K and the most expensive at $3.2M A lot of tourists Lots of traffic Figure C-1.

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8 Figure C-2. Miami Beach (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Miami Beach Location: Southeastern Coast Size: 7.7 sq. mi of land Demographic Census 1980 96,298 1990 92,639 2000 87,933 2010 87,779 Population Growth in 40 Years 22% Decline from Peak 8,519 Percent of Decline from Peak 91% Population Age and Rates Median Age 40.4 Birth Rate 5% Mortality Ra te 8% per thousand Average Birth Age of Mother 3539 Housing Total Houses 68,103 Occupied Homes 47,604 Vacant Homes 20,499 Owned Homes 18,566 Rented Homes 29,038 Ave. Household Size 1.8 Median Property Value $383,800 Econom ic Me dian Household Income $44,342 Unemployment Rate 4.9% Poverty Rate 15%

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8 Major Industries (Source: Data USA): Environmental Natural Disasters: overall US average. Earthquake is 99% smaller than US average (2006 5.9, 2002 5.7) Miami Dade County has 8 natural disasters, US average is 13. o 2 major disasters o 3 emergencies Natural disasters: o Hurricanes: 6 o Tropical Storms: 2 Tornados: 1 Political Policies & Legislation: Pursuant t o the Miami Beach City Code (Sec 142 1111) (Miami Beach Land Development Regulation Chapter 142, Article IV, Division 3) vacation/short term rentals are prohibited in all single family homes and in Figure C-.

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9 many multi family housing buildings in certain zoning di stricts of Miami Beach. Site Visits

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9 Panhandle Cities Tables Figure C 3 DeFuniak Spring s (City Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: DeFuniak Springs Location: Northwest Pan handle Size: 13.84 sq. mi of land Demographic Census 1980 5,563 1990 5,120 2000 5,089 2010 5,177 Population Growth in 40 Years 17% Decline from Peak 386 Percent of Decline from Peak 93% Population Age and Rates Median Age 63.5 Birth Rate 3% Mortality Rate 10% per thousand Average Birth Age of Mother 3034 Housing Total Houses 2,850 Occupied Homes 2,109 Vacant Homes 741 Owned Homes 1,340 Rented Homes 769 Ave. Household Size 2.3 Median Property Val ue $109,800 Ec on o mi c Median Household Income $28,675

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9 Unemployment Rate 4.1% Poverty Rate 19.9% Major Industries (Source: Data USA): Environmental Natural Disasters: Earthquake 2006 5.9 and 5.3, 2004 4.4 Walton County has 24 natural disasters, US average is 13. o 17 major disasters o 5 emergencies Natural disasters: o Hurricanes: 10 o Tropical Storms: 4 o Storms: 5 o Tornados: 3 o Floods: 7 o Heavy Rain: 1 Figure C-.

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9 o Wind: 3 o Fires: 4 Politica l Policies & Legislation: short term growth Site Visits Visited DeFuniak Springs on September 2, 2017 People got off I 10 to go to fast foods and gas stations and returned to the interstate. Small city barely to no traffic in the urban core More traffic located around I 10 The urban core is composed of a historic district with many abandoned shops and empty homes. Train tracks along the historic district and a small train station A chicken processing plant operated by Perdue Farms at DeFuni ak Springs was closed in April 2004 (Miami Herald, 2004) Figure C-.

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9 Figure C-4. Perry (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Perry Location: North Panhandle Size: 9.38 sq. mi of land Demographic Census 1980 8,254 1990 7,151 2000 6,847 2010 7,017 Population G rowth in 40 Years 37% Decline from Peak 1,237 Percent of Decline from Peak 85% Population Age and Rates Median Age 37.2 Birth Rate 9% Mortality Rate 12 per thousand Average Birth Age of Mother 20 24 Housing Total Houses 3,216 Oc cupied Homes 2,695 Vacant Homes 521 Owned Homes 1,865 Rented Homes 830 Ave. Household Size 2.52 Median Property Value $76,800 Econom ic Median Household Income $31,706 Unemployment Rate 5.70% Poverty Rate 22.9%

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9 Major Industries (Source : Data USA): Environmental Natural Disasters: US average. Earthquake is 95% smaller than US average (1 in 2006, 5.9; 1 in 2003, 4.9) Taylor County had 16 natural disasters, US a verage is 13. o 13 major disasters o 2 emergencies Natural disasters: o Hurricanes: 8 o Tropical Storms: 4 o Storms: 1 o Tornados: 2 o Floods: 2 Figure C-.

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9 o Freeze: 1 o Wind: 1 o Fires: 2 Political Policies & Legislation: None Site Visits Visited Pe rry on September 3, 2017 Very small city, almost deserted A few lumber mills and lumber yards Various abandoned homes and gas stations No urban core Figure C-.

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9 In land City Table Figure C-5. Pahokee (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Pahokee Location: Southeast Size: 5.54 sq. mi of land Demographi c Census 1980 6,346 1990 6,822 2000 5,985 2010 5,649 Population Growth in 40 Years 27% Decline from Peak 1,173 Percent of Decline from Peak 83% Population Age and Rates Median Age 34.3 Birth Rate 4% Mortality Rate 12% per tho usand Average Birth Age of Mother 25 29 Housing Total Houses 2,058 Occupied Homes 1,822 Vacant Homes 236 Owned Homes 1,056 Rented Homes 766 Ave. Household Size 3.2 Median Property Value $79,100 Econom ic Median Household Income $ 27,348 Unemployment Rate 34.2% Poverty Rate 33.8%

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9 Major Industries (Source: Data USA): Natural Disasters: Earthquake 2006 5.9, 2002 4.4, 2001 3.3 Palm Beach County has 24 natural disasters, US average is 13. o 15 major disasters o 8 emergencies Natural disasters: o Hurricanes: 15 o Tropical Storms: 4 o Storms: 1 o Floods: 1 o Freeze: 1 o Other: 1 o Fires: 3 Pol itic al Policies & Legislation: Figure C-.

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9 Taxable property values dropped from about $99 million i n 2007 to $66 million in 2014 Site Visits Visited Pahokee on August 18, 2017 Abandoned Commercial properties Abandoned homes Fishing community 2 restaurants, 1 fast food Tree Farms Sugar Cane Farms Homes along Lake Okeechobee No traffic Mor e than one in four people who want to work have no job. Taxable property values dropped from about $99 million in 2007 to $66 million in 2014. And a fifth of the population has fled in the past 15 years. It is one of two Palm Beach County cities the othe on a suggest it has been on the list continually since 1994. Figure C-.

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Coastal/Panhandle Cities Tables Figure C-6. Pensacola (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Pensacola Location: Northwestern Panhandle Coast Size: 22.60 sq. mi of land Demographic Census 1980 57,619 1990 58,165 2000 56,255 2010 51,923 Population Growth in 40 Years 25% Decline from Peak 5,696 Percent of Decline from Peak 90% Population Age and Rates Median Age 40.3 Birth Rate 5% Mortality Rate 10 per thousand Average Birth Age of Mother 3034 Housing Total Houses 25,523 Occupied Homes 22,103 Vacant Homes 3,420 Owned Homes 12,988 Rented Homes 9,115 Ave. Household Size 2.27 Median Prope rty Value $142,400 Econom ic Median Household Income $45,527 Unemployment Rate 5.2% Poverty Rate 15.7%

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10 Major Industries (Source: Data USA): Environmental Natural Disasters: e overall US average. Earthquake is 93% smaller than US average (2 in 2006, 5.3 and 5.9) Escambia County has 23 natural disasters, US average is 13. o 17 major disasters o 5 emergencies Natural disasters: o Hurricanes: 12 o Tropical Storms: 4 o Storms: 4 o Tornados: 3 o Floods: 5 o Heavy Rain: 1 Figure C-.

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10 o Wind: 2 o Fires: 2 Political Policies & Legislation: The county has adopted a strict no smoking policy effective October 1. The mandatory drug test will include a test for tobacco usage after that date. If a pers on tests positive for tobacco, they will not be hired for any county position. (2008) Site Visits Visited Pensacola on September 2, 2017 Various neighborhoods with abandoned houses. Moderate traffic on major roads but no congestions Inter national Airport (PNS) No nightlife Well maintained Historical district It had a Port with a few ships Abandoned commercial spaces in the urban core Hy Lite Blocks, a division of U.S. Block Windows since March 2009, molds acrylic architectural blocks for use in privacy windows, indoor radius walls, and partitions. The company recently moved its molding operation back from China to its company headquarters and manufacturing facility in Pensacola, Florida. (Pensacola News Journal, 2 015) Figure C-.

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10 Figure C-7. Fort Walton Beach (City-Data) (DATA USA) (United States Census Bureau) (Town Charts) City Name: Fort Walton Beach Location: Northwestern Panhandle Size: 7.52 sq. mi of land Demographic Census 1980 20,826 1990 21,471 2000 19,973 2010 19,507 Population Growth in 40 Years 16% Decline from Peak 1,964 Percent of Decline from Peak 91% Population Age and Rates Median Age 40.3 Birth Rate 6% Mortality Rate 8 per thousand Average Birth Age of Mother 25 29 Housing Total Houses 9,803 Occupied Homes 8,502 Vacant Homes 1,301 Owned Homes 4,762 Rented Homes 3,740 Ave. Household Size 2.33 Median Property Value $159,600 Econom ic Median Household Income $47,149 Unemployment Rate 4.0% Poverty Rate 17.5%

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10 Major Industries (Source: Data USA): Environmental Natural Disaste rs: overall US average. Earthquake is 94% smaller than US average (2 in 2006, 5.3 and 5.9) Okaloosa County has 24 natural disasters, US average is 13. o 20 major disasters o 4 emergenci es Natural disasters: o Hurricanes: 12 o Tropical Storms: 5 o Storms: 4 o Tornados: 3 o Floods: 7 o Heavy Rains: 2 o Wind: 3 Figure C-.

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10 o Fires: 1 Political Policies & Legislation: The most severe home price slump, Fort Walton Beach, Fla. went down 30.4 percent in 2007. Site Visits Visited Fort Walton Beach on September 2, 2017 No physical shrinkage Beautiful Historic District Nice Neighborhoods A lot of local businesses and a few chains Moderate traffic and no congestion Figure C-.

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10 APPENDIX D SUBURBANIZATION MAPS Figure D 1. Holmes Beach Suburbanization Map

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10 Figure D 2. Miami Beach Suburbanization Map

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10 Figure D 3. DeFuniak Springs Suburbanization Map

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10 Fi gure D 4. Perry Suburbanization Map

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10 Figure D 5. Miami Beach Suburbanization Map

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11 Figure D 6. Pensacola Suburbanization Map

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11 Figure D 7. Miami Beach Suburbanization Map

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11 APPEN DIX E MAPS OF POPULATION CHANGE IN FLORIDA AND SHRINKING CITIES Figure E-1. Holmes Beach Population Change 1990, 2000, 2010. Figure E-2. Miami Beach Population Change 1990, 2000, 2010

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11 Figure E-3. DeFuniak Spring Population Change 1990, 2000, 2010 Figure E-4. Perry Population Change Map 1990, 2000, 2010

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11 Figure E-5. Pahokee Population Change Map 1990, 2000, 2010 Figure E-6. Pensacola Population Change Map 1990, 2000, 2010

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11 Figure E-7. Fort Walton Beach Population Change Map 1990, 2000, 2010

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11 LIST OF REFERENCES Berg, J. (2017, May 13). Homeowner, displaced travelers question city after Sartell bans Airbnb. SCTimes Burney, D. M. Simmonds, K., & Queeley, G. (2007). The Relationship between Socio Economic Conditions and the Impact of Natural Disasters on Rural and Urbanized Regions Level of Preparedness and Recovery Retrieved 9 15, 2017, from https://questia.com/library/journal/1 g1 192639818/the relationship between socio economic conditions Census (2010). Retrieved May 19, 2017, from census.gov City Data. (n.d.). Retrieved from http://www.city data.com/city/Florida.html Cresswell, J. W. (2002). Research Design: Qualitative, Quan titative, and Mixed Methods Approaches (2nd ed.). Nebraska: Sage Publications. Cresswell, J. W. (2012). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Nebraska: Sage Publications. Cross, J. A. (2014). Disaster devastati on of US communities: long term demographic consequences. Environmental Hazards, 13 (1), 73 91. Retrieved 9 15, 2017, from http://tandfonline.com/doi/abs/10.1080/17477891.2013.864594 DATA USA. (n.d.). Retrieved from https://datausa.io Dreyfuss, E. (2016, De cember 31). The Year in Housing: The Middle Class Can't Afford to Live in Cities Anymore. Wired Edgmand, M., Moowaw, R., & Olson, K. (1996). Economics and Contemporary Issues (3rd ed. ed.). Dryden Press. Elzerman, K. (2009). A Future with Shrinkage. Flori da Trend. (2016, August 10). Florida's population exploding; expected to reach 20.7 million by end of 2016. Orlando, FL. Retrieved from http://www.floridatrend.com/article/20472/florida population growth exploding -to reach 207 million by end of 2016 Fusse ll, E. (2015). The Long Hurricane Katrina. American Behavioral Scientist, 59 (10), 1231 1245. Retrieved 9 15, 2017, from https://ncbi.nlm.nih.gov/pmc/articles/pmc4752119 Gabaix, X., Makse, H. A., Rozenfeld, H. D., & Rybski, D. (2011). The Area and Population of Cities: New Insights from a Different Perspective on Cities. American Economic Review 2205 2225.

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11 Haase, A., Rink, D., Grossmann, K., Bernt, M., & Mykhnenko, V. (2014). Conceptualizing Urban Shrinkage. En vironment and Planning A, 46 000 000. Herscher, A. (2013). Detroit Art City: Urban Decline, Aesthetic Production, Public Interest. In M. Dewar, & J. Thomas, The City After Abandonment (pp. 64 84). University of Pennsylvania Press. Hollander, J. B., Pallag st, K., Schwarz, T., & Popper, F. J. (2009). Planning Shrinking Cities. Kabisch, S., Haase, A., & Haase, D. (n.d.). Beyond Growth Urban Development in Shrinking Cities as a Challenge for Modeling Approaches. UFZ Centre for Environmental Research Kohlb acher, F. (2006, January). The Use of Qualitative Content Analysis in Case Study Research. Forum: Qualitative Social Research, 7 (1). Larkman, P. J. (1999). Preface. In R. Harris, & P. J. Larkman, Changing Suburbs: Foundation, Form, and Function. NY: Routle dge. Mathews, T. J., & Hamilton, B. (2016). Mean age of mothers is on the rise: United States, 2000 2014. National Center for Health Statistics. McGreal, C. (2010, December 17). Detroit mayor plans to shrink city by cutting services to some areas. The Guar dian Miami Herald. (2004, February). Perdue Closing Plant In Florida Panhandle. National Center for Health Statistics. (n.d.). Average Age Of U.S. First Time Mothers (1970 2014). Nimwegen, N. v., & Heering, L. (2009). Van groei naar krimp: een demografis che omslag in beeld; samenvatting en discussie. The Hague: NIDI Oswalt, P., & Rienits, T. (2006). Atlas of Shrinking Cities. Pallagst, K. (2009, May). Shrinking Cities in the United States of America: Three cases, three planning stories. The Future of Sh rinking Cities 81 88. Pallagst, K. M. (2007). Das Ende der Wachstumsmaschine, in Berliner Debatte Initial. 4 13. Pallagst, K., & Aber, J. (2009, May). The Future of Shrinking Cities: Problems, Patterns and Strategies of Urban Transformation in a Global Co ntext. Institute of Urban and Regional Development 1 4.

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11 Pallagst, K., & et, a. (2009, May 11). The Future of Shrinking Cities: Problems, Patterns and Strategies of Urban Transformation in a Global Context. Institute of Urban and Regional Development Pen sacola News Journal. (2015, January 17). 'Reshoring' brings jobs back to U.S., including Pensacola. Pensacola News Journal Population Reference Bureau. (n.d.). Human Population: Future Growth Retrieved from Population Reference Bureau: http://www.prb.org /Publications/Lesson Plans/HumanPopulation/FutureGrowth.aspx Rieniets, T. (2009). Shrinking Cities: Causes and Effects of Urban Population Losses in the Twentieth Century. Nature & Culture, 4 (3), 231 254. Retrieved 10 24, 2017 Rist, R. C. (1977). On the re lations among educational research paradigms: From disdain to detente. Anthropology & Education QUarterly, 8 42 49. Roberts, M. (n.d.). Million by 2030? Retrieved from Florida Chamber of C ommerce: http://www.flchamber.com/did you know that floridas population could increase to nearly 26 million by 2030/ Schett, S. (2012). An Analysis of Shrinking Cities. Urban Ecology Seltzer, N., & Nobles, J. (2017). Post disaster fertility: Hurricane Kat rina and the changing racial composition of New Orleans. Population and Environment, 38 (4), 465 490. Retrieved 9 15, 2017, from https://link.springer.com/article/10.1007/s11111 017 0273 3 Sirotin, N. (2016, December 6). Technological advances will drive ou r quest to live longer. The National Sommer, R., & Sommer, B. (2012). A Practical Guide to Behavioral Research: Tools and Techniques (5th ed.). New York: Oxford University Press. South, S. J., & Crowder, K. D. (1997). Residential mobility between cities a nd suburbs: race, suburbanization, and back to the city moves. Demography, 34 (4), 525 538. Stake, R. E. (1995). The art of case study research. Stansel, D. (2010). Why Are Some Cities Growing While Others Are Shrinking Retrieved 10 23, 2017, from https:// papers.ssrn.com/sol3/papers.cfm?abstract_id=1613092 Swanborn, P. G. (1994). Methods of Social Research. Meppel: Boom. Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research. California, US: Sage Publications.

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1 The World Trade Report 201 3. (2013). Factors Shaping the Future of World Trade. The World Trade Organization. Titscher, S., Meyer, M., Wodak, R., & Vetter, E. (2000). Methods of text and discourse analysis. London: Sage. Town Charts. (n.d.). Retrieved from http://www.towncharts.com / United States Census Bureau. (2010). Florida Census. United States Census Bureau. (2010). Urban and Rural Retrieved from United States Census Bureau: https://www.census.gov/geo/reference/ua/urban rural 2010.html US Bureau of Labor Statistics. (2016, Jun e 7). US Bureau of Labor Statistics Retrieved from United States Department of Labor: https://www.bls.gov/bls/glossary.htm#C

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12 BIOGRAPHI CAL SKETCH Alv imarie Corales-Cuadrado receiv ed her Bachelor of Arts in ociology in the spring of 2015 from the University of Puerto Rico, Mayaguez campus. During her two in half years in the Univ ersity of Florida she worked at the Disability Resource Center mapping ADA compliances on campus, performed an audit on Post Disaster Redev elopment for Manatee County Florida, and worked for the GeoPlan Center geocoding car accidents in Signal4 Geocoding. Alv imarie graduated with a Master of Urban and Regional Planning in the fall of 2017 from the Univ ersity of Florida.