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The Use of GIS in allocating employment centers that minimize land use conflict and satisfy regional economic potential

University of Florida Institutional Repository
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1 THE USE OF GIS IN ALLOCATING EMPL OYMENT CENTERS THAT MINIMIZE LAND USE CONFLICT AND SATISFY RE GIONAL ECONOMIC POTENTIAL By IRIS E. PATTEN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URB AN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2007

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2 2007 Iris E. Patten

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3 To all the tortoises in the world: you really can beat the hares.

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4 ACKNOWLEDGMENTS First, I would like to thank my parents fo r teaching me that with faith I can move mountains. Secondly, I would like to acknowledg e and thank my committee members. They have each provided an unquantifiable amount of advice, motivation and encouragement during the past two years. I thank Dr. Paul Zwic k for his refreshing enthusiasm for Geographic Information Systems (GIS) and urban planning. He continually pushes the envelope on what can be done technically, while encourag ing his students to think even further outside of the box. As a leader, researcher and scholar he has impressed upon me ideals that I hope to utilize in the future. I thank Peggy Carr for pushing me just a little bit harder. Her pass ion for the natural landscape has truly affected me, and her talents as an author, professor and mentor have not gone unnoticed. Before arriving at the University of Florida (UF) tw o years ago I promised that I would never touch GIS again. I would like to th ank Stanley Latimer for not only his time and efforts during this thesis pro cess but also for strengthening my foundation of GIS and restoring my confidence. Lastly, but certa inly not least, I thank Phil Laur ien. He is proof that although Ohio State University (OSU) was unable to win any championships this year, OSU produces great people (smile!). His ideas and vision for Ea st Central Florida give me hope that Florida will one day realize its potential of becoming a great state. He has been more than an employer. I consider him a great thinker and mentor. These are all amazing people and I thank them for their patience during this study a nd for their continued support. In addition, I would like to tha nk Samer Bitar and Claudia Paskauskas of the East Central Florida Regional Planning Council; Ella Littles, Evelyn Cairns, and Nelda Schneider of the University of Florida Urban and Regional Pla nning Department; Mosi Harrington and Ava Kuo of Housing Initiative Partnership; and Alexis Thomas and the entire GeoPlan staff. I thank them for their technical assistance, administrative help, and patience while I completed this study.

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5 Finally, I thank my family, close friends, a nd my fellow students for their kind words and encouragement. Their kindness was not overlooked. This study and paper have become more th an a series of pages and words that acknowledge that I learned a thi ng or two about planning during th e past two years. I hope it signals a new phase of my life where I push myse lf to explore topics and take chances that require a little faith to get through. The poet Pa trick Overton said, When you have come to the edge of all light that you know and are about to drop off into the darkness of the unknown, Faith is knowing one of two things will happen: ther e will be something solid to stand on or you will be taught to fly. Having comple ted this thesis, I can now fly.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES................................................................................................................ .......10 LIST OF EQUATIONS.............................................................................................................. ...11 ABSTRACT....................................................................................................................... ............12 CHAPTER 1 INTRODUCTION................................................................................................................... ..14 2 LITERATURE REVIEW..........................................................................................................16 The Rise of the Megalopolis...................................................................................................16 GlobalizationDid You Know the World is Flat?..................................................................19 The Economy and Development Patterns...............................................................................21 The Creative Class............................................................................................................. .....22 Indicators of Creative Centers................................................................................................25 How Shall We Grow?.............................................................................................................27 Traditional and Conventional Views of Regional Growth.....................................................27 The Use of Scenario Modeling...............................................................................................28 Creating a Vision.............................................................................................................. ......29 Select Aspects of Region al Visioning Exercises....................................................................30 The Use of GIS in Scenario Modeling...................................................................................32 Metro 2040 Growth Concept...........................................................................................32 Federal Highway Administ ration Funded Projects.........................................................35 Summary........................................................................................................................ .........41 3 CENTRAL FLORIDA GROWTH VISION..............................................................................49 Project Goal................................................................................................................... .........49 Scenario Modeling in Central Florida....................................................................................50 Land Use Conflict Identification Strategy (LUCIS)...............................................................52 Phase 2: Community Input.....................................................................................................53 Summary........................................................................................................................ .........56 4 STUDY AREA..................................................................................................................... .....69 The Economy.................................................................................................................... ......70 Transportation................................................................................................................. ........71 Summary........................................................................................................................ .........74

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7 5 METHODOLOGY....................................................................................................................85 Explanation of the Model.......................................................................................................85 Identifying the Indicators..................................................................................................... ...87 Creative Indicators...........................................................................................................87 Clustering of Creative Industries.....................................................................................88 Data Collection................................................................................................................ .......93 Data Collection Methods for the Urban Stakeholder......................................................93 Suitability Modeling........................................................................................................... ....95 Weighted Suitability........................................................................................................... ....96 Conflict Identification........................................................................................................ .....96 Allocating Employment Centers...........................................................................................101 Allocate Industry by County.........................................................................................103 6 FINDINGS AND RESULTS...................................................................................................141 The Creative Index............................................................................................................. ..141 The Creative Class Share of the Workforce..................................................................141 Talent Index...................................................................................................................144 Innovation Index............................................................................................................144 Milken Index Tech Pole................................................................................................146 The Diversity Index.......................................................................................................146 The Gay Index........................................................................................................147 The Melting Pot Index............................................................................................147 Spatial Comparisons............................................................................................................ .148 Spatial Findings.............................................................................................................150 Results........................................................................................................................ ...........155 Sensitivity Analysis.......................................................................................................155 Sensitivity Analysis Findings.................................................................................156 7 CONCLUSION..................................................................................................................... ...168 Universal Applicability........................................................................................................ .169 Economic Implications.........................................................................................................170 Human Capital.................................................................................................................. ....170 Influence to Surrounding Regions........................................................................................171 Recommendations for Future Research................................................................................172 Performing Additional Sensitivity Analysis..................................................................172 Projecting Other Employment Sectors..........................................................................172 Modeling/Predicting New Transportation Corridors.....................................................173 Model Each Alternative Scenario of the Central Florida Growth Vision.....................173 LIST OF REFERENCES.............................................................................................................174 BIOGRAPHICAL SKETCH.......................................................................................................180

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8 LIST OF TABLES Table page 2-1. Major regional visioning projects........................................................................................ ..43 2-2. Primary RLIS layers...................................................................................................... ........45 2-3. RLIS procedure for identifying bu ildable lands and calculating housing and employment capacities.......................................................................................................46 3-2. Assumptions used in baseline and 3 alternative scenarios of myregion.org Central Florida Growth Vision.......................................................................................................59 3-3. Potential future population di stribution by scenario and county...........................................61 4-1. Largest cities and population, for each county.......................................................................75 4-2. Regional 2005 population and 2050 projected population, by county...................................76 4-3. Major private sector employers........................................................................................... ..76 4-4. Employment patterns of workers 16 years and over who live in a MSA..............................80 4-5. Transportation corridors (roads) within the East Central Florida region...............................81 4-6. Ports and airports within th e East Central Florida region......................................................82 5-1. Goals and objectives used for optimiz ing choice for agricultu re, conservation, and urban land uses................................................................................................................ .106 5-2. Data sources for Creative Indices.........................................................................................129 5-3. Categories for land use analysis data...................................................................................133 5-4. Assigned SUA values...................................................................................................... ....133 5-5. AHP importance categories................................................................................................ .134 5-6. Combinations of preference rankings..................................................................................135 5-7. Employment Cent er Building Sizes....................................................................................136 5-8. Cells needed to acco mmodate building allocation..............................................................136 6-1. Description of Class Occupations. The Cr eative Class has two major sub-components: a Super-Creative Core and Creative Professionals.............................................................158 6-2. Creative Class share of the Service Sector in 2050.............................................................158

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9 6-3. Number of patents issued between 1990 and 1999..............................................................159 6-4. Gay population and share of total population.......................................................................159 6-5. Regional population, by ethnic group..................................................................................160 6-6. Average location quotie nt of Creative Industries................................................................160 6-7. Distribution of new em ployment centers, 2005-2050.........................................................160 6-8. Distribution of new employees, 2005-2050........................................................................161 6-9. Land area calculations for suitability analysis.....................................................................162 6-10. County land area calculations........................................................................................... .162 6-11. Polk County conflict summary..........................................................................................162 6-12. Breakdown of land usage for Brevard County..................................................................163 6-13. Conflict Surface stakeholder share for Brevard County....................................................163 6-14. Land most preferable for urban use (from the collapsed urban surface)...........................163

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10 LIST OF FIGURES Figure page 3-1. Illustration of growth patt erns for Baseline Scenario ..........................................................62 3-2. Illustration of growth patter ns for Scenario A (Green Areas)...............................................63 3-3. Illustration of growth patt erns for Scenario B (Centers).......................................................64 3-4. Illustration of growth patt erns for Scenario C (Corridors)....................................................65 3-5. Description of Phase 2 Scenarios from Central Florida Growth Vision...............................66 3-6. Central Florida Growth Vision survey results.......................................................................67 4-1. Study Area, 7 county Ea st Central Florida region.................................................................83 4-2. Metropolitan Statistical Areas (MSAs) within the East Central Florida Region..................84 5-1. Diagram of hierarchical relationships of goals, object ives and sub-objectives for agricultural land use su itability analysis..........................................................................106 5-2. Diagram of hierarchical relationships of goals, object ives and sub-objectives for conservation land use suitability analysis........................................................................112 5-3. Diagram of hierarchical relationships of goals, object ives and sub-objectives for conservation land use suitability analysis......................................................................1128 5-4: LUCIS strategy process flow............................................................................................... .137 5-5: Final preference map..................................................................................................... ......138 5-6: Suitable locations for Urban Areas and Creative Environments..........................................139 5-7. Example employment allocation calculation.......................................................................140 6-1. Peoples Choice Map with new Creative Industry locations for Volusia County................164 6-2. Peoples Choice Map with new Creative Industry locations for Osceola County...............165 6-3. Peoples Choice Map with new Creative Indu stry locations for Orange and Seminole Counties....................................................................................................................... ....166 6-4. Final Conflict Surface.................................................................................................... .......167

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11 LIST OF EQUATIONS Equation page 4-1: Equation to determine conflict............................................................................................ ...99 5-2. Equation to Determine Creative and Urban Environments..................................................100 5-3. Equation to determine areas that ar e Creative, Urban and Suitable for Office Commercial..................................................................................................................... .101

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12 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Urban and Regional Planning THE USE OF GIS IN ALLOCATING EMPL OYMENT CENTERS THAT MINIMIZE LAND USE CONFLICT AND SATISFY RE GIONAL ECONOMIC POTENTIAL By Iris E. Patten May 2007 Chair: Paul Zwick Cochair: Margaret Carr Major: Urban and Regional Planning Over the next 50 years the vast projec ted population increase will present several challenges to the East Central Florida region. These challenges include pursuing opportunities associated with competing in the global economy, protecting environmental and agricultural resources, and developing a transportation plan that will address expected demand. The Land Use Conflict Identification Strategy (LUCIS), developed by Margaret Carr and Paul Zwick, employs role playing and suitability modeling to predict areas where future land use conflict will likely occur. Applying LUCIS results in an il lustration of development patterns and provides a means to distinguish the driving forces behind the outcome, given specific policy decisions and assumptions. This study modified the LUCIS strategy by incorporating a more detailed economic analysis and included indicators of Richard Flor idas Creative Class to determine whether the East Central Florida region has suitable envi ronments for creative industries. Creative environments satisfy three conditions: 1) lands with socio-cultural and socio-economic qualities preferable to creative individua ls; 2) lands preferable for urban development; and 3) lands suitable for office/commercial development. Th ese locations attract oc cupations that are high-

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13 quality, high-earning, and service oriented (a lso known as Creative Occupations). Using projections of new employment for creative industries through 2050, this study allocates employment centers and distributes employment resulting in a sust ainable development plan that accommodates highly skilled workers and maximizes the economic potential of the region. The creative are members of the service se ctor and this study shows that of the 832,400 new service employees within the East Cent ral Florida Region over the next 45 years, environments conducive to the creative exist in all seven counties. Unfortunately, there was only enough contiguous land available to accommodate 88 new centers of creative employment, which would employ 62,760 employ ees or 7.5% of new service employment. Additionally, using indices developed by Rich ard Florida that measure dive rsity, tolerance, and economic potential necessary to compete in a Creativ e Economy, we found that Brevard and Orange counties were most suitable for innovative and high-t ech industries. The results of this analysis suggest that although the region has the necessary tools in place to compete globally and attract higher quality occupations, the region does not have enough suitable land available to accommodate industries that minimize land use conflict and have values related creative environments. This study is loosely based on the Central Fl orida Regional Growth Vision, coordinated by myregion.org (MyRegion); a coa lition of organizations promo ting Central Floridas economic competitiveness and quality of life (myregion.org, 2006, p. 11). Some results from the myregion.org alternative land use scenarios were us ed as inputs into the LUCIS model for this study. The myregion.org visioning process utilized community i nput with suitability modeling and other tools that provide comp arisons of the monetary and phys ical significance of alternative patterns of growth.

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14 CHAPTER 1 INTRODUCTION Good urban form is all about the shape of our cities. This includes how cities are designed and structured, where development occurs what areas are protected, and how areas are connected and support each other. Cities across the country ar e looking for ways to link good urban form with residents who possess a high econo mic potential. These residents are the highly skilled, highly educated, and highl y paid. The primary benefit of having these residents in your jurisdiction is that they create th e effective demand for a location. Richard Florida, a sociologist, indicates that the main reason these residents move to specific cities is root ed in economics. The physical proximity of talented, highly educat ed people has a powerful effect on innovation. When large numbers of entreprene urs, financiers, engineers, desi gners, and other smart, creative people are constantly bumping into one anothe r, the more quickly business ideas form, are refined, then executed (Florida, 2006, p. 36). In both early agricultural and industrial ec onomies, overall population growth defined economic growth. In a global, creative, postindust rial economy, thats no longer true. Changing technology, increased trade, and the ability to outsource routine functions have made highly skilled workers less reliant on the collocation of the unskilled and moderately skilled. What matters today isnt where most people settle, bu t where the greatest number of the most-skilled does (Florida, 2006, p. 37). It is a matter of location. The East Central Florida region anticipates over 3.6 million new residents by the year 2050 (Bureau of Economic and Business Research, 2 006; Carr, 2006). Of these, 832,400 (Bitar, 2007) will be employed in service industry occupations Creative occupations are within the service industry and these professions increase the econo mic potential of the region. In planning for the anticipated growth policymakers are using regional visioning ex ercises and GIS technology to

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15 determine how to accommodate creative employees with the least imp act on the environment and cultural resources. This study attempts to place employment centers within the East Central Florida region that will accomplish these goals. While the provision of housing is important, citie s must also plan for employment. Cities must evaluate what types of employment are needed that not only maximize revenue and diversify the economy but make best use of the la nd and attract these highly skilled workers. Moreover, how do you translate the values into a plan that provides good urban form and creates an atmosphere conducive to a creative class of workers? Using the results from the Central Florida Growth Vision and a modification of the LUCIS methodology this study attempts to achieve these objectives.

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16 CHAPTER 2 LITERATURE REVIEW Economic growth is spurred by dynamic regions, not nations. The geographer Jean Gottman coined the term megalopolis to desc ribe urban regions that are large and highly connected. A megalopolis is defi ned by a distinct region. A region is an area of distinctive communities, cities, and counties where residents share the following: a geographic identity and are socially, economically, and cu lturally interdependen t; a capacity for planning and function; and a capacity to create competitive advant age (ULI, 2005, p. 21). Since 1957 when Jean Gottman created the term megalopolis, an idea called the New Megas has emerged (Florida, 2006). New Megas produce the bu lk of wealth for the greater geographic area which attract highly skilled and talented workers as well as ge nerate the majority of innovation. New Megas are regions linked by environmental systems, tr ansportation networks, economies and cultures (America 2050, 2005, p. 3). East Central Florid a has a rich ecology, is served by major transportation corridors and has a strong economy, so does it have the potential to become a New Mega? The Rise of the Megalopolis The term megalopolis was developed by Jean Gottman, recognizing the string of urbanized areas extending from Boston to Washington, DC as the main street of America (Morrill, 2006, p. 155). Since the late 1950s, the original me galopolis has become a longer area of closely interconnected metropolises extending between Fredericksburg, Virginia, to Portsmouth and Dover-Rochester, in southern Maine. In 1961, this area was home to about 32 million people; today its population has risen to 55 million, more than 17% of all Americans. The region generates $2.5 trillion in economic activity, maki ng it the worlds fourth largest economy, bigger than France and the United Kingdom (Florida, 2006). Since the orig inal North Atlantic

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17 megalopolis was defined, several other mega lopolitan regions have been acknowledged, including urban southern California and th e urbanized southern Great Lakes region. A megalopolis is a unique combination of gr eat population numbers and density, history, wealth, physical and social dive rsity, and dynamism (Borchert, 1992, p. 3). Compared with its population, a megalopolis has a di sproportionately large share of the nations wealth, personal income, commerce, and industry (Borchert, 1992, p. 4). The core megalopolitan cities, such as Washington DC and Boston, serve as the econom ic hinge of innovation (Morrill, 2006, p. 155; Borchert, 1992, p. 3). Along with their dispropor tionate shares of the nations wealth and commerce, these places have comparably dispropor tionate shares of the nations poverty, crime, and ignorance (i.e., inadequate knowledge) (Borchert, 1992, p. 5). A megalopolis is defined by th ree criteria: 1) urban agglom erations with populations greater than 50,000; 2) urban territo ry consistently defined as con tiguous areas with densities of more than 1,000 persons per square mile; and 3) interconnected commuting patterns. Richard Morrill, a geographer from the University of Washington, studied the changing geography of the megalopolis from 1950 through 2000. In addition to the megalopolitan co re principles listed above, there are also five settlement patterns th at dominate megalopolita n areas, including 1) sheer economic and demographic growth, 2) ph ysical decentralization in the form of suburbanization, 3) extension of metropolitan commuting fields and the physical coalescence of formerly physically separate areas, 4) rise or restruct uring of and reaching out to formerly distant satellites, and 5) restructuri ng and revitalization of high-leve l metropolitan cores (Morrill, 2006, p. 158). In Jean Gottmans original book, he uses the above settlement patterns and the presence of high levels of commuting to metropolitan centers as an indicator of metropolitan dominance.

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18 Richard Morrill provides a chronology of se ttlement changes through three time periods, 1950-1970, 1970-1990, and 1990-2000. Although it is impor tant to understand the evolution of the megalopolis, for the purposes of this study, my concern lies in the ch anges that occurred during 1990-2000. During this time period larger downtowns and nearby historic areas were gentrified as middle and upper class households reclaimed parts of the core. Economic restructuring massively increased service em ployment, as business services and finance demonstrated a preference for central high-rise venues. Core populations rose, in part by attracting a creative class; younger, lateror not-marrying professiona ls and empty-nesters. This was due in part from a resurgent large-scale i mmigration, especially in the 1990s, from Asia, the Carribean, and Eastern Europe (Morrill, 2006, p. 159). This time period also signaled massive grow th on the suburban fringe with absolute population and jobs exceeding that of the ur ban core. This happened through continued industrial, commercial and resi dential expansion. During the 1990s the term smart growth evolved to describe well planned devel opment that protects open space and farmland, revitalizes communities, keeps housing affordab le and provides more transportation choices (Smart Growth America, 2006). Much of the exurban development concentrated in older, formerly independent satellite towns and cities became incorporated into the megalopolis web (Morrill, 2006, p. 159). Modern versions of the megalopolis are ke y regions or New Megas and include regions like Beijing to Shanghai in China and Bangalore to Hyderabad in India. These regions dont just focus on a single industry, such as high tech, but they are real economic organizing units, producing the bulk of its wealth, attracting a large share of its talent and generating the lions share of innovation (Florida, 2006). Megalopolise s grow when people cluster in one place, then

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19 the people and the place become more productive thus establishing a ke y region. Key regions encourage collective creativity; the creation of us eful new forms using knowledge as the primary means of production (Florida 2006; Jacobs, 1961). The idea of the New Mega is rooted in severa l concepts. First, New Megas arent made overnight. They take extensive planning and are founded upon sustainable practices and smart growth concepts. Secondly, New Megas are comp etitive in the global marketplace. To achieve a competitive advantage New Megas are regionall y based. As individual local governments become more and more fiscally constrained it simply makes both short and long term economic sense to cooperate and share in the economies of scale (Teeple, 2006, p. 4). An example is with respect to transportation planning. Many smaller jurisdictions would be unable to make local transportation improvements due to limited budgets and other development priorities if it were not for shared resources derived from larger jurisdictions with in their regions. Many regions have come to the realization that we are an in terdependent society and that many issues in our local communities have substantia l effect on our neighbors and that a larger context needs to be provided to balance competing in terests (Teeple, 2006, p. 4). The in terlinkage within a region is not only conducive to providing services but it is the foundation upon which corridors such as Washington DC to Boston were developed and enabled these regions to graduate from a megalopolis with shared population and commuting patterns to a New Mega that produces 20% of the nations Gross Domestic Product (GDP) w ith almost 18% of the population and only 2% of the land area (America2050, 2005, p. 11). These key regions have gained power through increased economic activity, innovati on and efficient production methods. GlobalizationDid You Know the World is Flat? Given the projected doubling of Floridas population by 2050, metropol itan areas within the central Florida region that are considered large today, including Or lando and Daytona, will

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20 continue to grow, and their expansion will begin to incorporate new adjacent municipalities. Facing serious competition for future economic growth, economic efficiency is becoming more significant for local jurisdictions. Many cities in this region eith er continue to rely on existing industries to remain competitive or compete against neighboring ci ties in the pursuit of the best opportunities for economic development. In 2007 the dynamics are changing, and in order to remain a relevant location for residents and a ttract creativity, new sp atial and organizational challenges must be met to compete within a globalizing world. Brian Teeple of the North Central Florida Region al Planning Council states that in order to compete and win globally, we must think regi onally. As our individual local governments become more and more fiscally constrained it simply makes both short and long term economic sense to cooperate and share in the economies of scale (Teeple, 2006, p. 4) Globalization is a dynamic ongoing process with a unique set of ru les, demographic trends, and demographic measurements. Most importan tly, globalization has one overa rching featureintegration. Thomas Friedman, a New York Times foreign affa irs columnist and author, defines globalization as the inexorable integration of markets, na tion-states and technologies to a degree never witnessed beforein a way that is enabling indivi duals, corporations and nation-states to reach around the world farther, faster, deeper and cheaper than ever before, and in a way that is enabling the world to reach into i ndividuals, corporations and nationstates farther, faster, deeper, cheaper than ever before (Friedman, 2000, p. 9). Professors at several leading universities in China have studied the importance of urban land uses in understanding the in teractions of urban economic ac tivities with th e environment and urban expansion. Guided by the theory of Metropolis Globalization, th ese professors believe that changes in urban land use are directly related to the development of the economy due to two

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21 factors. First, the demands of economic deve lopment create an impetus for urban sprawl. Secondly, the possibility of urba n sprawl lies in economic capacity (Wang et al., 2004, p. 1). As the economy becomes more efficient, employment centers will develop pa tterns that encourage increased productivity and greater innovation. Many economists, including Alfred Marshall, argue that firms cluster in agglomeration to ga in productive efficiencies. An alternative view is based on Robert Putnams soci al capital theory. It says th at regional economic growth is associated with tight-knit communities where pe ople and firms form and share strong ties (Florida, 2002, p. 220). A result of either of thes e views is that urban land use patterns will change and the incentives for sprawl will also change. The Economy and Development Patterns Globalization is such a significant process that the level of economic activity it creates is literally transforming the urba n landscapes of developing countries. David Dowall (1999), a professor of city and regional planning at the Univ ersity of California at Be rkeley, thinks that to effectively exploit the benefits of inward investment flows a nd to ensure that social and environmental goals are met; the public sector need s to take the lead in planning and formulating urban land management strategies to promot e sustainable urban economic development. Bruce Katz of the Brookings Institution offe rs three central themes as evidence of inefficient development. First, we must unders tand the broad demographic, economic, fiscal and cultural forces that promote de nsity, diversity, and urbanity. Looking at these forces in the greater context, outside of how they exclusiv ely encourage sprawl and decentralization, will promote innovation and improve attractiveness fo r foreign direct investment and employment generation (Katz, 2005, p. 2). The second principle is an extension of the first in that these forces are significantly altering the shape and composition of many subur bs. This change has fueled an uneven and incomplete resurgence of American c ities. Lastly, land use and growth changes are

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22 occurring in spite of state and federal policies that almost unifo rmly favor sprawl, concentrate poverty, and undermine cities and ol der suburbs (Katz, 2005, p. 2). The pace of demographic change due to an aging population and the massive influx of immigrants are matched only by the intensity of economic transformation. Katz attributes the changing local landscape to th e global economic shift. Globalization and technologica l innovation are reshaping a nd restructuring our economy and altering what Americans do and where they do it. These forces have accelerated the shift of our economy from manuf acture of goods to the conception, design, marketing, and delivery of goods, services, and ideas. Th ese forces are placing a high premium on education and skills, with communities and firms now engaging in a fierce competition for talented workers who can fuel innovation and prosperity. These forces are changing the ways businesses manage their disparate opera tions enabling large firms to locate headquarters in one city, research and desi gn somewhere else, produc tion facilities still somewhere else, and back-office functions with in our outside the firm in still other places. (2005, p. 3) The evidence shows that tight urban form is not only competitively wise, but fiscally sound. Bruce Katz contends that we have known for decades that compact development is more cost efficientboth because it lowers the cost of delivering essential government services and because it removes the demand for costly new infrastructure (Katz, 2005, p. 4). Broad demographic, market, and cultural forces coupled with a wave of innovation at the local level are improving the economic and social potential of cities. The Creative Class Individual jurisdictions are cons tantly in search of a purpose. Bruce Katz believes that just as changing demographics have shifted the face of neighborhoods, the restructuring of the American economy gives cities and urban pla ces a renewed economic function and purposea function that holds out hope for re-centering re gions and using land more efficiently (Katz, 2005, pp. 3-4).

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23 An economy based on knowledge bestows new importance on institutions of knowledge universities, medical research cente rsmany of which are located in the heart of central cities and urban communities. The shift to an economy based on ideas and innovation changes the value and function of density. Fundamentally, this creates an economy powered by creativity. Constant revision and enhancement of products, pr ocesses, and activities require access to a high concentration of talented and cr eative people. This dense concen tration of creative people, in addition to members of other em ployed classes, contributes to labor productivity. Increased residential density contributes to this paradigm sh ift by creating a quality of place that attracts knowledge workers and enabling interactions and knowledge-sharing among workers and firms, within and across industries (Katz, 2005, p. 4). The globalization system is built around what Thomas Friedman calls super empowered individuals. Super empowered i ndividuals are unique in that th ey reject the deeply ingrained tendency to think in terms of hi ghly segmented, narrow areas of expertise, which ignores the fact that the real world is not divide d up into such neat political and technological affairs (Friedman, 2000, p. 24). Super empowered people think as globalists-these are the creative class. Merriam-Websters (2004, p. 169) dictionary defi nes creativity as the ability to create meaningful new forms. Creativity is the decisive source of competitive advantage and emanates from people who require a social and economic environment to nurture its many forms (Florida, 2002, p. 5). In the current informa tion age many say that geography is dead and place doesnt matter anymore. Nothing could be fu rther from the truth. It is geographic place and access to assets, such as people, that dete rmine where companies must compete (Florida, 2002, p. 6).

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24 The Creative Class are individuals whose econo mic function is to create new ideas, new technology and/or new creative cont ent. These people engage in complex problem solving that involve a great deal of indepe ndent judgment and require high levels of education or human capital. The key difference between the Creative Cl ass and other classes lies in what they are paid to do. Those in the Working Class and Service Class are primarily paid to execute according to plan, while those in the Creative Class are primarily paid to create and have considerably more autonomy and flexibility than the other two classes to do so (Florida, 2002, p. 8). Although the Creative Class remains somewhat smaller than the Service Class, its crucial economic role makes it the most influential. Th e Creative Class is the norm-setting class of our time. The Creative Class dominates wealth and income, with members earning nearly twice as much on average as members of the other two classes (Florida, 2002, p. 9). According to Thomas Friedman, the nexus between the Creative Class and the global economy is similar to the connection between the Lexus (yes, the car) and the olive tree. Olive trees are our foundation. Family, religion, and the place we call homethese are things that root us, anchor us, and identify us in this world. We fight so intensely at times over our olive trees because, at their best, they provide the feelings of self-esteem and belonging that are as essential for human survival as food in the belly (F riedman, 2000, p. 31). Localities and, to a lesser extent, regions have been unable to compete in a global economy because they dont understand that they are the olive treethe ultimate expr ession of whom we be long tolinguistically, geographically and historically. Alone you cannot be complete. To be complete you must be part of, and rooted in, an olive grove (Friedman, 2000, p. 31). Furthermore, Friedman believes the Lexus represents human drivethe drive for sustenance, improvement, prosperity and mode rnization. The Lexus represents all the

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25 burgeoning global markets, financ ial institutions and computer technologies with which we pursue higher living standards today. Of course for millions of people in developing countries, the quest for material improvement still involves walking to a well, subsisting on a dollar a day, plowing a field barefoot behind an ox or gather ing wood and carrying it on their heads for five miles (Friedman, 2000, p. 31). While in Tokyo, Friedman toured a Lexus manu facturing plant. He was amazed at the interaction between man and machine, although th e machine was doing most of the work. Each time the robotic arm swung around to snip off wires or glue pieces together Friedman thought to himself about the planning, design and technology to achieve such precision. Then it struck him that the Lexus and the olive tree were pretty goo d explanations of globali zation. Since the Cold War, half the world has been intent on buildi ng a better Lexus, dedicated to modernizing, streamlining, and privatizing their economies in order to thrive in a global system. And half the worldsometimes the same country, sometimes th e same personwas still caught up in the fight over who owns the olive tree (Friedman, 2000, p. 31). The challenge in this era of globalizationfor countries and individualsis to find a healthy balance between preserving a sense of identity, home and community and doing what it takes to survive within the global world system. Friedman believes that a countr y without healthy olive trees will never feel rooted or s ecure enough to open up fully to th e world and reach out into it. But a country that is only olive tr ees, that is only roots, and has no Lexus, will never go, or grow, very far. Keeping the two in balance is a constant struggle (Friedman, 2000, p. 42). Indicators of Creative Centers Richard Florida developed the Creative Index as a baseline indicator of a regions overall standing in the Creative Economy and as a barome ter of a regions longterm economic potential. The Creativity Index is a mix of f our equally weighted factors: (1 ) the Creative Class share of the

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26 workforce; (2) innovation, measured as patents per capita; (3) high-te ch industry, using the Milken Institutes widely accepted Tech Pole Index; and (4) diversity, measured by the Gay Index, a reasonable proxy for an areas openness to different kinds of people and ideas. This composite indicator is a better measure of a regions underlying creative capabilities than the simple measure of the Creative Class, because it reflects the joint effects of it concentration and of innovative economic outcomes (Florida, 2002, p. 245). Determining the location of creative people relies not only on a numeric indicator but requires basic criteria for the built environment as well. Former Seattle mayor Paul Schell once said that success lies in creating a place wher e the creative experience can flourish (Kitsner, 2001, p. 44). Urban centers have long been cruc ibles for innovation and creativity. Now they are coming back. Their turnaround is driven in large measure by the attitudes and location choices of the Creative Class. In a Creative Economy, the Creative Class make s up more than 35% of the workforce. Creative Economies are not depe ndent upon the size of the regi on or city. For instance, Gainesville, Florida, and Minneap olis both have Creative Economie s. The presence of major universities, research facilities, or state governments helps boos t the concentra tion of creative people. The presence of a major research unive rsity is a huge potential source of competitive advantage in the Creative Economy. Universi ties contribute to re gional growth through technology, talent and tolerance. Technology : Universities are centers for cutting-edge research in fields from software to biotechnology and important sources of ne w technologies and spin-off companies. Talent: Universities are amazingly effective talent attractors, and their effect is truly magnetic. By attracting eminent researchers a nd scientists, universities in turn attract graduate students, generate spin-off companie s and encourage other companies to locate nearby in a cycle of self-reinforcing growth. Tolerance: Universities also help to create a pr ogressive, open and tolerant people climate that helps attract and retain members of the Creative Class. (Florida, 2002, p. 292)

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27 How Shall We Grow? How do we decide where to live and work? Wh at really matters to us in making this kind of life decision? How has this changed, and why? Creative Centers repr esent the new creative mainstream. These areas are large urban centers that are desirable places to live and work (Florida, 2002, p. 275). The Creativ e Centers are clusters that te nd to be the economic winners of our age. Not only do they have high concentr ations of Creative Class people, they have high concentrations of creative econom ic outcomes, in the form of innovations and high-tech industry growth. They also show strong signs of overall regional vitality, such as increases in regional employment and population. The Creative Centers are not thriving for such traditional economic reasons as access to natural res ources or transportation routes. Nor are they thriving because their local governments have given away the stor e through tax breaks and other incentives to lure business. They are succeeding largely because creative people want to live there. The companies then follow the peopleor, in many cases, are started by them (Florida, 2002, p. 218). Traditional and Conventional Views of Regional Growth Economists and geographers have always accep ted the conventional view of growth-that economic growth is regionalthat it is driven by and spread from specific regions, cities or even neighborhoods. The traditional view, however, is that places grow either because they are located on transportation routes or because they have endowments of natural resources that encourage firms to locate there. Successful region-based urbanization is the product of traditional and conventional planning methods. Trad itional methods result in policies that ensure greater sustainability of metropolita n areas, particularly in their intersection with the hinterland (Friedman, 2000, p. 10). According to the conv entional view, the economic importance of a place is tied to the efficiency with which it can make things and do business (Florida, 2002, p. 219).

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28 Urbanization proceeds by increasing the de nsity within and extending the periphery, frequently at the expense of open space and nature. Ian McHarg believed that man is potentially the most destructive force in na ture and its greatest e xploiter so mans view s and values placed on the environment are important (McHarg, 1969, p31) Growth responds to natural processes, which are clearly visible in the pattern and dist ribution of development in its density (McHarg, 1969, p. 160). Using scenario modeling and suitab ility surfaces, McHarg developed methods that relied upon the land to dictate its best sites for development (McHarg, 1969, p. 197). To ensure that society protects the values of natural processes, id entification of lands which possess inherent values and constraints would ensure the opera tion of vital natural processes and employ lands unsuited to developm ent in ways that would leave them unharmed by these often violent processe s (McHarg, 1969, pp. 55-56). Histori cally, growth models using mapping techniques and GIS have e volved that visually display the costs of development and growth. These models show the increased value as lands are converted to alternate uses, but the costs incurred from conversion should not involve irreversible losses (McHarg, 1969, p. 34). The Use of Scenario Modeling Ian McHarg developed a method of mapping su itability, which is sometimes referred to as the McHargian Overlay (Knaap, Bolen & Seltzer, 2003, p. 9). Themes or layers were identified and mapped suitability as high, medium, or low. Exampl es of layers include historic resources, residential distributi on, water, wildlife, and recreation. The suitability value of each layer was represented on a transparency and laye rs were superimposed upon one another. This resulted in a summary map revealing the sum of the physical and social su itabilities evaluated. This summary map reflects social values and cost s that directly contribute to the potential for future development. The darkest tone represents areas whose sum of suitabilities suggests they

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29 are least appropriate for development; the lightes t tone reveals the areas most suitable for new development (McHarg, 1969, p. 35). The Uncontrolled Growth Model was another early growth model, conceived by David Wallace, which revealed the consequences of unpl anned future growth in the valleys. This model identified the nature of the pressures and demands which would impact natural values. The model required familiarity with state and co unty proposals for highways, sewers, and zoning as well as knowledge about future population projections. Additional analysis included a housing market analysis from which housing demand by type, price and location might be determined. Knowing these parameters enabled Wa llace to simulate the pa ttern of growth that might occur in the absence of a pl an or new powers (McHarg, 1969, p80). When executing growth models it is important to understand that the resulting synthesis of values is not a plan. It merely shows the impli cations that the land and its processes display for prospective development and its form (McH arg, 1969, p. 160). A plan includes the entire question of demand and the resolution of demand relative to supply, inco rporating the capacity of the society or institution to re alize its objectives. In order to make a plan, it is necessary to calculate demand for the constituent land uses and the locational and formal requirements of these, and to recognize the instru ments available to society in both the public and private domain (McHarg, 1969, p. 105). Creating a Vision Currently, more than 90% of Floridians live in urban areas, which continue to expand farther into undeveloped areas. Most of the built environment that will exist in the next 25 years has yet to be constructed (ULI, 2005, p. 11). Grow th isnt limited to just urban environments. By 2020, more than 2.6 million acres of agricultura l land.5% of the states total land areais expected to be converted to urban uses (ULI, 2005, p. 11).

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30 The residents of East Central Florida have pa rticipated in regional visioning efforts in an attempt to correct unbalanced growth and inject community values into future development plans. Unbalanced growth undermines the econo mic efficiency of metropolitan markets (Katz, 2004, p. 3). Visioning is a strate gy for understanding th e necessity of appropriate growth and development. It is a collaborative effort am ong residents, the public sector, and the private sector to move beyond parochial interests and r ecognize the needs and desi res of all members of the community (ULI, 2005). All stakeholders mu st play a proactive role in the process; otherwise the outcomes will not be inclusive. A regional visioning process is an initiative to develop a long-term plan or policy to guide the future development of a region. Visioni ng allows leaders to take untested ideas, model them, and analyze the impacts, often with unexp ected results (Cartwright & Wilbur, 2005, p. 15). It is the subsequent action which creates a plan for quality growth (ULI, 2005, p. 5). Select Aspects of Regional Visioning Exercises The diversity of values and outcomes from each regional visioning process reinforces the precept that every region has different needs. The challenge lies in developing a method to translate values and current land use into a repr esentation of growth poten tial. Individually, the Envision Utah, Chicago Metropo lis 2020, the Los Angeles Comp ass Project, and Chattanoogas Vision 2000 couldnt be more geographically or demographically different. Although these projects have different regional needs, they ea ch share a common goal of developing a plan and policies that preserve the landscap e and accommodate regional needs. Envision Utah is a successful visioning proj ect formed in 1997 which studied the long term effects of growth in the Wasatch Valley. The pr ocess involved five years of research, public involvement, and analysis of alternative growth sc enarios, all of which le d to the development of the Quality Growth Strategy. Leaders of the Envi sion Utah project credit its success to including

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31 from the beginning: stakeholders who played an integral role in implementation, the identification of widely shared values and contin ued input of important id eals, and the use of a variety of communication strategies that were cl ear, educational and encouraged open feedback (Cartwright & Wilbur, 2005, p. 14). Until the 1996 Chicago Metropolis 2020 region al visioning process, the most recent regional vision was Daniel Burnhams Chi cago Plan of 1909. The purpose of Chicago Metropolis 2020 was to outline strategies for attrac ting needed investment and creating new jobs for the region. This plan used the Envision Utah process as a model but added an analysis that created a regional plan. The anticipated outcomes of the plan would be (1) the completion of a detailed assessment of regional housing needs and (2) land use pla nning for freight transportation centers (Cartwright & Wilbur, 2005, p. 15). Again using Envision Utah as a model, the Southern California Association of Governments (SCAG), the larges t regional government in the country, undertook the Compass Project to create a regional visi on for Los Angeles. The result of the Compass analysis is known as the 2% Strategy, in recognition of the fact that only 2 % of the la nd in the region had to change substantially for the entire region to r eap the resulting transportation and environmental benefits. The 2% Strategy calls for concentr ating mixed-use development near transportation corridors, transit stations, and regional centers. The benefits of this strategy are demonstrated using geographic information systems (GIS) so th at municipalities can vi sualize the effects to future growth management at the local level under different scenarios. Th ese scenarios include: what the current zoning allows, how much reve nue will accrue to local governments through different types of development, and what the return on investment to project proponents would be (Cartwright & Wilbur, 2005, p. 17).

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32 Chattanooga, Tennessees Vision 2000 plan bega n in 1984 and during a nine year period implemented 232 specific initiatives which result ed from the visioning process. Visioning leaders used a center-out approach focusing on identifying both the positive and negative aspects of the community and exploring possibili ties for the future instead of focusing on fixing problems. Instead of narrowing the scope of disc ussion to a few top priori ties, visioning leaders chose to put every issue on the table and encourag ed community residents to talk about all of them. This approach yielded a broad agenda supported by passionate, interested people who could effect change (Cartwright & Wilbur, 2005, p. 19). This wa s the same approach used by residents and leaders in Birmi ngham, Alabamas Region 2020 vision. The Use of GIS in Scenario Modeling As early as 1956, primitive versions of today s GIS based scenario models were used by mathematicians to model traffic flows (Anas, 1987, p. 63) and in 1960 Herbert and Stevens attempted to use similar mathematic programs to forecast the spatial arrangement of households in an urban area by assigning each land parcel to the highest bidding land use (Anas, 1987, p. 73). During the late 1950s and 60s mathem aticians and geographers were faced with computational limits in their analysis and found difficulty in describing an urban area by means of a grid or other regular geometry (Anas, p. 79). Today it is second-nature to use various systems of GIS to model growth and analyze fiscal, land use and transportation impacts. Metro 2040 Growth Concept Over the past two decades, over 15 major regi onal visioning projects (Table 2-1) have occurred across the United States. Current vi sioning projects are measured against Portland, Oregons regional visioning process known as th e Metro 2040 Growth Concept, developed in December 1995. The Metro 2040 Growth Concep t was coordinated by Metro, Portlands metropolitan area regional government. Metro is also responsible for managing the Portland

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33 metropolitan regions urban growth boundary (UGB) 1 and is required by st ate law to have a 20 year supply of land for future residential develo pment inside the boundary. Every five years, the Metro Council is required to conduct a review of land supply and, if necessary, expand the boundary to meet that requirement (Metro, 2007 b). The Metro 2040 Growth Concept sought to exceed the 20 year planning requirement and instead plan 50 years ahead, starting from 1990, to determine the amount of land and resources required to accommodate approximately 720,000 additional residents, 350,000 additi onal jobs (Metro, 2007a), and fu ture transportation demands. The 2040 Growth Concept was a means to addre ss questions about the impact of growth pressures to the urban growth boundary, satisfy the concerns of farmers who were concerned about how additional growth would impact thei r land, and address the issues of municipal service providers who were concerned about facil ities needs for new communities. This process engaged the public, including those from the e nvironmental and development community, in a manner that would provide public officials with gr eater certainty about th eir decisions and make them more resolute (personal communications with David Ausherman, formerly a GIS modeler for Fregonese and Calthorpe, Po rtland Oregon, October 9, 2006). The Regional Land Information System (RLIS) is a GIS used in every land use plan and in the evaluation of every policy in Portland to monitor land development and future growth capacity. RLIS provides accurate and detailed inventories of vacant land and Metro saw two compelling ways to use this system in future growth management. First was to update the UGB 1 Oregons Statewide Planning Guide defines as urban growth boundary as a boundary that provides for an orderly and efficient transition from rural to urban land use. Ur ban growth boundaries shall be established to identify and separate urbanizable land from rural land. Establishment and change of the boundaries shall be based upon considerations of the followi ng factors: (1) demonstrated need to acco mmodate long-range urban population growth requirements consistent with LCDC (Land Conservation and Development Commission) goals; (2) need for housing, employment opportunities, and livability; (3) or derly and economic provision for public facilities and services; (4) maximum efficiency of land uses within and on the fringe of the existing urban area; (5) environmental, energy, economic, and social consequences; (6) retention of agricultural land with four levels of priority; (7) compatibility of the proposed urban uses with nearby agricultural activities (Davidson and Dolnick, 2004, p. 433)

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34 according to new growth management legislati on and the second was to use it as the primary technical tool in the 2040 Growth Concept. The applic ation of RLIS in the 2040 Growth Concept was to create a McHar gian overlay (Knaap, Bolen & Se ltzer, 2003, p. 9) using a set of current GIS layers to combine data and crea te a map displaying suitable locations for development (Tables 2-2 and 2-3). This system was beneficial for ev aluating regional planning goals as well as illustrating future growth plans in a manner that would be understood by the general public. The RLIS methodology to determine suitable land for development is based upon identifying vacant land and determining growth pot ential of that land. Vacant land is primarily identified using aerial photogra phy overlaid with the tax lots within the Portland metropolitan area. The growth potential of each tax lot is dete rmined through the assignment of an attribute of vacant, partly vacant, undeveloped, or developed. At this point no consideration is given to suitability for building, zoning, redevelopment poten tial, or any other crit eria (Knaap, Bolen & Seltzer, 2003, p. 5). Restrictions on development (i.e. environmental constraints, presence of hazards or regulatory protections) are evaluated fu rther along in the process. RLIS then models development patterns according to a base line and three alternative scenarios. Key to the development of the 2040 plan was a Baseline Scenario a nd three alternative growth scenarios, each utilizi ng different assumptions for devel opment. The baseline concept continued existing patterns of development. The result was adding 121,000 acres with a total UGB area of 354,000 areas within an expanded UGB and a high level of growth at the outer edges. Scenario A was based upon growing out and included a significant expansion of the UGB with new growth at the urban edge mostly in the form of housing. This scenario resulted in an additional 51,000 acres within the UGB for a total UGB area of 284,000 acres. Scenario B

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35 was based upon growing up and pr ohibits (or limits) the expansi on of the UGB with growth accommodated through development of existing la nd within the urban growth boundary by using infill and more compact development. The result restricts the current UGB area to 234,000 acres. The last scenario, Scenario C, provides for n eighboring cities. This scenario moderately expands the UGB with growth focused primarily on centers, corridors, an d neighboring cities. This scenario adds 22,000 acres for a total of 257,000 acres within the UGB (The EcoTipping Point Project, 2007). Residents were presented with the base scenario and three alternative scenarios then asked to choose preferred elem ents of each. A hybrid Growth Concept was developed from the best elements chosen by th e public and is currently used to guide the region in the development of policy and in growth management decision making process. Federal Highway Administration Funded Projects The Federal Highway Administration has funded projects around the country that analyze, assess and communicate the impacts of transpor tation and land use decisions on mobility, the environment, and economic development through the use of GIS. One of the major challenges faced by organizations using GIS in visioning and development s cenario modeling has been to construct regional growth scenarios that are not entirely hypothetical, but instead are consistent with existing constraints on development and realis tic land use policy alternatives. Projects in Utah; Charlottesville, Virginia; and Maryland dem onstrate the use of GIS in scenario modeling (Federal Highway Ad ministration, 2005a). The outcomes of scenario modeling are only as good as its GIS inputs. Data collection for the Envision Utah visioning effo rt in northern Utah recognized the importance of comprehensive data collection and used GIS land use data from comprehensive plans, remote sensing data, and state inventories to determine suitable land for urban development. Land use constraints were determined using state databases of wetlands, slope s, floodplains, and riparian buffers. Potential

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36 areas for redevelopment and infill also were identified using property value data from tax parcel data and densities identified from local genera l plans. This project integrated GIS with community input by creating a base map of the laye rs mentioned above and allowing participants to identify on a paper map desired areas of green space and locations for future development in their community. The layers provided on the paper map allowed the participants to avoid unbuildable areas and make more informed decisions. Alternative scenarios provide an illustrati on of impacts and pattern s of growth based on various assumptions. The baseline represented fu ture conditions if no changes were made to existing development policy and regulations. Demographically, this meant accommodating an additional 3.4 million people over 50 years within a 1,350 square mile area. The baseline offers a benchmark to compare each alternative scenario. After a series of public workshops in which 700 residents, mayors and city council members were asked to place future population on paper maps and identify the types of development that would best se rve each area, a composite map was created from their input. Consultants reviewed the com posite maps and noticed common land use patterns. Further analys is enabled them to indicate where and how often industrial, office, retail and various types of residential de velopments should occur and what percentage of growth should be accommodated in walkable and non-walkable designs (Envision Utah, 2002). From these patterns, consultants deve loped four scenarios (Table 2-4). The next step involved allocating future project ed population into traffic analysis zones. Summary by TAZ allowed for seamless transportati on analysis to test the transportation impacts of each alternative. The four scenarios were further analyzed by technical consultants to determine water consumption, infrastructure cost s, air quality, and transportation needs. Using five indicators, housing, transpor tation, land use, costs, water c onsumption, and air quality, the

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37 impacts of each scenario was evaluated. The results were then summarized and presented to residents and state and local officials. Following is a summary of each scenario. In Scenario A people would live farther apart wi th an increase in average single-family lots from 0.32 in 1998 to 0.37 in 2020. Scenario A would also present a 95% increase in urbanized areas which would increase vehicle travel and increase the need for highway development. Infrastructure, personal tran sportation and housing costs would significantly increase. The demand for water would increase and the necessity of a vehicle for travel would significantly lower air quality. In Scenario B the average size of single-fam ily lots would remain the same as current levels but would offer fewer housing choices than scenarios C or D. Urbanized areas would grow by 75%, consuming open space and fa rmland more rapidly than in scenarios C or D. Fewer transportation choices w ould increase reliance upon the automobile contributing to increased congestion. Higher personal costs would incur due to a longer distance between housing and employment. A lthough this scenario has the second most expensive infrastructure and second highest c onsumption of water, it has the second best air quality of all scenarios. In Scenario C homes are closer together offering a wider va riety of housing options than scenarios A or B and the average size of the single-family lot decreases from 0.32 acres today to 0.29 acres in 2020. Much of new hous ing would be located in villages and towns situated along major roads and rail lines. New development is placed within existing urban areas slowing new urbanized growth to 29% from 1998 to 2020. New development is placed within existing urban areas and cluste red around transit routes, leaving more land open for space and agriculture. The expanded transit system provides additional transportation options and lowe r transportation costs. Di verse housing options offer a variety of affordable housing options. Of all scenarios, Scenario C has the best air quality and the second-lowest consumption of water. In Scenario D a higher density of housing and wider variety of housing options is available than all other scenarios. Land consumption is slower than all othe r scenarios and a large portion of new development is placed within existing urban areas and clustered around transit routes, leaving more land for open sp ace and farmland than any other scenario. More transportation options enable 32% of the population to have easy access to rail transit. This scenario has the lowest pe rsonal transportation cost and second lowest infrastructure cost of all othe r scenarios. This scenario has the lowest water consumption of all scenarios and the best air qualit y of all scenarios except Scenario B. (Envision Utah, 2002, pp. 32-35) The final step in the process allowed the publ ic to choose specific elements of each scenario that appealed to them. The responses from the public were compiled and presented to the public and then Governor Michael Leavitt. Fr om these results, Governor Leavitt decided that the time was right to develop a growth initiati ve, which later became the Quality Growth Act of

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38 1999. This legislation established a Quality Grow th Commission and provides incentives to help communities pursue quality growth. This had fiscal and land use implications in that the state government would no longer fund sprawl and contri buted a percentage of local taxes to fund open space. Reflecting on the Portland visioni ng efforts, Envision Utah understood the importance of public involvement and continued to inform the public at step of the process, which aided in implementing strategies outlined w ithin the Quality Growth Act. Envision Utah was more than a visioning process; it was a movement that utilized input from all stakeholders to develop a sustainable growth m odel and signaled a paradigm shift in growth management and future regional visioning efforts. In Charlottesville, Virginia the Eastern Pla nning Initiative constructed future growth scenarios by connecting regional land development patterns with socioeconomic characteristics and site-specific developmen t guidelines (Renaissance Plan ning Group, 2007). The regional land use map identifies community element boundaries, and each assigned element has very specific land use, building and infrastructure guidelines (Renaissance Planning Group, 2007) that is input into a spreadsheet based model known as CorPlan. CorPlan adds population to each community element (i.e. village, office, high de nsity residential) resu lting in a variety of scenarios based upon how elements are arranged. CorPlan estimates development potential and how potential translates into households a nd jobs (Renaissance Planning Group, 2007). The spreadsheet is then linked with ArcView to graphically display the proposed development scenario. Traffic analysis zone (TAZ) leve l forecast population and employment for each scenario is exported for input into a travel demand model (Federal Highway Administration, 2005a). Additional features of CorPlan include modeling fiscal impacts, community assessment and quality of life.

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39 The Charlottesville project utilized reside nt participation to determine community preferences for future development using different scenarios. Project staff constructed three alternative regional development scenarios (Urban Core, Town Centers, and Dispersed) based upon principles identified at community worksh ops (Federal Highway Administration, 2005a). In this project Charlottesville utilized existi ng community elements including the historical downtown of Charlottesville, the University of Virginia, olde r residential areas surrounding downtown, newer cul-de-sac residential subdivi sions, highway oriented shopping centers and small towns that ring the city (Renaissance Pl anning Group, 2007). The inputs and outputs were visually depicted using photos and graphics fo r the community elements and resulting potential development scenarios. This enabled the comm unity to qualitatively and quantitatively compare each growth scenario. The Eastern Planning Initiative re sulted in a set of eight key success factors, listed below, to support the publics preference for a clustered devel opment pattern (Federal Highway Administration, 2005b). The recommendations identif y specific locations for development and address future transportation demands. The Ea stern Planning Initiative was the first time a regional land development planning model conn ected to site-specific community elements. Grow only in designated development areas Maintain small towns and villages Define and maintain hard edges Create urban and enhanced suburban communities Invest in supportive infrastructure Preserve rural areas Regional equity Ensure affordability (Federal Highway Ad ministration, 2005b) In Maryland, their scenario modeling was actua lly reverse land use analysis. Instead of determining the consequences of development on tr ansportation, an assessment of the impact of

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40 different transportation scenarios on land use patterns was comple ted. To evaluate the land use impacts of either widening a two-lane road to a four-lane road or keeping the original two-lane road and adding a two-lane, limited access road, th e Maryland Department of Planning applied GIS data analysis and mapping in conjunction with a Departme nt of Transportation (DOT)sponsored expert-panel approach to land us e forecasting. Their process was as follows: The commute-shed of the road of current demand and each alternative was evaluated by mapping the origin locations of work-trips us ing the road, as estimated from the travel model covering the Baltimore metropolitan regi on. Land use data in the commute-shed were then mapped, including zoned densities, the amount of land re cently developed, and the amount of land available for developmen t. This provided background on growth trends, pressures, and opportunities in the ar ea. For each alternative, the increase in accessibility to jobs (measured as the change in number of jobs accessible within 45 minutes) was then estimated from the region al travel demand model and mapped for each planning area. This showed the potential impact of the highway project on the desirability of each area for new development. (F ederal Highway Administration, 2005a) In 2000 an expert panel of politicians, planne rs, and engineers individually and jointly assessed the potential impact of each alternative. Opinions vari ed, but most panelists provided the following recommendations: the four-lane alternative would provide a si gnificant incentive for increased development in the study area, while the two-lane limited access alternative would not. Staff from the Department of Planning therefore concluded that the four-lane op tion would contradict Smart Growth objectives and the desire to pres erve rural land within the commute-shed. In contrast, they concluded that the two lane option would addr ess safety concerns without contradicting Smart Growth principles (Federal Highway Administration 2005a) The PLACE3S (PLAnning for Community Energy, Environmental, and Economic Sustainability) model, which is similar to th e CorPlan model, supports community land use and transportation planning at the parcel level. It is a spreadsheet based model that can be integrated into ArcView to graphically display development scenarios. It is designed to estimate the community, environmental, economic, and transpor tation benefits associat ed with alternative development scenarios. This model requires de tailed input including ch aracteristics of each scenario, a street layout and land use type and planned densities by parcel (Federal Highway

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41 Administration, 2005a). Interact ive use of this model enabled residents of the Mid-City neighborhood in San Diego to understand the impacts of different zoning policies on redevelopment potential, ener gy use, vehicle travel, and other performance measures. Summary The first goal of this review of literature was to understand the characteristics of a megalopolis and New Megas. Then a discussi on of how the plight to maintain economic efficiency, which many New Mega s and megalopolises have attained, has led many regions to seek inclusion in the globalized economy. Next the traits of the Creative Class were outlined and were followed by a discussion of how creative i ndividuals and industries further the goals of New Megas. Next examples of visioning effort s across the country were listed and a discussion of how this technique has enabled cities and re gions to proactively pl an for population growth, transportation demands, and a stronger economy. Cities can no longer insulate themselves from the concerns and issues facing adjacent areas. Visioning allows the driv ing forces behind these issues to surface and initiate a conversation on possible resolutions. More importantly, they introduce the concept of how individual municipa lities contribute to the larger function of the region. Finally, a discussion of GIS growth models used in various vision ing projects identified the steps involved in developing scenarios which help identify constraints and alternatives to accommodate future population. Visioning efforts in Portland and Utah have become the benchmark for GIS modeling and policy implementation in visioning exercises since their completion. The Metro 2040 Growth Concept was the flagship of regional visioning. The McHargian overlay concept and many other modeling techniques and methods used to encourag e community involvement are just as relevant today as they were ten years ago. Organizers of the Envision Utah proces s credit the continued support and success of their exercise and implementa tion efforts to the residents, developers, and

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42 politicians who were and continue to be involved. The use of creating indicators to evaluate each scenario then used to develop a final co mposite map was adapted from the Envision Utah exercise for the Central Florid a Growth Vision, the visioning ex ercise for which my study was based upon. The projects funded by the Federal Highway Administra tion use unique GIS modeling techniques to accomplish the same task, but each has slightly different benefits. Envision Utah, which was funded by the Federal Highway Administ ration was the first growth model to allocate population by TAZ, which allows a seamless conve rsion into traffic modeling programs. The CorPlan model used in the Charlottesville, Virg inia exercise was the fi rst to populate community elements (i.e. downtowns, historic districts, and universities) and de velop a result that translates growth potential into jobs and households figur es. Lastly, in Maryla nd the typical modeling process of determining what is needed to accommodate future growth was modified to model predetermined transportation alternatives. The output from these scenarios provides the framework for regions and local governments to begin policy discussions and ultimately aid in developing a sm arter growth plan. The next chapter will discuss methods used for develo ping the Central Florida Growth Vision.

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43 Table 2-1. Major region al visioning projects Region Process name Creation Website Partners Atlanta Vision 2020 Atlanta 2006 www.atlantaregional.com Atlanta Regional Commission Austin Envision Central Texas May 2004 www.envisioncentraltexas.org Capital Metropolitan Planning Organization, Capital Metropolitan Transportation Authority, Bastrop, Caldwell, Hays, Travis and Williamson Counties, Central Texas Regional Mobility Authority, The Greater Austin Chamber of Commerce Baltimore Vision 2030: Shaping the region's future together 2003 www.baltometro.org/vision2030 Baltimore Regional Transportation Board, Baltimore Metropolitan Council, Baltimore Regional Partnership Central Florida How Shall We Grow? In progress http://www.myregion.org/ (see page 5) Chicago Chicago Metropolis 2020 1999 www.chicagometropolis2020.org/ The Commercial Club of Chicago; Cook, DuPage, Lake, McHenry, Kane and Will counties Denver Blueprint Denver 2020 2002 www.drcog.org Denver City Council, Denver Planning Board, Land Use and Transportation Advisory Committee, City Staff Phoenix ValleyVision 2025 2000 www.mag.maricopa.gov Maricopa Association of Governments Portland Metro 2040 Plan 1995 www.metro-region.org Portland Area Metro government, City of Portland, Multnomah County, Hillsboro County, Clark County, neighboring cities Salt Lake City Envision Utah 1997 www.envisionutah.org Coalition for Utah's Future, Mountainland Association of Governments, Wasatch Front Regional Council, Utah DOT, Utah Transit Authority, Envision Utah

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44 Table 2-1. Continued Region Process name Creation Website Partners San Diego 2030 Vision 2004 San Diego Associati on of Government Association of Bay Area Governments, Metropolitan Transportation Commission,, Bay Area Air Quality Management. District, Bay Conservation and Development Commission, Regional Water Quality Control Board, Bay Area Alliance for Sustainable Development San Francisco Smart Growth Strategy / Regional Livability Footprint Project 2020 2003 www.bayareavision.org Seattle VISION 2020, Destination 2030 Late 1980's, updated in 1995 www.psrc.org/projects/vision/ Puget Sound Regional Council Los Angeles Compass Blueprint June 2004 www.compassblueprint.org Southern California Association of Governments, Compass Blueprint Partnership Southern Louisiana Regional Vision for Southern Louisiana 2006 www.louisianaspeaks.org Louisiana Recovery Authority, State of Louisiana, LRA Support Foundation St. Louis Gateway Blueprint 2004 http://www.ewgateway.org/ East-West Gateway Coordinating Council St. Louis Regional Chamber and Growth Association (RCGA) (Adapted from the Federal Highway Administra tion. (2005a). Retrieved November 11, 2006, from http://www.fhwa.dot.gov/tcsp/case7.html)

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45 Table 2-2. Primary RLIS layers Layer Description GIS Base Layers Tax Lots Property assessment tax lots Streets Streets, highways, bus/light rail lines, bike routes, sidewalks/trails GIS Overlays Vacant land Vacant lots, partia lly developed lots with acre or more land vacant Developed land Reverse of vacant land layer Land use Derived from tax codes Zoning Local land use zones Comprehensive plans Local comprehensive plans Parks and open space Parks and public/private open spaces Aerial photographt Natural color ortho-rectified digital imagery Jurisdictional boundaries Boundari es, e.g. UGB, schools, service districts Places Hospitals, schools, police, etc. Building permit Location of issued permits U.S. Census Census data for 1980, 1990 and 2000 Environmental Layers Rivers, streams, wetlands, and watersheds Location and attribute information for water features Tree canopy and land cover Urban fore st canopy and vegetative/other land cover Flood plains 100 year Flood Plain Steep slopes 10% and 25% slopes Soils Soils by type and class Elevation contours 5 ft elevation contours Digital Terrain Model (DTM) Digital terrain data for georeferencing of info. Earthquake hazard 4 zones depict relative hazard for urban area (Knaap, G., Bolen, R., & Seltzer, E. (2003). Metros Regional Land Information System: The virtual key to Portlands growth management success Lincoln Institute of Land Policy Working Paper.; p. 21)

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46 Table 2-3. RLIS procedure for identifying buildable lands and calculating housing and employment capacities Step 1 Calculate the number of acres in side the Metro Urban Growth Boundary (UGB). Step 2 Subtract acres of co mmitted and developed land. Step 3 Subtract acres of platted, vacant single-family residential land. Step 4 Subtract vacant, environmentally constrained acres to arrive at vacant, unconstrained land. Step 5 Subtract land for future facilities (streets, schools, parks, churches, fraternal organizations, government facilities) to arrive at net buildable, vacant acres. Step 6 Calculate development capacity of vacant land under current comprehensive plans for housing. Step 7 Adjust current comprehensive plan capacity for single-family under-build. Step 8 Adjust housing for platted lots. Step 9 Rezone for 2040 Growth Concep t and calculate housing and employee capacity. Step 10 Adjust the Metro 2040 Growth Con cept capacity for residential under-build. Step 11 Adjust the Metro 2040 Growth Con cept housing capacity for platted singlefamily lots. Step 12 Adjust the Metro 2040 Growth Con cept housing and employment capacity for physical development barriers. Step 13 Adjust density assumptions to al low cities and counties time to implement 2040 type regulations (ramp-up). Step 14 Estimate redevelopment potential and adjust capacity calculation for housing and employment. Step 15 Estimate infill housing on lands categorizes as developed, increase employment densities on devel oped lands and adjust capacity. Step 16 Consider the farm or forest use assessment acreage in UGB. Step 17 Compare UGB capacity with forecast ed 20 year need and determine acres of UGB expansion by land use type. (Reprinted from Knaap, G., Bolen, R., & Seltzer, E. (2003). Metros Regional Land Information System: The virtual key to Portl ands growth management success Lincoln Institute of Land Policy Working Paper, p. 25)

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47 Table 2-4. Alternative growth scenarios for Envision Utah Scenario Description Scenario A Scenario A proj ected how the region could develop if the dispersed pattern of development occurring in some Greater Wasatch Area communities today were to continue. New development would primarily take the form of single-family homes on larger, suburban lots (0.37 acre average). Most development would focus future transportation investments on convenience for auto users. Scenario B (This scenario is an update of the Baseline Scenario) Scenario B depicted how the region could develop if state and local governments follow their 1997 municipal plans. Development would continue in a di spersed pattern, much like it has for the past 20 years, but not as widely dispersed as in Scenario A. New development would primarily take the form of single family homes on larger, suburban lots (0.32 acre average). Most development would focus on convenience for auto users and transportation investment s would support auto use. Scenario C (This scenario represents a combination of all scenarios) Scenario C shows how the region could grow if new development were focused on walkable communities containing nearby opportunities to work, shop, and play. Communities would accommodate a portion of new growth within existing urbanized areas, leaving more undeveloped land for open space and agriculture. New development would be clustered around a town center, with a mixture of retail services a nd housing types close to transit lines. These communities would be designed to encourage walking and biking, and would contain a wide variety of housing types, allowing people to move to more or less expensive housing without leaving a particular community. Average lot size would be slightly smaller (0.29 acre) than Scenarios A and B.

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48 Table 2-4. Continued Scenario Description Scenario D Scenario D shows how the Greater Wasatch Area might develop if Scenario C was taken one step further, focusing nearly half of all new growth within existing ur ban areas. This would leave more undeveloped land for open space and agriculture than any other scenario. When new land is used, development would be clustered around a town center, with a mixture of commercial and housing types close to some portion of a greatly e xpanded transit system. These communities would be designed to permit and encourage walking and biking, contain the widest vari ety of housing types of any scenario, and also have the smallest average lot size (0.27 acre). (Reprinted with permission from Envision Utah, 2004, p. 31)

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49 CHAPTER 3 CENTRAL FLORIDA GROWTH VISION Project Goal In early 2000, myregion.org was established to coordinate th e development of a plan for Central Floridas future growth. In Spring 2005 the University of Pennsylvania design studio produced PennDesign VII: Central Florida, a report illustrating the growth potential of Central Florida in the year 2050. The PennDesign final report indicated extensive amounts of sprawl and unsustainable infrastructure if current devel opment patterns continued into the future. PennDesign also created an alternative scenario illustrating a potential alternative pattern of growth if natural resources were preserved and mo re efficient infrastructure were in place. The alternative offered hope to the East Central Fl orida region. The Centra l Florida Growth Vision was a coordinated effort between residents, pr ivate, public and civic organizations to pursue more sustainable growth pattern s by developing proactive strate gies to accommodate future populations. The Central Florida Growth Vision consists of a three step process. During Phase 1, residents, activists and leaders from throughout the 7 county region identified key issues to be addressed. From the collective input of 3,000 people, myregion.org organizers began crafting strategies to positively impact the future. Phase 2 involved collecting existing regional conditions about strengths, weaknesses, values, demographics, and future population growth. This research was later used as inputs for the actual visioning proce ss. Specific research included a regional profile and i ndicators report; research on dem ographic trends; surveys of the regions values and social capital; development of illustrative future development scenarios; and identification of critical environmental reso urces. Phase 2 also engaged over 7,000 residents in a series of community meetings and online survey s in an effort to gather their input and shape

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50 elements to visually illustrate a preferred pattern of growth for the future. Phase 3, which began in April 2007, involves further coordination with regional leaders and residents as myregion.org prepares to develop policies and strategies that further the outcomes of Phase 2. Scenario Modeling in Central Florida At the request of the Metropolitan Center for Regional Studies at the University of Central Florida, the Penn Design studio was asked to illu strate patterns of development within the 7 county East Central Florida regi on in 2050 using population projections data from the Bureau of Economic and Business Research. The PennDes ign Model applied curre nt population, number of households and average household size rate to determine the curr ent gross density of development and determine the number of acres needed to accommodate future population projections. The gross density of development is the ratio of all developed acres, including residential and all other uses, to the numb er of housing units (myregion.org, 2006, p. 9). Existing urban areas were iden tified using the 2000 USGS La nd Cover Analysis and current conservation areas were determined using The Nature Conservancy and Florida Natural Areas Inventory GIS data. The PennDesign model employed several assumpti ons with respect to the environment, the economy, and transportation; pa rticularly, that no additiona l conservation land would be permanently preserved between 2000 and 2050. The model also assumed no additional economic activity centers and the existing cent ers would draw a cons tant proportion of new development throughout the region. The region curre ntly relies heavily on road networks and the model continued this premise and only utiliz ed the current road ne twork, not attempting to project when or where future road s will be built (Barnett, 2005, p. 31). There were several factors not considered in the design of this model. These factors include water availability, current zoning and future land use desi gnations, and individual parcel

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51 data. These factors were not considered due to the size of the projec t and accounting for these individual factors would prove to be impractical (Barnett, 2005, p. 30). PennDesign engaged in a five step process to create each scenario. First, the seven county study area was added to a GIS and divided into individual cells each representing 1 acre. Then surface water bodies, current conservation, or exis ting developed areas were masked [taken out] out of the GIS map. These areas were masked so that future population would not be allocated into these areas. It is important to note that we tlands remained in the map as an area that could accommodate future population. Then three factors were chosen which were deemed to influence the direction of development in Florida. These factors are: access to economic activity centers; access to already devel oped areas; and existence of wetla nds. Each factor received an equal weight of 0.33, meaning they were each as likely to draw or repel future development (Barnett, 2005, p. 32). When the model considered access to econom ic activity centers, each economic center was given a different weight, based on how much new development that center was likely to draw. These weights were not based on employment num bers, but rather on how strongly the centers currently influence the direction of new development (Barnett, 2005, p. 32). The access to designation was the manner by wh ich the model takes into account Central Floridas transportation system. Each cell was weighted based on how easy or difficult it would be to travel from that cell to economic activity centers (Factor #1) and to already urbanized areas (Factor #2) via the curr ent road network. To account for th e challenge that wetlands pose to development, wetlands were given a low weight meaning that the wetland could be still be developed by the model, but that it is less desi rable than other cells for development (Barnett, 2005, p. 32).

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52 Next, these weights were applied to each cell, resulting in a ranking which identified the likelihood that cell would be developed. Finall y, population was allocated using a predetermined density and the model displayed the acres ne eded to accommodate population growth for each decade. The acres selected by the model were the highest-rated acres as determined in the previous step (Barnett, 2005, p. 34). The conclusions drawn from the PennD esign trend scenario were that: The cost of providing roads, ut ilities, and other services to newly developed land in the region has been estimated at $90,000/acre. Developing the acres needed to accommodate the expected population growth will cost more than $104 billion. Assuming current land use trends continue and no additional land is conserved; 60,871 acres of currently unprotected sens itive lands will be developed by 2050. (Barnett, 2005, p. 33) Land Use Conflict Identification Strategy (LUCIS) The Land Use Conflict Identification Strategy (L UCIS) is another example of a scenario modeling strategy that employs role playing a nd suitability modeling to predict areas where future land use conflict will likely occur. The st rategys six step process includes 1) developing a hierarchical set of goals and objectives that become suitabil ity criteria, 2) inventory of available data, 3) determining suitabilities, 4) co mbining suitabilities to represent preference, 5) reclassifying preference into three categories of high, medium, and low, and 6) comparing areas of preference to determine the quantity and sp atial distribution of pot ential land use conflict (Carr and Zwick, 2005, p. 89). LUCIS stops short of representing alternat ive futures, but instead focuses on the comparison of the results of three suitability an alysis purposefully designed to capture biases inherent in the motivations of three stakeholde r groups: conservationists, developers and farmers

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53 and ranchers dedicated to an agri cultural future. The comparison of the suitabilities results in the identification of areas of potential future land use conflict (Carr a nd Zwick, 2005, p. 90). The results of LUCIS can be used to deve lop alternative scenarios for allocation of projected population when combined with defi ned assumptions about (1) the sequencing and speed of the conversion of existing conservation and agricultural lands to urban use; (2) the densities at which new population can be allocated; and (3) permanent set asid es (or lack thereof) of lands with high conservation a nd agricultural suitability. The re sult is a range of alternative future land use scenarios with associated bui ld out populations and dates (Carr and Zwick, 2005, p. 93). Phase 2: Community Input In preparation for the Central Florida regions anticipated growth-principles, indicators, and scenarios were developed (myregion.org, 200 6, p. 33). A principle is a comprehensive and fundamental law, doctrine, or assumpti on (myregion.org, 2006, p. 32). An indicator is a value or group of statistical va lues that taken together give an indication of the status or condition of an article or ite m (myregion.org, 2006, p. 33). These indicators were used to compare the scenarios developed from community input. Table 3-1 provides a summary of considered measures which relate to the five highest-priority principl es identified by the community (myregion.org, 2006, pp. 33-34). Community participation played a significant role in the Central Florida Regional Growth Vision: first in completing the draft of the Guiding Principles then in determining which indicators were most important to the residents of this region. The community was then tasked with determining where future density should be placed during the How Shall We Grow Chip Game and Dot Game. These games required residents to accommodate future population within their counties by placing do ts of varying density on a map. Dots from all seven counties

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54 were ultimately synthesized and, using GIS t echnology, serve as the f oundation for the The Peoples Choice Map, which is a reflection of the regions deve lopment preferences and values. The results of the PennDesign model were the ca talyst for the formal visioning effort. The myregion.org staff and its regional partners desi red to use a model similar to PennDesign to analyze input from the community that would illust rate its values but also depict future growth patterns. The LUCIS model was chosen to model three of the four scenar ios because it provided the flexibility to allocate population into TAZs would accommodate the ecological and physical constraints within the region, a nd it considers the suitability of lands for urban development before allocating future populat ion. The remaining scenario was modeled by Renaissance Planning Group using the PLAC3S model (see page 39 for a description of the PLAC3S model). A scenario provides alterna tive methods or development patterns of accommodating future population growth (myregion.org, 2006, p. 33) Each scenario emphasized a theme which, to various degrees, encompassed the re gions growth principles. Notable themes included a continuation of current trends, an emphasis on the cr eation of new compact centers, and an emphasis on alternative modes of transit. In addition to the baseline scenario, these themes formed the basis of th e three alternative scenarios th at were modeled by Renaissance Planning Group and the University of Florida. Before scenarios were populated, assumptions (Table 3-2) relating to trans portation networks, non-developa ble areas, and density were established and used as cons traints during allocation. The Baseline scenario accommodates future growth according to existing density and policy. Scenario A, the Green Areas Scenario, set aside the most critical la nds and habitat before population was allocated to the region. Scenario B, Centers, connected cities, towns and villages using a basic ra il network. Scenario C, Corridors, included a more extensive rail trans portation, including streetcar, light rail, and

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55 commuter rail. The scenarios were analyzed a nd compared using performance measures that address the regions principles (Figures 3-1 through 3-5 and Ta ble 3-3). During analysis, GIS and specialized modeling software (i.e. for land us e, transportation, and ai r quality) was used to evaluate additional quantitative impacts. The LUCIS was used to model every scenario except Scenario B, which was modele d by Renaissance Planning Group. If current development policies and density cont inue as many green fields (i.e. farms, fields, woods, and wetlands) will urbanize in the ne xt 45 years as were urbanized in the last 440 (Laurien, 2007, slide 54). The baseline scenario will urbanize more than 10 times more threatened/endangered species habita t than any of the other three al ternative scenarios. Scenario A (Green Areas) preserves more sensitive land (4 ,627 square miles) than any of the other scenarios. Scenario B (Centers) preserves th e next largest amount of sensitive land at 4,198 square miles and Scenario C (Corridors) follows at 3,816 square miles. With regard to traffic and air quality, Scenarios B and C will require Ce ntral Florida residents to spend lesser amounts of time in the car than the Baseline or S cenario A, which will result in lesser CO2 emissions. Scenario C also offers more affordable housing options and will produce more than $450 billion more Gross Regional Product than the Baseline S cenario. Overall, the Baseline Scenario provides the least opportunity in terms of housing options, preservation of sensitive environments, and economic strength. Each of the three alternative scenarios provides some level of economic efficiency with lesser impact s to the physical environment than the Baseline Scenario. As mentioned previously, comm unity input played a significant role throughout the East Central Florida Growth Vision. The final scen arios were a compilation of the input from the community, and their involvement was even more valuable after the final scenarios were

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56 released to the public. In January 2007, the resi dents of the East Central Florida region were asked to vote on each scenario and choose whic h overall element best represented how they would like their region to grow ove r the next 50 years. They were also asked to select their preferences to other individual elements, just like those who participated in Envision Utah, relating to amount of conserved land, amount of developed land, air quality, water demand, transportation choices, commute tim e, and economic impact. Figure 3-6 depicts the final survey results of the three growth scenarios. Phil Laurien, Executive Director of the East Central Florida Regional Planning Council summarized the results as follows: The Trend was the least preferred alternative by 86.5 % of respondents. The Green Areas scenario was the most pr eferred by 27.2 % of respondents, and second choice of 18%. The Centers scenario was the most preferre d by 38.2 % of respondents, and second choice of 41.4 %. The Corridors scenario was the most pr eferred by 31.1 % of respondents and second choice of 31.1 %. Clear Loser: the Trend These results spoke loudly that the Trend, th e current development pattern of low density sprawl in central Florida is not what people want for the future But the other alternatives provided no clear winner. No Clear Winner Just a few percentage points separated the Ce nters, Corridors, and the Green Areas. This was understandable, even predictable, since all three had a strong conservation element, and some alternative transportati on (transit, streetcar, light rail, commuter rail). As a result none of these three scenarios was markedly different to the average respondent. (email communication Phil Laurien, East Central Fl orida Regional Planning Council on March 14, 2007) Summary The Baseline and 3 alternative scenarios depict 4 significantly different futures for East Central Florida. The survey taken from reside nts after the final scen arios were developed

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57 indicated that residents are ready for a cha nge. The idea that 3.6 million people are coming seems like more of a reality when you see larg e portions of your county that are currently undeveloped colored a shade of yellow in 2050 indi cating that some type of urban development will occur. Although the LUCIS model determines which lands are more preferable for urban development and allocates population into those la nds first, in each scenario in which LUCIS was used, population was also alloca ted in lands preferre d for agriculture just to satisfy future demand. So why is this important to my study? It is clear fr om the Central Florida Growth Vision that the population and connectivity will ex ist in 2050. The econo mic analysis performed for the Central Florida Growth Vision also indicates that if a scenario other than the trend is realized then the Gross Regional Product2 will be at least $28 billi on more than that in the Baseline Scenario. Although to generate this le vel of economic growth high wage jobs that require highly skilled and educated workers, also known as the Creative Class, must exist within the region. This study will determine whether the East Central Florida can accommodate the new influx of creative em ployee expected by 2050. 2 A Gross Regional Product (GRP) is defined as a measure of total income in a given area. The GRP includes employee compensation, property income, and proprietary income plus indirect business taxes. The GRP is equal to total value added and is the local or regional equivalent of the national measure of economic growth, the Gross Domestic Product (Southern Forest Resource Assessment, 2001).

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58 Table 3-1. Summary of potential meas ures according to each guiding principle Principle Topic Area Potential Measure (Examples) Total acres of open spaces farmland, and critical environmental areas protected Preserving open space, recreation areas, farmland, water resources, and critical environmental areas Open spaces, farmland, natural beauty and critical environmental areas Total length hard versus so ft edges between urban and open, farm, environmental land (hard edges defines as densities greater than 4 dus per acre) Ratio of population to educati on capacity at all levels Education Number and percentage of people living within walking access to schools Number of uninsured Provide universal access to the highest quality of education, healthcare, and cultural amenities Health care Number and percentage of people living within x miles of a hospital Number and percentage of system miles in network (roads, transit, bike/ped) Availability of choices Percentage of population and employment within onequarter mile of public transportation service Total daily, per capita and per household vehicle miles and hours of travel Provide a variety of transportation choices Efficiency of choices Total daily, per capita an d per household hours of delay Population and employment cap acity within 2 miles of regional transit stations and highway interchanges Encourage a diverse, globally competitive economy Population within 20 minutes of regional activity centers Number and percentage of people living and working in well-designed/mixed communities Foster distinctive, attractive and safe places to live Total size of urban footprint Regional distribution of housing by type Create a range of obtainable housing opportunities and choices Number and percentage of housing units in mixed versus single use communities (myregion.org, 2006, p. 5)

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59 Table 3-2. Assumptions used in baseline and 3 alternative scenarios of myregion.org Central Florida Growth Vision Scenario Description of assumptions Baseline Population is allocated according to existing gross density and policy Scenario A: Green Areas Preserve primary sensitive lands plus threatened and endangered species habitat, which goes beyond just the seven environmental jewels3. New urban development is mostly placed outside sensitive areas and habitat. 20% of new growth will occur in redevelopment. 80% of new growth to green fields; use development preference map for guidance. Transportation Improvements: Planned Florida Interstate Highways; Strategic Inter-modal System Roads; 2025-2030 Long Range Transportation Plan Cost Feasible Plans; DOT commuter rail Deland to Kissimmee; Active Freight Rail; Existing Arterials Scenario B: Centers Transportation Improvements: Renaissance Planni ng Group designs 370 mile new road network and 413 mile rail and streetcar network in addition to Planned Florida Interstate Highways, Strategic Inter-modal System Roas, 2025-2030 Long Range Transportation Plan Cost Feasible Plans, DOT commuter rail Deland to Kissimmee, Active Freight Rail, Existing Arterials. Seven environmental jewels preserved and enhanced. 20% of new growth will occur in urban redevelopment in existing centers. 80% of new growth to green fields; most will go to new centers of mixed use densities ranging from 4-10 units per acre, higher near rail stations. 3 The seven environmental jewels are seven areas identif ied by The Florida Natural Areas Inventory as sensitive lands that have significant regional, national, and in some cases, global ecological and economic value. The seven locations are St. Johns Mosaic/Econlockhatchee River, the Indian River Lagoon, the Kiss immee Prairie, the Volusia Conservation Corridor, the Green Swamp, the Weki va-Ocala Greenway, and the Lake Wales Ridge.

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60 Table 3-2. Continued Scenario Description of assumptions Scenario C: Corridors Spend more money on transit than on road improvement. o DOT commuter rail enhanced o Additional light rail and streetcar network Planned Florida Inters tate and Strategic Inter-modal System Roads, 2025-2030 Long Range Transportation Plan Cost Feasible Plans, Active Freight Rail, Existing arterials Seven environmental jewels mostly preserved. 20% new growth goes to urban redevelopment, then as much new growth placed within 1/3 mile of transit stops as can be absorbed at 30 units/acre, first redeveloping old commercial sites and then remaining growth goes to green fields using development preference map as a guide, leaving quality existing neighborhoods intact. (Adapted from the Laurien, 2007)

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61 Table 3-3. Potential future populati on distribution by scenario and county County 2005 Population BEBR 2050 Trend 2050 Green Areas 2050 Centers 2050 Corridors 2050 Brevard 531,970 932,704 888,333 914,981 958,939 967,129 Lake 263,017 653,766 531,942 831,354 662,686 652,410 Orange 1,043,437 2,230,650 1,819,062 1,477,974 2,203,565 2,203,642 Osceola 235,156 688,296 413,624 669,095 752,315 588,742 Polk 541,840 969,088 1,507,076 1,595,293 977,565 1,097,067 Seminole 411,744 775,265 623,145 593,375 681,169 589,836 Volusia 494,649 874,001 1,340,569 1,041,647 894,077 1,022,564 Total Population 3,521,813 7,123,770 7,123,751 7,123,719 7,130,317 7,121,390 (Reprinted with permission from Laurien, P. (2007). Myregion.org presents: How Shall We Grow? Central Floridas Four Futures. Slides used for Media Week January 2007 East Central Florida Regional Planning Council)

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62 Figure 3-1. Illustration of growth patterns for Baseline Scenario (Reprinted with permission from Trend Scenario 2050 [Map]. (2007). East Central Florida Regional Planning Council)

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63 Figure 3-2. Illustration of growth patte rns for Scenario A (Green Areas) (Reprinted with permission from Green Areas Scenario 2050 [Map]. (2007). East Central Florida Regional Planning Council)

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64 Figure 3-3. Illustration of growth patterns for Scenario B (Centers) (Reprinted with permission from Centers Scenario 2050 [Map]. (2007). East Central Florida Regional Planning Council)

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65 Figure 3-4. Illustration of growth patterns for Scenario C (Corridors) (Reprinted with permission from Corridors Scenario 2050 [Map]. (2007). East Central Florida Regional Planning Council)

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66 Figure 3-5. Description of Phase 2 Scenar ios from Central Florida Growth Vision (Reprinted with permissi on from myregion.org. (2007). Survey Audit Final Report Retrieved April 20, 2007, from www.myregion.org)

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67 Figure 3-6. Central Florida Gr owth Vision survey results (Reprinted with permissi on from myregion.org. (2007). Scenario Descriptions and Survey Ballot Retrieved April 20, 2007, from www.myregion.org)

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68 Figure 3-6. Continued (Reprinted with permissi on from myregion.org. (2007). Scenario Descriptions and Survey Ballot Retrieved April 20, 2007, from www.myregion.org)

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69 CHAPTER 4 STUDY AREA The East Central Florida region, composed of 7 counties has a tota l land area of 7,488 square miles. The counties within this re gion are Brevard, Lake, Orange Osceola, Polk, Seminole, and Volusia. This region is furthe r divided into 4 Metropolitan Statistical Areas1 (MSAs); Melbourne-Titusville-Pa lm Bay, Lakeland-Winter Have n, Orlando, and Daytona Beach (Figure 4-2). Using spatial statistics within GI S, the geographic center of the region is within Orange County. Major cities (Tab le 4-1) within the region incl ude Palm Bay (Brevard County), Melbourne (Brevard County), Clermont (Lake County), L eesburg (Lake County), Orlando (Orange County), Apopka (Orange County), Kissimmee (Osceola County), St. Cloud (Osceola County), Lakeland (Polk County) Winter Haven (Polk County) Sanford (Seminole County), Altamonte Springs (Seminole County), Daytona Beach (Volusia County), and Port Orange (Volusia County). The total regional population in 2005 was 3,521,8132. Through the year 2050 population is expected to more than double to over 7.1 million people (Table 4-2). The region is composed of 97 municipali ties, each with a uni que set of land use regulations. With the exception of Polk County, six counties are under the auspices of the East Central Florida Regional Planning Council (ECFRPC) Polk County is a member of the Central Florida Regional Planning Council. Polk County was included in this exercise not only because of its shares borders with thr ee other counties within the ECFR PC, but also because the large number of Polk County residents who commute to a county within the EC FRPC region for work, 1 The U.S. Census Bureau defines a metropolitan statistical area as a geographic entitybased on the concept of a core area with a large population nucl eus, plus adjacent communities having a high degree of economic and social integration with that core. Qualification of an MSA requires the presence of a city with 50,000 or more inhabitants, or the presence of an urbanized area and a total population of at least 100,000 (75,000 in New England) (Davidson and Dolnick, 2004, p. 269). 2 The 2005 population is referenced because the analysis perfor med in this paper uses 2005 as the temporal baseline.

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70 shopping, or recreation. When a job is lost or ho using costs in the ECFRPC region increase, the effects are felt in Polk County. The implications of future growth ar e felt across all 7 seven counties through the local and regional ec onomy, transportation and housing sectors. The Economy In 2006, the largest private employer within th e region was Walt Disney World in Orange County with 53,500 employees (Table 4-3). Orange County is not only th e geographic center of the region, but it is also the economic heart. If Orange County were removed from the region, the largest regional private em ployer, located in Volusia County, would employ almost 9,000 people. If you include Orange County, there are five employers who employ more than 9,000 people. More importantly, 9 out of 10 of Ora nge Countys top private employers are creative industries. Creative industries hire individuals that are highl y skilled and stimulate economic growth. Among the regions highest ranked largest private employe r with the least number of employees is Leesburg Regional Medical Center located in Lake County at 2,300 employees. Lake County also has the least number of creative indus tries within its top ten employers. The presence of creative individuals in the East Central Florida regi on is the core of my study. The concentration of creative indus tries contributes to the Creativity Index, which in Richard Floridas research is a statistically significant in dicator of of a regions economic potential and is discussed further in Chapter 5. Residents of a particular count y often fall victim to the assu mption that economic shifts in a neighboring county or county within the larger region has no effect on they live. This couldnt be further from the truth. This is evident fr om the number of people who work outside of the MSA in which they reside (Table 4-4). Individua ls of Volusia and Polk County have the highest percentage of residents who work outside of th eir MSA of residence, 20% and 16% respectively. Individuals who live in Orange C ounty have the lowest percentage of residents who work outside

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71 of their MSA. It is important to understand th at a MSA is a statistical area, not a political boundary, and the Orlando MSA constitutes more than one county. Since the Orlando MSA has the lowest percentage, it reinforces the concept of interlinkages between counties within a region, especially with respect to commuting patterns and housing. Transportation Transport corridors can be seen as backbones of transportation networks linking major articulation points (e.g. hubs) a nd towards which freight and pa ssenger fluxes converge. Most often, they lie at the intersection of economic, demographic and geographic spaces as they perform both market-serving and market-connecting functions (Slack, 2006). The transportation corridors within East Central Florida are comple x and its major interchanges are suggestive of rapidly growing places of work and residence. Orange County is the most accessible county by roadway (Table 4-5) in the region with 1 federal interstate, 2 federal highways, and 10 stat e highways. The arrangement of these systems encourages economic activity and linkages betw een adjacent counties. Walt Disney World, Orange Countys largest employer, is situated near the convergence of Interstate 4, Floridas Turnpike and State Highway 417. From Osceola County Walt Disney World is adjacent to US192 and is not far from the Florida Turnpike. Th e University of Centra l Florida (UCF), the 9th largest employer in Orange County, is proximal to SR-417 and SR-50. Floridas Turnpike and Interstate 95 are principal arteri als that feed into major thoroughf ares that will lead you to the UCF campus. Other counties within the region offer as many accessibility options the potential for growth is there. In Brevard County the major fe deral interstate is I-95 with federal highways US-1 and SR-A1A providing acce ss to the coast. Residents in coastal neighborhoods have expressed additional transportation access routes between the mainland and the barrier islands,

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72 which would assist in evacuations during hurrica nes and support the addi tional demand resulting from barrier island development. The importance of Polk County as an area that links the east coast of Florida with the west is inherent in layout of its tran sportation corridors. In terstate 4 connects residents and businesses of Orange and Osceola to Polk County and vice ve rsa. Access to Intersta te 75 on the west side of Polk County provides additional access to Hillsborough and Pasco Coun ties as well as all points north and south. Interstate 75 bisects the state of Florida but provides a way for residents of Floridas east coast to access the Hillsborough County and its port without ever going through its county seat, Tampa. The numerous transpor tation options available to residents of Polk County support the 16.6% of residents who work outsi de the Lakeland-Winter Haven MSA. It is important to note that the current cost feasible transportation plans do not call for new roads, only improvements to existing infrastructure. Other modes of transport within the region include rail and water (Table 4-5 and 4-6). Active rails exist within each county but Orange and Volusia Counties are the only two counties with passenger rail. Amtrak provides nonstop auto train service betw een the northeast and Sanford. Rail is the primary mode of transporta tion for mining and agricu lture, especially in Polk and Osceola counties. Rail in Central Flor ida is also the cheapest way to move materials such as timber and building materials to l ong distance destinations. For example, CSX Transportation services the major seaports with in Florida and has access to 23,000 miles of rail, reaching 23 states (JAXPORT, 2007). Industries that are core to the Central Florida economy rely heavily upon rail freight to meet their transportation needs. Port Canaveral is located in Brevard County and is the major seaport on the west coast. This port supports the cruise and cargo industries, commercial fish ing, the foreign trade zone and

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73 industrial park, federal government agencies, and the recreational park s all of which support economic activities within the region. This port has significant economic impact on Brevard County, the Central Florida Region, and the State of Florida. In Brevard County, Port Canaveral generates more than 34,000 jobs, $1.1 billion in wages, $1.5 billion in economic impact, and accounts for almost 17% of the countys total ec onomic worth. In Central Florida, Port Canaveral generates more than 50,000 jobs, $1.8 billion in wages, and produces an economic impact of $2.3 billion. Around the state, Port Canaveral generates more than 90,000 jobs, $3 billion in wages, and has an economic impact of $3.9 billion (Fishkind and Associates, 2005). Its physical location opens up the Atlantic Ocean and eastern seaboard for trade and future economic growth. The Port of Tampa is the closest port to Po lk County. The Port of Tampa is Floridas largest port, handling approximately 50 million tons of cargo per year. This port is the largest economic engine in West Central Florida. The port is located in Tampa and provides the most direct route to Mexico, Latin America, and the Caribbean. Tampa is also the closest full service U.S. port to the Panama Canal (Tampa Port Auth ority, 2007). In additi on to cargo services the port also provides passenger cruise lines and is adjacent to the mixed use retail, entertainment and residential Channelside District. Two major regional airports (Tab le 4-6) are located within th e East Central Florida region; Orlando/Sanford International Airport in Semi nole County and Orlando In ternational Airport (OIA) in Orange County. Located just 18 miles north of Orlando, the Orlando/Sanford International Airport services 6 airlines and provides domestic and international service to destinations including Europe a nd the Caribbean. Orlando Internati onal Airport is located within Orlando and is the largest air port in the region with 4 runw ays spanning 12,005 feet. Orlando

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74 International Airport serves more global destinati ons than any other airport within the region; 3 airlines service the Bahamas, 3 airlines serv ice Canada, 3 airlines service the Caribbean, 4 airlines service Central America an d 5 airlines service Europe. Th is airport also provides cargo service and FedEx has a fleet based at OIA to pr ovide regional shipping services. The airports around the region serve as entry and exits points for cargo, bu siness travel and tourism. Summary Transportation corridors and th e presence of large industries are important indicators of economic activity and growth. The East Centra l Florida region has the economic potential to become a major powerhouse in global and region al economies due to its complex transportation networks and presence of several industries that hire large numbers of creative individuals. The creative and creative indust ries are attracted to re gions with mass transit. One downfall of the East Central Florida region is that they have not yet integrated mass transit into its transportation infrastructure. This study will try to evaluate whether this region can still attract creative industries given future employment demands and existing infrastructure.

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75 Table 4-1. Largest cities and population, for each county (Adapted from Bureau of Economic a nd Business Research (BEBR). (2006). Florida Population Studies (Detailed Bulletins 145) Gainesville, Florida: University of Florida) County City County Population* City 2005 Population* % of County Population Brevard 531,970 Palm Bay 91,888 17.2% Melbourne 75,060 14.1% Unincorporated 210,260 39.5% Lake 263,017 Clermont 20,017 7.6% Leesburg 17,467 6.6% Unincorporated 146,221 55.6% Orange 1,043,437 Orlando 217,567 20.8% Apopka 34,801 3.3% Unincorporated 677,185 64.9% Osceola 235,156 Kissimmee 58,223 24.8% St. Cloud 24,700 10.5% Unincorporated 152,233 64.7% Polk 541,840 Lakeland 90,851 16.8% Winter Haven 28,724 5.3% Unincorporated 338,250 62.4% Seminole 411,744 Sanford 49,252 12.0% Altamonte Springs 42,616 10.4% Unincorporated 203,021 49.3% Volusia 494,649 Daytona Beach 65,129 13.2% Port Orange 54,630 11.0% Unincorporated 114,961 23.2% Population as of April 1, 2005

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76 Table 4-2. Regional 2005 population a nd 2050 projected population, by county County 2005 Population* 2050 Population* Brevard 531,970 932,704 Lake 263,017 653,766 Orange 1,043,437 2,230,650 Osceola 235,156 688,296 Polk 541,840 969,088 Seminole 411,744 775,265 Volusia 494,649 874,001 TOTAL POPULATION 3,521,813 7,123,770 (Adapted from Bureau of Economic a nd Business Research (BEBR). (2006). Florida Population Studies (Detailed Bulletins 145) Gainesville, Florida: University of Florida) Table 4-3. Major private sector employers County Employer Business Line Number of Employee Brevard United Space Alliance NASA Space Flight Operations Contractor 6,500 Harris Corporation International Communications Equipment Company 6,500 Health First, Inc. Integrated Healthcare Delivery System 6,100 Space Gateway Support Base Operations for NASA & 45th Space Wing 3,000 Wuesthoff Health System Full-Service Healthcare System 2,500 Northrop Grumman Corporation Global Aerospace & Defense Company 2,000 The Boeing Company Payload Processing for Shuttle Operations 1,800 Sea Ray Boats, Inc. Boat Manufacturer 1,200 MC Assembly PC Board Assembly 1,200 Rockwell Collins Avionics Systems Manufacturer 1,120

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77 Table 4-3. Continued County Employer Business Line Number of Employee Lake Leesburg Regional Medical Center Healthcare 2,300 Village of Lake Sumter, Inc. Retirement Community 2,200 Florida Hospital Waterman Healthcare 1,400 Sprint Telecommunications 811 G&T Conwyar Company N/A 550 Bailey Industries Manufacturing 509 Accent Architecture 500 Dura-Stress Concrete Supply & Storage 425 Lake Port Square N/A 400 Casmin Incorporated Construction 300 Orange Walt Disney World Entertainment 53,500 Orange County Public Schools Education 22,807 Adventist Health Systems Healthcare 17,059 Universal Orlando Entertainment 14,500 Orlando Regional Healthcare System Healthcare 12,000 Orange County Government Government 6,577 Lockheed Martin Combat System 5,700 Central Florida Investments Real Estate Developers 5,000 University of Central Florida Education 4,808 Darden Restaurants Corporate Headquarters 4,675

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78 Table 4-3. Continued County Employer Business Line Number of Employee Osceola McLane/Sunset, Inc. N/A 900 Florida Hospital Kissimmee Healthcare 794 Osceola Regional Medical Center Healthcare 522 Hyatt Orlando Kissimmee Hotel/Resort 500 Walt Disney Imagineering Artistic Production 450 Splendid China Amusement Park 400 Orange Lake Resort & Country Club Resort & Country Club 400 Mercury Marine Maine Electronic Equipment 400 Tupperware Corporation Housewares 300 Lerio Corporation Plastic Products 120 Polk Publix Super Markets Retail Food 8,500 Wal-mart Retail General Merchandise 5,500 Lakeland Regional Medical Center Hospital/Medical 4,000 MOSAIC Phosphate Mining 3,000 Winter Haven Hospital Hospital/Medical 2,500 GEICO Insurance 2,000 State Farm Insurance Insurance 1,500 Watson Clinic Medical 1,300 GC Services Call Center 1,200 Florida Natural Growers Citrus Processors 1,000

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79 Table 4-3. Continued County Employer Business Line Number of Employee Seminole Seminole County Public Schools Education 8,824 Convergys Corporation Billing Software 1,747 Seminole Community College Education 1,673 Sprint PCS Telecommunications 1,550 Siemans ICN Telecommunications 1,500 Seminole County Government Government 1,247 First USA Credit Card Processing 1,200 U.S. Postal Processing Plant Postal Service 1,000 American Automobile Association Travel Services 825 Florida Hospital Altamonte Springs Healthcare 800 Volusia Volusia County School Board School Board 8,998 Halifax Staffing Medical 6,330 Publix Super Markets Grocery 2,798 Wal-Mart Retail 2,206 Vision HW Inc. Management Services 1,667 Embry-Riddle Aeronautical University University 1,513 Florida Hospital Ormond Memorial Medical 1,403 Daytona Beach Community College Community College 1,334 Winn Dixie Stores Inc Grocery 1,290 (Adapted from Enterprise Florida. (2007). C ounty Profiles. Retrieved April 20, 2007, from http://www.eflorida.com/countyprofiles/Count yProfiles.asp?level1=3&level2=127&level3 =335®ion=)

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80 Table 4-4. Employment patt erns of workers 16 years a nd over who live in a MSA County MSA Total Workers 16+ living in a MSA (Estimate) Total Workers 16+ worked in MSA of residence (Estimate) % of Total Workers in MSA Total Workers 16+ worked outside MSA of resident (Estimate) % of Total Workers in MSA Brevard MelbourneTitusvillePalm Bay 228,806 214,597 93.8% 13,075 5.7% Lake Orlando 106,111 98,900 93.2% 5,291 5.0% Orange Orlando 490,871 477,595 97.2% 12,622 2.6% Osceola Orlando 106,445 100,813 94.7% 5,489 5.2% Polk Lakeland/ Winter Haven 227,493 187,099 82.2% 37,864 16.6% Seminole Orlando 198,737 189,913 95.6% 8,037 4.0% Volusia Daytona Beach 203,068 158,579 78.1% 40,796 20.1% NOTE: All estimates are with in 1% standard of error. (U.S. Census Bureau; 2005 American Community Survey; generated by Iris Patten; using American Factfinder; < http://factfinder.census.gov/servlet/Data setMainPageServlet?_program=ACS&_submenuI d=&_lang=en&_ts=>; (08 March 2007).

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81 Table 4-5. Transportation corridors (roads ) within the East Central Florida region County Federal Interstates Federal Highways State Highways Railroads Brevard I-95 US-1, US 192 SR-A1A; 5; 46; 50; 405; 407; 501; 520, 524; 528 Florida East Coast Railway Lake None US-27, US 441 SR-19, 33, 40, 44, 46, 50, 56, 439, 445, 561 CSX Transportation, Florida Central Orange I-4 US-441, US 17/92 SR-15, 408, 417, 419, 426, 436, 482, 520, 525, 527 Amtrak, CSX, Florida Central Osceola I-4 US-192, US-441, US 17/92 SR-15, 424, 419, 530, 532, 545, Florida Turnpike CSX Transportation, Florida Central Polk I-4 US-27, US 98 SR-60 CSX Rail Seminole I-4 US-17/92 SR-46, 417, 419, 426, 427, 434, 436 CSX Transportation, Florida Central Volusia I-4; I-95 US-1, US-17, US-40, US-92 CSX, Florida East Coast Railway, Amtrak (Adapted from Enterprise Florida. (2007). C ounty Profiles. Retrieved April 20, 2007, from http://www.eflorida.com/countyprofiles/Count yProfiles.asp?level1=3&level2=127&level3 =335®ion=)

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82 Table 4-6. Ports and airports within the East Central Florida region Nearest Airport with Scheduled Commercial Airline Service # Runways Longest Paved Runway (ft) General Aviation Airports Local Deep Water Port Miles to Closest Port Brevard Melbourne International Airport 3 10,200 Space Coast Regional Airport; Merritt Island Airport Port Canaveral 1 Lake Orlando International Airport 3 12,005 Leesburg Regional Airport Canaveral Port Authority 73 Orange Orlando International Airport 4 12,005 Orlando Executive Airport Canaveral Port Authority 46 Osceola Orlando International Airport 4 12,005 Kissimmee Municipal Airport Canaveral Port Authority 49 Polk Tampa International Airport 3 11,002 Lakeland Linder Regional Tampa Port Authority 49 Seminole Orlando/Sanford International Airport 4 9,600 Orlando/ Sanford Airport Canaveral Port Authority 48 Volusia Daytona Beach International Airport 3 10,500 Deland Municipal Airport; Ormond Municipal Airport; New Smyrna Municipal Canaveral Port Authority 72 (Adapted from Enterprise Florida. (2007). C ounty Profiles. Retrieved April 20, 2007, from http://www.eflorida.com/countyprofiles/Count yProfiles.asp?level1=3&level2=127&level3 =335®ion=)

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83 Figure 4-1. Study Area, 7 county East Central Florida region

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84 Figure 4-2. Metropolitan Statis tical Areas (MSAs) within the East Central Florida Region

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85 CHAPTER 5 METHODOLOGY This paper builds on the East Central Flor ida Regional Visioning process and applies advanced GIS technologies to determine whether the economic potential exists within the East Central Florida region to accommodate indus tries that require high-quality, innovative employees and skills. A major outcome of th e original visioning process was to understand potential growth patterns given various assump tions. The original visioning efforts also identified several economic indicators includi ng projected gross domes tic product and average per capita salary for each alternative. An outcome not iden tified, but one my study addresses, is the identification of lands within the region suita ble for innovation and high tech industries. This paper will also examine whether the land suitable for these industries are sufficient in area to accommodate future employment projections in a way that minimizes land use conflict and diversifies the economy. Explanation of the Model Creative individuals drive the economy, therefore they seek locations that stimulate innovation and provide opportunities that validate their identity and enable them to flourish. Keeping this in mind, what matters to the Creative individual is far different than what mattered to those in our parents gene ration. Building a model that w ould measure growth patterns and potential locations for employment centers in the year 2050 that are conducive to attracting the Creative Class involved modifying the 5 step LUCIS strategy. The Carr and Zwick methodology considers agricultural, conservati on, and urban land uses as indi vidual stakeholders. In Carr and Zwicks LUCIS model, each stakeholder uses a five step process to determin e its suitability of a given land area. The outcome of this process is a surface1 that visually depicts the most 1 The term surface is used interchangeably with raster.

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86 preferable use of land with respect to agriculture conservation or urban land uses. The five step process (Figure 5-4), as define d by Carr and Zwick (p. 12) are Goals and objectives : Define goals and objectives that b ecome the criteria for determining suitability. Data inventory : Identify data resources potentially relevant to each goal and objective. Suitability : Analyze data to determine relative suitability for each goal. Preference : Combine the relative suitabilities of goa l to determine preference for the three major land-use categories. Conflict : Compare the three land-use preference to determine likely areas of future landuse conflict. The first basic step of my study was to modify the Carr and Zwick LUCIS model to include the goals, objectives, and su b-objectives that reflect the valu es of creative individuals. In addition to the Carr and Zwick hierarchical rela tionship of goals, objectives, and sub-objectives for agriculture, conservation and urban stakeholde rs (Table 5-1 and Figures 5-1, 5-2, and 5-3), the hierarchical relationship of the Creative was added as Goal 5 under the urban land use type (Table 5-1 and Figure 5-3). The second basic st ep involved collecting th e appropriate GIS and quantitative data to measure those indicators. The third step involved us ing models to measure the suitability of land within the region for each stakeholder. The fourth step created a community/stakeholder preference value (represe nted in terms of a weighted value, and calculated using the analytic al hierarchy process (AHP2)), combined the suitability surface3 for each stakeholder, and created a map that identifi ed conflict between all stakeholders. The fifth step involved using the conflict surface and the fi nal suitability surface for Goal 5 to determine appropriate locations for creative industries and allocating creative centers of employment that minimized land-use conflict and satisfied the local demand for creative industries. 2 AHP is the non-generic form of pairwise comparison. AHP was developed by T. L. Saaty in 1980 at the University of Pennsylvanias Wharton School of Business and is a systematic method that compares a list of objectives or alternatives (Virginia Tech University, 2006)

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87 Identifying the Indicators The first step identified indicators that measur e the values and bias of the Creative Class as well as values and bias of each land use. Th is project divided the in dicators into two groups: those that identify land use suitability for the Crea tive Class and those that identify suitability of the agriculture, conservation, and urban land uses. Once all indicators were identified, they were translated into a set of corresponding goals and ob jectives representative of each stakeholder. As mentioned in the previous section, this study applied the original goals and objectives for agriculture, conservation, and urban as defined by Carr and Zwick (2007, p. 229)4. Through previous research, Richard Flor ida (Florida, 2002) iden tified values of the Creative. It is believed that these values have never been previo usly translated into a formal set of goals and objectives that can serve as criteria to measure land use suitability. This is why the indicators for the Creative and those for agriculture, conserva tion, and urban land uses were initially divided into two groups. Ultimately, this study added th e goals and objectives of the Creative Class to those developed by Carr and Zwick. The Creativ e Class goals and objectives can be found as Goal 5 under the urban stakeholder (Figure 5-3). A complete list of goals and objectives used for each stakeholder is available in Table 4-1. Below is an explanation of indicators that quantify the attractiveness of an area fo r the Creative Class (also known as Creative Centers). Creative Indicators The Creative Class seek places that provide economic opportunity, are highly efficient, offer a high level of amenities and are environmen tally stimulating. Creati ve Centers, which are areas composed of a high concentration of crea tive people, thrive because creative people want 4 The surfaces and models for determining the suitability of the agriculture and conservation stakeholders used in my study are a product of the work done by Dr. Paul Zwick and Margaret Carr for the East Central Florida Regional Growth Vision.

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88 to live there. Companies that re quire the skills offered by these individuals follow them or, in many cases, are started by the Creative them selves. Creative Centers have four common characteristics: High concentrations of innovation and high tech industry growth Increases in regional employment and population High-quality amenities and experiences Openness to diversity of all kinds These characteristics were used in developing indicators for determining suitability of land within the study area for potential loca tions of future creative industries. Clustering of Creative Industries The advent of the Internet a nd modern telecommunications st imulated an ideology that it was no longer necessary to work or be together. Yet after more th an 25 years since the inception of the Internet, people remain highly concentr ated. Residential grow th patterns around the country indicate that people are looking to beco me even more concentrated, moving back into central cities or into areas that are higher dens ity, promote walkability, provide cultural amenities and posses a sense of place. The high-tech, kno wledge-based creative-c ontent industries that drive economic growth continue to concentrate in specific places such as Austin, Silicon Valley, or New York (Florida, 2004, p. 219), primarily due to the tendency of firms to cluster together. Clustering produces a producti ve efficiency (Florida, 2004, p. 220). Companies cluster in order to draw from concen trations of talented people w ho power innovation and economic growth (Florida, 2004, p. 220). Clustered industrie s are typically similar and/or related that together create competitive advantages for me mber firms and the regional economy (Barkley and Henry, 2001, p. 2). David Barkley and Mark He nry, economists at Clemson University, cite four benefits of industry cluste ring that support Richard Florida s productive efficiency theory:

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89 Clustering Strengthens Localization Economies Cost savings are achieved through greater availability of specia lized input suppliers and busines s services; a larger pool of trained, specialized workers; pub lic infrastructure investment s geared to the needs of a particular industry; financial markets fam iliar with the industry; and an enhanced likelihood of interfirm technology and information transfers. Clustering facilitates Industrial Reorganization Increased global competition and the emergence of new production technologies (e.g., computer-aided manufacturing) encourage reorganization between large firms engaged in mass production to small firms focused on specialty production. Proximity be tween the more specialized firms and their input suppliers and product markets enhan ces the flow of goods through the production system. Ready access to product and input mark ets also enables firms to more quickly adapt to market changes. And a spatial con centration of firms provi des the pool of skilled labor required by the computer-aided technologies. Clustering Encourages Ne tworking Among Firms. Networking is cooperation among firms to take advantage of complementaries, exploit new markets, integrate activities, or pool resources or knowledge. This cooperati on occurs more naturally and frequently within industry clusters. Ne tworking firms are more likely than non-networking firms to engage in collaborating and information shar ing in marketing, new product development, and technological upgrading. The networking firms also report that their competitiveness and profitability are enhanced by in terfirm cooperation and collaboration. Clustering Permits Greater Fo cusing of Public Resources. A cluster approach enables regions to focus their recruitment, re tention and expansion, and small business development programs rather than attempti ng to provide assistance for many different business types. Also, because of linkages among firms in a cluster, programs supporting specific businesses will have relatively larg e multiplier effects for the area economy. The total employment and income gains from recr uiting (or retaining) cluster members will likely exceed those associated with non-cluster firms of similar size. The creative often look for regions that have a diversified econom y, since the creative often dont anticipate staying with the same company for very long. Creative markets must offer a thick labor market conduciv e to a horizontal career pat h. The gathering of people, companies and resources into particular places with particular speci alties and capabilities generate efficiencies th at power economic growth. These spatial efficiencies also encourage a more transparent flow of knowledge (Feldman, Aharonson, and Baum, 2005). Breschi and Lissoni describe knowledge spillovers as pure externalities but suggest that information flows more easily among agents located within the

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90 same area, thanks to social bonds that foster r eciprocal trust and freque nt face-to-face contacts. Therefore, geographical clusters offer more innovation opportunities than scattered locations (Breschi and Lissoni, 2001, p. 258). From this discussion the first indicator was drawn: Indicator #1: Identify lo cations proximal to existing creative industries. Author and futurist Joel Kotkin believes that wealth accumulates wherever intelligence clusters evolve (Florida, 2004, p. 220). Intellig ent people are far less c onstrained than other determinants of economic productivity such as th e abundance of raw materials or the proximity to dense populations or modes of transport. The true importance of knowledge relates to the theory of human capital. University of Chi cago economist Robert Lucas identifies two special characteristics of human capital. 1. With effort, human capital can be ac quired without limit and it doesnt take more effort to acquire it when you have more of it. This allows economies to grow without slowing as they become rich er, a possibility that the neoclassical model of Petty, Smith and Becker denied5. 2. Higher average levels of human capital in an economy raise the level of productivity of everybody in that econom y, not just the productivity of those whose human-capital level is higher. Thus human capit al is an externality. (Nowlan, 1997, p. 1) Furthermore, research by Patricia Beeson, an urban economist at the University of Pittsburgh, has cited that investments in higher ed ucation infrastructure predict subsequent city 5 The original human capital theory was rooted in work done by British economists Sir William Petty and Adam Smith during the 17th and 18th centuries. During the early 20th century their work was further developed by American economists Gary Becker and Theodore Schultz. Th e neoclassical view explains that the expenditure on training and education is costly, and should be considered an investment since it is undertaken with a view to increasing personal incomes (Economy Professor, 2007).

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91 and regional growth far better th an investments in physical infras tructure like canals, railroads, or highways (Florida, 2004, p. 222). Th erefore, the second indicator is: Indicator #2: Identify locations proximal to educated people. Kevin Lynch observed that the distinct qualiti es of an urban area that appeal to an individuals aesthetic senses a ffect an individuals perceptual satisfaction with the urban environment (Chapin and Kaiser, 1979, p. 284). Th e siting of key functional areas and buildings in relation to residential and r ecreational opportun ities is significant in general land use planning but also contributes to the quali ty of place for the Creative Class. Sociologists Richard Lloyd and Terry Nichols Clark of the University of Chic ago note that workers in the elite sectors of the postindustrial city make quality of life de mands, and increasingly act like tourists in their own city (Florida, 2004, p. 224). Modern creative work demand unpredictable work schedules and readily accessi ble recreation. This leads to the third indicator: Indicator #3: Identify locations proximal to existing trails, parks, or recreational opportunities. A city or regions ability to facilitate the interaction between people and the community is important in a highly creative environment. Plac es that embrace the culture of the Creative Age (i.e. places where the Creative can fit in) are an important gauge of the Creative. Nightlife is a key indicator, especially one w ith a wide mix of experiential options. These include music venues, neighborhood art galleries, performance spaces and theaters. Previ ous studies indicate the highest-rated nightlife options were cultural attractions (from the symphony and theater to music venues) and late-night dining, followed by small jazz and music clubs and coffee shops. Bars, large dance clubs and afte r-hours clubs ranked much fart her down the list (Florida, 2002, p. 225). Amenities such as historic buildings, bouti ques, and non-franchised stores and restaurants create an authentic environment, which contribut es to unique and original experiences. Thus,

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92 the Creative are attracted to unique environments and forms the framework for the fourth indicator: Indicator #4: Identify locations proximal to cultural activities, historic structures, and nightlife. The Creative Capital Theory, deve loped by Richard Florida, stat es that regional growth is driven by the location choices of creative peoplethe holders of creative capitalwho prefer places that are diverse, tolerant, and open to ne w ideas. The gay index signifies tolerance and is measured by the number of gay individuals in an ar ea. Floridas research indicates that the gayindex does better than other individual measures of social and cultural diversity to predict hightech location (Florida and Gates, 2002, p. 33); becau se high tech industries locate in diverse, tolerant areas. The presence of gays in me tropolitan areas signals di versity and progressive environments. Furthermore, Richard Floridas st udies show that when compared to the Milken Institute Tech Growth Index, which measures gr owth in output of high-tech industries within metropolitan areas, the concentration of gays also indicate the potential for economic growth. Thus the fifth indicator is drawn: Indicator #5: Identify areas with concentratio ns of gay populations. Social cohesion and business ties are base d upon trust and often facilitate inter-firm cooperation and the exchange of ideas. Furthermore, diversity encourages participation and serves as an asset in global economic advantag e. Smallbone, Bertotti, and Ekanem (2005, p. 49) consider ethnic diversity as a potential source of creativity an d innovation as well as informal networking. Creative industr ies that either locate within ar eas of highly diverse populations or employ a diverse workforce are exposed to a ble nded knowledge base and cu ltural perspectives. This creates international network links, which ar e a source of competitive advantage, and enable these companies to use their cross-cultural know ledge and experience in product development

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93 and marketing (Smallbone, Bertotti, and Ek anem, 2005, p. 49). Taube (2006, pp. 3-4) also indicates that diversity creates an additional connection for non-local sources of information and knowledge. The potential for additional economic growth due to embracing diversity is the underlying premise behind th e sixth indicator: Indicator #6: Identify areas with concentrations of diverse cultures. Data Collection GIS data used to measure land use suitability can be grouped into seven broad categories (Zwick and Carr, 2007, p. 90). These categorie s are: geophysical, biol ogical/ecological, demographic, economic, political, cultural, and in frastructure (Table 5-3). Once the goals and objectives were established, data was collected for each objective and sub-objective then mapped and measured, using ArcGIS6, for spatial accuracy. It is importa nt to note that this study used the final suitability surfaces for the agriculture and conservation land uses that were previously run by Carr and Zwick for the East Central Flor ida Regional Growth Vision. Therefore, there was no need for this study to recollect data or run the models used for these two stakeholders. Although, this study employed the same objectives and sub-objectives as the Carr and Zwick model and included the following asp ects: 1) we modified several of the inputs for the urban land use; 2) added the additional Cr eative goal; and 3) reran the urba n stakeholder. The following discussion reflects those methods undertaken for the urban stakeholder. Data Collection Methods for the Urban Stakeholder The data collected for all three stakeholders is static (Table 5-1). For the urban stakeholder, the data was available and collected from primarily two sources; the United States 6 ArcGIS is a family of software products produced by ESRI that form a complete GIS (Geographic Information System) most often used by planners, developers and researchers (maps-gps-info.com, 2007)

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94 Bureau of the Census (Census) or the Florida Geographic Data Library (FGDL) 7. Continual updates to FGDL provide a source of relativ ely recent and accurate GIS data. Temporal attributes of FGDL data varies. Due to costs associated with th e acquisition of current parcel data and limitations of data dist ribution cited in Florida Statue 193.114 (5), the parcel data used in this study represent platte d parcels documented through 2004. For each indicator whose source is the Census, 2000 decennial data is used. Spatially, Census data is represented accordi ng to tract level, when possible. The attribute table for Census data was edited to include fiel ds that appropriately quantify each indicator. For example, concentrations of demographic populations we re measured using the appropriate Census population indicator divided by the nu mber of people in that tract. For indicators that measured land value, values were calculated per acre according to each section-township-range as identified by the Public Land Survey System Common tools within ArcMap ArcToolbox were used for data manipulation and analysis (Table 5-5). Data obtained from FGDL was downloaded in ei ther shapefile or raster format. Basic vector and raster analys is tools (i.e. Select by Attribute, Merge, Append, Intersect, etc.) were used to combine individual county datasets and c lip state datasets to th e extent of the region. Vector8 data were converted to raster9 format. Rasters reduce model processing time and greatly increase efficiency of the land-use modeling process (Zwick and Carr, 2007, p. 98). 7 The Florida Geographic Data Library (FGDL) is data warehouse that distributes GIS data free of charge for spatial geographies for the entire State of Florida and its counties. 8 Vectors are coordinate based data models that represents geographic features as points, lines and polygons (Wade and Sommer, 2006, p. 224). 9 Rasters are cellular GIS datasets constructed to repres ent spatial phenomena or geog raphic features within a framework of uniform cells (Carr and Zwick, 2007a, p. 18).

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95 Suitability Modeling The third step makes use of ArcGIS ModelBuilder10 and methods developed by Carr and Zwick to diagram and simulate land use suitability. This step also employs Spatial Analysis tools to measure proximity and suitability of each quarter acre of land within the region with respect to the values and bias of each stakeholder. De pending upon the intent of the objective and/or subobjective, models utilize various raster analysis tools to m easure proximity. Commonly used tools include Euclidean Distance, Extract by Attr ibutes, Extract by Mask, Zonal Statistics, and Reclassify. Suitability is measured in units of utility value, also known as a single utility assignment (SUA). The Reclassify tool within ArcGIS is used to assign a utility value to each cell11 within the dataset that represents an indicator. LUCIS employs a value range of 1 to 9 (Table 5-4), with 1 representing low suitability a nd 9 representing high suitabilit y. NoData can be assigned to unsuitable areas, but care must be taken because once used in a model, NoData will eliminate attribute information from subsequent analyses for the cells containing the NoData value (Carr and Zwick, 2007a, p. 103). For additional details and a step-by-step explana tion of how to create an SUA, refer to Chapter 8 in Carr and Zwicks Smart Land-Use Analysis: The LUCIS Model (Carr and Zwick, 2007a). Once a SUA has been created for each object ive and/or sub-object ive, the SUAs are combined to create a simple multiple utility assignment (MUA). The MUA process combines 10 ModelBuilder is an application within ArcGIS in which you create, edit, and manage models (ESRI 2006). Input data and geoprocessing tools can be strung together, with the output of one tool serving as the input for another, and the whole model can be run as a single operation with the click of a button. With the ability to place GIS data and geoprocessing tools in a visual program, the GIS analyst can create complex programs without having to learn a programming language (Carr and Zwick, 2007a, p. 26). 11 A cell is the smallest unit of information in raster data usually square in shape (Wade and Sommer, 2006, p. 27). For this project each cell repr esents an area of 31 square meters, which is equivalent to a quarter acre.

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96 layers using weights or percen tage of influence that equal 1.0 (100%) (Carr and Zwick, 2007a, p. 57; p. 103). The MUA process can also include the use of conditional statements, which process multiple raster data by selecting cells for the output raster based on if-then-else processing (Carr and Zwick, 2007a, p. 37). A MUA was de veloped for each urban stakeholder goal. Weighted Suitability Before a final suitability raster for the ur ban stakeholder was crea ted, a preference value for each urban goal was developed, this is step f our. The Expert Choice software was used to develop a preference value using the analytical hierarchy process listed below, as outlined by Carr and Zwick: First, a model is created and th e project goal is stated. The goal for pairwise comparison is a written statement defining pair comparisons. For example, which habitat is more suitable for residential development, A or B? Second, all feature types within the dataset are inserted as components of the overall goal. Then, each unique pair of habitat types is compared for their usefulness in supporti ng residential development. Habitats are compared using values from 1 (equally important/useful) to 9 (extremely more important/useful). Next, the pairwise comparisons are evaluated within a matrix for all pairs of values to produce final pairwise util ity values. Last, the final pairwise utility values are transformed into single utility assi gnment values ranging from 1 to 9 (Table 55). The AHP analysis produces a weight for each stakeholder goal. These goals are combined using a map algebra equation (i.e. the Single Ut ility Assignment tool) according to the weight produced from AHP. The result is a final pr eference map for each stakeholder. Each map depicts areas preferred for a specific land use. Conflict Identification Step five identifies conflict. Three main tasks are required to complete this step: (1) remove lands whose use will not change, (2) norma lize and collapse preference results, and (3) combine the normalized and collapsed preference rasters to identify areas of conflict (Carr and Zwick, 2007a, p. 137).

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97 The LUCIS model identifies preference values for various land use types and indicates future changes in land uses, although there are ar eas whose land use are permanently designated and will not change. These areas include open water, roads, existing urban areas (excluding vacant platted), and existing conservation lands. A single raster mask is created from these datasets and cells whose land use will not cha nge are removed before identifying conflict. A raster mask is created by convert ing the above datasets to a grid12 using the same cell size, 31, as used in all raster analysis associat ed with this project. The following methods, employed by Carr and Zwick indicate how to create a raster mask. Using the Reclassify tool on each grid, NoData values are assigned to areas to be excluded from consideration. All remaining cells are a ssigned a value of 1, thereby making all cells with a value of 1 available for conflict an alysis. Next, using the Single Output Map Algebra tool, the three individual mask layers were multiplied to create a final mask of areas available for future land-use developm ent considerations. The final development mask was then used in ModelBuilder to limit conflict analysis to those areas where development actually has the potential to occur. (Carr and Zwick, 2007a, p. 138) The final preference raster developed in step three for each stakeholder has values that range between 1 and 9, but may not include the valu e 9. For a value of 9 to result, at least one cell in the study area would have to be optimally suited for every measure of suitability included in the goals, objectives, and sub-objectives for th at land-use category (C arr and Zwick, 2007a, p. 139). The probability of this occurring is very low, so Carr and Zwick recommend normalizing the values before comparing preferences (Carr and Zwick, 2007a, p. 139). The following methods, taken from Carr and Zwick, ar e used to normalize preference surfaces: The development mask is designated as a pa rameter in the ModelBuilder environment settings, so normalization only occurs on the areas with developmen t potential. Preference normalization is accomplished using the Divide tool (Spatial Analyst Tools > Math toolset), which divides each cell value in a land-use prefer ence raster by the highest individual cell value in that raster. The resu lting raster will cont ain cell values ranging from 0 to 1.0 (depending on the number of decimal places assigned to the calculation). At 12 A grid is a synonym for raster. The two terms are used interchangeably throughout this paper.

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98 the extremes, it is possible the results might include as many unique values as there are cells in the study area. On the other hand, ther e may be as few as two values; but usually the normalization process results in a large number of unique cell values. (Carr and Zwick, 2007a, p. 139) Once the surfaces are normalized, each of the three normalized rasters is combined into three classes that co rrespond to high, medium, and low pref erences. This method is called collapsed preference and identifies the relati onships among the three land uses. Collapsed preference mapping identifies place s where conflict between land-us e categories exists and how strong the conflict might be (C arr and Zwick, 2007a, p. 139). For this study, values were reclassified using the standard deviation method. This method was chosen to produce as even a distribution of preference values as possible. The di stributed values are characterized using a designation of 1, 2, and 3, which desc ribe the level of preference. To visualize the conflict betw een the surfaces of the three stakeholders, a method referred to as conceptualized conflict by Carr and Zwic k was used. As mentioned above, the collapsed preference surface is characterized using values of 1, 2 and 3. Cells with the value of 1 indicate low preference, 2 medium preference, and 3 high pr eference. To combine a ll three surfaces into one, each collapsed preference surface must be on a di fferent scale. This paper utilizes the same categories as Carr and Zwick: agriculture pref erence is collapsed to produce values of 100, 200, and 300; conservation preference is collapsed into categorie s of 10, 20, and 30; and urban preference maintains its current categories of 1, 2, and 3 (Ca rr and Zwick, 2007a, p. 149). Using the Single Output Map Algebra function in Ar cMap, a statement was developed that would combine all three surfaces and multiply each re spective surface by the appropriate factor to achieve categories that would id entify the conflict within that land use type (Equation 4-1).

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99 Eq. 4-1: Equation to determine conflict (Z:\thesis\Workspace\Resu lts\collap_agri 100) + (Z:\thesis\Workspace\Resu lts\collap_cons 10) + (Z:\thesis\Workspace\Results\collap_urb) The values in the resultant final preferen ce surface will range fr om 111 to 333. The first digit in each number is representative of agricu lture preference, the seco nd digit representative of conservation preference and the th ird digit represents the urban preference. Additionally, areas of moderate conflict are identified when two la nd use types have the same preference value and severe conflict occurs when all three land use types have the same preference value. For example, preference value 122 has a moderate conflict between conservation and urban land uses. Severe conflict exists with three of the pot ential preference valu e combinations, 111, 222, or 333 (Table 5-6 and Figure 5-6). The objective of this project was to build upon the work done by Carr and Zwick for East Central Florida to identify areas that, because of physical and economic characteristics, can be described as Creative Environments and enc ourage the location of creative industries. A Creative Environment is conducive to creative industries, which c ontribute to attracting a higher level of jobs and increase the quality of place. The final preference surface described above identifies areas that are more appropriate for a sp ecific land use. Included in areas where urban preference dominates are residential, comme rcial, industrial and creative environments. To distinguish areas that are most appropr iate for urban development but are most appropriately categorized as Creative Environments (as opposed to residentia l or industrial), the next step requires us to remove from the fina l preference map areas that are preferred for urban uses (i.e. Urban Wins) and areas suitable for Creative Environments (Figure 5-3).

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100 Urban Goal 5 represents the suitability surface for Creative Environments. Areas identified with a value of 813 or higher indicate a high degr ee of suitability. We use a conditional statement to identify areas in Urban Goal 5 that have valu es of 8 or higher and intersect with those areas in th e final preference surface that are suitable for urban development (Equation 5-2). Eq. 5-2. Equation to Determine Creative and Urban Environments CON (Z:\thesis\Workspace\Results\final_pref14 == 112 or Z:\thesis\Workspace\Results\final_pref == 113 or Z:\thesis\Workspace\Results\final_pref == 123 or Z:\thesis\Workspace\Results\final_pref == 213 or Z:\thesis\Workspace\Res ults\final_pref == 223 AND con(Z:\thesis\Workspace\Results\creative\ug5_final GE 8, 9)) Among the four classifications (i .e. residential, retail, offi ce/commercial, and industrial) within the definition of urban represented by the Carr and Zw ick conflict surface, creative occupations more generally fit within the description of office/commercial. The specific occupations that place great value in working in an environment classified as creative are: Computer and mathematical occupations Architectural and engineering occupations Life, physical, and social science occupations Education, training and library occupations 13 Earlier discussion indicated that the suitability values assigned by Carr and Zwick range between 1 and 9, with 9 as the highest suitability. In this study, the suitability surface for Urban Goal 5 resulted in a high value of 8.625. Therefore, when examining areas with in Urban Goal 5 that are most su itable for Creative Environments, we consider those areas greater than 8. If our suitability surf ace had values equal to 9, then we would only seek to separate out those cells with values of 9. 14 The file name final_pref is the raster gr id which represents th e final conflict surface.

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101 Arts, design, entertainment, sports, and media occupations Management occupations Business and financial occupations Legal occupations Healthcare practitioners and technical occupations High-end sales and sales management (Florida, 2002, p. 328) Thus far we have identified areas that provi de a high quality of place and are appropriate for urban development. To further narrow dow n available lands for developing employment centers that require high quality employees, we us e a conditional statement to identify areas that are also suitable for Office/Commer cial development (Equation 5-3). Eq. 5-3. Equation to determine areas that are Creative, Urban and Suitable for Office Commercial CON (Z:\thesis\Workspace\Results\creative_dev2 eq 9 AND Z:\thesis\Workspace\Results\c ommercial\ug2_final GT 7, 9) Allocating Employment Centers The economic impact forecasting tool REMI (2007) determines employment projections and sector impact analysis by industrial sector. The three industrial ca tegories are extraction, manufacturing, and service. Interaction between people and the propagation of knowledge and creativity is the basis of the se rvice sector. In the year 2050 th e East Central Florida region will employ a total of 1,796,734 people. Compared to 2005 employment figures, this includes an increase of 832,400 people in the service sector. Of these, over one-third will be new employees

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102 in occupations classified as creative15. To allocate industries th at employ creative individuals and those that support the creative we first iden tify land areas necessary to accommodate these industries. This project allocates three building sizes th at represent future employment centers for creative industries. As the build ings are placed within the region, the number of employees each building holds is subtracted from the new empl oyees expected from 2005 to 2050. Table 5-7 lists the sizes and respective number of employees per building. The surface developed thus far indicates la nds suitable for employment centers that provide a good quality of place and also offers good quality jobs. The attribute table associated with the grid lists the number of cells that fit this criterion. Each cell is comparable to a land area of 1 quarter acre. The cells listed in the attribute ta ble represent contiguous and individual cells. To identify lands that have a contiguous area suitable for different building sizes we use the Region Group16 function within ArcMap. The Num ber of Neighbors option within the Region Group dialog box can be changed to include more or less neighbori ng cells in evaluating connectivity17. Each record in the attribute table of the resultant surface represents a contiguous set of cells with the tota l size of that record identified by the number in the Count field. This produces a result that allows you to classify areas of contiguous available land by size and place small, medium and large employment centers. 15 Employment figures and projections for 2005 and 2050 were developed using the REMI (Regional Economic Models, Inc.) software package. This software generates re alistic year-by-year estimates of the total regional effects of any specific policy initiative (REMI, 2007) 16 The Region Group function Selects and groups cells that are contiguous and have the same value. 17 The Number of neighbors to use indicator provides two neighbor sizes that can be used for analysis, eight or four. By selecting four, connectivity will be defined between cells of the same value that are directly to the right or left or above or below each other. Diagonal cells are no t considered. Conversely, by selecting eight, connectivity between cells of the same value that are to the right or le ft, above or below, or diagona l to each other will be defined (ESRI, 2006).

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103 Allocate Industry by County The Bureau of Labor Statistics defines the lo cation quotient as a ratio that compares the concentration of a resource or activ ity, such as employment, in a de fined area to that of a larger area or base (Bureau of Labor Statistics, 2007b). Indicators such as the location quotient provide a means to determine the economic base of a county or region. In this analysis, the location quotient is used to determine the concen tration of creative speci alizations within the local (county) economy compared to the states economy. When the location quotient is less than 1, local employment for creative industries is less than was expected for a given creative industry. Therefore, that spec ific creative industry is not m eeting local demand for a given creative good or service. If the location quotient is equal to 1 the local creative employment is exactly sufficient to meet the local demand for a given creative good or service. Lastly, if the location quotient is greater than 1 the local empl oyment for creative industrie s is greater than the need (Florida State University, 2006). For each county the location quotient for creati ve industries was calculated. The sectors that include occupations charac terized as creative are: NAICS18 51 Information; NAICS 52 Finance and insurance; NAICS 54 Professional and technical services; NAICS 55 Management of companies and enterprises; NAICS 61 Educati onal services; NAICS 62 Health care and social assistance; and NAICS 71 Arts, entertainment, and recreation19. For each county the average location quotient for these sectors were calculated and the result represents the local demand for creative occupations. 18 NAICS (North American Industry Classification System ) codes succeed the Standard Industrial Classification (SIC) system and is used to classify business es tablishments (Bureau of Labor Statistics, 2007b) 19 In this analysis sector level NAICS code were used.

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104 To build upon existing local demand for high-t ech innovative services the seven counties were sequentially ordered from greatest to least location quotient. This represents the procession of employment center allocation. Once I created individual rast ers for each county containing the groupings of adjacent cells, I began to allocat e the first cluster of employment centers. The first round of allocation placed large then medium then small buildings. To accomplish this, I selected records from the newly created count y grids of grouped cells with enough contiguous cells to at least accommodate the largest size building (Table 5-8). To avoid wasting greenfields and avoid creating a homogeneous urban landscape of mono-sized buildings, I created an Excel spreadsheet that took the total count of cells and allocated buildings of each size. The number of buildings that could be accommodated from each group of contiguous cells depended upon how many multiples (each multiple represents 1 build ing) of each respectiv e building cell count (Table 5-8) could be extracted from the total number of contiguous cells. For example, first we select records with gr ouped cells of larger than 38 for the county with the highest location quotient. The attribute tabl e of the records that fit this criteria will often have counts that are much larger than 38 and allow for the allocation of more than 1 large building. If there are cells remaining following the allocation of all multiples of 38 then we allocate medium buildings by determining the number of 19 cell multiples in the remainder. When the spreadsheet looks for multiples of 19, it is allocates medium size employment centers. From the remaining cells after the medium si ze building allocation, the spreadsheet looks for multiples of 10. Small employment centers are roughly 2.3 acres, whic h is equivalent to 10 cells. It is possible to have cells remaining after the last employment center allocation. These could be urban parks or some other use that benefits the overall working and physical environment.

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105 To determine the number of employees acco mmodated through each countys allocation, take the number of new employees expected fo r the region and subtract the capacity of the building allocated. For example, if the original number of ne w employees expected in 2050 is 832,400 and during the first allocation described above we placed 2 large, 2 medium and 1 small employment center in Orange County, then we would have 826,880 employees left after the Orange County allocation (Figure 5-3). After placing employment centers using the largemediumsmall building progression, the process begins again but this time only allocate medium and small build ings; allocating medium buildings first then small. During this allocation select records with grouped cells greater than or equal to 19 and less than 38 from the same county used for the first allocation. The number of medium size buildings that can be accommodated within e ach county by using the Excel spreadsheet which looks for multip les of 19 within each contiguous group of cells. From the remaining cells after the medium size building allocation, the spreadsheet looks for multiples of 10, which represents the number of cells needed for a small building. All remaining cells after the small building allocation can be used for urban parks or another sustaina ble use. If there are employees remaining after the medium small bu ilding allocation, select those cells from your each countys attribute table that are greater than or equal to 10 cells but less than 19. With these cells small buildings can be allocated using th e same process. Once all allocation sequences have been exhausted for a given county, repeat th e process using the county with the next highest location quotient The process described above outlines a met hod for allocating employment centers to satisfy future creative employme nt projections while creating a diverse clustered development pattern for industries that incite economic growth.

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106 Table 5-1. Goals and objectives used for optimizing choice for agriculture, cons ervation, and urban land uses25 Agriculture Suitability: Statement of Intent: Identify lands most suitable for agricultural use. Figure 5-1. Diagram of hierarch ical relationships of goals, obj ectives and sub-objectives for agricultural land use suitabilit y analysis 25 The goals and objectives used in this study, with the except ion of those to determine Creative suitability, were derived from Carr and Zwick (Carr and Zwick, 2007, pp. 229-237).

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107 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 1 Identify lands suitable for croplands/row crops Objective 1.1 Identify lands physically suitable for croplands/row crops Sub-objective 1.1.1 Identify soils that are most suitable for croplands Regional soils (corn, snap beans, cabbage, tomatoes) Sub-objective 1.1.2 Identify current croplands as suitable Land use and parcels identified as agricultural Goal 1 Identify lands suitable fro cropland/row crops Objective 1.2 Determine lands economically suitable for croplands/row crops Sub-objective 1.2.1 Assign economic suitability for croplands/row crops based on dollar value per acr e thresholds for lands that might realistically remain as croplands/row crops Land value raster Sub-objective 1.2.2 Identify lands proximal to markets for croplands/row crops City limits

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108 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for intensively managed livestock Objective 2.1 Identify lands physically suitable for intensively managed livestock Sub-objective 2.1.1 Identify underlying geology suitable for intensively managed livestock Aquifer suitability Sub-objective 2.1.2 Identify existing intensively managed livestock lands as suitable Land use and parcels identified as uses that are intensively managed Objective 2.2 Determine lands economically suitable for intensively managed livestock Sub-objective 2.2.1 Identify lands proximal to markets for intensively managed livestock Land use and parcels identified as feeding operations, fruits, vegetables and meat packing Sub-objective 2.2.2 Determine proximity to potentially troublesome adjacent land uses (e.g. residential land uses whose users will be displeased with intensively managed livestock). Land use and parcels identified as residential

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109 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for intensively managed livestock Objective 2.2 Determine lands economically suitable for intensive managed livestock Sub-objective 2.2.3 Identify lands with values suitable for intensively managed livestock Land value raster Goal 3 Determine lands suitable for low-intensity livestock activities Objective 3.1 Identify lands physically suitable for low intensity livestock activities Sub-objective 3.1.1 Identify soils most suitable for low-intensity livestock activities Soils associated with pastures Sub-objective 3.1.2 Identify lands with existing low-intensity livestock as suitable Land use and parcels identified as pasture Goal 3 Determine lands suitable for low intensity livestock activities Objective 3.2 Determine lands economically suitable for low-intensity livestock activities Sub-objective 3.2.1 Identify lands proximal to markets for low-intensity livestock Land use and parcels identified as feeding operations, fruits, vegetables and meat packing

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110 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 3 Determine lands suitable for low intensity livestock activities Objectives 3.2 Determine lands economically suitable for low intensity livestock activities Sub-objective 3.2.2 Identify lands with values suitable for low-intensity livestock Land value raster Goal 4 Identify lands suitable for Timber/Silviculture Objective 4.1 Determine lands physically suitable for timber Sub-objective 4.1.1 Identify soils that are most suitable for silviculture Soils suitable for timber production Sub-objective 4.1.2 Identify current timberlands as suitable Land use and parcels identified as timberland Objective 4.2 Determine lands economically suitable for silviculture Sub-objective 4.2.1 Assign economic suitability for timberlands based on dollar value per acre threshold for lands that might realistically remain as timberland Land value raster and proximity to city limits/market locations Sub-objective 4.2.2 Identify lands proximal to markets for timber and pulpwood Land use and parcels identified as timberland; city limits/market locations

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111 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 5 Identify lands suitable for specialty agricultural activities including nurseries, aquaculture, and orchards Objective 5.1 Identify current specialty agricultural land as suitable Sub-objective 5.1.1 Identify soils suitable for specialty agriculture where appropriate Soils suitable for nurseries Sub-objective 5.1.2 Identify current specialty agricultural lands as suitable Soils suitable for specialty agriculture production Objective 5.2 Determine lands economically suitable for specialty agriculture Sub-objective 5.2.1 Assign economic suitability for specialty agriculture based on a dollar value per acre threshold for lands that might realistically remain in specialty agriculture Land value raster Sub-objective 5.2.2 Identify lands proximal to markets for specialty agricultural products City limits/market locations

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112 Conservation Suitability: Statement of Intent: Identify lands most suitable for permanent protection thr ough the application of c onservation strategies. Figure 5-2. Diagram of hierarch ical relationships of goals, obj ectives and sub-objectives for conservation land use suitabilit y analysis

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113 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 1 Identify lands suitable for protecting biodiversity Objective 1.1 Identify lands with high native biodiversity Sub-objective 1.1.1 Identify priority wetland habitats Priority wetland habitat Sub-objective 1.1.2 Identify strategic habitat conservation areas Strategic habitats Sub-objective 1.1.3 Identify biodiversity hot spots Biodiversity hotspots Sub-objective 1.1.4 Identify habitats with high native biodiversity Habitat and landcover Objective 1.2 Identify lands with relatively low road density Road density Objective 1.3 Identify areas proximate to existing conservation lands Florida Natural Areas Inventory-Existing Conservation

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114 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for protecting surface and groundwater quality Objective 2.1 Identify lands suitable for protecting groundwater quality Sub-objective 2.1.1 Identify lands with high recharge potential/aquifer vulnerability Aquifer recharge facilities Sub-objective 2.1.2 Identify sinkholes and associated buffers of a size sufficient to filter runoff from adjacent lands Sinkholes Objective 2.2 Identify lakes, rivers and streams, and associated buffers of a size sufficient to filter runoff from adjacent lands to protect surface waters Hydrography and springs Goal 3 Identify lands suitable for protection of important ecological processes Objective 3.1 Identify lands important for the movement of fire across the landscape

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115 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 3 Identify lands suitable for protection of important ecological processes Objective 3.1 Identify lands important for the movement of fire across the landscape Sub-objective 3.1.1 Identify fire-maintained communities Habitat and landcover Sub-objective 3.1.2 Identify non-burnable areas and associated buffers of a size sufficient to protect them from fire Areas classified as urban from parcels and land use data Goal 3 Identify lands suitable for protection of important ecological processes Objective 3.2 Identify lands important for maintenance of the process of flooding and flood storage in the landscape Sub-objective 3.2.1 Identify wetlands Habitat and landcover Sub-objective 3.2.2 Identify rivers and associated buffers of a size sufficient to protect th eir flood storage function Rivers Sub-objective 3.2.3 Identify open water and associated buffers of a size sufficient to protect th eir flood storage function Hydrography

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116 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 4 Identify lands suitable for resource based outdoor recreation Objective 4.1 Identify existing areas used for resource-based outdoor recreation, including hunting areas Sub-objective 4.1.1 Identify existing resource-based parks and recreation areas Existing conservation lands Sub-objective 4.1.2 Identify existing and po tential trail corridors Existing and proposed trails Sub-objective 4.1.3 Identify cultural and hi storic sites potentially compatible with outdoor recreation Structures with architectural and historic significance Goal 4 Identify lands suitable for resource based outdoor recreation Objective 4.2 Identify all surface water features with the potential for use for outdoor recreation Goal 4 Identify lands suitable for resource based outdoor recreation Objective 4.3 Identify existing linear infrastructure with the potential for use as trail corridors Sub-objective 4.3.1 Identify abandoned rail lines Existing rails

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117 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 4 Identify lands suitable for resource based outdoor recreation Objective 4.3 Identify existing linear infrastructure with the potential for use as trail corridors Sub-objective 4.3.2 Identify utility corridor that might serve as trail corridors Goal 4 Identify lands suitable for resource based outdoor recreation Objective 4.4 Identify native habitat suitable for resourcebased recreation Habitat and landcover Goal 5 Identify lands suitable for enhancing existing conservation areas, within the Florida Ecological Network Objective 5.1 Identify native habitats with the potential to enhance existing conservation lands Habitat and landcover Goal 5 Identify lands suitable for enhancing existing conservation areas, within the Florida Ecological Network Objective 5.2 Identify lands within close proximity to existing conservation lands Florida Natural Area Inventory-Existing Conservation Goal 6 Identify lands with property values suitable for permanent protection through the application of conservation strategies Land value

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118 Urban Suitability Statement of Intent: Identify lands most suitable for urban development Figure 5-3. Modified LUCIS goals and objectives, which include values of the Creative Class Urban Stakeholder Goal 1 (Residential) Goal 2 (Office/ Commercial) Goal 3 (Retail) Goal 1 Objective 1 (1.1) Goal 1 Objective 2 (1.2) Goal 2 Objective 1 (2.1) Goal 2 Objective 2 (2.2) Goal 3 Objective 1 (3.1) Goal 4 (Industrial) Goal 5 (Creative Class) Goal 3 Objective 2 (3.2) Goal 4 Objective 1 (4.1) Goal 4 Objective 2 (4.2) Goal 5 Objective 1 (5.1) Goal 5 Objective 2 (5.2) Goal 5 Objective 2 Sub-objective 1 ( 5.2.1 ) Goal 5 Objective 2 Sub-objective 2 ( 5.2.2 ) Goal 5 Objective 1 Sub-objective 1 ( 5.1.1 ) Goal 5 Objective 1 Sub-Objective 2 ( 5.1.2 ) Goal 4 Objective 2 Sub-objective 1 ( 4.2.1 ) Goal 4 Objective 2 Sub-objective 2 ( 4.2.2 ) Goal 4 Objective 1 Sub-objective 1 ( 4.1.1 ) Goal 4 Objective 1 Sub-objective 2 ( 4.1.2 ) Goal 3 Objective 2 Sub-objective 1 ( 3.2.1 ) Goal 3 Objective 2 Sub-objective 2 ( 3.2.2 ) Goal 3 Objective 1 Sub-Objective 1 ( 3.1.1 ) Goal 3 Objective 1 Sub-objective 2 ( 3.1.2 ) Goal 2 Objective 2 Sub-objective 1 ( 2.2.1 ) Goal 2 Objective 2 Sub-objective 2 ( 2.2.2 ) Goal 2 Objective 1 Sub-objective 1 ( 2.1.1 ) Goal 2 Objective 1 Sub-objective 2 ( 2.1.2 ) Goal 2 Objective 2 Sub-objective 1 ( 2.2.1 ) Goal 2 Objective 2 Sub-objective 2 ( 2.2.2 ) Goal 1 Objective 1 Sub-objective 1 ( 1.1.1 ) Goal 1 Objective 1 Sub-objective 2 ( 1.1.2 )

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119 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 1 Identify lands suitable for residential development Objective 1.1 Determine lands physically suitable for residential development Sub-objective 1.1.1 Identify soils suitable for residential land use Soil drainage, soil corrosion Sub-objective 1.1.2 Identify lands that are free of flood potential Habitat and Landcover (gfchab03), FEMA floodplain data Sub-objective 1.1.3 Identify quiet areas Active rails, major highways, regional airports Sub-objective 1.1.4 Identify lands free of hazardous waste EPA National Priority List Sites, hazardous waste sites, radon locations Sub-objective 1.1.5 Identify lands with good air quality Powerplants, sewage treatment plants, land use and parcel data identified as heavy manufacturing, other land use classification associated with negative contributors to air quality

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120 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 1 Identify lands suitable for residential development Objective 1.2 Determine lands economically suitable for residential development Sub-objective 1.2.1 Identify lands proximal to existing residential development Land use and parcel data identified as residential Sub-objective 1.2.2 Identify lands proximal to schools Schools classified as public and private elementary, middle, and high schools Sub-objective 1.2.3 Identify lands proximal to hospitals as well as care facilities for the aging Medical centers and hospitals, retirement homes and elder care facilities Sub-objective 1.2.4 Identify lands proximal to roads Road network supplied by East Central Florida Regional Planning Council Sub-objective 1.2.5 Identify lands proximal to airports Regional airports Sub-objective 1.2.6 Identify lands proximal to parks, other recreational opportunities, protected c onservation lands and/or cultural and historic sites Significant architectural and historical structures, parks and zoos, recreational facilities, and existing conservation lands

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121 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 1 Identify lands suitable for residential development Objective 1.2 Determine lands economically suitable for residential development Sub-objective 1.2.7 Identify lands proximal to existing public water, sewer, and municipal utilities service Water plants, sewage treatment plants, those categories identified as utilities and communications facilities from the Landuse raster Sub-objective 1.2.8 Identify lands with values suitable for residential use Land value raster Goal 2 Identify lands suitable for office/commercial development Objective 2.1 Determine lands physically suitable for office/commercial development Sub-objective 2.1.1 Identify soils suitable for office/commercial land use Soil drainage, soil corrosion Sub-objective 2.1.2 Identify lands free of flood potential Habitat and landcover, FEMA flood zone data Sub-objective 2.1.3 Identify quiet areas Active rails, major highways, regional airports Sub-objective 2.1.4 Identify lands free of hazardous waste EPA National Priority List Sites, hazardous waste sites, radon locations

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122 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for office/commercial development Objective 2.1 Determine lands physically suitable for office/commercial development Sub-objective 2.1.5 Identify lands with good air quality Powerplants, sewage treatment plants, land use and parcel data identified as heavy manufacturing, other land use classification associated with negative contributors to air quality Goal 2 Identify lands suitable for office/commercial development Objective 2.2 Determine lands economically suitable for office/commercial development Sub-objective 2.2.1 Identify lands proximal to existing residential development and approved DRIs Residential and mixed use DRIs, land use and parcels identified as residential Sub-objective 2.2.1 Identify lands proximal to existing residential development and approved DRIs Residential and mixed use DRIs, land use and parcels identified as residential Sub-objective 2.2.2 Identify lands within and proximal to existing city limits City limits

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123 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for office/commercial development Objective 2.2 Determine lands economically suitable for office/commercial development Sub-objective 2.2.3 Identify lands proximal to roads Major roads Sub-objective 2.2.4 Identify lands proximal to intersections of major roads Intersections Sub-objective 2.2.5 Identify lands proximal to airports Regional airports Sub-objective 2.2.6 Identify lands proximal to parks, other recreational opportunities, protected c onservation lands and/or cultural and historic sites Structures of architectural and historical significance, parks and zoos, recreational facilities, and existing conservation lands Sub-objective 2.2.7 Identify lands proximal to existing public water and sewer service Water plants, sewage treatment plants, those categories identified as utilities and communications facilities from the Landuse raster

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124 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 2 Identify lands suitable for office/commercial development Objective 2.2 Determine lands economically suitable for office/commercial development Sub-objective 2.2.8 Identify lands with values suitable for office/commercial use Land value raster Sub-objective 2.2.9 Identify lands proximal to existing office/commercial land uses Office, commercial, and mixed use DRIs; land use and parcels identified as office/commercial Goal 3 Identify lands suitable for retail use Objective 3.1 Determine lands physically suitable for retail land use Sub-objective 3.1.1 Identify soils suitable for retail land use Soil drainage, soil corrosion Sub-objective 3.1.2 Identify lands that are free of flood potential Habitat and landcover, FEMA flood zone data Sub-objective 3.1.3 Identify lands free from hazardous wastes EPA National Priority List Sites, hazardous waste sites, radon locations Goal 3 Identify lands suitable for retail use Objective 3.2 Determine lands economically suitable for retail land use

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125 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 3 Identify lands suitable for retail use Objective 3.2 Determine lands economically suitable for retail land use Sub-objective 3.2.5 Identify lands proximal to existing public water and sewer service Water plants, sewage treatment plants, those categories identified as utilities and communications Sub-objective 3.2.1 Identify lands proximal to existing residential development Residential and Mixed use DRIs; land use and parcels identified as residential Sub-objective 3.2.2 Identify lands proximal to existing retail development Residential, mixed use and retail DRIs; land use and parcels identified as retail Sub-objective 3.2.3 Identify lands proximal to roads Major roads Sub-objective 3.2.4 Identify lands proximal to intersections of major roads Intersections Sub-objective 3.2.5 Identify lands proximal to existing public water and sewer service Water plants, sewage treatment plants, those categories identified as utilities and communications Sub-objective 3.2.6 Identify lands with values suitable for retail use Land value grid Sub-objective 3.2.7 Identify lands within and proximal to existing city limits City limits

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126 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 4 Identify lands suitable for industrial land use Objective 4.1 Identify lands physically suitable for industrial use Sub-objective 4.1.1 Identify soils suitable for industrial use Soil drainage; soil corrosion Sub-objective 4.1.2 Identify lands that are free of flood potential Habitat and landcover, FEMA flood zone data Goal 4 Identify lands suitable for industrial land use Objective 4.2 Identify lands economically suitable for industrial use Sub-objective 4.2.1 Identify lands away from existing residential development Residential and mixed use DRIs; land use and parcels identified as residential Sub-objective 4.2.2 Identify lands proximal to existing industrial development Industrial DRIs; land use and parcels identified as industrial Sub-objective 4.2.3 Identify lands proximal to roads Major roads Sub-objective 4.2.4 Identify lands proximal to railroads Active railroads Sub-objective 4.2.5 Identify lands proximal to airports Regional airports

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127 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 4 Identify lands suitable for industrial land use Objective 4.2 Identify lands economically suitable for industrial use Sub-objective 4.2.6 Identify lands proximal to existing public water and sewer service Water plants, sewage treatment plants, those categories identified as utilities and communications Sub-objective 4.2.7 Identify lands with values suitable for industrial use Land value grid Goal 5 Identify lands suitable for the Creative Class Objective 5.1 Identify lands suitable for the Creative Class Sub-objective 5.1.1 Identify lands proximal to existing trails, parks, or recreational opportunities Architectural and historic structures, parks and zoos, recreational facilities, existing conservation lands Sub-objective 5.1.2 Identify lands proximal to cultural activities, historic structur es, and nightlife Cultural centers (i.e. museums & galleries, theaters, planetariums, botanical gardens, etc.); community centers, structures of architectural significance

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128 Table 5-1. Continued Goal Objective Sub-objective Data used Goal 5 Identify lands suitable for the Creative Class Objective 5.1 Identify lands suitable for the Creative Class Sub-objective 5.1.3 Identify lands with concentrations of gay populations Gay and lesbian populations (as a percentage of the census tract population) Sub-objective 5.1.4 Identify lands with preferences that reflect the personal values of the region IDW surface from the East Central Florida Regional Growth Visioning exercises Sub-objective 5.1.5 Identify lands with concentrations of diverse cultures Ethnicities of Black/African American, Asian, Hispanic, Multiple Races, and ethnicities classified as Other from census data-as a percentage of the census tract population Goal 5 Identify lands suitable for the Creative Class Objective 5.2 Identify lands economically suitable fro the Creative Class Sub-objective 5.2.1 Identify lands proximal to existing creative industries Density grid of existing creative industries Sub-objective 5.2.2 Identify lands proximal to advanced educational opportunities Universities and colleges within the region from the statewide schools dataset

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129 Table 5-2. Data sources for Creative Indices Name Source Method of manipulation Indicator 1 Identify locations proximal to existing creative industries (Objective 5.1, Sub-objective 5.2.1) Florida Geographic Data Library (parcel data) Creative industries were selected from the parcel data based upon the DESCRIPT field. The Density function within Sp atial Analyst was performed. The output was reclassified, usi ng Spatial Analyst, according to distance of historical gr owth for creative industries. Indicator 2 Identify locations proximal to educated people (Objective 5.1, Sub-objective 5.2.2) Florida Geographic Data Library (statewide schools data) Extracted public, private, and technical colleges and universities located within th e region from the statewide schools dataset. This became the regional college dataset. Determined the straight line distance from each of these point features using the Eucl idean Distance tool Reclassified the resultant Euclidean Distance raster based upon the number and availabil ity of high level degrees conferred. For example, technical and vocational institutions were given a value of 1, whereas 4-year non-liberal arts institutions were given values of 9. Liberal arts schools were given a value of 5 and institutions that conferred a low percentage of degrees that apply to creative occupations lowered their reclassification value. A separate model diagram was cr eated for institutions that conferred PhDs that apply to a creative occupation were selected from the regional college dataset. All values in this raster were reclassified and given a value of 9. The results of the previous tw o bullet points were combined.

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130 Table 5-2. Continued Name Source Method of manipulation Indicator 3 Identify locations proximal to existing trails, parks, or recreational opportunities (Objective 5.1, Sub-objective 5.1.1) Florida Geographic Data Library Extracted recreation sites, park s and zoos from a dataset of combined land use and parcel data The straight line distance was determined from each of these datasets as well as from the dataset representing struct ures of architectural and historic significance as well as from the Florida Natural Areas Inventory (existing conservation). We determined the spatial relationship between the location of each of these features to the location of existing office/commercial development and creative industries using the Zonal Statistics tool. Using the average distance and standard deviation of the relationship identified in the pr evious step, we reclassified each distance raster. The closer the proximity of the recreational area, the higher the preference value assigned. The resultant reclassified raster was combined. Indicator 4 Identify locations proximal to cultural activities, historic structures, and nightlife (Objective 5.1, Sub-objective 5.1.2) Florida Geographic Data Library The straight line distance was determined, using Euclidean Distance, for data representing community centers, cultural centers and structures of architectural and historic significance. We determined the spatial relationship between the location of each of these features to the location of existing office/commercial development and creative industries using the Zonal Statistics tool. Using the average distance and standard deviation of the relationship identified in the pr evious step, we reclassified each distance raster. The closer the proximity of the amenity, the higher the preference value assigned. The resultant reclassified raster was combined.

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131 Table 5-2. Continued Name Source Method of manipulation Indicator 5 Identify concentrations of gay populations (Objective 5.1, Sub-objective 5.1.3) Census (statistics representing gay and lesbian populations), Florida Geographic Data Library (Census tract features) I downloaded the Census 2000 Summary File, 100 Percent Data, Detailed Table PCT14: Un married-Partner Households by Sex of Partners from the US Census website. Using the field name STFID I joined the Census summary table to the FGDL Census Tract polygon dataset according to tract number. The gay population was measured using the field PCT014003: Households: Unma rried-partner households; Male householder and male partner. The lesbian population was measured by the Census field PCT014005: Households: Unmarried-partner households; Female householder and female partner. I created two new fields within the feature dataset. One was a calculati on of the gay population per tract divided by the total households w ithin that tract. The second new field reflected the lesbia n population divided by the total households within that tract. I then grided the feat ure dataset, using th e Convert to Raster function, on the concentration of gay population per tract, represented by the first new field added. The original feature class wa s again grided but based upon the concentration of lesbian popul ation per tract, represented by the second new field added. Each new grid was reclassified using 9 equal intervals of concentration. The higher th e concentration, the higher the preference value assigned.

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132 Table 5-2. Continued Name Source Method of manipulation Indicator 6 Identify concentrations of diverse populations (Objective 5.1, Sub-objective 5.1.4) Census; Florida Geographic Data Library I downloaded the Census 2000 Summary File 3, Sample Data, Detailed Table PCT21: Place of Birth by Citizenship Status from the US Census website. Using the Census statistic STFID, I joined the Census table to the FGDL Census Tract polygon dataset according to tract number. I created five new fields within the feature dataset. Each field is a calculation of a specific ethnic grouped divided by the total tract population. I then grided the feature dataset five separate times, each time based on a different ethnic group, using the Convert to Raster function. Each new grid was reclassified according to concentration using 9 equal intervals. The higher the concentration, the higher the preferen ce value assigned.

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133 Table 5-3. Categories for land use analysis data GIS data category Description Geophysical Datasets that describe abiotic ( nonliving) native charac teristics, including geology, soils, hydrology, hydrogeography, climate, and aspect. Biological/Ecological Datasets th at describe biotic (living) native characteristics, including vegetation, animal habitat, specie s distribution, and measures of biological diversity. Demographic Datasets that describe human populations and their dist ribution, including census, population densities, ethnicity, in come levels, age, and people per household. Economic Datasets that describe landowners hip, costs and associated cost trends, property parcels, market value per acre, assessed valu e per parcel, and year built. Political Datasets that represent politically derived constructs like zoning districts, comprehensive plan units, city limits, county limits, water management district boundaries, re gional planning c ouncil boundaries, state boundaries, and publicly owned conservation lands. Cultural Datasets that capture the distri bution and character of cultural features, including land use and land cover, nati onal register historic sites, state register historic sites, eligible histor ic sites, and cultural features sorted by period of historic significance. Infrastructure Datasets that represent th e spatial distribution and character of the physical infrastructure needed to sup port human settlement like roads, sanitary sewers, storm sewers, airports, and railroads. (Carr, M., & Zwick, P. (2007a). Smart Land-Use Analysis: The LUCIS Model Redlands, California: ESRI Press. P. 90) Table 5-4. Assigned SUA values Suitability value Description 9 Highest suitability 8 Very high suitability 7 High suitability 6 Moderately high suitability 5 Moderate suitability 4 Moderately low suitability 3 Low suitability 2 Very low suitability 1 Lowest suitability (Carr, M., & Zwick, P. (2007b). An applicati on of LUCIS (Land Use Conflict Identification Strategy) in South Central Florida: An ex ample of GIS for land use planning & design. LAA 6656 & URP 6341 Class Slides University of Florida slide 18)

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134 Table 5-5. AHP importance categories Category Value Extremely more important 9 Very strongly to extremely more important 8 Very strongly more important 7 Strongly to very strongly more important 6 Strongly more important 5 Moderately to strongly more important 4 Moderately more important 3 Equally to moderately more important 2 Equally important 1 (Carr, M., & Zwick, P. (2007a). Smart Land-Use Analysis: The LUCIS Model Redlands, California: ESRI Press. p. 62)

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135 Table 5-6. Combinations of preference rankings Areas of conflict Areas of no conflict Code Description CodeDescription 111 All in conflict, all low prefer ence 112 Urban preference dominates 122 Moderate conservation preference conflicts with moderate urban preference 113 Urban preference dominates 133 High conservation preference conflicts with high urban preference 121 Conservation preference dominates 233 High conservation preference conflicts with high urban preference 123 Urban preference dominates 221 Moderate agriculture preference conflicts with moderate conservation preference 131 Conservation preference dominates 212 Moderate agriculture preference conflicts with moderate urban preference 132 Conservation preference dominates 222 All in conflict, all moderate prefer ence 211 Agriculture preference dominates 313 High agriculture pr eference conflicts with high urban preference 213 Urban preference dominates 323 High agriculture pr eference conflicts with high urban preference 223 Urban preference dominates 331 High agriculture pr eference conflicts with high conservation preference 231 Conservation preference dominates 332 High agriculture pr eference conflicts with high conservation preference 232 Conservation preference dominates 333 All in conflict, all high preference 311 Agriculture preference dominates 312 Agriculture preference dominates 321 Agriculture preference dominates 322 Agriculture preference dominates (Carr, M., & Zwick, P. (2007a). Smart Land-Use Analysis: The LUCIS Model Redlands, California: ESRI Press. p. 148)

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136 Table 5-7. Employment Center Building Sizes Building Size Building Capacity (Employees per 1000 GSF1) Total number of Employees Under 100,000 GSF (2.3 acres) 4.8 480 100,000 to 200,000 GSF (4.6 acres) 4.4 880 Over 200,000 GSF (9 acres) 3.5 1,400 (Institute of Transportation Engineers. 1998. Trip Generation: Trip Generation Rates, Plots, and Equations 6th Ed. Washington, D.C.: Institute of Transportation Engineers) Table 5-8. Cells needed to accommodate building allocation Building Size Acres Cells Large Building Under 100,000 GSF 2.3 acres 10 100,000 to 200,000 GSF 4.6 acres 19 Over 200,000 GSF 9 acres 38 (Institute of Transportation Engineers. 1998. Trip Generation: Trip Generation Rates, Plots, and Equations 6th Ed. Washington, D.C.: Institute of Transportation Engineers) 1 Institute of Transpor tation Engineers. 1998. Trip Generation: Trip Generatio n Rates, Plots, and Equations 6th Ed. Washington, D.C.: Institute of Transportation Engineers.

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137 Figure 5-4: LUCIS st rategy process flow

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138 Figure 5-5: Final preference map

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139 (a) (b) Figure 5-6: Suitable locations fo r Urban Areas and Creative Envir onments. A) the intersection of areas where the urban preference dominates and B) that are suitable for creative environments produce areas

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140 Beginning New Employment 832,400 Large Building Allocation (2 @ 1,400/building) 2,800 Medium Building Allocation (2 @ 880/building) 1,760 Small Building Allocation (2 @ 480/building) 960 Employment Remaining for Allocation 826,880 Figure 5-7. Example employm ent allocation calculation

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141 CHAPTER 6 FINDINGS AND RESULTS This thesis focuses on the use of census and other relevant economic data to reveal the potential of the East Central Florida region to accommodate industries that employ creative individuals with ideas, talents, and services that sustain the economy. The following chapter will report findings for each Creative indicator separately then relate this to the spatial findings that resulted from our suitability analysis. The tre nds revealed from the quantitative analysis and how they correspond to the spatial arrangement of Creative Environments is worth discussing. The Creative Index Richard Florida uses the Creative Index as a way to measure a re gions position in the Creative Economy. The Creative In dex is comprised of four fact ors: (1) the Creative Class share of the workforce; (2) innovation, measured as patents per capita; (3) high-tech industry, using the Milken Institutes widely accepted Tech Pole Index (a.k.a the High-Tech Index); and (4) diversity measured by the Gay Index, the Melting Pot Index and the Bohemian Index (Florida, 2002, p. 244; p. 261). These socio-economic factors influence the inventiveness of regions and provide a reasonable indicator of future economic growth (Ceh, 2001). The next four sections apply the factors of the Creative Index to the seven county East Central Florida region. The final section in this chapter is a discussion of whether the Creative Index for the East Central Florid a region correlates in any way to the results concluded from the spatial analysis outlined in Chapter 5. The sa me indicators established in the Methodology are used in developing the Creative Index. The Creative Class Share of the Workforce According to Florida, there are four major cl asses of occupations; Cr eative Class, Working Class, Service Class, and Agriculture (Table 61). REMI projects that the share of Creative

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142 Class employment in 2050, with resp ect to its share of th e service sector as well as its share of the overall workforce in Orange County (Table 62) is twice as much as the Polk County, the county with the next highest creative employment rate. Orange County is the center of the region and is presently home to large companies that offer creative occupations (Table 4-3). Orange County also offers high quality educa tional opportunities that offer advanced degrees (i.e. University of Central Florid a) that have historically serv ed as incubators for high tech industries (i.e. Burnham and I nnovation Way). Compared to othe r counties within the region, Orange offers greater transportation options (i.e. Orlando Internationa l Airport, Floridas Turnpike, 1-4 Corridor; Tables 4-5 and 4-6) as compared to other counties within the region. The Creative Class share of the Servic e Sector in 2050 (Table 6-2) indicates that across the region people have sorted themselves into classe s not associated with creative occupations. Interestingly, higher percentages of the Creat ive Class are not limited to areas that you would typically associate with high-tech corridors or artistic centers. An example is Polk County. REMI projects that following Orange C ounty, it has the next highest percentage of creative occupations in 2050. Polk County is typically associated with agricultural or manufacturing activities; namely companies such as Tropicana or IMC-Agrico Phosphate Mining Company. Although the location of Po lk County, between Hillsborough and Orange, offers significant economic and ge ographic benefits, these benefits can be applied to attracting industries that are not resource de pendent and generate greater shares of wealth. The proximity to Interstate 75 and, to a greater degree, Inters tate 4 creates a portal fo r knowledge spillover from Hillsborough and Orange County to flow into Po lk County and vice versa. When comparing Polk to Volusia or Brevard, who also have relatively high shares of creative employment as a percentage of the entire service sector, Polk is nt landlocked by the ocean and therefore creates a

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143 seam for the greater Central Florida region th at extends from Pinellas County eastward to Volusia and Brevard Counties. Polk County ha s countless benefits be cause of location and access which can be exploited to encourage cr eative industries to lo cate in the county. The notion of propelling a multi-ton rocket against gravity into other galaxies takes a bit of creativity and specific knowledge. The Kenne dy Space Center (KSC) in Brevard County employs hundreds of individuals with specific knowle dge about rocket propulsion and physics so that research and exploration is possible outside of the Earth that we know. The presence of KSC in Brevard County and the collocation of industries around KSC that support the space program can be attributed to the high share of creative individuals within the county. According to the Bureau of Labor Statistics, in 2006 ma nufacturing had the highest location quotient, 2.24 (Bureau of Labor Statistics, 2007a), among all Brevard County industries; indicating that although creativity is needed to support the missi on and future ideas of the space program, it is the physical materials its supporting services that sustain the Br evard County economy. Other counties, such as Osceola and Seminol e are being passed by. Osceola County is historically known as a bedroom community2 to Orange and Polk Count ies. Maps indicate that Osceola County is primarily composed of undeve loped land, although much of this land belongs to large land holders. If cu rrent development patterns ar ound the region continue, Osceola County will continue to serve as a bedroom community. An ex ample is the proposed Innovation Way project in Southeast Orange County, which w ill add an additional 20 million square feet of manufacturing, light industri al, biotech, warehouse, teleco mmunications, office and small businesses. This project offe rs countless economic benefits but early speculation from local government officials indicates that affordable hou sing in the immediate vicini ty of the project is 2 A bedroom community is classic residential suburbs. The offer little in the way of employment but plenty of housing (Title Company of the Rockies, 2007).

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144 inadequate. This leads me to believe that the burden for accommodating those in need of affordable housing will continue to rest upon Osceola County. A lthough projects like Innovation Way look to neighboring counties more for providi ng amenities than talent, the potential exists for Osceola and Seminole Counties to increa se their share of creative employment. Talent Index Within the region there are a total of 33 co lleges and universities. Twenty-two are community colleges, ten are private colleges, and one is a state university. Among these only eleven offer degrees beyond a Bachelors. The University of Central Florida (UCF) conferred the most degrees (53.3% or 5,4603) related to creative occupations. The ability of UCF to confer such a large number of degrees has translated into a burgeoning technolog y and research industry in southeast Orange County. Other universities that deserve menti on include Florida Institute of Technology and Bethune Cookman Un iversity who also have high rates of conferred technical and innovative degrees, 38.0%4 and 36.2%5, respectively. Innovation Index Economic growth is driven by innovation and invention, both of wh ich are vital to a regions economic vitality. Duri ng the first part of the twentie th century, three quarters of economic growth in the U.S. was linked to i ndustrial inventions (Ceh, 2001, p. 298). Feldman and Florida (1994) indicate that the capacity of regions to invent is ev er more dependent on the agglomeration of [specialized] skills, knowledge, institutions and resources that [mold] the technical infrastructure of regions. (Ceh, 2001, p. 298) 3 This percentage reflects the most recent full academic year data availabl e, which was from 2005-2006 (Office of Institutional Research University of Central Florida, 2007). 4 This percentage reflects the most recent data available, which was from academic year 20 05-2006 (Office of Institutional Research Florida Institute of Technology, 2007). 5 This percentage reflects the most recent data available, which was from academic year 20 05-2006 (Office of Institutional Research Bethune Cookman College).

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145 Patent information can also be used by co rporations to measure the success of R&D employees and spending (Ceh, 2001, pp. 300-301). This study uses the presence of new technology as measured by the number of patents issued6 to determine an areas level of innovation. Within the region, Brevard County (32.5%) issued the highest number of patents over a ten year period7 followed by Orange County (26.7%) (Table 63). Brevard County is the location of Kennedy Space Center and relies upon new technol ogy to support its space program. Protection of new technologies and inventi ons is important and this can be achieved through the use of patents. Supporting patents may be acquired by co mpanies which are located in close proximity to KSC and capitalize upon the efforts of the spac e program. This is illu strated by the 199 patents issued to Lockheed Martin scientists and engi neers for technologies related to the aerospace industry (Space War, 2003). In previous research done by Richard Florid a, he found a statistic al correlation between concentrations of innovation and educational opportunities. Furthe r support for Brevard Countys Innovation Index rank could be attributed to the location of Florida Institute of Technology (FIT). As mentioned in the previous section, FIT is an inst itution specializing in technical training, especially trai ning needed for creative industries Students that earn doctoral degrees possess a higher amount of creative capital. The University of Central Florida conferred the most PhDs among any of the regional univers ities and ranks second in the number of patents issued per capita. 6 The statistics used in this discussion reflect calculations based on the total patents issued by county from 1990 to 1999. The calculations are expressed in per capita figures per 1,000 population. 7 Statistics were not yet available by county after 1999. Recent detailed statistics were available by MSA, which is considered too large of an area to determine r eal patterns (U.S. Department of Commerce, 2000).

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146 Milken Index Tech Pole The Milken Index provides a measure to ra nk Metropolitan Statisti cal Areas (MSAs) on their economic performance and their ability to cr eate, as well as keep, the greatest number of jobs in the nation. The East Central Florida region is comprised of 4 MSAs: MelbourneTitusville-Palm Bay, Lakeland-Winter Haven, Or lando, and Daytona Beach. Among the largest 200 cities, in 2005 three out of four region MSAs rank in the top ten of the Milken Index Tech Pole. Lakeland ranks at #33 but improved from nu mber 87 the previous year. The top ten have similar characteristics: strong and growing se rvice sectors, a robust tourism, growing populations, and an increase in the number of retirees (Milken, 2007). The number one ranked best performing city is the Palm Bay-Melbourne -Titusville area, located in Brevard County. This supports Richard Floridas theory that t echnology and talent, both indices that Brevard County ranks high in, are complementary and support the overall cr eative environment. The Diversity Index Diversity is indicative of a mix of influences and differences, which thrive in environments with a tolerance for strangers and intolerance for mediocrity (Florida, 2002, p. 227). Talented individuals seek environments in which they liv e and work to be open to dissimilarity. During previous research, Florida sta tistically determined that the Gay Index, Melting Pot Index, and Bohemian Index (collectively known as the Com posite Diversity Index or CDI) were the strongest predictors of creative population gr owth during 1990 and 2000 (Florida, 2002, p. 263). The population of the East Central Florida Region will surpass 7.5 million people (BEBR, 2006) by the year 2050. This estimate doubles the cu rrent regional population. According to Richard Floridas research, the Bohemian Index and the CDI are the only signifi cant predictors of population and employment growth in regions with an average population of 2.2 million. He recommends these regions develop strategies to bolster their openness to diversity and invest

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147 their resources in the development of vibrant local artistic and cultural communities (Florida, 2002, p. 263). The Gay Index Diversity can be measured in numerous ways but it is a good indicator for an areas openness and tolerance to different kinds of pe ople and ideas (Florida, 2002, p.45; p. 258). The Gay Index is a measure of the overand under-re presentation of coupled gay people in a region relative to the United States as a whole (Flo rida, 2002, p. 333). Once ag ain, Orange County had the highest percentage of gay population, 0.32% (Table 6-4). In previous statistical research by Richard Florida, five of the top ten 2000 Gay I ndex regions also rank amo ng the nations top ten high-tech regions. As we have seen in indicat ors described previously in this study, Orange County has been within the t op two counties with respect to innovation and education, both indicators of a strong high tech industry. The Melting Pot Index The Melting Pot Index, as defined by Richard Fl orida, measures the relative percentage of foreign-born people in a region (Florida, 2002, p. 333). This study does not include the concentration and influence of immigrants in calculating the Melting Pot Index8. Instead, I examined the concentration of the four minor ity ethnic groups recognized by the U.S. Census Bureau (Blacks/African Americans, Asians, Hisp anics, Other and Multirace) and investigated the regional concentration of minor ities to determine if there were any spatial patterns existed. The statistics indicate that Orange County has the highest percentage of Blacks/African Americans and Asians; compared to its total popula tion (Table 6-5). Orange County also has the highest level of diversity (gay population) and is home to UCF, which confers the most degrees 8 Sufficient evidence could not be found that supported the statistical correlation between concentrations of immigrants and the suitability of an area for creativity.

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148 for creative occupations. In addition to academics, UCF also has the highest enrolled number of minority students9. With the exception of Blacks and Asians, Os ceola County is the only other county with high percentages of minority populations. Furthe r analysis would be needed to determine whether creative employees are commuting from Osceola to Orange or Brevard counties; counties that rank high in other indices. Using projected popula tion estimates and average rates of growth for each ethnic group through 2050, Osceola County continues to lead the region with the highest concentrations of three of the five minority ethnic groups. Spatial Comparisons Steps 1 through 4 of the Chapter 4 produce a GIS raster that identifie s locations around the seven county region that provide a good quality of life and are c onducive to high quality jobs. First, I identified undeveloped land s preferable for urban uses in the future. These uses include residential, office/commer cial, retail, and industrial. The to tal land area for the region preferable for urban uses is 314,792 acres. I then develope d a surface that graphically displayed areas not only preferable for urban uses but were specific ally suitable for creative environments. These areas are tolerant, have high leve ls of diversity and are in area s that promote clustering among industry and the transfer of ideas. The total land area for these Creat ive Environments was 20,287. Finally, we identified areas that were no t only Creative Environments but also were suitable for office/commercial us es. This further narrowed the amount of available land to 9,058 acres. 9 This statistic excludes Bethune Cookman University. Bethune Cookman is a HBCU (Historically Black College and University) so enrollment for students of Black/Afri can-American ethnicity are typi cally higher than those of other races/ethnicities.

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149 Sam Bitar, an economist with the East Centra l Florida Regional Planning Council, used the REMI economic modeling software to determ ine employment for the years 2005 through 2050. He determined that 832,400 new people will be employed in the service sector for the East Central Florida region. Over one-third of these new employees will work in industries classified as creative10. The foundation of my research was to determine whether the region could accommodate employment centers that would sati sfy new employment, minimize impact to other land uses, and distribute employme nt centers to counties within the region in a manner that would satisfy regional dema nd for creative industries. I developed a very simplistic method to dist ribute 2050 service empl oyment and allocate employment centers. Although roughly one-thi rd of the 832,400 individuals will work in creative industries, I attempted to allocate the en tire projected new service employment using the assumption that remaining employees would provide support services to th e creative. First, employment centers were categorized as either small, medium, or large. Then the average location quotient for creative industries was cal culated for each county (Table 6-6). The sequence of allocation depended upon the current st rength of creative indu stries for each county. Next, employment centers were allocated from th e largest building size to the smallest among a contiguous land area. This created a variation of company sizes with in a small area, thus cluster of industries (Tables 6-7 and 6-8). The distribution of new employees is affected not only by a countys need to fulfill the economic demand for creative industries, but by th e amount of land area w ithin the county that has an environment which creates the quality of place industries want to locate. Using my model, I determined that the region could only accommodate 62,760 employees, or 7.5%, of the 832,400 10 For a breakdown of occupations within each class, see Table 5-1

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150 new service employees the region projects by the y ear 2050 (Table 6-8). According to allocation sequence, Volusia County was the first county in which the potential existed to allocate employment centers. Orange and Polk coun ties did not have enough contiguous land area available to accommodate large employment cente rs. Brevard and Lake Counties suffered the same problem of inadequate contiguous land area, and were unable to receive any large employment centers. Osceola County did have enough contiguous land available to accommodate large employment centers, but it is Volusia County that could absorb most employees in employment centers of at least 9 acres. For the second allocation only medium and sma ll buildings were placed. Once again, Orange County did not have enough contiguous land area in creative environments to accommodate buildings of 4.6 acres. All other c ounties in the region are able to accommodate medium size employment centers. Again, it is Volusia County who absorbed the most employment; accounting for 58% of all crea tive employment that occupy medium size employment centers. For the third and final allocation in which only small employment centers are placed, all counties were able to absorb some share of new employment. Once again Volusia absorbed the most employment accounting for 18 buildings and 8,640 employees. In tota l Volusia will attract 55% of the new creative employment and Orange County will attract the least by absorbing only 1,440 employees accommodated in three new employment centers. Spatial Findings As constraints were applied to lands suitabl e for urban land uses (as identified by the collapsed preference surface and classified by the final conflict surface as Urban Wins), the amount of viable land for creative environments (as identified by the urban goal 5 preference surface) and creative environments that are also suitable for office/commercial use significantly

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151 decreased (Table 6-9). More importantly, the constraint that caused the most significant reduction of land suitable for creative environm ents is when lands appropriate for creative environments and suitable for office/commercial developmen t were selected (Table 6-10). The cells that fit these criteria are considered creat ive environments. This is important in that it validates the importance of the added Creative Go al (i.e. Goal 5 under th e Urban stakeholder). Areas that are ideal for office/commercial deve lopment are not necessarily suitable creative environments. Earlier discussion within this chapter indi cated that once employment centers were allocated, Volusia County absorbed the largest numb er of anticipated employment. This is due in large part to the amount of contiguous land available for development. For the sake of simplicity, this study allocated employment cente rs under the assumption that each employment center would be a 1 story building. In areas li ke Orange County where the largest contiguous area is 18 acres, the only possi bility of accommodating large nu mbers of people would be to build upwards. Therefore if we were to place a building of 2 stories on the land area with 18 contiguous cells only then would we be able to allocate a large employment center which would accommodate an additional 1,440 employees within the region. Furthermore, it is possible to accommodate additional creative em ployees throughout the region by manipulating the floor area ratio11. Volusia County will absorb the largest share of new creative industries and employees over the next 50 years. According to 2006 employ ment estimates, Volusia Countys employment 11 The floor area ratio (FAR) is the rela tionship of the floor area to the lot area computed by dividing the floor area by the lot area (Davidson and Dolnick, 2004, p. 190). It is important to note that each jurisdiction has different guidelines for calculating floor area ratio.

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152 currently meets the loca l demands for service sector jobs that are high-tech and innovative12. Although Volusia County has the most scattered sp atial arrangement of new creative industries the most notable spatial pattern is that the more dense concentrati ons of new creative employment are located near major transportation corridors or clusters of major arterial roads (Figure 6-1). Proximity to major roads was a sub-objective within every goal of the urban stakeholder but was not included as a sub-objective of the Crea tive Goal. Also, since Volusia County will absorb the most new creative industr ies within the region, this study also implies that Volusia will play a more significant role in the regional economic growth. Osceola County, which has the lowest loca tion quotient, will accommodate the third highest number of new creative industries a nd employees. Osceola County has the largest contiguous land area available for allocation of employment centers. In terestingly, the next largest contiguous land area in Osceola County is 19 cells or 4.5 acres. All potential creative environments in Osceola County identified by this study are in close proximity to the Orange County line. These industries can benefit from knowledge spillover from Orange County (i.e. Innovation Way) (Figure 6-2). Orange County ranked seventh within the re gion for lands suitable for new creative employment. According to 2006 employment estimates, creative employment exceeds the county demand by 50% (Table 6-6). The lack of land area presents spatial challenges to Orange County since the county has significant tr ansportation (i.e. I-4 Corridor and Orlando International Airport), economic, and educational opportunities (i.e the University of Central Florida). For these reasons it is advantageous fo r companies to continue to locate within Orange County. It is important to note that the larger concentrations of new creative industries occur 12 The location quotient is used to determine whether a countys local demand for service sector employment is being met.

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153 near the Seminole County line. In Seminole C ounty larger concentrat ions of new creative industries occur in the same rela tive area as the Orange County cr eative industries (Figure 6-3). A relationship like this does not occur along th e shared county boundary of Orange County and any other county. As indicated by the People s Choice Map, residents would like increased densities in these areas, theref ore taller buildings with sma ller footprints may be more appropriate for areas suitable for ne w creative industry in Orange County. Seminole County is the smallest county within the region, with respec t to land area, yet it absorbs the second highest number (42 buildings ) of creative industries. This study also indicates that the distribution of employment between medium and small industries was almost equal, collectively contributing 11.7% (7,360 jobs) to regional em ployment. Seminole County is currently experiencing phenomenal growth in the Sanford (Figure 6-4) and Heathrow area (Figure 6-3), especially within high-tech innovative i ndustries. The key to Seminoles continued growth will be increasing floor area ratios (i.e. bu ilding up), take advantage of the transportation infrastructure (i.e. Interstate 4 and Sanford Airport), and expl oiting its proximity to Orange County and the resources that Orange County provides. Lake County ranks fourth in the percentage of new creative employment for 2050 that is absorbed. Like Seminole County, Lake County has a unique geographic location due to its proximity to Orange County, the economic engine of the region. Lake County also serves as a gateway to North Central and West Central Fl orida counties. Existi ng development patterns within Lake favor residential development serving those who work in Marion, Sumter, Volusia, Orange, Polk and Seminole Counties. Local offici als have suggested that they would like to find a balance between encouraging additional office/ commercial developmen t, residential demand,

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154 and the preservation of the local ecological resources. Consequentl y, these three goals appeal to and are essential in attracting the creative. The results of this study indicate that due to the large lot development patterns that currently exist, it would be diffi cult to locate an employment cen ter with a large footprint in Lake County. The largest footprin t conducive to a creativ e industry is 5.7 acres This resulted in the placement of only medium and small buildin gs which collectively absorb a total of 5,520 future creative employees. Although Lake County is less dense than Seminole, it faces the same problem of accommodating a needed population of individuals that drive economic growth. I offer the same solution for Lake County that I s uggested for Seminole: increase floor area ratios (i.e. building up), take advantage of the transportation infrastructu re (i.e. Florida Turnpike and Interstate 75), exploit its shar ed boundaries with the neighboring seven counties, and exploit the high-tech economic resources that Ora nge and Seminole Countys can provide. Polk County has the largest land area ( 1,874 sq. miles; 1,199,360 acres) than any other county but will only accommoda te 6.6% of new creative employment through 2050. An examination of the final conflict surface reveal s that urban clearly wins 72,768 acres; if you remove existing urban areas this is only 7.5% of the total land area for Po lk County. The conflict surface suggests Polk County has more land suitable for agriculture or where agriculture is in conflict with conservation (Table 6-11 and Figure 6-4). This is one potential reason why Polk County does not have many lands su itable for creative industries. Brevard County ranks just behind Polk in the su itability of lands for creative industries. Polk absorbs only 2,800 new employees, primarily due to the lack of available land to place new employment centers. Accordi ng to the Creative Index, Brevar d County is one of the most suitable counties for innovation and high-tech companies. The problem is that after

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155 accommodating existing urban areas, open water, ro ad networks, and existing conservation lands only 34% of the county is available for future development (Table 6-12). Of the remaining 224,379 acres available for future development the conflict surface (Figure 6-4) indicates that Brevard County is primarily in major conflict betw een all three stakeholders (Table 6-13). This presents a problem when accommodating future cr eative employment. According to this study, Brevard County can only support 5 new creative employment centers employing 2,800 people. As in the cases of Seminole and Lake Counties, Brevard should explore building up in order to accommodate more creative employees. Results Using the model and methods described in the previous chapter, the results do not support my hypothesis that the East Cent ral Florida region can accommodate future creative employment centers, according to the principles of the crea tive class, if current policy and development patterns persist. Although the Creativity Index indicated that several counties within the region (i.e. Orange and Brevard) currently have the re sources and amenities available to compete in a Creative Economy, the region is unable to acco mmodate the needed number of industries that command a high level of skills, encourage cluste ring of industries that innovate, and provide services or products that ar e used in the global economy. Sensitivity Analysis The discussion that accompanied the findings wi thin this chapter offer explanations about the numerical and spatial results. The importance of the indicators used a nd the effects variables within those indicators placed upon the final result can be verified through the use of sensitivity analysis. Marian Scott of the University of Glasgow defines sens itivity analysis as: Sensitivity analysis (SA) is a general met hodology used to evaluate the sensitivity of model output to changes in model input, i.e. th e rate of change of the response function relative to the input pa rameters. (Scott, 2003)

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156 As an example, I offer the following discussion which outlines the steps taken and findings of manipulating the weight of various ethnicities on the suitability of lands for creative industries. Sensitivity Analysis Findings Within the Melting Pot Index I examined the concentration of five ethnicities (Blacks/African Americans, Asians, Hispanic s, Other and Multi-race) around the region and question whether any spatial patterns exist. This index was also sp atially represented and evaluated as Goal 5, Objective 5.1, and Sub-Objec tive 5.1.4. Within the suita bility model, this sub-objective weights each ethnicity equally as 20%. To determine the sensitivity of each respective ethnic group in determ ining suitable creative environm ents, I performed the following four aspects: 1) modified the we ight of each ethnicity so that one ethnicity was given a weight of 40% and the remaining ethnicities were given an equal weight of 15%; 2) created a new preference surface for Urban Goal 5; 3) created a new collapsed preference surface for the Urban Goal; and finally, 4) created a new conflict surface. By changing the weight of each ethnic ity the amount of land suitable for urban development varies (Table 6-14). An implication that cannot be seen from the data tables is depending upon the ethnicity that was weighted the most, the high preference value for the diversity sub-objective and the number of values within each st andard deviation interval used to develop the collapsed preference su rface changed. This explains the variation in the amount of land suitable for urban use. To determine whether the sensi tivity of ethnicity influenced the amount of land suitable for creative environments I calculated the amount of land iden tified as highly preferable (i.e. values greater than 8) from the Creative Goal surface. I then used these areas to calculate the number of acres that were identified as U rban Wins in the final conflict surface and were preferable for office/commercial development. What I lear ned was that the amount of acres suitable for

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157 creative environments remained the same, rega rdless of which ethnicity possessed the greatest weight. Therefore, the results of this sensitivity analysis indicate that diversity influences the amount of land suitable for urban environments but has negligible influence on creating additional creating environments.

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158 Table 6-1. Description of Class Occupations. Th e Creative Class has two major sub-components: a Super-Creative Core a nd Creative Professionals. Class Name Description Super-Creative Core Computer and mathematical occupations Architectural and engineering occupations Life, physical, and social science occupations Education, training and library occupations Arts, design, entertainment, sports, and media occupations Creative Professionals Management occupations Business and financial occupations Legal occupations Healthcare practitioners and technical occupations High-end sales and sales management Working Class Construction and extraction occupations Installation, maintenance, and repair occupations Production occupations Transportation and materi al moving occupations Service Class Health care support occupations Food preparation and food-se rvice-related occupations Building and grounds cleaning and maintenance occupations Personal care and service occupations Low-end sales and related occupations Office and administrative support occupations Community and social services occupations Protective service occupations Agriculture Farming, fishing, and forestry occupations (Florida, R. (2002). The rise of the creative class Nee York: Basic Books, p. 328) Table 6-2. Creative Class share of the Service Sector in 2050 County % of Service Sector % of Total Workforce Brevard 8.7% 4.4% Lake 5.8% 2.9% Orange 19.7% 10.1% Osceola 5.2% 2.7% Seminole 5.3% 2.7% Volusia 9.2% 4.7% Polk 9.8% 5.0% (Regional Economic Models, Inc. (2007). Policy Insight Retrieved March 16, 2007 from http://www.remi.com/software/software.shtml)

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159 Table 6-3. Number of patents issued between 1990 and 1999 County # of patents (1990-1999) % of patents issued within region Brevard 1188 32.5% Lake 129 3.5% Orange 974 26.7% Osceola 73 2.0% Seminole 632 17.3% Volusia 349 9.6% Polk 309 8.5% (U.S. Department of Commerce, 2000) Table 6-4. Gay population and share of total population # of gays County Males Females % of gays (total) Total county pop (2000) Brevard 420 440 .18 476,230 Lake 250 201 .21 210,528 Orange 1737 1170 .32 896,344 Osceola 200 185 .22 172,493 Seminole 381 371 .21 365,196 Volusia 499 475 .22 443,343 Polk 545 469 .21 483,924 (U.S. Census Bureau; Census 2000, Summary File 3 (SF 3); generated by Iris Patten; using American Factfinder; ; (08 March 2007).)

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160 Table 6-5. Regional po pulation, by ethnic group Blacks Multi-Race Hispanics Asians Other County # % # % # % # % # % Brevard 40,000 8.4 8,429 1.8 21,970 4.6 7,152 1.5 5,168 1.1 Lake 17,503 8.3 2,477 1.2 11,808 5.6 1,667 0.8 3,966 1.9 Orange 162,899 18.2 30,771 3.4 168,361 18.8 30,033 3.4 53,889 6.0 Osceola 12,702 7.4 6,257 3.6 50,727 29.4 3,802 2.2 15,631 9.1 Seminole 34,764 9.5 7,944 2.2 40,731 11.1 9,115 2.5 11,175 3.1 Volusia 41,198 9.3 6,347 1.4 29,111 6.6 4,430 1.0 8,071 1.8 Polk 65,545 13.5 8,253 1.7 45,933 9.5 4,515 0.9 18,466 3.8 (Adapted from U.S. Census Bureau; Census 2000, Summary File 3 (SF 3); generated by Iris Patten; using American Factfinder; ; (08 March 2007).) Table 6-6. Average location quo tient of Creative Industries County Average Location Quotient of Creative Industries Orange 1.43 Polk 1.06 Volusia 1.01 Seminole 0.94 Brevard 0.81 Lake 0.71 Osceola 0.57 (Adapted from Bureau of Labor and Statistics (BLS). (2007a). Location Quotient Retrieved March 18, 2007, from http://www.bls.gov/data) Table 6-7. Distribution of ne w employment centers, 2005-2050 County Large Buildings Medium Buildings Small Buildings Orange 0 0 3 Polk 0 2 5 Volusia 9 15 18 Seminole 0 4 8 Brevard 0 1 4 Lake 0 3 6 Osceola 2 1 7

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161 Table 6-8. Distribution of new employees, 2005-2050 County Large Buildings (# of bldgs) Cap Medium Buildings (# of bldgs) Cap Small Buildings (# of bldgs) Cap Total (# of bldgs) Cap % of total new regional creative employment Orange (0) (0) (3) 1,440 (3) 1,440 2.3% Polk (0) (2) 1,760 (5) 2,400 (7) 4,160 6.6% Volusia (9) 12,600 (15) 13,200 (18) 8,640 (42) 34,440 54.9% Seminole (0) (4) 3,520 (8) 3,840 (12) 7,360 11.7% Brevard (0) (1) 880 (4) 1,920 (5) 2,800 4.5% Lake (0) (3) 2,640 (6) 2,880 (9) 5,520 8.8% Osceola (2) 2,800 (1) 880 (7) 3,360 (10) 7,040 11.2% TOTAL (88) 62,760 100%

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162 Table 6-9. Land area calculations for suitability analysis Suitability # of acres Collapsed Urban: Areas with high preference (Preference Value = 3) 321,482 Final Conflict: Urban Wins 305,511 Goal 5, Creative Class: Values greater than and equal to 813 17,632 Areas where Urban Wins and are preferred for Creative Environments 1,473 Areas where Urban Wins, preferred for Creative Environments, and preferable for office/commercial development 893 Table 6-10. County land area calculations County Total # of acres Greatest number of contiguous cells Greatest number of contiguous land (in acres) Least number of contiguous cells Entire 7 County Region 893 84 19.9 1 Orange 455 18 4.3 1 Polk 377 28 6.6 1 Volusia 1,233 66 15.7 1 Seminole 438 31 7.4 1 Brevard 322 36 8.5 1 Lake 462 24 5.7 1 Osceola 475 84 19.9 1 Table 6-11. Polk County conflict summary Conflict Type # of cells # of acres Agriculture Wins 540,639 129,812 Conflict: Agriculture and Conservation 460,346 109,319 Conflict: Agriculture and Urban 272,898 64,805 Urban Wins 306,426 72,768 13 Typically, the highest suitability value is classified as thos e cells with a suitability value of 9. In this case, the final suitability surface had a high value of 8.25; therefore a ll areas classified as greater than 8 were classified as most suitable for creative environments.

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163 Table 6-12. Breakdown of land usage for Brevard County Use Land Area (Acres) Total County Land Area 651,520 Lands available for future development (This figure removes existing urban, roadways, open water, and existing conservation) 224,379 Table 6-13. Conflict Surface stakeholder share for Brevard County Conflict type # of cells # of acres Agriculture Wins 17,542 4,166 Conservation Wins 112,563 26,731 Urban Wins 123,608 29,354 Severe Conflict 260,430 61,845 Table 6-14. Land most preferable for urba n use (from the collapsed urban surface) Method 1 (equal weight given to all ethnicities; in acres) Method 2 (40% weight given to Asians; in acres) Method 3 (40% weight given to Other; in acres) Method 4 (40% weight given to Mixed Races; in acres) Method 5 (40% weight given to Hispanics; in acres) Method 6 (40% weight given to Black/African Americans; in acres) Collapsed Urban (High Preference) 321,482 348,446 348,532 348,517 349,384 348,086 Final Conflict: Urban Wins 305,511 324,948 324,991 324,954 325,668 324,686

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164 Figure 6-1. Peoples Choice Map with new Creat ive Industry locations for Volusia County

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165 Figure 6-2. Peoples Choice Map with new Creat ive Industry locations for Osceola County

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166 Figure 6-3. Peoples Choice Map with new Creativ e Industry locations for Orange and Seminole Counties

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167 Figure 6-4. Final Conflict Surface

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168 CHAPTER 7 CONCLUSION A megalopolis is an interconnected corridor. The connection is typically defined by commuting patterns or an economic interdependence. This study attempted to use the principles of the creative class to determ ine whether the East Central Flor ida region could attract the types of industries and people that encourage higher rates of clustered high tech, highly innovative industries necessary for a megalopolis to thrive Through detailed discu ssion (Chapter 4) we concluded that social and economic traits of the Creative Class ali gn with those of the individuals and industries in other megalopolis regions around the U.S. We then used weighted suitability analysis and the identification of socio-cultural and economic indicators of the Creative to evaluate the spatial suitability of land areas within the region so that we could allocate approximate locations of creative em ployment centers that would satisfy the new employment through 2050. We hypothesize that the seven county East Central Florida region will attract creative individuals and industries to satisfy the demands of each countys economy collectively allowing this region to compete in a global market. Empl oyment projections indicated that if current development patterns and policy continue through 2050 an additional 832,400 people will be employed in the service sector in occupations that are high tech and/or innovative. This study concludes that the East Central Florida region is incapable of s upporting creative industries due a lack of contiguous land areas to support the number of industries needed to support new creative employment. Within the region only 62,760 creative employ ees can be accommodated in employment centers in areas conducive to cr eative environments. This tran slates into 88 new employment centers. Although the spatial dema nd for creative industries was not met, the Creativity Index, a

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169 quantitative measure of a regions suitability for cr eative people and industries, indicates that the region has the qualities necessary to attract high tech innovative firms that can successfully compete in a global economy. This study demonstr ates the capabilities of suitability analysis, economic modeling, and geographic information sy stems to address economic and employment issues in a proactive manner by anticipating a regions economic potential. The unmet demand for creative i ndustries raises an issue about density and development patterns. Although the skyline across the re gion is changing, most jurisdictions enforce regulations that encourage low density office/commercial development. This study allocated employment centers according to the assumption th at new employment centers are single story. In counties like Orange, Lake, and Seminole highe r density development with greater floor area ratios will present additional opportunities to accommodate future cr eative employment. The creative thrive in enviro nments where there is a dense flow of knowledge and increased densities would provide a mechanism to build creative capital. Universal Applicability The expansion of urban areas due to increase d population growth is blurring jurisdictional boundaries thus creating urban corrid ors. In terms of housing and ur ban services this growth has become problematic. With respect to econom ic potential, additional growth creates an opportunity for increased economic efficiency, which could be an inducement to serve greater populations. Cities are looking to provide the amenities that will attract those individuals and companies in turn maximizing the local and regional economy. This study can be adapted to any region, city or neighborhood to ev aluate whether they have the potential to become a creative industry. The results from this study do not signify a plan but instead is an illustrati on of potential development pattern s and should be used to identify the driving forces behind the pr eference of creativ e industries. Before any region welcomes new

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170 industries, they must understand th at growth cannot occur independently of other societal factors. Without the help of the larger region and identifi cation of values that en courage populations that will stimulate and contribute to the economy, growth will occur slow ly if at all. Several key dynamics must be in place for the most talent ed to not only locate to a region but to start companies and attract other companies to locate in the region as well. This model proves that it is possible to spatially measure a system of qu alitative indicators and determine the potential regional economic growth in East Central Florida. Economic Implications This model is an endogenous growth model. Th at is, we believe that the influence of the goals we have included within the suitability analysis are indicative of future spatial development patterns of high-tech and innovative industries, which will drive regional economic growth. For example, if you consider ex isting transportation in frastructure, no new improvements to roads were considered, but tran sportation plays a significant role in where people live and work. If rail or some other type of more effici ent more sustainable system of mass transit was included as a transit option, th e employment base of the region would expand exponentially. Rail would allow valuable land to be used for alternative uses (i.e. conservation areas) aside from parking facili ties. Although we didnt have an indicator which evaluated transportation, the larger idea va lue of more compact connected urban form was represented in the Peoples Choice Map. To compete in a larger economy, the East Central Florida region must harness their complementary instead of competiti ve advantage and enable regions to compete across the nation and around the globe. Human Capital As individuals increase th eir education and pursue degr ees beyond high school they are increasing their capital. As me ntioned earlier, the University of Central Florida (UCF) confers

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171 the largest numbers of overall degrees and a dvanced degrees (i.e. those beyond a Bachelors) among the regions 33 colleges and universities. If you survey the area around the main UCF campus in east Orange County you will notice an in creased rate of growth among R&D firms. As UCF, and similar institutions, continue to increase the human capital stock (increasing the highly educated labor force) the economy in Orange County will grow at faster rates than those counties with fewer opportunities for advanced education. An example is the new Burnham Institute campus in Orlando. Plans call fo r the new Burnham facility to employ 200-300 individuals who have expertis e in biomedicine and nanotechnol ogy. More importantly, the new institute will be located next to the University of Central Floridas Colleg e of Medicine at Lake Nona. The new employment center will create opportunities for partnerships with UCF researchers and a medical city cluster of bi otechnology companies and research (University of Central Florida, 2006). The knowledge spillove r from the new Burnham Institute will help smaller R&D firms in the region because skilled workers endowed with a high level of capital are a mechanism by which economic knowledge is transmitted (Audretsch and Feldman, 1996). A historical example is Boston, In Boston alone there are 12 major uni versities that are either Ivy League or rank in the top 50 of most college polls yet the city and univers ities have continued to thrive due to government and industry support for basic research and prof essional training that would improve the quality of their workforce. If the East Central Florida region were to increase support from industries as well as the level of e ducation offered by colleges and universities, the region would increase the number of highly educated in the labor force and make other areas outside of Orlando and UCF mo re attractive for industry. Influence to Surrounding Regions It is not only important to measure the eff ects of new industry with in the region, but the effect of regional economic activity is most im portant outside of the seven county region. The

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172 significance of this project lies beyond determining where industries are spatially suitable to increase regional economic activity. If the ne w industries do not produce a product that can be marketed and sold outside of the immediate re gion, regional growth is limited. High-tech industries and R&D as it relate s to economic growth is meas ured and considered on the aggregate or national level. Take other me galopolis regions like Silicon Valley or the Washington, DC to Boston corridor. The compan ies that have made these regions thrive are those that compete in global and national market s (i.e. software companies, banking & finance, engineering, etc.), not those with products relevant only w ithin their imme diate region. Therefore, product innovation must be considered on a larger scale. Recommendations for Future Research Overall, this model appears to be effective in determining the spatial preferences of hightech innovative firms. However, specific aspect s of the model could be adjusted to increase its effectiveness, calling for additional research: Performing Additional Sensitivity Analysis Sensitivity analysis, as described in Chapter 4, tests the influence of individual variables on the outcome. Applying sensitivity analysis on e ach creative indicator would provide evidentiary support of my conclusions. This statistical te st would also enable users of my method to determine which variables drive suitabi lity for a creative environment. Projecting Other Employment Sectors The results of this project stop at projecting the spatial patterns of office/commercial developments. Future research should include projecting spatial pattern s of office/commercial, retail and industrial as well as the ability of the region to accommodate future employment projections. This was consider ed during this project, but deve loping criteria establishing how retail and industrial reac t to the location of office/commer cial building can be tricky. For

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173 example, for every one office/commercial buildin g does a McDonalds or big box store like Walmart or Target result? Modeling/Predicting New Transportation Corridors How people flow through and around regions can indicate economic connections of areas. In the Findings and Results (Chapter 5) seve ral conclusions were dr awn about correlation between the location of new cr eative employment centers and highway access. In future research I would like to examine the efficacy of di fferent modes of transit of the spatial pattern of creative industries. Model Each Alternative Scenario of the Central Florida Growth Vision The result of the year-long myregion.org community workshops and visioning exercises were a trend scenario and three alternatives, each based upon different patterns of growth. In the future it would be interesting to model each al ternative scenario and determining whether the region can truly accommodate the new employment calculated as a result of the alternative development pattern.

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174 LIST OF REFERENCES America2050. (2007). America 2050 Prosp ectus. Retrieved April 20, 2007, from www.america2050.org Anas, A. (1987). Modeling in urban and regi onal economics. New York: Harwood Academic Publishers. Audretsch, D. B. and M. P. Feldman. (1996) R&D Spillovers and the Geography of Innovation and Production. American Economic Review 86: 630-640. Barkley, D.L., & Henry, M. S. (2001). Advantages and disadvantages of targeting industry clusters. Clemson, South Carolina: Clemson Un iversity Public Service Activities. Barnett, Jonathan. (2005). PennDesign VII Central Florida: Our region in the year 2050 City Planning 702 Urban Design Studio. Borchert, J. R. (1992). Megalopolis: Washington to Boston New Jersey: Rutgers University Press. Bitar, S. (2007). [Employment statistics fr om the Regional Economic Models, Inc. (REMI) software]. Unpublished raw data. Bolen, R. (2002). GIS: Essent ial technology for urban growth management: Portland, Oregon Metropolitan Area. Presented at the RLIS @ Ten Sympos ium, Sponsored by Lincoln Institute of Land Policy, March 14, 2002. Breschi, B, & Lissoni, F. (2001). Localised knowledge spillovers vs. innovative milieux: Knowledge "tacitness" reconsidered Papers in Regional Science 80 (3), 255 Borchert, J. (1992). Megalopolis: Washington, D.C., to Boston New Brunswick, N.J.: Rutgers University Press. Bureau of Economic and Business Research (BEBR). (2006). Florida Population Studies (Detailed Bulletins 145). Gainesville, Florida: University of Florida. Bureau of Labor and Statistics (BLS). (2007a). Location quotient Retrieved March 18, 2007, from http://www.bls.gov/data Bureau of Labor and Statistics (BLS). (2007b). Glossary Retrieved April 9, 2007, from http://www.bls.gov/bls/glossary.htm Carr, M. (2006). [Moderate population projectio ns from 2030 to 2050]. Unpublished raw data. Carr, M., & Zwick, P. (2005). Using GIS suitability analysis to identify potential future land use conflicts in North Central Florida. Journal of Conservation Planning (1): 89-105.

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175 Carr, M., & Zwick, P. (2007a). Smart Land-Use Analysis: The LUCIS Model Redlands, California: ESRI Press. Carr, M., & Zwick, P. (2007b). An application of LUCIS (Land Use C onflict Identification Strategy) in South Central Florida: An ex ample of GIS for land use planning & design. LAA 6656 & URP 6341 Class Slides University of Florida Cartwright, S. D., & Wilbur, V. R. (2005). ULI Community Catalyst Report: Translating a regional vision into action Washington, D.C.: Urban Land Institute. Ceh, B. (2001, April 9). Regional innovation potentia l in the United States: Evidence of spatial transformation. Papers in Regional Science, 80, 297-316. Central Florida Regional Growth Vision: Lessons learned from regiona l visioning processes. (2006, September). Florida Department of Transportation (FDOT) Davidson, M. & Dolnick, F. (2004). A planners dictionary Chicago, IL: Planning Advisory Service. Deal, B., Pallathucheril, V. G., Sun, Z ., Terstriep, J., & Hartel, W. (2005). LEAM Technical Document: Overview of the LEAM Approach University of Illi nois at Urbana-Champaign. Dowall, D. E. (1999, January). Globalization, st ructural change and urban land management. Land Lines Retrieved November 10, 2006, from http://www.lincolninst.edu/ pubs/PubDetail.aspx?pubid=378 Economy Professor. (2007). Human Capital Theory Retrieved March 5, 2007, from http://www.economyprofessor.com/economictheories/human-capital-theory.php Enterprise Florida. (2007). County Profiles Retrieved April 20, 2007, from http://www.eflorida.com/countyprofiles/Count yProfiles.asp?level1=3&level2=127&level3 =335®ion= Envision Utah. (2004). A history of Envision Utah Retrieved April 23, 2007, from http://www.envisionutah.org/ plans.phtml?type=research ESRI. (2006). Understanding reclassifi cation. Retrieved December 4, 2006, from http://webhelp.esri.com/arcgisdesktop/9.2/in dex.cfm?TopicName=Understanding_reclassif ication Federal Highway Administration. (2005a). GIS tools for transportati on and community planning Retrieved November 11, 2006, from http://www.fhwa.dot.gov/tcsp/case7.html Federal Highway Administration. (2005b). Case study: Charlottesville, Virginia: Jefferson Area Eastern Planning Initiative Retrieved April 20, 2007, from http://www.fhwa.dot.gov/tcsp/cvadeflt.html

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176 Feldman, M. & Florida, R. (1994). The geogr aphic sources of i nnovation: Technological infrastructure and product i nnovation in the United States. Annals of the Association of American Geographers 84: 210-229. Fishkind and Associates. (2005). Supporting and strengthening our local economy: Figures for 2003. Retrieved April 23, 2007, from http://www.portcanaveral.org/general/economics.php Fishman, R. (2000). The death and life of Am erican Regional Planning. In B. Katz (Ed.), Reflections on regionalism (pp. 107-123). Washington, D.C.: Brookings Institution Press. Florida, R. (2002). The rise of the creative class Nee York: Basic Books. Florida, R. (2006, July). Richar d Florida: The New Megalopolis. Newsweek International Retrieved November 8, 2006, from http://msnbc.msn.com /id/13528839/site/newsweek Florida, R., & Gates, G. ( 2002). Technology and tolerance: Di versity and high tech growth. The Brookings Review 20, 32-35. Florida State University Department of Urban and Regional Planning. (2006). Planning Methods III: Forecasting, loca tion quotient technique Retrieved March 15, 2007, from http://garnet.acns.fsu.edu/~tchap in/urp5261/topics/econbase/lq.htm Frey, W. H. (2001). Melting Pot Suburbs: A Census 2000 study of suburban diversity The Brookings Institution Center on Urban & Metropolitan Policy: Washington, DC. Friedman, T. (2000). The Lexus and the olive tree New York: Anchor Books. GIS Tools for Transportation a nd Community Planning. (2005). Federal Highway Administration (FHWA) Retrieved August 26, 2006, from http://www.fhwa.dot.gov/tcsp/case7.html How Shall We Grow? Creating a sh ared vision for Central Florid a, Mid-Project Report. (2006, September). myregion.org Institute of Transportation Engineers. (1988). Employment center parking facilities Committee 6F-24 Informational Report. Washington, D.C. : Institute of Transportation Engineers. Jacobs, J. (1961). The death and life of great American cities New York: Random House. JAXPORT. (2007). Seaports-Services-Rail a nd Road Connections. Retrieved April 23, 2007, from http://www.jaxport.com/sea/sea_rail.cfm Katz, B. (2005, March 9). This land is our land: A call to arms for state and federal policy reform [Presentation]. Funders Network for smart growth and livable communities Albuquerque, New Mexico

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177 Katz, B. (2004). A progressive agenda for metr opolitan America. Policy Report Retrieved November 10, 2006, from http://www.brookings.edu/ur ban/pubs/KatzGreenBook.pdf Kitsner, S. (2001, May). Seattle reboots its future Fast Company, p. 44. Knaap, G., Bolen, R., & Seltzer, E. (2003). Metros Regional Land Information System: The virtual key to Portlands growth management success Lincoln Institute of Land Policy Working Paper. Knaap, G., & Song, Y. (2004). The fruits of growth management in the Sunshine State: An examination of urban form in Orange County, Florida Prepared for presentation at the Critical Issues Symposium on Evaluating Growth Management in Florida: January, 2005. Kochar, R. (2006). Growth in the foreign-born workfor ce and employment of the native born Pew Hispanic Center: Washington, DC. Latimer, S. (2006). URP 6270 Class Notes University of Florida. Laurien, P. (2007). Myregion.org presents: How Sha ll We Grow? Central Floridas Four Futures. Slides used for Media Week January 2007 East Central Florida Regional Planning Council Lockheed Martin awarded patent for a three-axis flap c ontrol system. (2003). Space War Retrieved April 11, 2007, from http://www.spacewar.com/reports/Lockheed_M artin_Awarded_Patent_for_a_ThreeAxis_F lap_Control_System.html Maps-gps-info.com (2007). Maps GPS glossary Retrieved March 16, 2007, from http://www.maps-gps-info.com/maps-gps-glossary.html McHarg, I. L. (1969). Design with nature New Jersey: Doubleday/Natural History Press. Merriam-Webster, Incorporated. (2004). The Merriam-Webster Dictionary Springfield, Massachusetts: Merriam-Webster. Metro (2007a). Land Use Planning: 2040 Growth Concept Retrieved April 20, 2007, from http://www.metro-region.org/ article.cfm?articleid=231 Metro (2007b). Land Use planning: urban growth boundary. Retrieved April 20, 2007, from http://www.metro-region.org/ article.cfm?articleID=277 Morrill, R. (2006, May). Classic Map Revi sted: The growth of megalopolis. The Professional Geographer Vol. 58, No. 2, 155-160. myregion.org (2006, September). How shall we grow? Creating a shared vision for Central Florida: Mid project report Orlando, Florida: myregion.org Nowlan, D. M. (1997). Jane Jacobs among the economists Retrieved April 2, 2007, from http://www.chass.utoronto. ca/~nowlan/papers/jacobs.pdf

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178 Office of Institutional Research, Fl orida Institute of Technology. (2007) Graduation Summary Report 2005-2006 Retrieved March 15, 2007, from http://www.fit.edu/oir/reportviewer.php?pageid=gradsum Office of Institutional Research, Be thune Cookman University. (2007). Total undergraduate degrees conferred 2001-02 2005-06 Daytona Beach, Florida: Office of Institutional Research. Office of Institutional Research, Univ ersity of Central Florida. (2007) Degrees conferred AY 2006-2007 Retrieved March 15, 2007, from http://www.iroffice.ucf.edu/de grees/0607degrees/0607contents.html Regional Economic Models, Inc. (2007). Policy Insight Retrieved March 16, 2007, from http://www.remi.com/software/software.shtml Renaissance Planning Group. (2007). Community element model Retrieved April 20, 2007, from, http://www.citiesthatwork.com/imodels/CorPlanInfoSheet2.htm Scott, M. (2003). Modeling, making inferences and making decisions: the role of sensitivity analysis Retrieved April 17, 2007, from http://www.rss.org.uk/main.asp?page=1142 Slack, B. (2006). Transport Corridors of Canada. Retrieved April 23, 2007, from http://people.hofstra.edu/geotrans /eng/ch2en/appl2en/ch2a2en.html Smallbone, D., Bertotti, M., and Ek anem, I. (2005). Diversificati on in ethnic minority business: The case of Asians in Londons creative industries. Journal of Small Business and Enterprise Development ; 2005; 12, 1; ABI/INFORM Global pg 41. Smart Growth America. (2006). Glossary American Farmland Trust Retrieved November 13, 2006 from http://www.farmland.org/farmingontheedge/about_glossary.htm Southern Resource Forest Assessment. (2001). Draft glossary of terms Retrieved April 22, 2007, from http://www.srs.fs.usda.gov/sust ain/data/authors/glossary.htm Tampa Port Authority. (2007). Tampa Port Authority. Retrieved April 23, 2007, from http://www.tampaport.com/index.asp Taube, F. A. (2006). Local clusters, ethnic ne tworks and diversity in knowledge-based industries: evidence from the Indian IT industry. Retrieved April 5, 2007, from http://www.people.hbs.edu/dkendall /Consortium/Summaries/taube.pdf Teeple, Brian (2006, September/October). The Re-Emergence of regionalism in Florida. Florida Planning pp. 1-2. The EcoTipping Points Project (2007). USA-Por tland, Oregon A livable city. Retrieved April 20, 2007, from http://www.ecotippingpoints.or g/indepth/usaportland.html

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179 The National Neighborhood Coalitions Neighbo rhoods, Regions and Smart Growth Project: Connecting Neighborhood and Region for Smarter Growth. (2000, April). National Neighborhood Coalition pp. 1-17. Title Company of the Rockies. (2007). Real estate dictionary Retrieved March 16, 2007, from http://www.titlecorockies.com/dictionary_b.htm ULI The Urban Land Institute (2005). Building Floridas Future: St ate strategies for regional cooperation Washington, D.C.: The Urban Land Institute. University of Central Florida. (2006). Burnham Institute to build next to UCF College of Medicine Retrieved March 18, 2007, from http://www.med.ucf.edu/news_releases/2006/aug/082306.htm U.S. Census Bureau; 2005 American Community Survey; generated by Iris Patten; using American Factfinder; http://factfinder.census.gov/servlet/Data setMainPageServlet?_program=ACS&_submenuI d=&_lang=en&_ts= ; (08 March 2007). U.S. Census Bureau; Census 2000, Summary File 3 (SF 3); generated by Iris Patten; using American Factfinder; http://factfinder.census.gov/ ; (08 March 2007). U.S. Department of Commerce. (2000). United States Patent Grant s by State, County, and Metropolitan Area Util ity Patents, 1990-1999 (A Technology Assessment and Forecast Report). Washington, DC: U.S. Government Printing Office. Virginia Tech University. (2006). Analytical hierarchy process. Retrieved March 18, 2007, from http://www.aoe.vt.edu/~cdhall /courses/aoe4065/AHPslides.pdf Wade, T. & Sommer, S. (2006). A to Z GIS Redlands, California: ESRI Press. Wang, L., Wu, H., Wang, F., & Hu, Y. (2004). Economic globalization and a case study of the urban land use growth of Wuhan, PR China. Retrieved November 8, 2006, from the International Society for Photogramme try and Remote Sensing Web site http://www.isprs.org/is tanbul2004/comm3/papers/334.pdf

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180 BIOGRAPHICAL SKETCH Iris Patten is currently pursuing her Masters of Arts in Urban and Regional Planning at the University of Florida (UF) in Gainesville, Florid a. She has a Bachelor of Arts in Environmental Science and Policy from the University of Mary land, College Park. She currently works at the GeoPlan Center at UF and after graduation plan s to pursue a Doctor of Philosophy degree, also from UF, in urban and regional planning. Iris masters studies have focused on the use of geographic information systems (GIS) in regional vi sioning efforts and spatia l analysis of growth patterns in accommodating future populations. As a budding urban planner, Iris past experience includes environmental planning and, most recen tly, she has worked with the East Central Florida Regional Planning Council on the Centra l Florida 2050 Regional Growth Visioning. As university representative and executive committee member for the Florida Chapter of the American Planning Association, Ir is represents over 150 students enrolled in undergraduate and graduate planning programs within the State of Flor ida. Iris future interests include working in the field of international development and enco uraging greater participation among underserved communities in the planning process.


Permanent Link: http://ufdc.ufl.edu/UFE0020997/00001

Material Information

Title: The Use of GIS in allocating employment centers that minimize land use conflict and satisfy regional economic potential
Physical Description: Mixed Material
Language: English
Creator: Patten, Iris E. ( Dissertant )
Zwick, Paul D. ( Thesis advisor )
Carr, Margaret H. ( Thesis advisor )
Latimer, Stanley ( Reviewer )
Laurien, Phil ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007
Copyright Date: 2007

Subjects

Subjects / Keywords: Urban and Regional Planning thesis, M.A.U.R.P
Dissertations, Academic -- UF -- Urban and Regional Planning
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
theses   ( marcgt )
Spatial Coverage: United States--Florida

Notes

Abstract: Over the next 50 years the vast projected population increase will present several challenges to the East Central Florida region. These challenges include pursuing opportunities associated with competing in the global economy, protecting environmental and agricultural resources, and developing a transportation plan that will address expected demand. The Land Use Conflict Identification Strategy (LUCIS), developed by Margaret Carr and Paul Zwick, employs role playing and suitability modeling to predict areas where future land use conflict will likely occur. Applying LUCIS results in an illustration of development patterns and provides a means to distinguish the driving forces behind the outcome, given specific policy decisions and assumptions. This study modified the LUCIS strategy by incorporating a more detailed economic analysis and included indicators of Richard Florida’s Creative Class to determine whether the East Central Florida region has suitable environments for creative industries. Creative environments satisfy three conditions: 1) lands with socio-cultural and socio-economic qualities preferable to creative individuals; 2) lands preferable for urban development; and 3) lands suitable for office/commercial development. These locations attract occupations that are high quality, high-earning, and service oriented (also known as Creative Occupations). Using projections of new employment for creative industries through 2050, this study allocates employment centers and distributes employment resulting in a sustainable development plan that accommodates highly skilled workers and maximizes the economic potential of the region. The creative are members of the service sector and this study shows that of the 832,400 new service employees within the East Central Florida Region over the next 45 years, environments conducive to the creative exist in all seven counties. Unfortunately, there was only enough contiguous land available to accommodate 88 new centers of creative employment, which would employ 62,760 employees or 7.5% of new service employment. Additionally, using indices developed by Richard Florida that measure diversity, tolerance, and economic potential necessary to compete in a Creative Economy, we found that Brevard and Orange counties were most suitable for innovative and high-tech industries. The results of this analysis suggest that although the region has the necessary tools in place to compete globally and attract higher quality occupations, the region does not have enough suitable land available to accommodate industries that minimize land use conflict and have values related creative environments. This study is loosely based on the Central Florida Regional Growth Vision, coordinated by myregion.org (“MyRegion”); a coalition of organizations promoting Central Florida’s economic competitiveness and quality of life (myregion.org, 2006, p. 11). Some results from the myregion.org alternative land use scenarios were used as inputs into the LUCIS model for this study. The myregion.org visioning process utilized community input with suitability modeling and other tools that provide comparisons of the monetary and physical significance of alternative patterns of growth.
Subject: creative, employment, GIS, growth, megalopolis, regional, UF
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 180 pages.
General Note: Includes vita.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2007.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003874523
System ID: UFE0020997:00001

Permanent Link: http://ufdc.ufl.edu/UFE0020997/00001

Material Information

Title: The Use of GIS in allocating employment centers that minimize land use conflict and satisfy regional economic potential
Physical Description: Mixed Material
Language: English
Creator: Patten, Iris E. ( Dissertant )
Zwick, Paul D. ( Thesis advisor )
Carr, Margaret H. ( Thesis advisor )
Latimer, Stanley ( Reviewer )
Laurien, Phil ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007
Copyright Date: 2007

Subjects

Subjects / Keywords: Urban and Regional Planning thesis, M.A.U.R.P
Dissertations, Academic -- UF -- Urban and Regional Planning
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
theses   ( marcgt )
Spatial Coverage: United States--Florida

Notes

Abstract: Over the next 50 years the vast projected population increase will present several challenges to the East Central Florida region. These challenges include pursuing opportunities associated with competing in the global economy, protecting environmental and agricultural resources, and developing a transportation plan that will address expected demand. The Land Use Conflict Identification Strategy (LUCIS), developed by Margaret Carr and Paul Zwick, employs role playing and suitability modeling to predict areas where future land use conflict will likely occur. Applying LUCIS results in an illustration of development patterns and provides a means to distinguish the driving forces behind the outcome, given specific policy decisions and assumptions. This study modified the LUCIS strategy by incorporating a more detailed economic analysis and included indicators of Richard Florida’s Creative Class to determine whether the East Central Florida region has suitable environments for creative industries. Creative environments satisfy three conditions: 1) lands with socio-cultural and socio-economic qualities preferable to creative individuals; 2) lands preferable for urban development; and 3) lands suitable for office/commercial development. These locations attract occupations that are high quality, high-earning, and service oriented (also known as Creative Occupations). Using projections of new employment for creative industries through 2050, this study allocates employment centers and distributes employment resulting in a sustainable development plan that accommodates highly skilled workers and maximizes the economic potential of the region. The creative are members of the service sector and this study shows that of the 832,400 new service employees within the East Central Florida Region over the next 45 years, environments conducive to the creative exist in all seven counties. Unfortunately, there was only enough contiguous land available to accommodate 88 new centers of creative employment, which would employ 62,760 employees or 7.5% of new service employment. Additionally, using indices developed by Richard Florida that measure diversity, tolerance, and economic potential necessary to compete in a Creative Economy, we found that Brevard and Orange counties were most suitable for innovative and high-tech industries. The results of this analysis suggest that although the region has the necessary tools in place to compete globally and attract higher quality occupations, the region does not have enough suitable land available to accommodate industries that minimize land use conflict and have values related creative environments. This study is loosely based on the Central Florida Regional Growth Vision, coordinated by myregion.org (“MyRegion”); a coalition of organizations promoting Central Florida’s economic competitiveness and quality of life (myregion.org, 2006, p. 11). Some results from the myregion.org alternative land use scenarios were used as inputs into the LUCIS model for this study. The myregion.org visioning process utilized community input with suitability modeling and other tools that provide comparisons of the monetary and physical significance of alternative patterns of growth.
Subject: creative, employment, GIS, growth, megalopolis, regional, UF
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 180 pages.
General Note: Includes vita.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2007.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003874523
System ID: UFE0020997:00001


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THE USE OF GIS IN ALLOCATING EMPLOYMENT CENTERS THAT MINIMIZE LAND
USE CONFLICT AND SATISFY REGIONAL ECONOMIC POTENTIAL




















By

IRIS E. PATTEN


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ARTS IN URBAN AND REGIONAL PLANNING

UNIVERSITY OF FLORIDA

2007

































O 2007 Iris E. Patten





























To all the tortoises in the world: you really can beat the hares.









ACKNOWLEDGMENTS

First, I would like to thank my parents for teaching me that with faith I can move

mountains. Secondly, I would like to acknowledge and thank my committee members. They

have each provided an unquantifiable amount of advice, motivation and encouragement during

the past two years. I thank Dr. Paul Zwick for his refreshing enthusiasm for Geographic

Information Systems (GIS) and urban planning. He continually pushes the envelope on what can

be done technically, while encouraging his students to think even further outside of the box. As

a leader, researcher and scholar he has impressed upon me ideals that I hope to utilize in the

future. I thank Peggy Carr for pushing me just a little bit harder. Her passion for the natural

landscape has truly affected me, and her talents as an author, professor and mentor have not gone

unnoticed. Before arriving at the University of Florida (UF) two years ago I promised that I

would never touch GIS again. I would like to thank Stanley Latimer for not only his time and

efforts during this thesis process but also for strengthening my foundation of GIS and restoring

my confidence. Lastly, but certainly not least, I thank Phil Laurien. He is proof that although

Ohio State University (OSU) was unable to win any championships this year, OSU produces

great people (smile!). His ideas and vision for East Central Florida give me hope that Florida

will one day realize its potential of becoming a great state. He has been more than an employer.

I consider him a great thinker and mentor. These are all amazing people and I thank them for

their patience during this study and for their continued support.

In addition, I would like to thank Samer Bitar and Claudia Paskauskas of the East Central

Florida Regional Planning Council; Ella Littles, Evelyn Cairns, and Nelda Schneider of the

University of Florida Urban and Regional Planning Department; Mosi Harrington and Ava Kuo

of Housing Initiative Partnership; and Alexis Thomas and the entire GeoPlan staff. I thank them

for their technical assistance, administrative help, and patience while I completed this study.









Finally, I thank my family, close friends, and my fellow students for their kind words and

encouragement. Their kindness was not overlooked.

This study and paper have become more than a series of pages and words that

acknowledge that I learned a thing or two about planning during the past two years. I hope it

signals a new phase of my life where I push myself to explore topics and take chances that

require a little faith to get through. The poet Patrick Overton said, "When you have come to the

edge of all light that you know and are about to drop off into the darkness of the unknown, Faith

is knowing one of two things will happen: there will be something solid to stand on or you will

be taught to fly." Having completed this thesis, I can now fly.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ................ ...............8............ ....


LIST OF FIGURES .............. ...............10....


LIST OF EQUATIONS ................. ...............11................


AB S TRAC T ............._. .......... ..............._ 12...


CHAPTER


1 INTRODUCTION .............. ...............14....


2 LITERATURE REVIEW .............. ...............16....


The Rise of the M egalopolis .................... ............. ...............16.....
Globalization-Did You Know the World is Flat? ............. ...............19.....
The Economy and Development Patterns............... ...............21
The Creative Class ................. ...............22........... ....
Indicators of Creative Centers ................ .............. .................. ...............25
How Shall W e Grow? .................. ... .. .... ..... ....... ....... ........2
Traditional and Conventional Views of Regional Growth ........................... ...............27
The Use of Scenario Modeling ................. ...............28........... ...
Creating a Vision .............. .... .......... ...............29......
Select Aspects of Regional Visioning Exercises ................. ...............30..............
The Use of GIS in Scenario Modeling .............. ...............32....
Metro 2040 Growth Concept................... .... ..............3
Federal Highway Administration Funded Proj ects ........._.___..... .__. ................3 5
Summary ........._.___..... ._ __ ...............41.....

3 CENTRAL FLORIDA GROWTH VISION............... ...............49.


Proj ect Goal ........._._... .........._. _... .. ...............49...
Scenario Modeling in Central Florida ..................... ...............50.
Land Use Conflict Identification Strategy (LUCIS) ................. ...............52...............
Phase 2: Community Input .............. ...............53....
Summary ................. ...............56.................

4 STUDY AREA .............. ...............69....


T he Economy ................. ...............70.......... ......
Transportation ................. ...............71.................
Sum m ary ................. ...............74.......... ......












5 METHODOLOGY .............. ...............85....


Explanation of the Model ................. ...............85.......... ....
Identifying the Indicators............... ...............8
Creative Indicators................. ..............8
Clustering of Creative Industries............... ...............8
D ata Collection ......... .... ........ .................. ...............9
Data Collection Methods for the Urban Stakeholder ................. ......... ................93
Suitability M odeling ................. ...............95.......... .....
W eighted Suitability .............. ...............96....
Conflict Identification............... .............9
Allocating Employment Centers ................. ...............101................
Allocate Industry by County .............. ...............103....


6 FINDINGS AND RESULTS ................ ...............141...............


The Creative Index .............. ......... ........... .........4
The Creative Class Share of the Workforce ................. ...............141........... ..
Talent Index ................. ...............144................
Innovation Index............... ...............144.
Milken Index Tech Pole .............. ...............146....
The Diversity Index ................. ...............146................
The Gay Index ................. ...............147................
The Melting Pot Index ................. ...............147...............
Spatial Comparisons ................ ...............148................
Spatial Findings ................ ...............150................
Re sults.................. ......... ...............155......
Sensitivity Analysis................ ...............15
Sensitivity Analysis Findings ................. ...............156................


7 CONCLUSION ................. ...............168................


Universal Applicability............... .............16
Economic Implications ................. ...............170......... ......
Human Capital .............. .... .......... ...............170......
Influence to Surrounding Regions ................ ...............171...............
Recommendations for Future Research ................. ...............172...............
Performing Additional Sensitivity Analysis............... ...............17
Proj ecting Other Employment Sectors ............__........... ....___ ............7
Modeling/Predicting New Transportation Corridors ................... ... ..._ ...............173
Model Each Alternative Scenario of the Central Florida Growth Vision ....................173


LIST OF REFERENCES ............_ ..... ..__ ...............174..


BIOGRAPHICAL SKETCH ............ _...... ._ ...............180...











LIST OF TABLES


Table page


2-1. Maj or regional visioning proj ects ................. ...............43............

2-2. Primary RLIS layers .............. ...............45....

2-3. RLIS procedure for identifying buildable lands and calculating housing and
employment capacities............... ...............4

3-2. Assumptions used in baseline and 3 alternative scenarios of myregion.org Central
Florida Growth Vision ................. ...............59........... ....

3-3. Potential future population distribution by scenario and county ................. ............... .....61

4-1. Largest cities and population, for each county ................. ...............75.............

4-2. Regional 2005 population and 2050 proj ected population, by county ................ ................76

4-3. Maj or private sector employers ................. ...............76......... ..

4-4. Employment patterns of workers 16 years and over who live in a MSA ................... ...........80

4-5. Transportation corridors (roads) within the East Central Florida region .............. ................81

4-6. Ports and airports within the East Central Florida region .............. ...............82....

5-1. Goals and objectives used for optimizing choice for agriculture, conservation, and
urban land uses............... ...............106.

5-2. Data sources for Creative Indices ............_ ..... ..__ ...............129.

5-3. Categories for land use analysis data ............_ ..... .. ...............133

5-4. Assigned SUA values ................ ...............133...............

5-5. AHP importance categories ............_ ..... ..__ ...............134.

5-6. Combinations of preference rankings............... ...............13

5-7. Employment Center Building Sizes .............. ...............136....

5-8. Cells needed to accommodate building allocation .............. ...............136....

6-1. Description of Class Occupations. The Creative Class has two maj or sub-components: a
Super-Creative Core and Creative Professionals ................. ............... ......... ...158

6-2. Creative Class share of the Service Sector in 2050 ................ ...............158............











6-3. Number of patents issued between 1990 and 1999 ........._.___..... ....._......... .159_

6-4. Gay population and share of total population............... ..............15

6-5. Regional population, by ethnic group............... ...............160.

6-6. Average location quotient of Creative Industries .....__.___ .... ... .___ ......_.... ........6

6-7. Distribution of new employment centers, 2005-2050 .............. .....................160

6-8. Distribution of new employees, 2005-2050 .............. .....................161

6-9. Land area calculations for suitability analysis............... ...............16

6-10. County land area calculations............... .............16

6-11. Polk County conflict summary ........._.._.. .......... ...............162.

6-12. Breakdown of land usage for Brevard County ................. ...._.._ ............. ......6

6-13. Conflict Surface stakeholder share for Brevard County ................. ...................._...163

6-14. Land most preferable for urban use (from the collapsed urban surface) ........................163










LIST OF FIGURES


Figure page

3-1. Illustration of growth patterns for Baseline Scenario ............. ...............62.....

3-2. Illustration of growth patterns for Scenario A (Green Areas) ....._____ ... ... ....__ ..........63

3-3. Illustration of growth patterns for Scenario B (Centers) ................ ................ ..........64

3-4. Illustration of growth patterns for Scenario C (Corridors) ................ .........................65

3-5. Description of Phase 2 Scenarios from Central Florida Growth Vision .............. ................66

3-6. Central Florida Growth Vision survey results ................. ...............67........... ..

4-1. Study Area, 7 county East Central Florida region............... ...............83.

4-2. Metropolitan Statistical Areas (MSAs) within the East Central Florida Region ..................84

5-1. Diagram of hierarchical relationships of goals, objectives and sub-objectives for
agricultural land use suitability analysis............... ...............10

5-2. Diagram of hierarchical relationships of goals, objectives and sub-objectives for
conservation land use suitability analysis............... ...............11

5-3. Diagram of hierarchical relationships of goals, objectives and sub-objectives for
conservation land use suitability analysis............... ...............112

5-4: LUCIS strategy process flow............... ...............137..

5-5: Final preference map ................ ...............138....._... ...

5-6: Suitable locations for Urban Areas and Creative Environments. ........._.._.. ............... ..139

5-7. Example employment allocation calculation............... ..............14

6-1. People' s Choice Map with new Creative Industry locations for Volusia County ................164

6-2. People's Choice Map with new Creative Industry locations for Osceola County ...............165

6-3. People's Choice Map with new Creative Industry locations for Orange and Seminole
Counties .............. ...............166....

6-4. Final Conflict Surface............... ...............167










LIST OF EQUATIONS


Equation page

4-1: Equation to determine conflict .............. ...............99....

5-2. Equation to Determine Creative and Urban Environments ................. ........................100

5-3. Equation to determine areas that are Creative, Urban and Suitable for Office
Commercial ................. ...............10. 1...............









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 Arts in Urban and Regional Planning

THE USE OF GIS IN ALLOCATING EMPLOYMENT CENTERS THAT MINIMIZE LAND
USE CONFLICT AND SATISFY REGIONAL ECONOMIC POTENTIAL

By

Iris E. Patten

May 2007

Chair: Paul Zwick
Cochair: Margaret Carr
Major: Urban and Regional Planning

Over the next 50 years the vast proj ected population increase will present several

challenges to the East Central Florida region. These challenges include pursuing opportunities

associated with competing in the global economy, protecting environmental and agricultural

resources, and developing a transportation plan that will address expected demand. The Land

Use Conflict Identification Strategy (LUCIS), developed by Margaret Carr and Paul Zwick,

employs role playing and suitability modeling to predict areas where future land use conflict will

likely occur. Applying LUCIS results in an illustration of development patterns and provides a

means to distinguish the driving forces behind the outcome, given specific policy decisions and

assumptions.

This study modified the LUCIS strategy by incorporating a more detailed economic

analysis and included indicators of Richard Florida' s Creative Class to determine whether the

East Central Florida region has suitable environments for creative industries. Creative

environments satisfy three conditions: 1) lands with socio-cultural and socio-economic qualities

preferable to creative individuals; 2) lands preferable for urban development; and 3) lands

suitable for office/commercial development. These locations attract occupations that are high-










quality, high-earning, and service oriented (also known as Creative Occupations). Using

proj sections of new employment for creative industries through 2050, this study allocates

employment centers and distributes employment resulting in a sustainable development plan that

accommodates highly skilled workers and maximizes the economic potential of the region.

The creative are members of the service sector and this study shows that of the 832,400

new service employees within the East Central Florida Region over the next 45 years,

environments conducive to the creative exist in all seven counties. Unfortunately, there was only

enough contiguous land available to accommodate 88 new centers of creative employment,

which would employ 62,760 employees or 7.5% of new service employment. Additionally,

using indices developed by Richard Florida that measure diversity, tolerance, and economic

potential necessary to compete in a Creative Economy, we found that Brevard and Orange

counties were most suitable for innovative and high-tech industries. The results of this analysis

suggest that although the region has the necessary tools in place to compete globally and attract

higher quality occupations, the region does not have enough suitable land available to

accommodate industries that minimize land use conflict and have values related creative

environments.

This study is loosely based on the Central Florida Regional Growth Vision, coordinated by

myregion.org ("MyRegion"); a coalition of organizations promoting Central Florida's economic

competitiveness and quality of life (myregion.org, 2006, p. 11). Some results from the

myregion.org alternative land use scenarios were used as inputs into the LUCIS model for this

study. The myregion.org visioning process utilized community input with suitability modeling

and other tools that provide comparisons of the monetary and physical significance of alternative

patterns of growth.









CHAPTER 1
INTTRODUCTION

Good urban form is all about the shape of our cities. This includes how cities are

designed and structured, where development occurs, what areas are protected, and how areas are

connected and support each other. Cities across the country are looking for ways to link good

urban form with residents who possess a high economic potential. These residents are the highly

skilled, highly educated, and highly paid. The primary benefit of having these residents in your

jurisdiction is that they create the effective demand for a location. Richard Florida, a sociologist,

indicates that the main reason these residents move to specific cities is rooted in economics. The

physical proximity of talented, highly educated people has a powerful effect on innovation.

When large numbers of entrepreneurs, Einanciers, engineers, designers, and other smart, creative

people are constantly bumping into one another, the more quickly business ideas form, are

refined, then executed (Florida, 2006, p. 36).

In both early agricultural and industrial economies, overall population growth defined

economic growth. In a global, creative, postindustrial economy, that' s no longer true. Changing

technology, increased trade, and the ability to outsource routine functions have made highly

skilled workers less reliant on the collocation of the unskilled and moderately skilled. What

matters today isn't where most people settle, but where the greatest number of the most-skilled

does (Florida, 2006, p. 37). It is a matter of location.

The East Central Florida region anticipates over 3.6 million new residents by the year 2050

(Bureau of Economic and Business Research, 2006; Carr, 2006). Of these, 832,400 (Bitar, 2007)

will be employed in service industry occupations. Creative occupations are within the service

industry and these professions increase the economic potential of the region. In planning for the

anticipated growth policymakers are using regional visioning exercises and GIS technology to









determine how to accommodate creative employees with the least impact on the environment

and cultural resources. This study attempts to place employment centers within the East Central

Florida region that will accomplish these goals.

While the provision of housing is important, cities must also plan for employment. Cities

must evaluate what types of employment are needed that not only maximize revenue and

diversify the economy but make best use of the land and attract these highly skilled workers.

Moreover, how do you translate the values into a plan that provides good urban form and creates

an atmosphere conducive to a creative class of workers? Using the results from the Central

Florida Growth Vision and a modification of the LUCIS methodology this study attempts to

achieve these obj ectives.









CHAPTER 2
LITERATURE REVIEW

Economic growth is spurred by dynamic regions, not nations. The geographer Jean

Gottman coined the term "megalopolis" to describe urban regions that are large and highly

connected. A megalopolis is defined by a distinct region. A "region" is an area of distinctive

communities, cities, and counties where residents share the following: a geographic identity and

are socially, economically, and culturally interdependent; a capacity for planning and function;

and a capacity to create competitive advantage (ULI, 2005, p. 21). Since 1957 when Jean

Gottman created the term megalopolis, an idea called the "New Megas" has emerged (Florida,

2006). New Megas produce the bulk of wealth for the greater geographic area which attract

highly skilled and talented workers as well as generate the majority of innovation. New Megas

are regions linked by environmental systems, transportation networks, economies and cultures

(America 2050, 2005, p. 3). East Central Florida has a rich ecology, is served by major

transportation corridors and has a strong economy, so does it have the potential to become a New

Mega?

The Rise of the Megalopolis

The term megalopolis was developed by Jean Gottman, recognizing the string of urbanized

areas extending from Boston to Washington, DC as the "main street of America" (Morrill, 2006,

p. 155). Since the late 1950s, the original megalopolis has become a longer area of closely

interconnected metropolises extending between Fredericksburg, Virginia, to Portsmouth and

Dover-Rochester, in southern Maine. In 1961, this area was home to about 32 million people;

today its population has risen to 55 million, more than 17% of all Americans. The region

generates $2.5 trillion in economic activity, making it the world's fourth largest economy, bigger

than France and the United Kingdom (Florida, 2006). Since the original North Atlantic










megalopolis was defined, several other megalopolitan regions have been acknowledged,

including urban southern California and the urbanized southern Great Lakes region.

A megalopolis is a unique combination of great population numbers and density, history,

wealth, physical and social diversity, and dynamism (Borchert, 1992, p. 3). Compared with its

population, a megalopolis has a disproportionately large share of the nation' s wealth, personal

income, commerce, and industry (Borchert, 1992, p. 4). The core megalopolitan cities, such as

Washington DC and Boston, serve as the economic hinge of innovation (Morrill, 2006, p. 155;

Borchert, 1992, p. 3). Along with their disproportionate shares of the nation's wealth and

commerce, these places have comparably disproportionate shares of the nation' s poverty, crime,

and ignorance (i.e., inadequate knowledge) (Borchert, 1992, p. 5).

A megalopolis is defined by three criteria: 1) urban agglomerations with populations

greater than 50,000; 2) urban territory consistently defined as contiguous areas with densities of

more than 1,000 persons per square mile; and 3) interconnected commuting patterns. Richard

Morrill, a geographer from the University of Washington, studied the changing geography of the

megalopolis from 1950 through 2000. In addition to the megalopolitan core principles listed

above, there are also five settlement patterns that dominate megalopolitan areas, including 1)

sheer economic and demographic growth, 2) physical decentralization in the form of

suburbanization, 3) extension of metropolitan commuting Hields and the physical coalescence of

formerly physically separate areas, 4) rise or restructuring of and reaching out to formerly distant

satellites, and 5) restructuring and revitalization of high-level metropolitan cores (Morrill, 2006,

p. 158). In Jean Gottman's original book, he uses the above settlement patterns and the presence

of high levels of commuting to metropolitan centers as an indicator of metropolitan dominance.









Richard Morrill provides a chronology of settlement changes through three time periods,

1950-1970, 1970-1990, and 1990-2000. Although it is important to understand the evolution of

the megalopolis, for the purposes of this study, my concern lies in the changes that occurred

during 1990-2000. During this time period larger downtown and nearby historic areas were

gentrified as middle and upper class households reclaimed parts of the core. Economic

restructuring massively increased service employment, as business services and finance

demonstrated a preference for central high-rise venues. Core populations rose, in part by

attracting a creative class; younger, later- or not-marrying professionals and empty-nesters. This

was due in part from a resurgent large-scale immigration, especially in the 1990s, from Asia, the

Carribean, and Eastern Europe (Morrill, 2006, p. 159).

This time period also signaled massive growth on the suburban fringe with absolute

population and j obs exceeding that of the urban core. This happened through continued

industrial, commercial and residential expansion. During the 1990s the term "smart growth"

evolved to describe "well planned development that protects open space and farmland,

revitalizes communities, keeps housing affordable and provides more transportation choices"

(Smart Growth America, 2006). Much of the exurban development concentrated in older,

formerly independent satellite towns and cities became incorporated into the megalopolis web

(Morrill, 2006, p. 159).

Modern versions of the megalopolis are key regions or New Megas and include regions

like Beijing to Shanghai in China and Bangalore to Hyderabad in India. These regions don't just

focus on a single industry, such as high tech, but they are real economic organizing units,

producing the bulk of its wealth, attracting a large share of its talent and generating the lion' s

share of innovation (Florida, 2006). Megalopolises grow when people cluster in one place, then










the people and the place become more productive thus establishing a key region. Key regions

encourage collective creativity; the creation of useful new forms using knowledge as the primary

means of production (Florida, 2006; Jacobs, 1961).

The idea of the New Mega is rooted in several concepts. First, New Megas aren't made

overnight. They take extensive planning and are founded upon sustainable practices and smart

growth concepts. Secondly, New Megas are competitive in the global marketplace. To achieve

a competitive advantage New Megas are regionally based. As individual local governments

become more and more fiscally constrained it simply makes both short and long term economic

sense to cooperate and share in the economies of scale (Teeple, 2006, p. 4). An example is with

respect to transportation planning. Many smaller jurisdictions would be unable to make local

transportation improvements due to limited budgets and other development priorities if it were

not for shared resources derived from larger jurisdictions within their regions. Many regions

have come to the realization that we are an interdependent society and that many issues in our

local communities have substantial effect on our neighbors and that a larger context needs to be

provided to balance competing interests (Teeple, 2006, p. 4). The interlinkage within a region is

not only conducive to providing services but it is the foundation upon which corridors such as

Washington DC to Boston were developed and enabled these regions to graduate from a

megalopolis with shared population and commuting patterns to a New Mega that produces 20%

of the nation' s Gross Domestic Product (GDP) with almost 18% of the population and only 2%

of the land area (America2050, 2005, p. 11). These key regions have gained power through

increased economic activity, innovation and efficient production methods.

Globalization-Did You Know the World is Flat?

Given the proj ected doubling of Florida' s population by 2050, metropolitan areas within

the central Florida region that are considered large today, including Orlando and Daytona, will









continue to grow, and their expansion will begin to incorporate new adj acent municipalities.

Facing serious competition for future economic growth, economic efficiency is becoming more

significant for local jurisdictions. Many cities in this region either continue to rely on existing

industries to remain competitive or compete against neighboring cities in the pursuit of the best

opportunities for economic development. In 2007 the dynamics are changing, and in order to

remain a relevant location for residents and attract creativity, new spatial and organizational

challenges must be met to compete within a globalizing world.

Brian Teeple of the North Central Florida Regional Planning Council states that in order to

compete and win globally, we must think regionally. As our individual local governments

become more and more fiscally constrained it simply makes both short and long term economic

sense to cooperate and share in the economies of scale (Teeple, 2006, p. 4). Globalization is a

dynamic ongoing process with a unique set of rules, demographic trends, and demographic

measurements. Most importantly, globalization has one overarching feature-integration.

Thomas Friedman, a New York Times foreign affairs columnist and author, defines globalization

as the "inexorable integration of markets, nation-states and technologies to a degree never

witnessed before-in a way that is enabling individuals, corporations and nation-states to reach

around the world farther, faster, deeper and cheaper than ever before, and in a way that is

enabling the world to reach into individuals, corporations and nation-states farther, faster, deeper,

cheaper than ever before" (Friedman, 2000, p. 9).

Professors at several leading universities in China have studied the importance of urban

land uses in understanding the interactions of urban economic activities with the environment

and urban expansion. Guided by the theory of Metropolis Globalization, these professors believe

that changes in urban land use are directly related to the development of the economy due to two









factors. First, the demands of economic development create an impetus for urban sprawl.

Secondly, the possibility of urban sprawl lies in economic capacity (Wang et al., 2004, p. 1). As

the economy becomes more efficient, employment centers will develop patterns that encourage

increased productivity and greater innovation. Many economists, including Alfred Marshall,

argue that firms cluster in "agglomeration" to gain productive efficiencies. An alternative view

is based on Robert Putnam's social capital theory. It says that regional economic growth is

associated with tight-knit communities where people and firms form and share strong ties

(Florida, 2002, p. 220). A result of either of these views is that urban land use patterns will

change and the incentives for sprawl will also change.

The Economy and Development Patterns

Globalization is such a significant process that the level of economic activity it creates is

literally transforming the urban landscapes of developing countries. David Dowall (1999), a

professor of city and regional planning at the University of California at Berkeley, thinks that to

effectively exploit the benefits of inward investment flows and to ensure that social and

environmental goals are met; the public sector needs to take the lead in planning and formulating

urban land management strategies to promote sustainable urban economic development.

Bruce Katz of the Brookings Institution offers three central themes as evidence of

inefficient development. First, we must understand the broad demographic, economic, fiscal and

cultural forces that promote density, diversity, and urbanity. Looking at these forces in the

greater context, outside of how they exclusively encourage sprawl and decentralization, will

promote innovation and improve attractiveness for foreign direct investment and employment

generation (Katz, 2005, p. 2). The second principle is an extension of the first in that these forces

are significantly altering the shape and composition of many suburbs. This change has fueled an

uneven and incomplete resurgence of American cities. Lastly, land use and growth changes are










occurring in spite of state and federal policies that almost uniformly favor sprawl, concentrate

poverty, and undermine cities and older suburbs (Katz, 2005, p. 2).

The pace of demographic change due to an aging population and the massive influx of

immigrants are matched only by the intensity of economic transformation. Katz attributes the

changing local landscape to the global economic shift.

Globalization and technological innovation are reshaping and restructuring our economy
and altering what Americans do and where they do it. These forces have accelerated the
shift of our economy from manufacture of goods to the conception, design, marketing, and
delivery of goods, services, and ideas. These forces are placing a high premium on
education and skills, with communities and firms now engaging in a fierce competition for
talented workers who can fuel innovation and prosperity. These forces are changing the
ways businesses manage their disparate operations enabling large firms to locate
headquarters in one city, research and design somewhere else, production facilities still
somewhere else, and back-office functions within our outside the firm in still other
places. (2005, p. 3)

The evidence shows that tight urban form is not only competitively wise, but fiscally

sound. Bruce Katz contends that we have known for decades that compact development is more

cost efficient-both because it lowers the cost of delivering essential government services and

because it removes the demand for costly new infrastructure (Katz, 2005, p. 4). Broad

demographic, market, and cultural forces coupled with a wave of innovation at the local level are

improving the economic and social potential of cities.

The Creative Class

Individual jurisdictions are constantly in search of a purpose. Bruce Katz believes that just

as changing demographics have shifted the face of neighborhoods, the restructuring of the

American economy gives cities and urban places a renewed economic function and purpose-a

function that holds out hope for re-centering regions and using land more efficiently (Katz, 2005,

pp. 3-4).









An economy based on knowledge bestows new importance on institutions of knowledge-

universities, medical research centers-many of which are located in the heart of central cities and

urban communities. The shift to an economy based on ideas and innovation changes the value

and function of density. Fundamentally, this creates an economy powered by creativity.

Constant revision and enhancement of products, processes, and activities require access to a high

concentration of talented and creative people. This dense concentration of creative people, in

addition to members of other employed classes, contributes to labor productivity. Increased

residential density contributes to this paradigm shift by creating a "quality of place" that attracts

knowledge workers and enabling interactions and knowledge-sharing among workers and firms,

within and across industries (Katz, 2005, p. 4).

The globalization system is built around what Thomas Friedman calls super empowered

individuals. Super empowered individuals are "unique in that they rej ect the deeply ingrained

tendency to think in terms of highly segmented, narrow areas of expertise, which ignores the fact

that the real world is not divided up into such neat political and technological affairs" (Friedman,

2000, p. 24). Super empowered people think as globalists-these are the creative class.

Merriam-Webster' s (2004, p. 169) dictionary defines creativity as "the ability to create

meaningful new forms". Creativity is the decisive source of competitive advantage and

emanates from people who require a social and economic environment to nurture its many forms

(Florida, 2002, p. 5). In the current information age many say that "geography is dead" and

place doesn't matter anymore. Nothing could be further from the truth. It is geographic place

and access to assets, such as people, that determine where companies must compete (Florida,

2002, p. 6).










The Creative Class are individuals whose economic function is to create new ideas, new

technology and/or new creative content. These people engage in complex problem solving that

involve a great deal of independent judgment and require high levels of education or human

capital. The key difference between the Creative Class and other classes lies in what they are

paid to do. Those in the Working Class and Service Class are primarily paid to execute

according to plan, while those in the Creative Class are primarily paid to create and have

considerably more autonomy and flexibility than the other two classes to do so (Florida, 2002, p.

8). Although the Creative Class remains somewhat smaller than the Service Class, its crucial

economic role makes it the most influential. The Creative Class is the norm-setting class of our

time. The Creative Class dominates wealth and income, with members earning nearly twice as

much on average as members of the other two classes (Florida, 2002, p. 9).

According to Thomas Friedman, the nexus between the Creative Class and the global

economy is similar to the connection between the Lexus (yes, the car) and the olive tree. Olive

trees are our foundation. Family, religion, and the place we call home-these are things that root

us, anchor us, and identify us in this world. We fight so intensely at times over our olive trees

because, at their best, they provide the feelings of self-esteem and belonging that are as essential

for human survival as food in the belly (Friedman, 2000, p. 31). Localities and, to a lesser

extent, regions have been unable to compete in a global economy because they don't understand

that they are the olive tree-the ultimate expression of whom we belong to-linguistically,

geographically and historically. Alone you cannot be complete. To be complete you must be

part of, and rooted in, an olive grove (Friedman, 2000, p. 31).

Furthermore, Friedman believes the Lexus represents human drive-the drive for

sustenance, improvement, prosperity and modernization. The Lexus represents all the










burgeoning global markets, Einancial institutions and computer technologies with which we

pursue higher living standards today. Of course, for millions of people in developing countries,

the quest for material improvement still involves walking to a well, subsisting on a dollar a day,

plowing a Hield barefoot behind an ox or gathering wood and carrying it on their heads for Hyve

miles (Friedman, 2000, p. 31).

While in Tokyo, Friedman toured a Lexus manufacturing plant. He was amazed at the

interaction between man and machine, although the machine was doing most of the work. Each

time the robotic arm swung around to snip off wires or glue pieces together Friedman thought to

himself about the planning, design and technology to achieve such precision. Then it struck him

that the Lexus and the olive tree were pretty good explanations of globalization. Since the Cold

War, half the world has been intent on building a better Lexus, dedicated to modernizing,

streamlining, and privatizing their economies in order to thrive in a global system. And half the

world-sometimes the same country, sometimes the same person-was still caught up in the Eight

over who owns the olive tree (Friedman, 2000, p. 31).

The challenge in this era of globalization-for countries and individuals-is to Eind a healthy

balance between preserving a sense of identity, home and community and doing what it takes to

survive within the global world system. Friedman believes that a country without healthy olive

trees will never feel rooted or secure enough to open up fully to the world and reach out into it.

But a country that is only olive trees, that is only roots, and has no Lexus, will never go, or grow,

very far. Keeping the two in balance is a constant struggle (Friedman, 2000, p. 42).

Indicators of Creative Centers

Richard Florida developed the Creative Index as a baseline indicator of a region's overall

standing in the Creative Economy and as a barometer of a region's longterm economic potential.

The Creativity Index is a mix of four equally weighted factors: (1) the Creative Class share of the










workforce; (2) innovation, measured as patents per capital; (3) high-tech industry, using the

Milken Institute's widely accepted Tech Pole Index; and (4) diversity, measured by the Gay

Index, a reasonable proxy for an area' s openness to different kinds of people and ideas. This

composite indicator is a better measure of a region's underlying creative capabilities than the

simple measure of the Creative Class, because it reflects the joint effects of it concentration and

of innovative economic outcomes (Florida, 2002, p. 245).

Determining the location of creative people relies not only on a numeric indicator but

requires basic criteria for the built environment as well. Former Seattle mayor Paul Schell once

said that success lies in "creating a place where the creative experience can flourish" (Kitsner,

2001, p. 44). Urban centers have long been crucibles for innovation and creativity. Now they

are coming back. Their turnaround is driven in large measure by the attitudes and location

choices of the Creative Class.

In a Creative Economy, the Creative Class makes up more than 3 5% of the workforce.

Creative Economies are not dependent upon the size of the region or city. For instance,

Gainesville, Florida, and Minneapolis both have Creative Economies. The presence of maj or

universities, research facilities, or state governments helps boost the concentration of creative

people. The presence of a maj or research university is a huge potential source of competitive

advantage in the Creative Economy. Universities contribute to regional growth through

technology, talent and tolerance.

* Technology: Universities are centers for cutting-edge research in fields from software to
biotechnology and important sources of new technologies and spin-off companies.
* Talent: Universities are amazingly effective talent attractors, and their effect is truly
magnetic. By attracting eminent researchers and scientists, universities in turn attract
graduate students, generate spin-off companies and encourage other companies to locate
nearby in a cycle of self-reinforcing growth.
* Tolerance: Universities also help to create a progressive, open and tolerant people climate
that helps attract and retain members of the Creative Class. (Florida, 2002, p. 292)









How Shall We Grow?

How do we decide where to live and work? What really matters to us in making this kind

of life decision? How has this changed, and why? Creative Centers represent the new creative

mainstream. These areas are large urban centers that are desirable places to live and work

(Florida, 2002, p. 275). The Creative Centers are clusters that tend to be the economic winners

of our age. Not only do they have high concentrations of Creative Class people, they have high

concentrations of creative economic outcomes, in the form of innovations and high-tech industry

growth. They also show strong signs of overall regional vitality, such as increases in regional

employment and population. The Creative Centers are not thriving for such traditional economic

reasons as access to natural resources or transportation routes. Nor are they thriving because

their local governments have given away the store through tax breaks and other incentives to lure

business. They are succeeding largely because creative people want to live there. The

companies then follow the people-or, in many cases, are started by them (Florida, 2002, p. 218).

Traditional and Conventional Views of Regional Growth

Economists and geographers have always accepted the conventional view of growth-that

economic growth is regional-that it is driven by and spread from specific regions, cities or even

neighborhoods. The traditional view, however, is that places grow either because they are

located on transportation routes or because they have endowments of natural resources that

encourage firms to locate there. Successful region-based urbanization is the product of

traditional and conventional planning methods. Traditional methods result in policies that ensure

greater sustainability of metropolitan areas, particularly in their intersection with the hinterland

(Friedman, 2000, p. 10). According to the conventional view, the economic importance of a

place is tied to the efficiency with which it can make things and do business (Florida, 2002, p.

219).









Urbanization proceeds by increasing the density within and extending the periphery,

frequently at the expense of open space and nature. Ian McHarg believed that man is potentially

the most destructive force in nature and its greatest exploiter so man's views and values placed

on the environment are important (McHarg, 1969, p31). Growth responds to natural processes,

which are clearly visible in the pattern and distribution of development in its density (McHarg,

1969, p. 160). Using scenario modeling and suitability surfaces, McHarg developed methods

that relied upon the land to dictate its best sites for development (McHarg, 1969, p. 197).

To ensure that society protects the values of natural processes, identification of lands

which possess inherent values and constraints would ensure the operation of vital natural

processes and employ lands unsuited to development in ways that would leave them unharmed

by these often violent processes (McHarg, 1969, pp. 55-56). Historically, growth models using

mapping techniques and GIS have evolved that visually display the costs of development and

growth. These models show the increased value as lands are converted to alternate uses, but the

costs incurred from conversion should not involve irreversible losses (McHarg, 1969, p. 34).

The Use of Scenario Modeling

lan McHarg developed a method of mapping suitability, which is sometimes referred to

as the "McHargian Overlay" (Knaap, Bolen & Seltzer, 2003, p. 9). Themes or layers were

identified and mapped suitability as high, medium, or low. Examples of layers include historic

resources, residential distribution, water, wildlife, and recreation. The suitability value of each

layer was represented on a transparency and layers were superimposed upon one another. This

resulted in a summary map revealing the sum of the physical and social suitabilities evaluated.

This summary map reflects social values and costs that directly contribute to the potential for

future development. The darkest tone represents areas whose sum of suitabilities suggests they









are least appropriate for development; the lightest tone reveals the areas most suitable for new

development (McHarg, 1969, p. 35).

The Uncontrolled Growth Model was another early growth model, conceived by David

Wallace, which revealed the consequences of unplanned future growth in the valleys. This

model identified the nature of the pressures and demands which would impact natural values.

The model required familiarity with state and county proposals for highways, sewers, and zoning

as well as knowledge about future population projections. Additional analysis included a

housing market analysis from which housing demand by type, price and location might be

determined. Knowing these parameters enabled Wallace to simulate the pattern of growth that

might occur in the absence of a plan or new powers (McHarg, 1969, p80).

When executing growth models it is important to understand that the resulting synthesis of

values is not a plan. It merely shows the implications that the land and its processes display for

prospective development and its form (McHarg, 1969, p. 160). A plan includes the entire

question of demand and the resolution of demand relative to supply, incorporating the capacity

of the society or institution to realize its obj ectives. In order to make a plan, it is necessary to

calculate demand for the constituent land uses and the locational and formal requirements of

these, and to recognize the instruments available to society in both the public and private domain

(McHarg, 1969, p. 105).

Creating a Vision

Currently, more than 90% of Floridians live in urban areas, which continue to expand

farther into undeveloped areas. Most of the built environment that will exist in the next 25 years

has yet to be constructed (ULI, 2005, p. 11). Growth isn't limited to just urban environments.

By 2020, more than 2.6 million acres of agricultural land-7.5% of the state' s total land area-is

expected to be converted to urban uses (ULI, 2005, p. 11).









The residents of East Central Florida have participated in regional visioning efforts in an

attempt to correct unbalanced growth and inj ect community values into future development

plans. Unbalanced growth undermines the economic efficiency of metropolitan markets (Katz,

2004, p. 3). Visioning is a strategy for understanding the necessity of appropriate growth and

development. It is a collaborative effort among residents, the public sector, and the private

sector to move beyond parochial interests and recognize the needs and desires of all members of

the community (ULI, 2005). All stakeholders must play a proactive role in the process;

otherwise the outcomes will not be inclusive.

A "regional visioning process" is an initiative to develop a long-term plan or policy to

guide the future development of a region. Visioning allows leaders to take untested ideas, model

them, and analyze the impacts, often with unexpected results (Cartwright & Wilbur, 2005, p. 15).

It is the subsequent action which creates a plan for "quality growth" (ULI, 2005, p. 5).

Select Aspects of Regional Visioning Exercises

The diversity of values and outcomes from each regional visioning process reinforces the

precept that every region has different needs. The challenge lies in developing a method to

translate values and current land use into a representation of growth potential. Individually, the

Envision Utah, Chicago Metropolis 2020, the Los Angeles' Compass Project, and Chattanooga' s

Vision 2000 couldn't be more geographically or demographically different. Although these

proj ects have different regional needs, they each share a common goal of developing a plan and

policies that preserve the landscape and accommodate regional needs.

Envision Utah is a successful visioning proj ect formed in 1997 which studied the long term

effects of growth in the Wasatch Valley. The process involved five years of research, public

involvement, and analysis of alternative growth scenarios, all of which led to the development of

the Quality Growth Strategy. Leaders of the Envision Utah project credit its success to including









from the beginning: stakeholders who played an integral role in implementation, the

identification of widely shared values and continued input of important ideals, and the use of a

variety of communication strategies that were clear, educational and encouraged open feedback

(Cartwright & Wilbur, 2005, p. 14).

Until the 1996 Chicago Metropolis 2020 regional visioning process, the most recent

regional vision was Daniel Burnham' s Chicago Plan of 1909. The purpose of Chicago

Metropolis 2020 was to outline strategies for attracting needed investment and creating new j obs

for the region. This plan used the Envision Utah process as a model but added an analysis that

created a regional plan. The anticipated outcomes of the plan would be (1) the completion of a

detailed assessment of regional housing needs and (2) land use planning for freight transportation

centers (Cartwright & Wilbur, 2005, p. 15).

Again using Envision Utah as a model, the Southern California Association of

Governments scagG), the largest regional government in the country, undertook the Compass

Proj ect to create a regional vision for Los Angeles. The result of the Compass analysis is known

as the 2% Strategy, in recognition of the fact that only 2 % of the land in the region had to

change substantially for the entire region to reap the resulting transportation and environmental

benefits. The 2% Strategy calls for concentrating mixed-use development near transportation

corridors, transit stations, and regional centers. The benefits of this strategy are demonstrated

using geographic information systems (GIS) so that municipalities can visualize the effects to

future growth management at the local level under different scenarios. These scenarios include:

what the current zoning allows, how much revenue will accrue to local governments through

different types of development, and what the return on investment to proj ect proponents would

be (Cartwright & Wilbur, 2005, p. 17).









Chattanooga, Tennessee's Vision 2000 plan began in 1984 and during a nine year period

implemented 232 specific initiatives which resulted from the visioning process. Visioning

leaders used a "center-out" approach focusing on identifying both the positive and negative

aspects of the community and exploring possibilities for the future instead of focusing on fixing

problems. Instead of narrowing the scope of discussion to a few top priorities, visioning leaders

chose to put every issue on the table and encouraged community residents to talk about all of

them. This approach yielded a broad agenda supported by passionate, interested people who

could effect change (Cartwright & Wilbur, 2005, p. 19). This was the same approach used by

residents and leaders in Birmingham, Alabama's Region 2020 vision.

The Use of GIS in Scenario Modeling

As early as 1956, primitive versions of today's GIS based scenario models were used by

mathematicians to model traffic flows (Anas, 1987, p. 63) and in 1960 Herbert and Stevens

attempted to use similar mathematic programs to forecast the spatial arrangement of households

in an urban area by assigning each land parcel to the highest bidding land use (Anas, 1987, p.

73). During the late 1950s and 60s mathematicians and geographers were faced with

computational limits in their analysis and found difficulty in "describing an urban area by means

of a grid or other regular geometry" (Anas, p. 79). Today it is second-nature to use various

systems of GIS to model growth and analyze fiscal, land use and transportation impacts.

Metro 2040 Growth Concept

Over the past two decades, over 15 maj or regional visioning proj ects (Table 2-1) have

occurred across the United States. Current visioning projects are measured against Portland,

Oregon's regional visioning process known as the Metro 2040 Growth Concept, developed in

December 1995. The Metro 2040 Growth Concept was coordinated by Metro, Portland's

metropolitan area regional government. Metro is also responsible for managing the Portland










metropolitan region's urban growth boundary (UGB) and is required by state law to have a 20

year supply of land for future residential development inside the boundary. Every five years, the

Metro Council is required to conduct a review of land supply and, if necessary, expand the

boundary to meet that requirement (Metro, 2007b). The Metro 2040 Growth Concept sought to

exceed the 20 year planning requirement and instead plan 50 years ahead, starting from 1990, to

determine the amount of land and resources required to accommodate approximately 720,000

additional residents, 350,000 additional jobs (Metro, 2007a), and future transportation demands.

The 2040 Growth Concept was a means to address questions about the impact of growth

pressures to the urban growth boundary, satisfy the concerns of farmers who were concerned

about how additional growth would impact their land, and address the issues of municipal

service providers who were concerned about facilities needs for new communities. This process

engaged the public, including those from the environmental and development community, in a

manner that would provide public officials with greater certainty about their decisions and make

them more resolute (personal communications with David Ausherman, formerly a GIS modeler

for Fregonese and Calthorpe, Portland Oregon, October 9, 2006).

The Regional Land Information System (RLIS) is a GIS used in every land use plan and in

the evaluation of every policy in Portland to monitor land development and future growth

capacity. RLIS provides accurate and detailed inventories of vacant land and Metro saw two

compelling ways to use this system in future growth management. First was to update the UGB


SOregon' s Statewide Planning Guide defines as urban growth boundary as a boundary that provides for an orderly
and efficient transition from rural to urban land use. Urban growth boundaries shall be established to identify and
separate urbanizable land from rural land. Establishment and change of the boundaries shall be based upon
considerations of the following factors: (1) demonstrated need to accommodate long-range urban population growth
requirements consistent with LCDC (Land Conservation and Development Commission) goals; (2) need for
housing, employment opportunities, and livability; (3) orderly and economic provision for public facilities and
services; (4) maximum efficiency of land uses within and on the fringe of the existing urban area; (5) environmental,
energy, economic, and social consequences; (6) retention of agricultural land with four levels of priority; (7)
compatibility of the proposed urban uses with nearby agricultural activities (Davidson and Dolnick, 2004, p. 433)









according to new growth management legislation and the second was to use it as the primary

technical tool in the 2040 Growth Concept. The application of RLIS in the 2040 Growth

Concept was to create a "McHargian overlay" (Knaap, Bolen & Seltzer, 2003, p. 9) using a set of

current GIS layers to combine data and create a map displaying suitable locations for

development (Tables 2-2 and 2-3). This system was beneficial for evaluating regional planning

goals as well as illustrating future growth plans in a manner that would be understood by the

general public.

The RLIS methodology to determine suitable land for development is based upon

identifying vacant land and determining growth potential of that land. Vacant land is primarily

identified using aerial photography overlaid with the tax lots within the Portland metropolitan

area. The growth potential of each tax lot is determined through the assignment of an attribute of

vacant, partly vacant, undeveloped, or developed. At this point no consideration is given to

suitability for building, zoning, redevelopment potential, or any other criteria (Knaap, Bolen &

Seltzer, 2003, p. 5). Restrictions on development (i.e. environmental constraints, presence of

hazards or regulatory protections) are evaluated further along in the process. RLIS then models

development patterns according to a baseline and three alternative scenarios.

Key to the development of the 2040 plan was a Baseline Scenario and three alternative

growth scenarios, each utilizing different assumptions for development. The baseline concept

continued existing patterns of development. The result was adding 121,000 acres with a total

UGB area of 3 54,000 areas within an expanded UGB and a high level of growth at the outer

edges. Scenario A was based upon 'growing out' and included a significant expansion of the

UGB with new growth at the urban edge mostly in the form of housing. This scenario resulted in

an additional 51,000 acres within the UGB for a total UGB area of 284,000 acres. Scenario B









was based upon growing up and prohibits (or limits) the expansion of the UGB with growth

accommodated through development of existing land within the urban growth boundary by using

infill and more compact development. The result restricts the current UGB area to 234,000 acres.

The last scenario, Scenario C, provides for "neighboring cities". This scenario moderately

expands the UGB with growth focused primarily on centers, corridors, and neighboring cities.

This scenario adds 22,000 acres for a total of 257,000 acres within the UGB (The EcoTipping

Point Project, 2007). Residents were presented with the base scenario and three alternative

scenarios then asked to choose preferred elements of each. A hybrid Growth Concept was

developed from the "best" elements chosen by the public and is currently used to guide the

region in the development of policy and in growth management decision making process.

Federal Highway Administration Funded Projects

The Federal Highway Administration has funded proj ects around the country that analyze,

assess and communicate the impacts of transportation and land use decisions on mobility, the

environment, and economic development through the use of GIS. One of the maj or challenges

faced by organizations using GIS in visioning and development scenario modeling has been to

construct regional growth scenarios that are not entirely hypothetical, but instead are consistent

with existing constraints on development and realistic land use policy alternatives. Projects in

Utah; Charlottesville, Virginia; and Maryland demonstrate the use of GIS in scenario modeling

(Federal Highway Administration, 2005a).

The outcomes of scenario modeling are only as good as its GIS inputs. Data collection for

the Envision Utah visioning effort in northern Utah recognized the importance of comprehensive

data collection and used GIS land use data from comprehensive plans, remote sensing data, and

state inventories to determine suitable land for urban development. Land use constraints were

determined using state databases of wetlands, slopes, floodplains, and riparian buffers. Potential









areas for redevelopment and infill also were identified using property value data from tax parcel

data and densities identified from local general plans. This proj ect integrated GIS with

community input by creating a base map of the layers mentioned above and allowing participants

to identify on a paper map desired areas of green space and locations for future development in

their community. The layers provided on the paper map allowed the participants to avoid

unbuildable areas and make more informed decisions.

Alternative scenarios provide an illustration of impacts and patterns of growth based on

various assumptions. The baseline represented future conditions if no changes were made to

existing development policy and regulations. Demographically, this meant accommodating an

additional 3.4 million people over 50 years within a 1,350 square mile area. The baseline offers

a benchmark to compare each alternative scenario. After a series of public workshops in which

700 residents, mayors and city council members were asked to place future population on paper

maps and identify the types of development that would best serve each area, a composite map

was created from their input. Consultants reviewed the composite maps and noticed common

land use patterns. Further analysis enabled them to indicate where and how often industrial,

office, retail and various types of residential developments should occur and what percentage of

growth should be accommodated in walkable and non-walkable designs (Envision Utah, 2002).

From these patterns, consultants developed four scenarios (Table 2-4).

The next step involved allocating future proj ected population into traffic analysis zones.

Summary by TAZ allowed for seamless transportation analysis to test the transportation impacts

of each alternative. The four scenarios were further analyzed by technical consultants to

determine water consumption, infrastructure costs, air quality, and transportation needs. Using

five indicators, housing, transportation, land use, costs, water consumption, and air quality, the










impacts of each scenario was evaluated. The results were then summarized and presented to

residents and state and local officials. Following is a summary of each scenario.

* In Scenario A people would live farther apart with an increase in average single-family lots
from 0.32 in 1998 to 0.37 in 2020. Scenario A would also present a 95% increase in
urbanized areas which would increase vehicle travel and increase the need for highway
development. Infrastructure, personal transportation and housing costs would significantly
increase. The demand for water would increase and the necessity of a vehicle for travel
would significantly lower air quality.
* In Scenario B the average size of single-family lots would remain the same as current
levels but would offer fewer housing choices than scenarios C or D. Urbanized areas
would grow by 75%, consuming open space and farmland more rapidly than in scenarios C
or D. Fewer transportation choices would increase reliance upon the automobile
contributing to increased congestion. Higher personal costs would incur due to a longer
distance between housing and employment. Although this scenario has the second most
expensive infrastructure and second highest consumption of water, it has the second best
air quality of all scenarios.
* In Scenario C homes are closer together offering a wider variety of housing options than
scenarios A or B and the average size of the single-family lot decreases from 0.32 acres
today to 0.29 acres in 2020. Much of new housing would be located in villages and towns
situated along major roads and rail lines. New development is placed within existing urban
areas slowing new urbanized growth to 29% from 1998 to 2020. New development is
placed within existing urban areas and clustered around transit routes, leaving more land
open for space and agriculture. The expanded transit system provides additional
transportation options and lower transportation costs. Diverse housing options offer a
variety of affordable housing options. Of all scenarios, Scenario C has the best air quality
and the second-lowest consumption of water.
* In Scenario D a higher density of housing and wider variety of housing options is available
than all other scenarios. Land consumption is slower than all other scenarios and a large
portion of new development is placed within existing urban areas and clustered around
transit routes, leaving more land for open space and farmland than any other scenario.
More transportation options enable 32% of the population to have easy access to rail
transit. This scenario has the lowest personal transportation cost and second lowest
infrastructure cost of all other scenarios. This scenario has the lowest water consumption
of all scenarios and the best air quality of all scenarios except Scenario B.
(Envision Utah, 2002, pp. 32-35)

The final step in the process allowed the public to choose specific elements of each

scenario that appealed to them. The responses from the public were compiled and presented to

the public and then Governor Michael Leavitt. From these results, Governor Leavitt decided that

the time was right to develop a growth initiative, which later became the Quality Growth Act of









1999. This legislation established a Quality Growth Commission and provides incentives to help

communities pursue quality growth. This had fiscal and land use implications in that the state

government would no longer fund sprawl and contributed a percentage of local taxes to fund

open space. Reflecting on the Portland visioning efforts, Envision Utah understood the

importance of public involvement and continued to inform the public at step of the process,

which aided in implementing strategies outlined within the Quality Growth Act. Envision Utah

was more than a visioning process; it was a movement that utilized input from all stakeholders to

develop a sustainable growth model and signaled a paradigm shift in growth management and

future regional visioning efforts.

In Charlottesville, Virginia the Eastern Planning Initiative constructed future growth

scenarios by connecting regional land development patterns with socioeconomic characteristics

and site-specifie development guidelines (Renaissance Planning Group, 2007). The regional

land use map identifies community element boundaries, and each assigned element has very

specific land use, building and infrastructure guidelines (Renaissance Planning Group, 2007) that

is input into a spreadsheet based model known as CorPlan. CorPlan adds population to each

community element (i.e. village, office, high density residential) resulting in a variety of

scenarios based upon how elements are arranged. CorPlan estimates development potential and

how potential translates into households and j obs (Renaissance Planning Group, 2007). The

spreadsheet is then linked with ArcView to graphically display the proposed development

scenario. Traffic analysis zone (TAZ) level forecast population and employment for each

scenario is exported for input into a travel demand model (Federal Highway Administration,

2005a). Additional features of CorPlan include modeling Eiscal impacts, community assessment

and quality of life.









The Charlottesville proj ect utilized resident participation to determine community

preferences for future development using different scenarios. Proj ect staff constructed three

alternative regional development scenarios (Urban Core, Town Centers, and Dispersed) based

upon principles identified at community workshops (Federal Highway Administration, 2005a).

In this proj ect Charlottesville utilized existing community elements including the historical

downtown of Charlottesville, the University of Virginia, older residential areas surrounding

downtown, newer cul-de-sac residential subdivisions, highway oriented shopping centers and

small towns that ring the city (Renaissance Planning Group, 2007). The inputs and outputs were

visually depicted using photos and graphics for the community elements and resulting potential

development scenarios. This enabled the community to qualitatively and quantitatively compare

each growth scenario.

The Eastern Planning Initiative resulted in a set of eight key success factors, listed below,

to support the public's preference for a clustered development pattern (Federal Highway

Administration, 2005b). The recommendations identify specific locations for development and

address future transportation demands. The Eastern Planning Initiative was the first time a

regional land development planning model connected to site-specific community elements.

* Grow only in designated development areas
* Maintain small towns and villages
* Define and maintain hard edges
* Create urban and enhanced suburban communities
* Invest in supportive infrastructure
* Preserve rural areas
* Regional equity
* Ensure affordability
(Federal Highway Administration, 2005b)

In Maryland, their scenario modeling was actually reverse land use analysis. Instead of

determining the consequences of development on transportation, an assessment of the impact of









different transportation scenarios on land use patterns was completed. To evaluate the land use

impacts of either widening a two-lane road to a four-lane road or keeping the original two-lane

road and adding a two-lane, limited access road, the Maryland Department of Planning applied

GIS data analysis and mapping in conjunction with a Department of Transportation (DOT)-

sponsored expert-panel approach to land use forecasting. Their process was as follows:

The commute-shed of the road of current demand and each alternative was evaluated by
mapping the origin locations of work-trips using the road, as estimated from the travel
model covering the Baltimore metropolitan region. Land use data in the commute-shed
were then mapped, including zoned densities, the amount of land recently developed, and
the amount of land available for development. This provided background on growth
trends, pressures, and opportunities in the area. For each alternative, the increase in
accessibility to j obs (measured as the change in number of jobs accessible within 45
minutes) was then estimated from the regional travel demand model and mapped for each
planning area. This showed the potential impact of the highway project on the desirability
of each area for new development. (Federal Highway Administration, 2005a)

In 2000 an expert panel of politicians, planners, and engineers individually and j ointly

assessed the potential impact of each alternative. Opinions varied, but most panelists provided

the following recommendations:

...the four-lane alternative would provide a significant incentive for increased development
in the study area, while the two-lane limited access alternative would not. Staff from the
Department of Planning therefore concluded that the four-lane option would contradict
Smart Growth objectives and the desire to preserve rural land within the commute-shed. In
contrast, they concluded that the two lane option would address safety concerns without
contradicting Smart Growth principles. (Federal Highway Administration 2005a)

The PLACE3 S (PLAnning for Community Energy, Environmental, and Economic

Sustainability) model, which is similar to the CorPlan model, supports community land use and

transportation planning at the parcel level. It is a spreadsheet based model that can be integrated

into ArcView to graphically display development scenarios. It is designed to estimate the

community, environmental, economic, and transportation benefits associated with alternative

development scenarios. This model requires detailed input including characteristics of each

scenario, a street layout and land use type and planned densities by parcel (Federal Highway









Administration, 2005a). Interactive use of this model enabled residents of the Mid-City

neighborhood in San Diego to understand the impacts of different zoning policies on

redevelopment potential, energy use, vehicle travel, and other performance measures.

Summary

The first goal of this review of literature was to understand the characteristics of a

megalopolis and New Megas. Then a discussion of how the plight to maintain economic

efficiency, which many New Megas and megalopolises have attained, has led many regions to

seek inclusion in the globalized economy. Next the traits of the Creative Class were outlined and

were followed by a discussion of how creative individuals and industries further the goals of

New Megas. Next examples of visioning efforts across the country were listed and a discussion

of how this technique has enabled cities and regions to proactively plan for population growth,

transportation demands, and a stronger economy. Cities can no longer insulate themselves from

the concerns and issues facing adjacent areas. Visioning allows the driving forces behind these

issues to surface and initiate a conversation on possible resolutions. More importantly, they

introduce the concept of how individual municipalities contribute to the larger function of the

region. Finally, a discussion of GIS growth models used in various visioning proj ects identified

the steps involved in developing scenarios which help identify constraints and alternatives to

accommodate future population.

Visioning efforts in Portland and Utah have become the benchmark for GIS modeling and

policy implementation in visioning exercises since their completion. The Metro 2040 Growth

Concept was the flagship of regional visioning. The McHargian overlay concept and many other

modeling techniques and methods used to encourage community involvement are just as relevant

today as they were ten years ago. Organizers of the Envision Utah process credit the continued

support and success of their exercise and implementation efforts to the residents, developers, and










politicians who were and continue to be involved. The use of creating indicators to evaluate

each scenario then used to develop a final composite map was adapted from the Envision Utah

exercise for the Central Florida Growth Vision, the visioning exercise for which my study was

based upon.

The proj ects funded by the Federal Highway Administration use unique GIS modeling

techniques to accomplish the same task, but each has slightly different benefits. Envision Utah,

which was funded by the Federal Highway Administration was the first growth model to allocate

population by TAZ, which allows a seamless conversion into traffic modeling programs. The

CorPlan model used in the Charlottesville, Virginia exercise was the first to populate community

elements (i.e. downtown, historic districts, and universities) and develop a result that translates

growth potential into jobs and households figures. Lastly, in Maryland the typical modeling

process of determining what is needed to accommodate future growth was modified to model

predetermined transportation alternatives.

The output from these scenarios provides the framework for regions and local governments

to begin policy discussions and ultimately aid in developing a smarter growth plan. The next

chapter will discuss methods used for developing the Central Florida Growth Vision.









Table 2-1. Major regional visioning. projects
Region Process name Creation Web site Partners
Atlanta Vision 2020 Atlanta 2006 www. atl antaregi onal.com Atlanta Regional Commission
Capital Metropolitan Planning
Organization, Capital Metropolitan
Transportation Authority, Bastrop,
Caldwell, Hays, Travis and Williamson
Counties, Central Texas Regional
Envision Central May Mobility Authority, The Greater Austin
Austin Texas 2004 www. envi si oncentraltexas.org. Chamber of Commerce
Vision 2030: Shaping Baltimore Regional Transportation
the region's future Board, Baltimore Metropolitan Council,
Baltimore together 2003 www.baltometro.orn/vision2030 Baltimore Regional Partnership
How Shall We In
Central Florida Grow? progress http://www.myreni on.orn/ (see page 5)
The Commercial Club of Chicago;
Chicago Metropolis Cook, DuPage, Lake, McHenry, Kane
Chicago 2020 1 999 www. chi cagometropoli s2020.orn/ and Will counties
Denver City Council, Denver Planning
Blueprint Denver Board, Land Use and Transportation
Denver 2020 2002 www. drcog.org Advisory Committee, City Staff
Phoenix ValleyVision 2025 2000 www.mag..mari copa.gnov Maricopa Association of Governments
Portland Area Metro government, City
of Portland, Multnomah County,
Hillsboro County, Clark County,
Portland Metro 2040 Plan 1995 www.metro-regi on.org neighboring. cities
Coalition for Utah's Future,
Mountainland Association of
Governments, Wasatch Front Regional
Council, Utah DOT, Utah Transit
Salt Lake City Envision Utah 1997 www. envi si onutah.org Authority, Envision Utah









Table 2-1. Contint ed
Region Process name Creation Web site Partners
San Diego Association of Government
Association of Bay Area Governments,
Metropolitan Transportation
Commission,, Bay Area Air Quality
Management. District, Bay
Conservation and Development
Commission, Regional Water Quality
Control Board, Bay Area Alliance for
San Diego 2030 Vision 2004 Sustainable Development
Smart Growth
Strategy / Regional
Livability Footprint
San Francisco Proj ect 2020 2003 www.bayareavision. org
Late
1980's,
VISION 2020, updated
Seattle Destination 2030O in 1995 www.p~src.org/proj ects/vision/ Puget Sound Regional Council
Southern California Association of
June Governments, Compass Blueprint
Los Angeles Compass Blueprint 2004 www. compas sblueprint.org. Partnership
Southern Regional Vision for Louisiana Recovery Authority, State of
Loui siana Southern Louisiana 2006 www.l oui si anasp eaks.org Louisiana, LRA Support Foundation
East-West Gateway Coordinating
Council St. Louis Regional Chamber
St. Louis Gateway Blueprint 2004 http://www. ewgateway.org/ and Growth Association (RCGA)


(2005a). Retrieved November 11, 2006, from


(Adapted from the Federal Highway Administration.
http://www.fhwa. dot.gov/tesp/case7 .html)










Table 2-2. Primary RLIS layers
Layer Description
GIS Base Layers
Tax Lots Propet assessment tax lots
Streets Streets, highways, bus/light rail lines, bike
routes, sidewalks/trails
GIS Overlays
Vacant land Vacant lots, partially developed lots with V/2
acre or more land vacant
Developed land Reverse of vacant land layer
Land use Derived from tax codes
Zoning Local land use zones
Comprehensive pans Local comprehensive plans
Parks and opnsace Parks and pblic/prvate opnsaces
Aerial photograht Natural color ortho-rectified digital imer
Jurisdictional boundaries Boundaries, e.g. UGB, schools, service
districts
Places Hospitals, schools, police, etc.
Building prit Location of issued prmits
U.S. Census Census data for 1980, 1990 and 2000
Environmental Layers
Rivers, streams, wetlands, and watersheds Location and attribute information for water
features
Tree canopy and land cover Urban forest canopy and vegetative/other land
cover
Flood plains 100 year Flood Plain
Ste sloe 10% and 25% slope
Soils Soils bytp and class
Elevation contours 5 ft elevation contours
Digital Terrain Model (DTM) Digital terrain data for georeferencing of info.
Earthquake hazard 4 zones depict relative hazard for urban area
(Knaap, G., Bolen, R., & Seltzer, E. (2003). Metro 's Regional Land Information System: The
virtual key to Portland'slt~l~lt~t~ltlt~ gi/ 1,n th management success. Lincoln Institute of Land Policy
Working Paper.; p. 21)










Table 2-3. RLIS procedure for identifying buildable lands and calculating housing and
employment capacities
Step 1 Calculate the number of acres inside the Metro Urban Growth Boundary
(UGB)
Ste 2 Subtract acres of committed and developed land.
Ste 3 Subtract acres of platted, vacant single-family residential land.
Step 4 Subtract vacant, environmentally constrained acres to arrive at vacant,
unconstrained land.
Step 5 Subtract land for future facilities (streets, schools, parks, churches, fraternal
oraizations, government facilities) to arrive at net buildable, vacant acres.
Step 6 Calculate development capacity of vacant land under current comprehensive
plans for housing.
Ste 7 Adjust current comprehensive plan capacity for single-family under-build.
Ste 8 Adjust housing for platted lots.
Step 9 Rezone for 2040 Growth Concept and calculate housing and employee
capacity .
Step 10 Adjust the Metro 2040 Growth Concept caacit for residential under-build.
Step 11 Adjust the Metro 2040 Growth Concept housing capacity for platted single-
family lots.
Step 12 Adjust the Metro 2040 Growth Concept housing and employment capacity for
physical development barriers.
Step 13 Adjust density assumptions to allow cities and counties time to implement
2040 typ creations (rampu)
Step 14 Estimate redevelopment potential and adjust capacity calculation for housing
and empoment.
Step 15 Estimate infill housing on lands categorizes as developed, increase
emplyent densities on develop lands and adust caacity
Step 16 Consider the farm or forest use assessment acreage in UGB.
Step 17 Compare UGB capacity with forecasted 20 year need and determine acres of
UGB expansion by land usetye
(Reprinted from Knaap, G., Bolen, R., & Seltzer, E. (2003). Metro 's Regional Land'Information
System: The virtual key to Portland'slt~l~lt~t~ltlt~ gi/ 1,n th management success. Lincoln Institute of
Land Policy Working Paper, p. 25)










Table 2-4. Alternative growth scenarios for Envision Utah
Scenario Description
Scenario A Scenario A proj ected how the region could
develop if the dispersed pattern of development
occurring in some Greater Wasatch Area
communities today were to continue. New
development would primarily take the form of
single-family homes on larger, suburban lots
(0.37 acre average). Most development would
focus future transportation investments on
convenience for auto users.
Scenario B Scenario B depicted how the region could
(This scenario is an update of the Baseline develop if state and local governments follow
Scenario) their 1997 municipal plans. Development
would continue in a dispersed pattern, much
like it has for the past 20 years, but not as
widely dispersed as in Scenario A. New
development would primarily take the form of
single family homes on larger, suburban lots
(0.32 acre average). Most development would
focus on convenience for auto users and
transportation investments would support auto
use.
Scenario C Scenario C shows how the region could grow
(This scenario represents a combination of all if new development were focused on walkable
scenarios) communities containing nearby opportunities
to work, shop, and play. Communities would
accommodate a portion of new growth within
existing urbanized areas, leaving more
undeveloped land for open space and
agriculture. New development would be
clustered around a town center, with a mixture
of retail services and housing types close to
transit lines. These communities would be
designed to encourage walking and biking, and
would contain a wide variety of housing types,
allowing people to move to more or less
expensive housing without leaving a particular
community. Average lot size would be slightly
smaller (0.29 acre) than Scenarios A and B.









Table 2-4. Continued
Scenario Description


Scenario D


Scenario D shows how the Greater Wasatch
Area might develop if Scenario C was taken
one step further, focusing nearly half of all new
growth within existing urban areas. This would
leave more undeveloped land for open space
and agriculture than any other scenario. When
new land is used, development would be
clustered around a town center, with a mixture
of commercial and housing types close to some
portion of a greatly expanded transit system.
These communities would be designed to
permit and encourage walking and biking,
contain the widest variety of housing types of
any scenario, and also have the smallest
average lot size (0.27 acre).


(Reprinted with permission from Envision Utah, 2004, p. 31)









CHAPTER 3
CENTRAL FLORIDA GROWTH VISION

Project Goal

In early 2000, myregion.org was established to coordinate the development of a plan for

Central Florida' s future growth. In Spring 2005 the University of Pennsylvania design studio

produced PennDesign VII: Central Florida, a report illustrating the growth potential of Central

Florida in the year 2050. The PennDesign final report indicated extensive amounts of sprawl and

unsustainable infrastructure if current development patterns continued into the future.

PennDesign also created an alternative scenario illustrating a potential alternative pattern of

growth if natural resources were preserved and more efficient infrastructure were in place. The

alternative offered hope to the East Central Florida region. The Central Florida Growth Vision

was a coordinated effort between residents, private, public and civic organizations to pursue

more sustainable growth patterns by developing proactive strategies to accommodate future

populations.

The Central Florida Growth Vision consists of a three step process. During Phase 1,

residents, activists and leaders from throughout the 7 county region identified key issues to be

addressed. From the collective input of 3,000 people, myregion.org organizers began crafting

strategies to positively impact the future. Phase 2 involved collecting existing regional

conditions about strengths, weaknesses, values, demographics, and future population growth.

This research was later used as inputs for the actual visioning process. Specific research

included a regional profile and indicators report; research on demographic trends; surveys of the

region's values and "social capital"; development of illustrative future development scenarios;

and identification of critical environmental resources. Phase 2 also engaged over 7,000 residents

in a series of community meetings and online surveys in an effort to gather their input and shape









elements to visually illustrate a preferred pattern of growth for the future. Phase 3, which began

in April 2007, involves further coordination with regional leaders and residents as myregion.org

prepares to develop policies and strategies that further the outcomes of Phase 2.

Scenario Modeling in Central Florida

At the request of the Metropolitan Center for Regional Studies at the University of Central

Florida, the Penn Design studio was asked to illustrate patterns of development within the 7

county East Central Florida region in 2050 using population proj sections data from the Bureau of

Economic and Business Research. The PennDesign Model applied current population, number

of households and average household size rate to determine the current gross density of

development and determine the number of acres needed to accommodate future population

proj sections. The "gross density of development" is the ratio of all developed acres, including

residential and all other uses, to the number of housing units (myregion.org, 2006, p. 9).

Existing urban areas were identified using the 2000 USGS Land Cover Analysis and current

conservation areas were determined using The Nature Conservancy and Florida Natural Areas

Inventory GIS data.

The PennDesign model employed several assumptions with respect to the environment, the

economy, and transportation; particularly, that no additional conservation land would be

permanently preserved between 2000 and 2050. The model also assumed no additional

economic activity centers and the existing centers would draw a constant proportion of new

development throughout the region. The region currently relies heavily on road networks and

the model continued this premise and only utilized the current road network, not attempting to

proj ect when or where future roads will be built (Barnett, 2005, p. 31i).

There were several factors not considered in the design of this model. These factors

include water availability, current zoning and future land use designations, and individual parcel










data. These factors were not considered due to the size of the proj ect and accounting for these

individual factors would prove to be impractical (Barnett, 2005, p. 30).

PennDesign engaged in a five step process to create each scenario. First, the seven county

study area was added to a GIS and divided into individual cells each representing 1 acre. Then

surface water bodies, current conservation, or existing developed areas were masked [taken out]

out of the GIS map. These areas were masked so that future population would not be allocated

into these areas. It is important to note that wetlands remained in the map as an area that could

accommodate future population. Then three factors were chosen which were deemed to

influence the direction of development in Florida. These factors are: access to economic activity

centers; access to already developed areas; and existence of wetlands. Each factor received an

equal "weight" of 0.33, meaning they were each as likely to draw or repel future development

(Barnett, 2005, p. 32).

When the model considered access to economic activity centers, each economic center was

given a different weight, based on how much new development that center was likely to draw.

These weights were not based on employment numbers, but rather on how strongly the centers

currently influence the direction of new development (Barnett, 2005, p. 32).

The "access to" designation was the manner by which the model takes into account Central

Florida's transportation system. Each cell was weighted based on how easy or difficult it would

be to travel from that cell to economic activity centers (Factor #1) and to already urbanized areas

(Factor #2) via the current road network. To account for the challenge that wetlands pose to

development, wetlands were given a low weight, meaning that the wetland could be still be

developed by the model, but that it is less desirable than other cells for development (Barnett,

2005, p. 32).









Next, these weights were applied to each cell, resulting in a ranking which identified the

likelihood that cell would be developed. Finally, population was allocated using a predetermined

density and the model displayed the acres needed to accommodate population growth for each

decade. The acres selected by the model were the highest-rated acres as determined in the

previous step (Barnett, 2005, p. 34).

The conclusions drawn from the PennDesign trend scenario were that:

* The cost of providing roads, utilities, and other services to newly developed land in the
region has been estimated at $90,000/acre.
* Developing the acres needed to accommodate the expected population growth will cost
more than $104 billion.
* Assuming current land use trends continue and no additional land is conserved; 60,871
acres of currently unprotected sensitive lands will be developed by 2050.
(Barnett, 2005, p. 33)

Land Use Conflict Identification Strategy (LUCIS)

The Land Use Conflict Identification Strategy (LUCIS) is another example of a scenario

modeling strategy that employs role playing and suitability modeling to predict areas where

future land use conflict will likely occur. The strategy's six step process includes 1) developing

a hierarchical set of goals and obj ectives that become suitability criteria, 2) inventory of

available data, 3) determining suitabilities, 4) combining suitabilities to represent preference, 5)

reclassifying preference into three categories of high, medium, and low, and 6) comparing areas

of preference to determine the quantity and spatial distribution of potential land use conflict

(Carr and Zwick, 2005, p. 89).

LUCIS stops short of representing alternative futures, but instead focuses on the

comparison of the results of three suitability analysis purposefully designed to capture biases

inherent in the motivations of three stakeholder groups: conservationists, developers and farmers









and ranchers dedicated to an agricultural future. The comparison of the suitabilities results in the

identification of areas of potential future land use conflict (Carr and Zwick, 2005, p. 90).

The results of LUCIS can be used to develop alternative scenarios for allocation of

proj ected population when combined with defined assumptions about (1) the sequencing and

speed of the conversion of existing conservation and agricultural lands to urban use; (2) the

densities at which new population can be allocated; and (3) permanent set asides (or lack thereof)

of lands with high conservation and agricultural suitability. The result is a range of alternative

future land use scenarios with associated build out populations and dates (Carr and Zwick, 2005,

p. 93).

Phase 2: Community Input

In preparation for the Central Florida regions' anticipated growth-principles, indicators,

and scenarios were developed (myregion.org, 2006, p. 33). A "principle" is a comprehensive

and fundamental law, doctrine, or assumption (myregion.org, 2006, p. 32). An "indicator" is a

value or group of statistical values that taken together give an indication of the status or

condition of an article or item (myregion.org, 2006, p. 33). These indicators were used to

compare the scenarios developed from community input. Table 3-1 provides a summary of

considered measures which relate to the five highest-priority principles identified by the

community (myregion.org, 2006, pp. 33-34).

Community participation played a significant role in the Central Florida Regional Growth

Vision: first in completing the draft of the Guiding Principles then in determining which

indicators were most important to the residents of this region. The community was then tasked

with determining where future density should be placed during the "How Shall We Grow" Chip

Game and Dot Game. These "games" required residents to accommodate future population

within their counties by placing dots of varying density on a map. Dots from all seven counties









were ultimately synthesized and, using GIS technology, serve as the foundation for the "The

People' s Choice Map", which is a reflection of the region' s development preferences and values.

The results of the PennDesign model were the catalyst for the formal visioning effort. The

myregion.org staff and its regional partners desired to use a model similar to PennDesign to

analyze input from the community that would illustrate its values but also depict future growth

patterns. The LUCIS model was chosen to model three of the four scenarios because it provided

the flexibility to allocate population into TAZs, would accommodate the ecological and physical

constraints within the region, and it considers the suitability of lands for urban development

before allocating future population. The remaining scenario was modeled by Renaissance

Planning Group using the PLAC3 S model (see page 39 for a description of the PLAC3 S model).

A "scenario" provides alternative methods or development patterns of accommodating

future population growth (myregion.org, 2006, p. 33). Each scenario emphasized a "theme"

which, to various degrees, encompassed the region's growth principles. Notable themes

included a continuation of current trends, an emphasis on the creation of new compact centers,

and an emphasis on alternative modes of transit. In addition to the baseline scenario, these

themes formed the basis of the three alternative scenarios that were modeled by Renaissance

Planning Group and the University of Florida. Before scenarios were populated, assumptions

(Table 3-2) relating to transportation networks, non-developable areas, and density were

established and used as constraints during allocation. The Baseline scenario accommodates

future growth according to existing density and policy. Scenario A, the Green Areas Scenario,

set aside the most critical lands and habitat before population was allocated to the region.

Scenario B, Centers, connected cities, towns and villages using a basic rail network. Scenario C,

Corridors, included a more extensive rail transportation, including streetcar, light rail, and









commuter rail. The scenarios were analyzed and compared using performance measures that

address the region's principles (Figures 3-1 through 3-5 and Table 3-3). During analysis, GIS

and specialized modeling software (i.e. for land use, transportation, and air quality) was used to

evaluate additional quantitative impacts. The LUCIS was used to model every scenario except

Scenario B, which was modeled by Renaissance Planning Group.

If current development policies and density continue as many "green fields" (i.e. farms,

fields, woods, and wetlands) will urbanize in the next 45 years as were urbanized in the last 440

(Laurien, 2007, slide 54). The baseline scenario will urbanize more than 10 times more

threatened/endangered species habitat than any of the other three alternative scenarios. Scenario

A (Green Areas) preserves more sensitive land (4,627 square miles) than any of the other

scenarios. Scenario B (Centers) preserves the next largest amount of sensitive land at 4,198

square miles and Scenario C (Corridors) follows at 3,816 square miles. With regard to traffic

and air quality, Scenarios B and C will require Central Florida residents to spend lesser amounts

of time in the car than the Baseline or Scenario A, which will result in lesser CO2 emiSSIOnS.

Scenario C also offers more affordable housing options and will produce more than $450 billion

more Gross Regional Product than the Baseline Scenario. Overall, the Baseline Scenario

provides the least opportunity in terms of housing options, preservation of sensitive

environments, and economic strength. Each of the three alternative scenarios provides some

level of economic efficiency with lesser impacts to the physical environment than the Baseline

Scenario.

As mentioned previously, community input played a significant role throughout the East

Central Florida Growth Vision. The final scenarios were a compilation of the input from the

community, and their involvement was even more valuable after the final scenarios were










released to the public. In January 2007, the residents of the East Central Florida region were

asked to vote on each scenario and choose which overall element best represented how they

would like their region to grow over the next 50 years. They were also asked to select their

preferences to other individual elements, just like those who participated in Envision Utah,

relating to amount of conserved land, amount of developed land, air quality, water demand,

transportation choices, commute time, and economic impact. Figure 3-6 depicts the final survey

results of the three growth scenarios. Phil Laurien, Executive Director of the East Central

Florida Regional Planning Council summarized the results as follows:

The Trend was the least preferred alternative by 86.5 % of respondents.

The Green Areas scenario was the most preferred by 27.2 % of respondents, and second
choice of 18%.

The Centers scenario was the most preferred by 38.2 % of respondents, and second choice
of 41.4 %.

The Corridors scenario was the most preferred by 3 1.1 % of respondents and second
choice of 31.1 %.

Clear Loser: the Trend

These results spoke loudly that the Trend, the current development pattern of low density
sprawl in central Florida is not what people want for the future. But the other alternatives
provided no clear winner.

No Clear Winner

Just a few percentage points separated the Centers, Corridors, and the Green Areas. This
was understandable, even predictable, since all three had a strong conservation element,
and some alternative transportation (transit, streetcar, light rail, commuter rail). As a result
none of these three scenarios was markedly different to the average respondent. (email
communication Phil Laurien, East Central Florida Regional Planning Council on March
14, 2007)

Summary

The Baseline and 3 alternative scenarios depict 4 significantly different futures for East

Central Florida. The survey taken from residents after the final scenarios were developed










indicated that residents are ready for a change. The idea that 3.6 million people are coming

seems like more of a reality when you see large portions of your county that are currently

undeveloped colored a shade of yellow in 2050 indicating that some type of urban development

will occur. Although the LUCIS model determines which lands are more preferable for urban

development and allocates population into those lands first, in each scenario in which LUCIS

was used, population was also allocated in lands preferred for agriculture just to satisfy future

demand. So why is this important to my study? It is clear from the Central Florida Growth

Vision that the population and connectivity will exist in 2050. The economic analysis performed

for the Central Florida Growth Vision also indicates that if a scenario other than the trend is

realized then the Gross Regional Product2 will be at least $28 billion more than that in the

Baseline Scenario. Although to generate this level of economic growth high wage jobs that

require highly skilled and educated workers, also known as the Creative Class, must exist within

the region. This study will determine whether the East Central Florida can accommodate the

new influx of creative employee expected by 2050.


















2 A Gross Regional Product (GRP) is defined as "a measure of total income in a given area. The GRP includes
employee compensation, property income, and proprietary income plus indirect business taxes. The GRP is equal to
total value added and is the local or regional equivalent of the national measure of economic growth, the Gross
Domestic" Product (Southemn Forest Resource Assessment, 2001).









Table 3-1. Summary of potential measures according to each guiding principle

Principle Topic Area Potential Measure (Examples)
Preserving open Open spaces, Total acres of open spaces, farmland, and critical
space, recreation farmland, environmental areas protected
areas, farmland, natural beauty
waerrsores nd ad r'ia Total length hard versus soft edges between urban and
critcal nvionmetal open, farm, environmental land (hard edges defines as
envionmnta ares aeasdensities greater than 4 dus per acre)
Provide universal Education Ratio of population to education capacity at all levels
access to the highest Number and percentage of people living within
quality of education, walking. access to schools
healthcare, and Health care Number of uninsured
cultural amenities Number and percentage of people living within x
miles of a hospital
Provide a variety of Availability of Number and percentage of system miles in network
transportation choices (roads, transit, bike/pd
choices Percentage of population and employment within one-
uatr mile of public transportation service
Efficiency of Total daily, per capital and per household vehicle miles
choices and hours of travel
Total daily, per capital and per household hours of
delay
Encourage a diverse, Population and employment capacity within 2 miles of
globally competitive regional transit stations and highway interchanges
economy Population within 20 minutes of regional activity
centers
Foster distinctive, Number and percentage of people living and working
attractive and safe in well-designed/mixed communities
places to live Total size of urban footprint

Create a range of Regional distribution of housing by type
obtainable housing
opportunities and Number and percentage of housing units in mixed
choices versus single use communities
(myregion.org, 2006, p. 5)










Table 3-2. Assumptions used in baseline and 3 alternative scenarios of myregion.org Central
Florida Growth Vision
Scenario Description of assumptions
Baseline Population is allocated according to
existing, gross density and polic
Scenario A: Green Areas Preserve primary sensitive lands plus
threatened and endangered species
habitat, which goes beyond just the seven
"environmental jewels"3
New urban development is mostly placed
outside sensitive areas and habitat.
20% of new growth will occur in
redevelopment.
80% of new growth to green fields; use
development preference map for
guidance.
Transportation Improvements: Planned
Florida Interstate Highways; Strategic
Inter-modal System Roads; 2025-2030
Long Range Transportation Plan Cost
Feasible Plans; DOT commuter rail
Deland to Kissimmee; Active Freight
Rail; Existing Arterials
Scenario B: Centers Transportation Improvements:
Renaissance Planning Group designs 370
mile new road network and 413 mile rail
and streetcar network in addition to
Planned Florida Interstate Highways,
Strategic Inter-modal System Roas,
2025-2030 Long Range Transportation
Plan Cost Feasible Plans, DOT
commuter rail Deland to Kissimmee,
Active Freight Rail, Existing Arterials.
Seven "environmental jewels" preserved
and enhanced.
20% of new growth will occur in urban
redevelopment in existing centers.
80% of new growth to green fields; most
will go to new centers of mixed use
densities ranging from 4-10 units per
acre, higher near rail stations.

3 The seven enviromnental jewels are seven areas identified by The Florida Natural Areas Inventory as sensitive
lands that have significant regional, national, and in some cases, global ecological and economic value. The seven
locations are St. Johns Mosaic/Econlockhatchee River, the Indian River Lagoon, the Kissimmee Prairie, the Volusia
Conservation Corridor, the Green Swamp, the Wekiva-Ocala Greenway, and the Lake Wales Ridge.









Table 3-2. Continued
Scenario Description of assumptions


Scenario C: Corridors


* Spend more money on transit than on
road improvement.
o DOT commuter rail enhanced
o Additional light rail and
streetcar network
* Planned Florida Interstate and Strategic
Inter-modal System Roads, 2025-2030
Long Range Transportation Plan Cost
Feasible Plans, Active Freight Rail,
Existing arterials
* Seven "environmental jewels" mostly
preserved.
* 20% new growth goes to urban
redevelopment, then as much new
growth placed within 1/3 mile of transit
stops as can be absorbed at 30 units/acre,
first redeveloping old commercial sites
and then remaining growth goes to green
fields using development preference map
as a guide, leaving quality existing
neighborhoods intact.


(Adapted from the Laurien, 2007)

























? Central Florida's Four Futures.


_ __


County 2005 BEBR 2050 Trend 2050 Geen Areas Centers 2050 Corridors 2050
Population 2050
Brevard 531,970 932,704 888,333 914,981 958,939 97, 129
Lae 263,017 653,766 531,942 831,354 62,686 652,410
Oage 1,043,437 2,230,650 1,819,062 1,477,974 2,03,565 2,203,642
Osceola 235,156 688,296 413,624 69,095 752,315 588,742
Polk 541,840 99,088 1,507,076 1,595,293 97,565 1,097,067
Seminole 411,744 775,265 623,145 593,375 61,169 589,836
Volusia 494,649 874,001 1,340,569 1,041,647 894,077 1,022,564
Total 3,521,813 7,123,770 7,123,751 7,123,719 7,130,317 7,121,390
Population


Table 3-3. Potential future population distribution b
y scenario and county


(Reprinted with permission from Laurien, P. (2007). Myregion.org presents: How Shall We Grow
Slides used for Media Week January 2007. East Central Florida Regional Planning Council)





Figure 3-1. Illustration of growth patterns for Baseline Scenario
(Reprinted with permission from Trend Scenario 2050 [Map]. (2007). East Central
Florida Regional Planning Council)











,P, .:I

'i:l


i Ll~_ _II I~ I IL I ___LC ___ _: ~ II _Y _I1I_ ___1* _II_ I_
r-- -; ~--YII-'- ~l'--L- '~~l---r L1 ~I I-C'---Y-~I- I r-
*Is larou;ar a~~~ rcr*ril arscr hlr


Figure 3-2. Illustration of growth patterns for Scenario A (Green Areas)

(Reprinted with permission from Green Area~s Scenario 2050 [Map]. (2007). East Central Florida
Regional Planning Council)

























































Figure 3-3. Illustration of growth patterns for Scenario B (Centers)


(Reprinted with permission from Centers Scenario 2050 [Map]. (2007). East Central Florida
Regional Planning Council)


64





Figure 3-4. Illustration of growth patterns for Scenario C (Corridors)

(Reprinted with permission from Corridors Scenario 2050 [Map]. (2007). East Central Florida
Regional Planning Council)










































._ --r --


ai~siirne rush r..al-.T r- qualisilmalistnlydwdlin
Iraw~Wna1 rus. uniwl~hsughall Innsa
..vki~Ci Lc lill -
4ave~me~*s ~ntuaterwardeatedihsetHwadethfbmsitkreunpaummtra


Figure 3-5. Description of Phase 2 Scenarios from Central Florida Growth Vision



(Reprinted with permission from myregion.org. (2007). Survey Audit Final Report. Retrieved April 20, 2007, from
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Pespondents have Ilved In Central Florida:
Less than five yeams 17 %
Between 5-15 years 31 9
Between 15-30 years 31 9
More than 30 yeams 20: %
I don't reside in Central Florida 1%



Under21 496
21-24 4 9
25-34 18 96
35-44 22 %
45-54 26 96
55-64 3:1 9
65 or older 5 %

PRespondents moved to Central Florida pri marily because of;
The weather 11 9
30b opportunities 31 9
Family in the area 17 96
Affoniable land and housing 5 %
The entertainment options 1%
The beaches 1%
I've lived here my entire life 17 %
Other 17 96

The results from this community survey are the latest piece In a 15-month puzzle to create a shared
Regenal VMseen tbr Centrd F~onda. Creatng regional strategies to support this usion requires input from
citrens, community leaders and elected officials and from the very beginning, "How Shall VWe Grow?" has
been seeking thoughtful Input fro m the residents of Central Fl ori da i n an open and I ncl usi v manner.

Over the last year, r~ecpnren rg trawled throughout the seven countes of Central Florida and conducted
more than 151: community meetngs and presentatons. During these tmes of learning and sharing, we
"talk ed with" and "Iistened" to over 11:,0:1:1: participants think ing together about how we view the future
of our region. From these gatherings, we developed the various growth scenarios that were presented to
the community asking residents "Howy Shall We Grow?"

Our work will conclude in August 21:l:7 with a Community Summit that will seek "Consensus on Action."
The final scenario framework will Include recommendatons to s rve as guidelines for community leaders
in their land and transportation plans, essentially, creating a guide for what Central Florida can become.

myrafal corg* 75 Scuth Ivarhoe Bodlevard Orland:., Fkcrida 3~3312 1.ECIO.9:O.5315 www.myregion~org
Spring 3:107



Figure 3-6. Central Florida Growth Vision survey results


(Reprinted with permission from myregion.org. (2007). Scenario Descriptions and Survey
Ballot. Retrieved April 20, 2007, from www.myregion.org)


How Shall We G3row;
Creating a Shared Vision
for Central Florida













How Shall We Grow;
Creating a Shared Vision *
for Central Florida


Surv ey Results
The message Is clear those who Ilve In Cent-al Florida want a future that Is very different than the path
we are now on when It comes to howy we'l| handle our future growth. In the final analysts, :E6.5% of all
partcipants (7,319 accepted surveys) said that the "current growth trend" was their "least preferred"
scenario when ast ed about "How Shall We Grow?"


Considerinq the issues in this sect on, respondents selected the following preferences:
Trend Gmeen
2050 Areas Centers Corridors
Developed Land 4 % 27 96 24 96 4E. 9
Conserved Land 5 % 46 96 25 96 24 96
Air Quality 1 % 12 % 66E9 9 18 96
Total W~ater Dem and 3 % 20 96 26 % 50 %
Transportation Choices 3 % 25 96 24 % 43 %
Average Commute Time % 11: 96 77 % 11 R'
Econom ic Im pact/ % 6 16 6
Number of New 7obs

Respondents ranked the following scenario choices for the future:
Trend Gmeen
2050 Areas Centers Corridors
Most Preferred 4 % 27 96 38 % 31 9
Second Choice 4 % 18 96 41 % 31%
Third Choice 5 % 51 96 15 % '8 96
Least Preferred 86 % 4 9 5 % 11: 96

When asked how i mportant is it for leaders from across the region to work together in the following
areas, respondents used the scale of 1-5 with 5 being -of high importance:
1 2 34 5


4 9 3 % 11: 96 18 96 65 96


1. Preserve green ameas
2. Provide access to education, healthcare
and cultural choices
3. Create safe, attractive, places to live
4. Create a variety of transportation
choices
5. Create a range of housing choices
6. Develop a diverse economy


-% % 11 96 2 %


53 S


6 9 5 % 13 96 28 96 49 St

5% 66 E % 15 96 22 52 *

9 % 9 % 24 % 27 96 31 %
5 9 5 % 3:1 9 27 % 43 96


Conbnued on reverse -.

inwg~pa7on rg 75 Scuth Ivarhoe BoLievard Orland:U FI.-rida 33302 1.E:10,9:10.5315 www.rnyregion.org
Spring 3:107



Figure 3-6. Continued


(Reprinted with permission from myregion.org. (2007). Scenario Descriptions and' Survey
Ballot. Retrieved April 20, 2007, from www.myregion.org)









CHAPTER 4
STUDY AREA

The East Central Florida region, composed of 7 counties has a total land area of 7,488

square miles. The counties within this region are Brevard, Lake, Orange Osceola, Polk,

Seminole, and Volusia. This region is further divided into 4 Metropolitan Statistical Areasl

(MSAs); Melbourne-Titusville-Palm Bay, Lakeland-Winter Haven, Orlando, and Daytona Beach

(Figure 4-2). Using spatial statistics within GIS, the geographic center of the region is within

Orange County. Major cities (Table 4-1) within the region include Palm Bay (Brevard County),

Melbourne (Brevard County), Clermont (Lake County), Leesburg (Lake County), Orlando

(Orange County), Apopka (Orange County), Kissimmee (Osceola County), St. Cloud (Osceola

County), Lakeland (Polk County), Winter Haven (Polk County), Sanford (Seminole County),

Altamonte Springs (Seminole County), Daytona Beach (Volusia County), and Port Orange

(Volusia County). The total regional population in 2005 was 3,521,8132. Through the year 2050

population is expected to more than double to over 7. 1 million people (Table 4-2).

The region is composed of 97 municipalities, each with a unique set of land use

regulations. With the exception of Polk County, six counties are under the auspices of the East

Central Florida Regional Planning Council (ECFRPC). Polk County is a member of the Central

Florida Regional Planning Council. Polk County was included in this exercise not only because

of its shares borders with three other counties within the ECFRPC, but also because the large

number of Polk County residents who commute to a county within the ECFRPC region for work,



SThe U.S. Census Bureau defines a metropolitan statistical area as "a geographic entity...based on the concept of a
core area with a large population nucleus, plus adjacent communities having a high degree of economic and social
integration with that core. Qualification of an MSA requires the presence of a city with 50,000 or more inhabitants,
or the presence of an urbanized area and a total population of at least 100,000 (75,000 in New England) (Davidson
and Dolnick, 2004, p. 269).

2 The 2005 population is referenced because the analysis performed in this paper uses 2005 as the temporal baseline.










shopping, or recreation. When a job is lost or housing costs in the ECFRPC region increase, the

effects are felt in Polk County. The implications of future growth are felt across all 7 seven

counties through the local and regional economy, transportation and housing sectors.

The Economy

In 2006, the largest private employer within the region was Walt Disney World in Orange

County with 53,500 employees (Table 4-3). Orange County is not only the geographic center of

the region, but it is also the economic heart. If Orange County were removed from the region,

the largest regional private employer, located in Volusia County, would employ almost 9,000

people. If you include Orange County, there are five employers who employ more than 9,000

people. More importantly, 9 out of 10 of Orange County's top private employers are creative

industries. Creative industries hire individuals that are highly skilled and stimulate economic

growth. Among the region's highest ranked largest private employer with the least number of

employees is Leesburg Regional Medical Center located in Lake County at 2,300 employees.

Lake County also has the least number of creative industries within its top ten employers. The

presence of creative individuals in the East Central Florida region is the core of my study. The

concentration of creative industries contributes to the Creativity Index, which in Richard

Florida' s research is a statistically significant indicator of of a region' s economic potential and is

discussed further in Chapter 5.

Residents of a particular county often fall victim to the assumption that economic shifts in

a neighboring county or county within the larger region has no effect on they live. This couldn't

be further from the truth. This is evident from the number of people who work outside of the

MSA in which they reside (Table 4-4). Individuals of Volusia and Polk County have the highest

percentage of residents who work outside of their MSA of residence, 20% and 16% respectively.

Individuals who live in Orange County have the lowest percentage of residents who work outside









of their MSA. It is important to understand that a MSA is a statistical area, not a political

boundary, and the Orlando MSA constitutes more than one county. Since the Orlando MSA has

the lowest percentage, it reinforces the concept of interlinkages between counties within a

region, especially with respect to commuting patterns and housing.

Transportation

Transport corridors can be seen as backbones of transportation networks linking maj or

articulation points (e.g. hubs) and towards which freight and passenger fluxes converge. Most

often, they lie at the intersection of economic, demographic and geographic spaces as they

perform both market-serving and market-connecting functions (Slack, 2006). The transportation

corridors within East Central Florida are complex and its maj or interchanges are suggestive of

rapidly growing places of work and residence.

Orange County is the most accessible county by roadway (Table 4-5) in the region with 1

federal interstate, 2 federal highways, and 10 state highways. The arrangement of these systems

encourages economic activity and linkages between adjacent counties. Walt Disney World,

Orange County's largest employer, is situated near the convergence of Interstate 4, Florida's

Turnpike and State Highway 417. From Osceola County Walt Disney World is adj acent to US-

192 and is not far from the Florida Tumnpike. The University of Central Florida (UCF), the 9th

largest employer in Orange County, is proximal to SR-417 and SR-50. Florida' s Tumnpike and

Interstate 95 are principal arterials that feed into maj or thoroughfares that will lead you to the

UCF campus.

Other counties within the region offer as many accessibility options the potential for

growth is there. In Brevard County the maj or federal interstate is I-95 with federal highways

US-1 and SR-A1A providing access to the coast. Residents in coastal neighborhoods have

expressed additional transportation access routes between the mainland and the barrier islands,









which would assist in evacuations during hurricanes and support the additional demand resulting

from barrier island development.

The importance of Polk County as an area that links the east coast of Florida with the west

is inherent in layout of its transportation corridors. Interstate 4 connects residents and businesses

of Orange and Osceola to Polk County and vice versa. Access to Interstate 75 on the west side

of Polk County provides additional access to Hillsborough and Pasco Counties as well as all

points north and south. Interstate 75 bisects the state of Florida but provides a way for residents

of Florida' s east coast to access the Hillsborough County and its port without ever going through

its county seat, Tampa. The numerous transportation options available to residents of Polk

County support the 16.6% of residents who work outside the Lakeland-Winter Haven MSA. It is

important to note that the current cost feasible transportation plans do not call for new roads,

only improvements to existing infrastructure.

Other modes of transport within the region include rail and water (Table 4-5 and 4-6).

Active rails exist within each county but Orange and Volusia Counties are the only two counties

with passenger rail. Amtrak provides nonstop auto train service between the northeast and

Sanford. Rail is the primary mode of transportation for mining and agriculture, especially in

Polk and Osceola counties. Rail in Central Florida is also the cheapest way to move materials

such as timber and building materials to long distance destinations. For example, CSX

Transportation services the maj or seaports within Florida and has access to 23,000 miles of rail,

reaching 23 states (JAXPORT, 2007). Industries that are core to the Central Florida economy

rely heavily upon rail freight to meet their transportation needs.

Port Canaveral is located in Brevard County and is the maj or seaport on the west coast.

This port supports the cruise and cargo industries, commercial fishing, the foreign trade zone and









industrial park, federal government agencies, and the recreational parks all of which support

economic activities within the region. This port has significant economic impact on Brevard

County, the Central Florida Region, and the State of Florida. In Brevard County, Port Canaveral

generates more than 34,000 jobs, $1.1 billion in wages, $1.5 billion in economic impact, and

accounts for almost 17% of the county's total economic worth. In Central Florida, Port

Canaveral generates more than 50,000 jobs, $1.8 billion in wages, and produces an economic

impact of $2.3 billion. Around the state, Port Canaveral generates more than 90,000 jobs, $3

billion in wages, and has an economic impact of $3.9 billion (Fishkind and Associates, 2005).

Its physical location opens up the Atlantic Ocean and eastern seaboard for trade and future

economic growth.

The Port of Tampa is the closest port to Polk County. The Port of Tampa is Florida' s

largest port, handling approximately 50 million tons of cargo per year. This port is the largest

economic engine in West Central Florida. The port is located in Tampa and provides the most

direct route to Mexico, Latin America, and the Caribbean. Tampa is also the closest full service

U.S. port to the Panama Canal (Tampa Port Authority, 2007). In addition to cargo services the

port also provides passenger cruise lines and is adj acent to the mixed use retail, entertainment

and residential Channelside District.

Two maj or regional airports (Table 4-6) are located within the East Central Florida region;

Orlando/Sanford International Airport in Seminole County and Orlando International Airport

(OIA) in Orange County. Located just 18 miles north of Orlando, the Orlando/Sanford

International Airport services 6 airlines and provides domestic and international service to

destinations including Europe and the Caribbean. Orlando International Airport is located within

Orlando and is the largest airport in the region with 4 runways spanning 12,005 feet. Orlando









International Airport serves more global destinations than any other airport within the region; 3

airlines service the Bahamas, 3 airlines service Canada, 3 airlines service the Caribbean, 4

airlines service Central America and 5 airlines service Europe. This airport also provides cargo

service and FedEx has a fleet based at OIA to provide regional shipping services. The airports

around the region serve as entry and exits points for cargo, business travel and tourism.

Summary

Transportation corridors and the presence of large industries are important indicators of

economic activity and growth. The East Central Florida region has the economic potential to

become a major powerhouse in global and regional economies due to its complex transportation

networks and presence of several industries that hire large numbers of creative individuals. The

creative and creative industries are attracted to regions with mass transit. One downfall of the

East Central Florida region is that they have not yet integrated mass transit into its transportation

infrastructure. This study will try to evaluate whether this region can still attract creative

industries given future employment demands and existing infrastructure.









Table 4-1. Largest cities and population, for each county
County City County City 2005 % of County
Population* Population* Population
Brevard 531,970
Palm Ba 91,888 17.2%
Melbourne 75,060 14.1%
Unincorporated 210,260 39.5%
Lake 263,017
Clermont 20,017 7.6%
Leesburg 17,467 6.6%
Unincorprated 146,221 55.6%
Orange 1,043,437
Orlando 217,567 20.8%
Apoka 34,801 3.3%
Unincorprated 677, 185 64.9%
Osceola 235,156
Kissimmee 58,223 24.8%
St. Cloud 24,700 10.5%
Unincorprated 152,233 64.7%
Polk 541,840
Lakeland 90,851 16.8%
Winter Haven 28,724 5.3%
Unincorprated 338,250 62.4%
Seminole 411,744
Sanford 49,252 12.0%
Altamonte Springs 42,616 10.4%
Unincorprated 203,021 49.3%
Volusia 494,649
Daytn Beach 65,129 13.2%
Port Orange 54,630 11.0%
Unincorporated 114,961 23.2%
* Population as of Aril 1, 2005
(Adapted from Bureau of Economic and Business Research (BEBR). (2006). Florida Population
Studies (Detailed Bulletins 145). Gainesville, Florida: University of Florida)









Table 4-2. Regional 2005 population and 2050 proj ected population, by count
County 2005 Population* 2050 Population*
Brevard 531,970 932,704
Lake 263,017 653,766
Orange 1,043,437 2,230,650
Osceola 235,156 688,296
Polk 541,840 969,088
Seminole 411,744 775,265
Volusia 494,649 874,001
TOTAL POPULATION 3,521,813 7, 123,770
(Adapted from Bureau of Economic and Business Research (BEBR). (2006). Florida Population
Studies (Detailed Bulletins 145). Gainesville, Florida: University of Florida)


Table 4-3. Major priva e sector employers
County Employer Business Line Number of Employee
Brevard
United Space Alliance NASA Space Flight 6,500
Ovations Contractor
Harris Corporation International 6,500
Communications
Eqiment Compn
Health First, Inc. Integrated Healthcare 6, 100
Delivery System
Space Gateway Base Operations for 3,000
Support NASA & 45th Space
Wing
Wuesthoff Health Full-Service 2,500
System Healthcare System
Northrop Grumman Global Aerospace & 2,000
Corporation Defense Compn
The Boeing Company Payload Processing 1,800
for Shuttle Operations
Sea Ray Boats, Inc. Boat Manufacturer 1,200
MC Assembly PC Board Assembly 1 ,200
Rockwell Collins Avionics Systems 1,120
Manufacturer










Table 4-3. Continued
County Employer Business Line Number of Employee
Lake
Leesburg Regional Healthcare 2,300
Medical Center
Village of Lake Retirement 2,200
Sumter, Inc. Community
Florida Hospital Healthcare 1,400
Waterman
Sprint Telecommunications 811
G& T Conwyar N/A 550
Company
Bailey Industries Manufacturing 509
Accent Architecture 500
Dura-Stress Concrete Supply & 425
Storage
Lake Port Square N/A 400
Casmin Incoprated Construction 300
Orange
Walt Disney World Entertainment 53,500
Orange County Public Education 22,807
Schools
Adventist Health Healthcare 17,059
Systems
Universal Orlando Entertainment 14,500
Orlando Regional Healthcare 12,000
Healthcare System
Orange County Government 6,577
Government
Lockheed Martin Combat System 5,700
Central Florida Real Estate 5,000
Investments Developr
University of Central Education 4,808
Florida
Darden Restaurants Corporate 4,675
Headqatr









Table 4-3. Continued
County Employer Business Line Number of Employee
Osceola
McLane/Sunset, Inc. N/A 900
Florida Hospital Healthcare 794
Kissimmee
Osceola Regional Healthcare 522
Medical Center
Hyatt Orlando Hotel/Resort 500
Kissimmee
Walt Disney Artistic Production 450
Imagieering
Splendid China Amusement Park 400
Orange Lake Resort Resort & Country 400
& Country Club Club
Mercury Marine Maine Electronic 400
Euipment
Tupperware Housewares 300
Corpration
Lerio Corporation Plastic Products 120
Polk
Publix Sue Markets Retail Food 8,500
Wal-mart Retail General 5,500
Merchandise
Lakeland Regional Hospital/Medical 4,000
Medical Center
MOSAIC Phosphate Mining 3,000
Winter Haven Hospital/Medical 2,500
Hospital
GEICO Insurance 2,000
State Farm Insurance Insurance 1,500
Watson Clinic Medical 1,300
GC Services Call Center 1,200
Florida Natural Citrus Processors 1,000
Growers









Table 4-3. Continued
County Employer Business Line Number of Employee
Seminole
Seminole County Education 8,824
Public Schools
Convergys Billing Software 1,747
Corporation
Seminole Community Education 1,673
Coll eg
Sprint PCS Telecommunications 1,550
Siemans ICN Telecommunications 1,500
Seminole County Government 1,247
Government
First USA Credit Card 1,200
Processing
U.S. Postal Processing Postal Service 1,000
Plant
American Automobile Travel Services 825
Association
Florida Hospital Healthcare 800
Altamonte Spis
Volusia
Volusia County School Board 8,998
School Board
Halifax Staffing Medical 6,330
Publix Sue Markets Grocer 2,798
Wal-Mart Retail 2,206
Vision HW Inc. Manaement Services 1,667
Embry-Riddle University 1,513
Aeronautical
University
Florida Hospital Medical 1,403
Ormond Memorial
Daytona Beach Community College 1,334
Community College
Winn Dixie Stores Inc Grocr 1,290
(Adapted from Enterprise Florida. (2007). County Profiles. Retrieved April 20, 2007, from
http://www.eflorida.com/countyprofiles/Counyrfie1splvl=3&level2=127&level3
=335®ion=)









Table 4-4. Employment petterns of workers 16 years and over who live in a MSA
County MSA Total Total % of Total Total % of Total
Workers Workers Workers in Workers Workers in
16+ living 16+ worked MSA 16+ worked MSA
in a MSA in MSA of outside
(Estimate) residence MSA of
(Estimate) resident
(Estimate)
Brevard Melbourne- 228,806 214,597 93.8% 13,075 5.7%
Titusville-
Palm Bay
Lake Orlando 106,111 98,900 93.2% 5,291 5.0%
Orange Orlando 490,871 477,595 97.2% 12,622 2.6%
Osceola Orlando 106,445 100,813 94.7% 5,489 5.2%
Polk Lakeland/ 227,493 187,099 82.2% 37,864 16.6%
Winter
Haven
Seminole Orlando 198,737 189,913 95.6% 8,037 4.0%
Volusia Daytona 203,068 158,579 78.1% 40,796 20.1%
Beach
NOTE: All estimates are within 1% standard of error.
(U.S. Census Bureau; 2005 American Community Survey; generated by Iris Patten; using
American Factfinder; <
http:.//factfinder. census. gov/serylet/DatasetMainPageSerylet?_progra=AS&_submenul
d=&_1ang=en&_ts=>; (08 March 2007).









Table 4-5. Transpottion corridors (roads) within the East Central Florida region
County Federal Federal State Highways Railroads
Interstates Highways
Brevard I-95 US-1, US 192 SR-A1A; 5; 46; Florida East Coast
50; 405; 407; Railway
501; 520, 524;
528
Lake None US-27, US 441 SR-19, 33, 40, CSX
44, 46, 50, 56, Transportation,
439, 445, 561 Florida Central
Orange I-4 US-441, US SR-15, 408, 417, Amtrak, CSX,
17/92 419, 426, 436, Florida Central
482, 520, 525,
527
Osceola I-4 US-192, US-441, SR-15, 424, 419, CSX
US 17/92 530, 532, 545, Transportation,
Florida Turnpike Florida Central
Polk I-4 US-27, US 98 SR-60 CSX Rail
Seminole I-4 US-17/92 SR-46, 417, 419, CSX
426, 427, 434, Transportation,
436 Florida Central
Volusia I-4; I-95 US-1, US-17, CSX, Florida East
US-40, US-92 Coast Railway,
Amtrak
(Adapted from Enterprise Florida. (2007). County Profl es. Retrieved April 20, 2007, from
http://www.eflorida.com/countyprofiles/Counyrfie1splvl=3&level2=127&level3
=335®ion=)









Table 4-6. Ports and airports within the East Central Florida region
Nearest Airport # Runways Longest General Local Deep Miles to
with Scheduled Paved Aviation Water Port Closest
Commercial Runway Airports Port
Airline Service (ft)
Brevard Melbourne 3 10,200 Space Port 1
International Coast Canaveral
Airport Regional
Airport;
Merritt
Island
Airport
Lake Orlando 3 12,005 Leesburg Canaveral 73
International Regional Port
Airport Airport Authority
Orange Orlando 4 12,005 Orlando Canaveral 46
International Executive Port
Airport Airport Authorit
Osceola Orlando 4 12,005 Kissimmee Canaveral 49
International Municipal Port
Airport Airport Authorit
Polk Tampa 3 11,002 Lakeland Tampa Port 49
International Linder Authority
AirportRegional
Seminole Orlando/Sanford 4 9,600 Orlando/ Canaveral 48
International Sanford Port
Airport Airport Authority
Volusia Daytona Beach 3 10,500 Deland Canaveral 72
International Municipal Port
Airport Airport; Authority
Ormond
Municipal
Airport;
New
Smyrna
Municipal
(Adapted from Enterprise Florida. (2007). County Profiles. Retrieved April 20, 2007, from
http://www.eflorida.com/countyprofiles/Counyrflsaplv 1 =3&level2=127&level3
=335®ion=)





Legend
SEast Central Florida Region
SExisting Conservation Lands
SOpen Walter
Major Higlways


Figure 4-1. Study Area, 7 county East Central Florida region





Y ;i


I:T 11

Legend C` II .
East Central Florida MbSA a
Existing Conservation Lands
SOpen Walter 'r Miles3~
0.1 3.757 5 15 22.5 30


Figure 4-2. Metropolitan Statistical Areas (MSAs) within the East Central Florida Region


.E-

N









CHAPTER 5
IVETHODOLOGY

This paper builds on the East Central Florida Regional Visioning process and applies

advanced GIS technologies to determine whether the economic potential exists within the East

Central Florida region to accommodate industries that require high-quality, innovative

employees and skills. A maj or outcome of the original visioning process was to understand

potential growth patterns given various assumptions. The original visioning efforts also

identified several economic indicators including proj ected gross domestic product and average

per capital salary for each alternative. An outcome not identified, but one my study addresses, is

the identification of lands within the region suitable for innovation and high tech industries. This

paper will also examine whether the land suitable for these industries are sufficient in area to

accommodate future employment projections in a way that minimizes land use conflict and

diversifies the economy.

Explanation of the Model

Creative individuals drive the economy, therefore they seek locations that stimulate

innovation and provide opportunities that validate their identity and enable them to flourish.

Keeping this in mind, what matters to the Creative individual is far different than what mattered

to those in our parents' generation. Building a model that would measure growth patterns and

potential locations for employment centers in the year 2050 that are conducive to attracting the

Creative Class involved modifying the 5 step LUCIS strategy. The Carr and Zwick methodology

considers agricultural, conservation, and urban land uses as individual stakeholders. In Carr and

Zwick' s LUCIS model, each stakeholder uses a five step process to determine its suitability of a

given land area. The outcome of this process is a surface that visually depicts the most


i The term "surface" is used interchangeably with "raster".










preferable use of land with respect to agriculture, conservation or urban land uses. The five step

process (Figure 5-4), as defined by Carr and Zwick (p. 12) are

* Goals and objectives: Define goals and objectives that become the criteria for determining
suitability.
* Data inventory: Identify data resources potentially relevant to each goal and objective.
* Suitability: Analyze data to determine relative suitability for each goal.
* Preference: Combine the relative suitabilities of goal to determine preference for the three
maj or land-use categories.
* Conflict: Compare the three land-use preference to determine likely areas of future land-
use conflict.

The first basic step of my study was to modify the Carr and Zwick LUCIS model to

include the goals, obj ectives, and sub-objectives that reflect the values of creative individuals. In

addition to the Carr and Zwick hierarchical relationship of goals, obj ectives, and sub-obj ectives

for agriculture, conservation and urban stakeholders (Table 5-1 and Figures 5-1, 5-2, and 5-3),

the hierarchical relationship of the Creative was added as Goal 5 under the urban land use type

(Table 5-1 and Figure 5-3). The second basic step involved collecting the appropriate GIS and

quantitative data to measure those indicators. The third step involved using models to measure

the suitability of land within the region for each stakeholder. The fourth step created a

community/stakeholder preference value (represented in terms of a weighted value, and

calculated using the analytical hierarchy process (AHP2)), combined the suitability surface for

each stakeholder, and created a map that identified conflict between all stakeholders. The fifth

step involved using the conflict surface and the final suitability surface for Goal 5 to determine

appropriate locations for creative industries and allocating creative centers of employment that

minimized land-use conflict and satisfied the local demand for creative industries.


2 AHP is the non-generic form of pairwise comparison. AHP was developed by T. L. Saaty in 1980 at the
University of Pennsylvania's Wharton School of Business and is a systematic method that compares a list of
objectives or alternatives (Virginia Tech University, 2006)










Identifying the Indicators

The first step identified indicators that measure the values and bias of the Creative Class as

well as values and bias of each land use. This proj ect divided the indicators into two groups:

those that identify land use suitability for the Creative Class and those that identify suitability of

the agriculture, conservation, and urban land uses. Once all indicators were identified, they were

translated into a set of corresponding goals and obj ectives representative of each stakeholder.

As mentioned in the previous section, this study applied the original goals and obj ectives

for agriculture, conservation, and urban as defined by Carr and Zwick (2007, p. 229)4. Through

previous research, Richard Florida (Florida, 2002) identified values of the Creative. It is

believed that these values have never been previously translated into a formal set of goals and

obj ectives that can serve as criteria to measure land use suitability. This is why the indicators for

the Creative and those for agriculture, conservation, and urban land uses were initially divided

into two groups. Ultimately, this study added the goals and objectives of the Creative Class to

those developed by Carr and Zwick. The Creative Class goals and objectives can be found as

Goal 5 under the urban stakeholder (Figure 5-3). A complete list of goals and obj ectives used for

each stakeholder is available in Table 4-1. Below is an explanation of indicators that quantify the

attractiveness of an area for the Creative Class (also known as "Creative Centers").

Creative Indicators

The Creative Class seek places that provide economic opportunity, are highly efficient,

offer a high level of amenities and are environmentally stimulating. Creative Centers, which are

areas composed of a high concentration of creative people, thrive because creative people want



4 The surfaces and models for determining the suitability of the agriculture and conservation stakeholders used in my
study are a product of the work done by Dr. Paul Zwick and Margaret Carr for the East Central Florida Regional
Growth Vision.









to live there. Companies that require the skills offered by these individuals follow them or, in

many cases, are started by the Creative themselves. Creative Centers have four common

characteristics:

* High concentrations of innovation and high tech industry growth
* Increases in regional employment and population
* High-quality amenities and experiences
* Openness to diversity of all kinds

These characteristics were used in developing indicators for determining suitability of land

within the study area for potential locations of future creative industries.

Clustering of Creative Industries

The advent of the Internet and modern telecommunications stimulated an ideology that it

was no longer necessary to work or be together. Yet after more than 25 years since the inception

of the Internet, people remain highly concentrated. Residential growth patterns around the

country indicate that people are looking to become even more concentrated, moving back into

central cities or into areas that are higher density, promote walkability, provide cultural amenities

and posses a sense of place. The high-tech, knowledge-based creative-content industries that

drive economic growth continue to concentrate in specific places such as Austin, Silicon Valley,

or New York (Florida, 2004, p. 219), primarily due to the tendency of firms to cluster together.

Clustering produces a "productive efficiency" (Florida, 2004, p. 220). Companies cluster

in order to draw from concentrations of talented people who power innovation and economic

growth (Florida, 2004, p. 220). Clustered industries are typically "similar and/or related that

together create competitive advantages for member firms and the regional economy" (Barkley

and Henry, 2001, p. 2). David Barkley and Mark Henry, economists at Clemson University, cite

four benefits of industry clustering that support Richard Florida' s productive efficiency theory:










* Clustering Strengthens Localization Economies. Cost savings are achieved through
greater availability of specialized input suppliers and business services; a larger pool of
trained, specialized workers; public infrastructure investments geared to the needs of a
particular industry; financial markets familiar with the industry; and an enhanced
likelihood of interfirm technology and information transfers.

* Clustering facilitates Industrial Reorganization. Increased global competition and the
emergence of new production technologies (e.g., computer-aided manufacturing)
encourage reorganization between large firms engaged in mass production to small firms
focused on specialty production. Proximity between the more specialized firms and their
input suppliers and product markets enhances the flow of goods through the production
system. Ready access to product and input markets also enables firms to more quickly
adapt to market changes. And a spatial concentration of firms provides the pool of skilled
labor required by the computer-aided technologies.

* Clustering Encourages Networking Among Firms. Networking is cooperation among
firms to take advantage of complementaries, exploit new markets, integrate activities, or
pool resources or knowledge. This cooperation occurs more naturally and frequently
within industry clusters. Networking firms are more likely than non-networking firms to
engage in collaborating and information sharing in marketing, new product development,
and technological upgrading. The networking firms also report that their competitiveness
and profitability are enhanced by interfirm cooperation and collaboration.

* Clustering Permits Greater Focusing of Public Resources. A cluster approach enables
regions to focus their recruitment, retention and expansion, and small business
development programs rather than attempting to provide assistance for many different
business types. Also, because of linkages among firms in a cluster, programs supporting
specific businesses will have relatively large multiplier effects for the area economy. The
total employment and income gains from recruiting (or retaining) cluster members will
likely exceed those associated with non-cluster firms of similar size.

The creative often look for regions that have a diversified economy, since the creative

often don't anticipate staying with the same company for very long. Creative markets must offer

a "thick labor market" conducive to a horizontal career path. The gathering of people,

companies and resources into particular places with particular specialties and capabilities

generate efficiencies that power economic growth.

These spatial efficiencies also encourage a more transparent flow of knowledge (Feldman,

Aharonson, and Baum, 2005). Breschi and Lissoni describe knowledge spillovers as pure

externalities but suggest that information "flows more easily among agents located within the










same area, thanks to social bonds that foster reciprocal trust and frequent face-to-face contacts.

Therefore, geographical clusters offer more innovation opportunities than scattered locations"

(Breschi and Lissoni, 2001, p. 258). From this discussion the first indicator was drawn:

*Indicator #1: Identify locations proximal to existing creative industries.

Author and futurist Joel Kotkin believes that wealth accumulates wherever intelligence

clusters evolve (Florida, 2004, p. 220). Intelligent people are far less constrained than other

determinants of economic productivity such as the abundance of raw materials or the proximity

to dense populations or modes of transport. The true importance of knowledge relates to the

theory of human capital. University of Chicago economist Robert Lucas identifies two special

characteristics of human capital.

1. With effort, human capital can be acquired without limit and it doesn't take

more effort to acquire it when you have more of it. This allows economies to

grow without slowing as they become richer, a possibility that the neoclassical

model of Petty, Smith and Becker denied".

2. Higher average levels of human capital in an economy raise the level of

productivity of everybody in that economy, not just the productivity of those

whose human-capital level is higher. Thus human capital is an externality.

(Nowlan, 1997, p. 1)

Furthermore, research by Patricia Beeson, an urban economist at the University of

Pittsburgh, has cited that investments in higher education infrastructure predict subsequent city



5 The original human capital theory was rooted in work done by British economists Sir William Petty and Adam
Smith during the 17t and 18t centuries. During the early 20t century their work was further developed by
American economists Gary Becker and Theodore Schultz. The neoclassical view explains that the "expenditure on
training and education is costly, and should be considered an investment since it is undertaken with a view to
increasing personal incomes" (Economy Professor, 2007).









and regional growth far better than investments in physical infrastructure like canals, railroads,

or highways (Florida, 2004, p. 222). Therefore, the second indicator is:

* Indicator #2: Identify locations proximal to educated people.

Kevin Lynch observed that the distinct qualities of an urban area that appeal to an

individual's aesthetic senses affect an individual's perceptual satisfaction with the urban

environment (Chapin and Kaiser, 1979, p. 284). The siting of key functional areas and buildings

in relation to residential and recreational opportunities is significant in general land use planning

but also contributes to the quality of place for the Creative Class. Sociologists Richard Lloyd

and Terry Nichols Clark of the University of Chicago note that "workers in the elite sectors of

the postindustrial city make 'quality of life' demands, and ... increasingly act like tourists in

their own city" (Florida, 2004, p. 224). Modern creative work demand unpredictable work

schedules and readily accessible recreation. This leads to the third indicator:

* Indicator #3: Identify locations proximal to existing trails, parks, or recreational
opportunities.

A city or region's ability to facilitate the interaction between people and the community is

important in a highly creative environment. Places that embrace the culture of the Creative Age

(i.e. places where the Creative can fit in) are an important gauge of the Creative. Nightlife is a

key indicator, especially one with a wide mix of experiential options. These include music

venues, neighborhood art galleries, performance spaces and theaters. Previous studies indicate

the highest-rated nightlife options were cultural attractions (from the symphony and theater to

music venues) and late-night dining, followed by small jazz and music clubs and coffee shops.

Bars, large dance clubs and after-hours clubs ranked much farther down the list (Florida, 2002, p.

225). Amenities such as historic buildings, boutiques, and non-franchised stores and restaurants

create an "authentic" environment, which contributes to unique and original experiences. Thus,










the Creative are attracted to unique environments and forms the framework for the fourth

indicator:

* Indicator #4: Identify locations proximal to cultural activities, historic structures, and
nightlife.

The Creative Capital Theory, developed by Richard Florida, states that regional growth is

driven by the location choices of creative people-the holders of creative capital-who prefer

places that are diverse, tolerant, and open to new ideas. The gay index signifies tolerance and is

measured by the number of gay individuals in an area. Florida' s research indicates that the gay-

index does better than other individual measures of social and cultural diversity to predict high-

tech location (Florida and Gates, 2002, p. 33); because high tech industries locate in diverse,

tolerant areas. The presence of gays in metropolitan areas signals diversity and progressive

environments. Furthermore, Richard Florida' s studies show that when compared to the Milken

Institute Tech Growth Index, which measures growth in output of high-tech industries within

metropolitan areas, the concentration of gays also indicate the potential for economic growth.

Thus the fifth indicator is drawn:

* Indicator #5: Identify areas with concentrations of gay populations.

Social cohesion and business ties are based upon trust and often facilitate inter-firm

cooperation and the exchange of ideas. Furthermore, diversity encourages participation and

serves as an asset in global economic advantage. Smallbone, Bertotti, and Ekanem (2005, p. 49)

consider ethnic diversity as a potential source of creativity and innovation as well as informal

networking. Creative industries that either locate within areas of highly diverse populations or

employ a diverse workforce are exposed to a blended knowledge base and cultural perspectives.

This creates international network links, which are a source of competitive advantage, and enable

these companies to use their cross-cultural knowledge and experience in product development










and marketing (Smallbone, Bertotti, and Ekanem, 2005, p. 49). Taube (2006, pp. 3-4) also

indicates that diversity creates an additional connection for non-local sources of information and

knowledge. The potential for additional economic growth due to embracing diversity is the

underlying premise behind the sixth indicator:

*Indicator #6: Identify areas with concentrations of diverse cultures.

Data Collection

GIS data used to measure land use suitability can be grouped into seven broad categories

(Zwick and Carr, 2007, p. 90). These categories are: geophysical, biological/ecological,

demographic, economic, political, cultural, and infrastructure (Table 5-3). Once the goals and

obj ectives were established, data was collected for each obj ective and sub-obj ective then mapped

and measured, using ArcGIS6, for spatial accuracy. It is important to note that this study used

the final suitability surfaces for the agriculture and conservation land uses that were previously

run by Carr and Zwick for the East Central Florida Regional Growth Vision. Therefore, there

was no need for this study to recollect data or run the models used for these two stakeholders.

Although, this study employed the same obj ectives and sub-obj ectives as the Carr and Zwick

model and included the following aspects: 1) we modified several of the inputs for the urban land

use; 2) added the additional Creative goal; and 3) reran the urban stakeholder. The following

discussion reflects those methods undertaken for the urban stakeholder.

Data Collection Methods for the Urban Stakeholder

The data collected for all three stakeholders is static (Table 5-1). For the urban

stakeholder, the data was available and collected from primarily two sources; the United States




6 ArcGIS is a family of software products produced by ESRI that form a complete GIS (Geographic Information
System) most often used by planners, developers and researchers (maps-gps-info.com, 2007)










Bureau of the Census (Census) or the Florida Geographic Data Library ("FGDL") 7. Continual

updates to FGDL provide a source of relatively recent and accurate GIS data. Temporal

attributes of FGDL data varies. Due to costs associated with the acquisition of current parcel

data and limitations of data distribution cited in Florida Statue 193.1 14 (5), the parcel data used

in this study represent platted parcels documented through 2004.

For each indicator whose source is the Census, 2000 decennial data is used. Spatially,

Census data is represented according to tract level, when possible. The attribute table for Census

data was edited to include fields that appropriately quantify each indicator. For example,

concentrations of demographic populations were measured using the appropriate Census

population indicator divided by the number of people in that tract. For indicators that measured

land value, values were calculated per acre according to each section-township-range as

identified by the Public Land Survey System. Common tools within ArcMap ArcToolbox were

used for data manipulation and analysis (Table 5-5).

Data obtained from FGDL was downloaded in either shapefile or raster format. Basic

vector and raster analysis tools (i.e. Select by Attribute, Merge, Append, Intersect, etc.) were

used to combine individual county datasets and clip state datasets to the extent of the region.

"Vector"s data were converted to "raster"9 format. Masters reduce model processing time and

greatly increase efficiency of the land-use modeling process (Zwick and Carr, 2007, p. 98).





SThe Florida Geographic Data Library (FGDL) is data warehouse that distributes GIS data free of charge for spatial
geographies for the entire State of Florida and its counties.
SVectors are coordinate based data models that represents geographic features as points, lines and polygons (Wade
and Sommer, 2006, p. 224).

9 Rosters are cellular GIS datasets constructed to represent spatial phenomena or geographic features within a
framework of uniform cells (Carr and Zwick, 2007a, p. 18).










Suitability Modeling

The third step makes use of ArcGIS ModelBuilderlo and methods developed by Carr and

Zwick to diagram and simulate land use suitability. This step also employs Spatial Analysis tools

to measure proximity and suitability of each quarter acre of land within the region with respect to

the values and bias of each stakeholder. Depending upon the intent of the obj ective and/or sub-

obj ective, models utilize various raster analysis tools to measure proximity. Commonly used

tools include Euclidean Distance, Extract by Attributes, Extract by Mask, Zonal Statistics, and

Reclassify.

Suitability is measured in units of utility value, also known as a single utility assignment

(SUA). The Reclassify tool within ArcGIS is used to assign a utility value to each cell" within

the dataset that represents an indicator. LUCIS employs a value range of 1 to 9 (Table 5-4), with

1 representing low suitability and 9 representing high suitability. NoData can be assigned to

unsuitable areas, but care must be taken because once used in a model, NoData will eliminate

attribute information from subsequent analyses for the cells containing the NoData value (Carr

and Zwick, 2007a, p. 103). For additional details and a step-by-step explanation of how to create

an SUA, refer to Chapter 8 in Carr and Zwick' s Smart Land'-Use Analysis: The LUCIS2~odel

(Carr and Zwick, 2007a).

Once a SUA has been created for each obj ective and/or sub-obj ective, the SUAs are

combined to create a simple multiple utility assignment (MUA). The MUA process combines



'o ModelBuilder is an application within ArcGIS in which you create, edit, and manage models (ESRI 2006). Input
data and geoprocessing tools can be strung together, with the output of one tool serving as the input for another, and
the whole model can be run as a single operation with the click of a button. With the ability to place GIS data and
geoprocessing tools in a visual program, the GIS analyst can create complex programs without having to learn a
programming language (Carr and Zwick, 2007a, p. 26).
11 A cell is the smallest unit of information in raster data, usually square in shape (Wade and Sommer, 2006, p. 27).
For this project each cell represents an area of 31 square meters, which is equivalent to a quarter acre.










layers using weights or percentage of influence that equal 1.0 (100%) (Carr and Zwick, 2007a, p.

57; p. 103). The MUA process can also include the use of conditional statements, which process

multiple raster data by selecting cells for the output raster based on "if-then-else" processing

(Carr and Zwick, 2007a, p. 37). A MUA was developed for each urban stakeholder goal.

Weighted Suitability

Before a Einal suitability raster for the urban stakeholder was created, a preference value

for each urban goal was developed, this is step four. The Expert Choice software was used to

develop a preference value using the analytical hierarchy process listed below, as outlined by

Carr and Zwick:

First, a model is created and the proj ect goal is stated. The goal for pairwise comparison is
a written statement defining pair comparisons. For example, "which habitat is more
suitable for residential development, A or B?" Second, all feature types within the dataset
are inserted as components of the overall goal. Then, each unique pair of habitat types is
compared for their usefulness in supporting residential development. Habitats are
compared using values from 1 (equally important/useful) to 9 (extremely more
important/useful). Next, the pairwise comparisons are evaluated within a matrix for all
pairs of values to produce Einal pairwise utility values. Last, the Einal pairwise utility
values are transformed into single utility assignment values ranging from 1 to 9 (Table 5-
5).

The AHP analysis produces a weight for each stakeholder goal. These goals are combined

using a map algebra equation (i.e. the Single Utility Assignment tool) according to the weight

produced from AHP. The result is a Einal preference map for each stakeholder. Each map

depicts areas preferred for a specific land use.

Conflict Identification

Step five identifies conflict. Three main tasks are required to complete this step: (1)

remove lands whose use will not change, (2) normalize and collapse preference results, and (3)

combine the normalized and collapsed preference rasters to identify areas of conflict (Carr and

Zwick, 2007a, p. 137).









The LUCIS model identifies preference values for various land use types and indicates

future changes in land uses, although there are areas whose land use are permanently designated

and will not change. These areas include open water, roads, existing urban areas (excluding

vacant platted), and existing conservation lands. A single raster mask is created from these

datasets and cells whose land use will not change are removed before identifying conflict.

A raster mask is created by converting the above datasets to a gridl2 USing the same cell

size, 31i, as used in all raster analysis associated with this proj ect. The following methods,

employed by Carr and Zwick indicate how to create a raster mask.

Using the Reclassify tool on each grid, NoData values are assigned to areas to be excluded
from consideration. All remaining cells are assigned a value of 1, thereby making all cells
with a value of I available for conflict analysis. Next, using the Single Output Map
Algebra tool, the three individual mask layers were multiplied to create a final mask of
areas available for future land-use development considerations. The final development
mask was then used in ModelBuilder to limit conflict analysis to those areas where
development actually has the potential to occur. (Carr and Zwick, 2007a, p. 138)

The final preference raster developed in step three for each stakeholder has values that

range between 1 and 9, but may not include the value 9. For a value of 9 to result, at least one

cell in the study area would have to be optimally suited for every measure of suitability included

in the goals, obj ectives, and sub-obj ectives for that land-use category (Carr and Zwick, 2007a, p.

139). The probability of this occurring is very low, so Carr and Zwick recommend normalizing

the values before comparing preferences (Carr and Zwick, 2007a, p. 139). The following

methods, taken from Carr and Zwick, are used to normalize preference surfaces:

The development mask is designated as a parameter in the ModelBuilder environment
settings, so normalization only occurs on the areas with development potential. Preference
normalization is accomplished using the Divide tool (Spatial Analyst Tools > Math
toolset), which divides each cell value in a land-use preference raster by the highest
individual cell value in that raster. The resulting raster will contain cell values ranging
from 0 to 1.0 (depending on the number of decimal places assigned to the calculation). At

12 A grid is a synonym for raster. The two terms are used interchangeably throughout this paper.









the extremes, it is possible the results might include as many unique values as there are
cells in the study area. On the other hand, there may be as few as two values; but usually
the normalization process results in a large number of unique cell values. (Carr and Zwick,
2007a, p. 139)

Once the surfaces are normalized, each of the three normalized rasters is combined into

three classes that correspond to high, medium, and low preferences. This method is called

"collapsed preference" and identifies the relationships among the three land uses. Collapsed

preference mapping identifies places where conflict between land-use categories exists and how

strong the conflict might be (Carr and Zwick, 2007a, p. 139). For this study, values were

reclassified using the standard deviation method. This method was chosen to produce as even a

distribution of preference values as possible. The distributed values are characterized using a

designation of 1, 2, and 3, which describe the level of preference.

To visualize the conflict between the surfaces of the three stakeholders, a method referred

to as conceptualizedd conflict" by Carr and Zwick was used. As mentioned above, the collapsed

preference surface is characterized using values of 1, 2 and 3. Cells with the value of 1 indicate

low preference, 2 medium preference, and 3 high preference. To combine all three surfaces into

one, each collapsed preference surface must be on a different scale. This paper utilizes the same

categories as Carr and Zwick: agriculture preference is collapsed to produce values of 100, 200,

and 300; conservation preference is collapsed into categories of 10, 20, and 30; and urban

preference maintains its current categories of 1, 2, and 3 (Carr and Zwick, 2007a, p. 149). Using

the Single Output Map Algebra function in ArcMap, a statement was developed that would

combine all three surfaces and multiply each respective surface by the appropriate factor to

achieve categories that would identify the conflict within that land use type (Equation 4-1).










Eq. 4-1: Equation to determine conflict

(Z:\thesis\Workspace\Results\collap_agri 100) +

(Z :\thesi s\Workspace\Results\collap_cons 10) +

(Z:\thesis\Workspace\Results\collap urb)

The values in the resultant Einal preference surface will range from 111 to 333. The first

digit in each number is representative of agriculture preference, the second digit representative of

conservation preference and the third digit represents the urban preference. Additionally, areas

of moderate conflict are identified when two land use types have the same preference value and

severe conflict occurs when all three land use types have the same preference value. For

example, preference value 122 has a moderate conflict between conservation and urban land

uses. Severe conflict exists with three of the potential preference value combinations, 1 11, 222,

or 333 (Table 5-6 and Figure 5-6).

The obj ective of this proj ect was to build upon the work done by Carr and Zwick for East

Central Florida to identify areas that, because of physical and economic characteristics, can be

described as "Creative Environments" and encourage the location of creative industries. A

Creative Environment is conducive to creative industries, which contribute to attracting a higher

level of jobs and increase the quality of place. The Einal preference surface described above

identifies areas that are more appropriate for a specific land use. Included in areas where "urban

preference dominates" are residential, commercial, industrial and creative environments.

To distinguish areas that are most appropriate for urban development but are most

appropriately categorized as Creative Environments (as opposed to residential or industrial), the

next step requires us to remove from the final preference map areas that are preferred for urban

uses (i.e. Urban "Wins") and areas suitable for Creative Environments (Figure 5-3).










Urban Goal 5 represents the suitability surface for Creative Environments. Areas

identified with a value of 813 Or higher indicate a high degree of suitability. We use a

conditional statement to identify areas in Urban Goal 5 that have values of 8 or higher and

intersect with those areas in the final preference surface that are suitable for urban development

(Equation 5-2).

Eq. 5-2. Equation to Determine Creative and Urban Environments

CON (Z:\thesi s\Workspace\Results\final_prefl4 == 112 or

Z:\thesi s\Workspace\Results\final_pref == 113 or

Z:\thesi s\Workspace\Results\final_pref == 123 or

Z:\thesi s\Workspace\Results\final_pref == 213 or

Z:\thesi s\Workspace\Results\final_pref == 223 AND

con(Z:\thesi s\Workspace\Results\creative\ug5_inal GE 8, 9))

Among the four classifications (i.e. residential, retail, office/commercial, and industrial)

within the definition of"urban" represented by the Carr and Zwick conflict surface, creative

occupations more generally fit within the description of office/commercial. The specific

occupations that place great value in working in an environment classified as creative are:

* Computer and mathematical occupations

* Architectural and engineering occupations

* Life, physical, and social science occupations

* Education, training and library occupations


13 Earlier discussion indicated that the suitability values assigned by Carr and Zwick range between 1 and 9, with 9
as the highest suitability. In this study, the suitability surface for Urban Goal 5 resulted in a high value of 8.625.
Therefore, when examining areas within Urban Goal 5 that are most suitable for Creative Enviromnents, we
consider those areas greater than 8. If our suitability surface had values equal to 9, then we would only seek to
separate out those cells with values of 9.
14 The file name "finalgpref' is the raster grid which represents the final conflict surface.