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THE USE OF GIS IN ALLOCATING EMPLOYMENT CENTERS THAT MINIMIZE LAND
USE CONFLICT AND SATISFY REGIONAL ECONOMIC POTENTIAL
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
O 2007 Iris E. Patten
To all the tortoises in the world: you really can beat the hares.
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
ACKNOWLEDGMENTS .............. ...............4.....
LIST OF TABLES ................ ...............8............ ....
LIST OF FIGURES .............. ...............10....
LIST OF EQUATIONS ................. ...............11................
AB S TRAC T ............._. .......... ..............._ 12...
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
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
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
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
Iris E. Patten
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
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
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.
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.
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
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,
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.
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
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.
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
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,
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
Strategy / Regional
San Francisco Proj ect 2020 2003 www.bayareavision. org
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
GIS Base Layers
Tax Lots Propet assessment tax lots
Streets Streets, highways, bus/light rail lines, bike
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
Places Hospitals, schools, police, etc.
Building prit Location of issued prmits
U.S. Census Census data for 1980, 1990 and 2000
Rivers, streams, wetlands, and watersheds Location and attribute information for water
Tree canopy and land cover Urban forest canopy and vegetative/other land
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
Step 1 Calculate the number of acres inside the Metro Urban Growth Boundary
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,
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
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-
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
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 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
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 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)
CENTRAL FLORIDA GROWTH VISION
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
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,
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
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
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
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
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
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
New urban development is mostly placed
outside sensitive areas and habitat.
20% of new growth will occur in
80% of new growth to green fields; use
development preference map for
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
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
o DOT commuter rail enhanced
o Additional light rail and
* Planned Florida Interstate and Strategic
Inter-modal System Roads, 2025-2030
Long Range Transportation Plan Cost
Feasible Plans, Active Freight Rail,
* Seven "environmental jewels" mostly
* 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
(Adapted from the Laurien, 2007)
? Central Florida's Four Futures.
County 2005 BEBR 2050 Trend 2050 Geen Areas Centers 2050 Corridors 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
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)
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-
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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)
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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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
(ml~l Erids m elag well .i
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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%
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
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:
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:
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
5. Create a range of housing choices
6. Develop a diverse economy
-% % 11 96 2 %
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
Figure 3-6. Continued
(Reprinted with permission from myregion.org. (2007). Scenario Descriptions and' Survey
Ballot. Retrieved April 20, 2007, from www.myregion.org)
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.
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.
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
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
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.
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
Palm Ba 91,888 17.2%
Melbourne 75,060 14.1%
Unincorporated 210,260 39.5%
Clermont 20,017 7.6%
Leesburg 17,467 6.6%
Unincorprated 146,221 55.6%
Orlando 217,567 20.8%
Apoka 34,801 3.3%
Unincorprated 677, 185 64.9%
Kissimmee 58,223 24.8%
St. Cloud 24,700 10.5%
Unincorprated 152,233 64.7%
Lakeland 90,851 16.8%
Winter Haven 28,724 5.3%
Unincorprated 338,250 62.4%
Sanford 49,252 12.0%
Altamonte Springs 42,616 10.4%
Unincorprated 203,021 49.3%
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
United Space Alliance NASA Space Flight 6,500
Harris Corporation International 6,500
Health First, Inc. Integrated Healthcare 6, 100
Space Gateway Base Operations for 3,000
Support NASA & 45th Space
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
Table 4-3. Continued
County Employer Business Line Number of Employee
Leesburg Regional Healthcare 2,300
Village of Lake Retirement 2,200
Sumter, Inc. Community
Florida Hospital Healthcare 1,400
Sprint Telecommunications 811
G& T Conwyar N/A 550
Bailey Industries Manufacturing 509
Accent Architecture 500
Dura-Stress Concrete Supply & 425
Lake Port Square N/A 400
Casmin Incoprated Construction 300
Walt Disney World Entertainment 53,500
Orange County Public Education 22,807
Adventist Health Healthcare 17,059
Universal Orlando Entertainment 14,500
Orlando Regional Healthcare 12,000
Orange County Government 6,577
Lockheed Martin Combat System 5,700
Central Florida Real Estate 5,000
University of Central Education 4,808
Darden Restaurants Corporate 4,675
Table 4-3. Continued
County Employer Business Line Number of Employee
McLane/Sunset, Inc. N/A 900
Florida Hospital Healthcare 794
Osceola Regional Healthcare 522
Hyatt Orlando Hotel/Resort 500
Walt Disney Artistic Production 450
Splendid China Amusement Park 400
Orange Lake Resort Resort & Country 400
& Country Club Club
Mercury Marine Maine Electronic 400
Tupperware Housewares 300
Lerio Corporation Plastic Products 120
Publix Sue Markets Retail Food 8,500
Wal-mart Retail General 5,500
Lakeland Regional Hospital/Medical 4,000
MOSAIC Phosphate Mining 3,000
Winter Haven 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 Citrus Processors 1,000
Table 4-3. Continued
County Employer Business Line Number of Employee
Seminole County Education 8,824
Convergys Billing Software 1,747
Seminole Community Education 1,673
Sprint PCS Telecommunications 1,550
Siemans ICN Telecommunications 1,500
Seminole County Government 1,247
First USA Credit Card 1,200
U.S. Postal Processing Postal Service 1,000
American Automobile Travel Services 825
Florida Hospital Healthcare 800
Volusia County School Board 8,998
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
Florida Hospital Medical 1,403
Daytona Beach Community College 1,334
Winn Dixie Stores Inc Grocr 1,290
(Adapted from Enterprise Florida. (2007). County Profiles. Retrieved April 20, 2007, from
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
Brevard Melbourne- 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/ 227,493 187,099 82.2% 37,864 16.6%
Seminole Orlando 198,737 189,913 95.6% 8,037 4.0%
Volusia Daytona 203,068 158,579 78.1% 40,796 20.1%
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
Brevard I-95 US-1, US 192 SR-A1A; 5; 46; Florida East Coast
50; 405; 407; Railway
501; 520, 524;
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,
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,
(Adapted from Enterprise Florida. (2007). County Profl es. Retrieved April 20, 2007, from
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
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
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
(Adapted from Enterprise Florida. (2007). County Profiles. Retrieved April 20, 2007, from
http://www.eflorida.com/countyprofiles/Counyrflsaplv 1 =3&level2=127&level3
SEast Central Florida Region
SExisting Conservation Lands
Figure 4-1. Study Area, 7 county East Central Florida region
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
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
* 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-
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").
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
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
* 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
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 #4: Identify locations proximal to cultural activities, historic structures, and
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.
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).
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
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.
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-
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.
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) +
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
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.