6.3 Land Use Policies' Impacts On

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2017 Urban Growth Patterns and Drivers in Florida, the United States: Parcel-Based New Measures and Modeling of Multi-Scale Factors Guang Xing

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COLLEGE OF SOCIAL SCIENCES AND PUBLIC POLICY

URBAN GROWTH PATTERNS AND DRIVERS IN FLORIDA, THE UNITED STATES:

PARCEL-BASED NEW MEASURES AND MODELING OF

MULTI-SCALE FACTORS

GUANG XING

A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy

2017 Guang Xing defended this dissertation on June 19 2017.

The members of the supervisory committee were:

Tingting Zhao

Professor Directing Dissertation

Richard Feiock

University Representative

Xiaojun Yang

Committee Member

Christopher Uejio

Committee Member

The Graduate School has verified and approved the above-named committee members, and

certifies that the dissertation has been approved in accordance with university requirements.

This dissertation is dedicated to my parents.

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ACKNOWLEDGMENTS

First, I would like to thank my advisor, Dr. Tingting Zhao. She is a great mentor and has always been smiling no matter how silly the questions I asked. Her knowledge, patience and warm- hearted personality make me enjoying my PhD time at FSU. This manuscript would have been impossible without Dr. Zhao’s guidance and support. I want to acknowledge Dr. Xiaojun Yang,

Dr. Richard Feiock, Dr. Christopher Uejio on my committee for providing additional guidance and associated training through taking courses with them. I want to give sincere thanks to Dr.

Tim Chapin, Dr. David C. Folch and Dr. James B. Elsner for many fruitful class discussions related with my dissertation. In addition, I want to thank my friends at FSU, Fang Zhang, Di Shi,

Xinyu Gao, Yingru Liu, Holly M Widen, AmirSasan Mahjoor, Karen Wertz, Sarah Strazzo, Luis

Santiago and many others. Finally, I would like to thank my parents and my husband. They have always been there to support and encourage me.

TABLE OF CONTENTS

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

ABSTRACT ...... ix

CHAPTER 1 INTRODUCTION ...... 1

1.1 Motivation ...... 1

1.2 Objectives ...... 6

1.3 Significance of the dissertation ...... 8

CHAPTER 2 BACKGROUND/LITERATURE REVIEW ...... 11

2.1 Defining urban sprawl ...... 11

2.2 Measuring urban sprawl ...... 12

2.3 Spatial/landscape metrics ...... 18

2.4 Urban growth driving forces ...... 20

2.5 Analysis/modeling of growth forces ...... 21

CHAPTER 3 STUDY AREA AND DATA ...... 25

3.1 Metro Orlando and its principal cities ...... 25

3.2 MSAs in Florida ...... 27

3.3 Florida cities ...... 32

CHAPTER 4 METHODOLOGY ...... 38

4.1 Measuring urban sprawl within a metropolis ...... 38

4.2 Measuring and comparing urban sprawl across metropolitan areas ...... 44

4.3 Global OLS and local GWR analyses of urban growth factors ...... 48

4.4 Multilevel analysis of urban growth drivers ...... 53

CHAPTER 5 RESULTS ...... 61

5.1 Urban sprawl in Metro Orlando ...... 61

5.2 Urban sprawl across Florida’s four metropolitan statistical areas ...... 72

5.3 Urban growth drivers across Florida cities: A study at city scale ...... 82

5.4 Urban growth drivers examined with multilevel modeling approaches ...... 88

CHAPTER 6 DISCUSSION ...... 97

6.1 The urban sprawl measures ...... 97

6.2 Parcel data ...... 102

6.3 Land use policies’ impacts on urban growth ...... 107

6.4 Multilevel modeling ...... 110

6.5 Future works ...... 111

CHAPTER 7 CONCLUSION...... 113

REFERENCES ...... 117

BIOGRAPHICAL SKETCH ...... 129

LIST OF TABLES

Table 3.1 Description of physical and demographic characteristics of four MSAs in Florida ..... 30

Table 3.2 Data sources used in the studies and their brief descriptions ...... 35

Table 3.3 Description of urban growth related variables at multiple levels ...... 36

Table 4.1 Cities’ classification scheme by population range and buffer radius ...... 50

Table 4.2 Description of variables in multilevel model ...... 56

Table 5.1 Summary of sprawl indices of density, mixed use and accessibility and overall sprawl index for the cities of Orlando, Kissimmee and Sanford ...... 67

Table 5.2 Means and standard deviations of average livable space per residential unit ...... 72

Table 5.3 Means and standard deviations of land use diversity ...... 75

Table 5.4 Means and standard deviations of accessibility to business hubs...... 79

Table 5.5 Sprawl Z scores and ranking ...... 81

Table 5.6 T test results for the three city groups in terms of four land use policy actions ...... 83

Table 5.7 OLS global model results for the three city groups ...... 84

Table 5.8 T test results at block group level for three city groups regarding four land use policies ...... 89

Table 5.9 Testing for city variation effects of three groups of cities ...... 92

Table 5.10 Multilevel models with block-group level variables for three groups of cities ...... 93

Table 5.11 Multilevel models with both block-group level and city level variables for three ..... 94

Table 6.1 Comparison of our sprawl ranking (2012) with Ewing’s sprawl ranking (2010) for the four studied Florida metropolitan areas ...... 102

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LIST OF FIGURES

Figure 3.1 Geographic location of Metro Orlando, and its three principal cities ...... 26

Figure 3.2 Geographic locations of the studied metropolitan areas ...... 29

Figure 3.3 Geographic location of Florida state and the surveyed cities ...... 33

Figure 4.1 The multilevel model framework on local urban development ...... 55

Figure 5.1 Spatial distribution of cell-based residential densities of four urban-rural categories for the cities of Orlando (a), Sanford (b), and Kissimmee (c) ...... 62

Figure 5.2 Statistical pattern of four types of residential densities cells against relative distance to city center for the cities of Orlando (a), Kissimmee (b) and Sanford (c) ...... 63

Figure 5.3 Distance of peak development is indicated with the vertical dashed line for each of the three principle cities. It was determined so that 75% of the urban-density cells are located within this relative distance measured from city center...... 64

Figure 5.4 Land-use diversity was counted as the total number of land-use types within each ... 65

Figure 5.5 Land-use evenness was calculated using Shannon’s entropy index, which ranges between 0 and 1 with a higher value indicating higher degree of mixed use...... 66

Figure 5.6 Proportions of the cells with low and high diversities for the cities of Orlando, Kissimmee and Sanford ...... 68

Figure 5.7 Proportions of the cells with low and high evenness for the cities of Orlando, Sanford, and Kissimmee ...... 68

Figure 5.8 Economic centers of service parcels for the cities of Orlando (a), Sanford (b), and .. 69

Figure 5.9 Distributions of the REDs from the residential-dominated land parcels to its nearest economic centers and their associated “separation” lines of 75th percentile of the REDs for the cities of Orlando (a), Sanford (b), and Kissimmee (c) ...... 71

Figure 5.10 Sprawl index of average livable space per residential unit on 1-km2 USNG cells ... 73

Figure 5.11 Sprawl index of land use diversity on 1-km2 USNG cells ...... 77

Figure 5.12 Sprawl index of accessibility to business hubs on 1-km2 USNG cells ...... 80

Figure 5.13 GWR for all-sized cities, small-sized cities and medium/large-sized cities ...... 87 viii

ABSTRACT

Urban growth or sprawl has been an interesting research topic for contemporary urban studies. The availability of remote sensing and GIS techniques facilitate a large number of empirical and practical studies in addition to traditional theoretical research. From definition, to spatial measures, to exploration of the driving forces and modeling/forecasting of urban growth or sprawl, this research topic has received increasing attention in multiple disciplinary fields, such as geography, urban planning, public policy and administration, environment science, and public health etc.

The subtopics of urban growth or sprawl are broad and multidisciplinary. In my dissertation research, I focused mainly on spatial aspects of urban growth and sprawl through examining their patterns and investigating driving forces. My approach integrated data from multiple sources at various geographic scales (ranging from parcels at individual housing scale to land-use policy survey data at city scale) and utilized GIS techniques and statistical methods.

The first two dissertation chapters provide an introduction of urban growth/sprawl issues and literature review of previous research. The remaining chapters present four major studies, with the first two focused on devising new urban sprawl measures dedicated to utilizing information-rich, fine-scale parcel data for urban sprawl assessment at aggregated scales. Census population/housing and remote sensing land-cover data have been used to characterize urban growth and urban sprawl effectively but not without limitations. These data at the aggregated level usually remove detailed information on housing types (such as single vs. multi-family residence), unit size, and land-use purposes (such as commercial vs. residential uses). With the increasing availability of parcel data especially in the United States, scholars began to explore

and utilize these fine spatial/temporal data with full-detailed attributes (such as land-use type, total living space, number of residential units, the actual year built, and etc.) for urban growth research. To take advantage of the information-rich fine-scale parcel data, two sets of new urban sprawl measures were designed to characterize urban sprawl patterns from different perspectives.

The two sets of sprawl measures are introduced in the dissertation as two relatively independent studies, given the variation of measures as well as distinctive study areas. For each set of sprawl measures, three indices were created (with some level of overlap) to capture urban sprawl from the aspects of housing characteristics (development density or housing size), land use diversity, and accessibility to business hubs. These measures of urban sprawl are based on fine-scale parcel data; and are able to capture patterns of sprawl at the city and metropolis levels. Our measures are easily transferable to cities of different development patterns and allow comparison across cities of various dimensions. They may also be used to compare growth of a city or metropolis in time sequence.

The third and fourth studies explore urban growth drivers that integrate factors such as socioeconomics, environment, and sustainable urban development policies using Geographic

Weighted Regression (GWR) as well as statistical multi-level modeling approaches. Single-level linear regression model is a common approach to examine the relationships between urban growth and associated driving factors. In the third study presented in this dissertation,

Geographic Weighted Regression (GWR) analysis, single-level model that takes into consideration of spatial adjacency and variation, was used to explore each driving factor’s influence on urban growth. The potential driving factors were first examined by a global OLS

(Ordinary Least Square) model to identify their global influential trends on urban growth across

cities in Florida. Then, a local GWR model is applied to detect local variations of these urban

growth drivers for cities at different locations. In the fourth study presented in this dissertation, a multilevel linear regression model frame was developed and applied to exploring impacts of

urban growth driving factors on urban development, attempting to capture influences at both city

and finer geographic scales. First, the null model was built to examine whether cities differ from each other on urban development, and then variables derived from census block-group scale were included to examine their relationships with urban development, and lastly variables derived from city scale were added in the multilevel model to further examine their relationships with urban development together with census block-group level variables. Compared to the traditional single-level linear regression model, multilevel modeling is a relatively new method to be used in analyzing urban growth and the associated driving factors.

Overall, the entire dissertation work enriches research of urban growth and urban sprawl,

in particular the measurement and modeling perspectives from the geography stance. The first

two studies present an innovative research attempt that suits well with the era of big data, which

geographers can provide unique contribution given the nature of the data we constantly handle.

The third and fourth studies target on the unknown relationship between fine-scale empirical

observation of urban growth (based on remote sensing data) and meso-scale land-use regulation

(based on survey data). This makes these studies unique in terms of integration of knowledge

gained in geography, urban planning, and public administration. Finally, the results of our

analysis benefit urban planners and policy makers through quantitative assessment of levels of

urban growth/sprawl, which provides a knowledge base for their planning or design of

sustainable urban development in the future. They may also benefit from our integrated

assessment of urban growth drivers, in particular the effectiveness of individual policies on curbing urban growth.

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CHAPTER 1

INTRODUCTION

1.1 Motivation

Urban growth, the expansion of cities and their suburbs, is commonly seen in many metropolitan areas across the world, due to urgent need of a wide variety of purpose of urban land use, such as residential, commercial, and industrial land uses, etc. It appears to be inevitable due to the rapid increase of urban population, enhanced road network and other municipal facilities, and insufficient or failing land-use regulations. The studies of urban growth started first in the US and European countries, and gradually extended to some other populous countries with fast growing population and increasing economic development, such as China, Indian, and

Turkey etc. throughout the world. Urban growth has become an international characteristic of urban growth in the 20th century (Stomp 2013).

In the United States approximately 220 million people dwelt in urbanized areas in 2010, more than twice the number of urban residents in 1950 (U.S. Census Bureau 1990). Such rapid addition of urban population, accompanied by enhanced transportation networks and extending municipal facilities, has resulted in a significant amount of growth in built-up land. From 1982 to

2010, development from rural lands (such as rangeland, forest, and crop land) increased by 58% nationwide (U.S. Department of Agriculture 2013). Most often those new built-up land uses were placed in medium to low densities at the outer skirt of existing urban areas or along rural transportation corridors. This has contributed to urban sprawl, a phenomenon characterized by decentralization of city population (Berube 2003; Birch 2003) as well as spread of suburbs and exurbia (Kotkin 2005; Carruthers and Vias 2005).

Descriptions of urban sprawl from urban planning and geography literature, include, but not limited to, that 1) urban sprawl is a type of suburban spreading development with negative outcomes (Batisani and Yarnal 2009), 2) urban sprawl is an improper suburban development with expanded costs on facility and sacrificed protect natural resources and agricultural lands

(Florida Growth Management Plan 1993), and 3) urban sprawl is the intrusion of low-density development into rural and undeveloped areas (Burchell and Shad 1999). In most cases, we know it is urban sprawl when we see it. Jaeger et al. (2000) stated that urban sprawl is visually perceptible. A landscape experiences urban sprawl if it is invaded by urban activities such as constructing roads and buildings. The more urban area present in a landscape and the more dispersed the urban patches, the higher the degree of urban sprawl.

According to related urban growth studies (Chapin 2007), the state of Florida has experienced rapid population growth and urban sprawl development since 1980s, and the trends of both are continuing. If governors of Florida do not take any effective actions such as incentives or regulative policies to discourage low-density urban development, by 2060, more than 7 million acres of current non-urban lands will be converted into urban lands (Florida 2060

Report 2006).

1.1.1 Drivers and impacts of urban growth/sprawl

Previous literature has discussed numerous factors from either socioeconomic or other aspects that may cause urban growth and/or sprawling development. The most influential factor that is population growth (Florida 2060 Report, 2006; Bhatta 2010; Burchfield et al. 2006). Other factors include topography, economic growth, industrialization, environmental issue, living and property cost, lack of affordable housing, demand of more living space, transportation, single-

family home, credit and capital market, government developmental policies, lack of proper

planning policies, housing investment, and large lots size (Bhatta 2010; Burchfield et al. 2006;

Squires 2002; Harvey and Clark 1965). Four categories of factors are concluded to have

influenced urban sprawling development.

1.1.1.1 Demographic growth

Bhatta (2010) stated that rapid urban sprawl is the result of fast population growth

including natural population increase and vast foreign residents’ migration (internal and

international migrations). Urbanization is a process of population concentration, and more and more people move from the countryside to the city directly cause rapid urban expansion (Skoga and Steinnes, 2016). According to FAO and ITOPS (2015), more than half of the world’s population was living in urban areas in 2014.

1.1.1.2 Physical constraints

Physical conditions, such as elevation, slope, road density, etc. are commonly used in urban growth/sprawl modelling (Bhatta 2010). City with natural barrier conditions (such as

wetlands, steep slopes, lakes, geological fragile zones, flooding zones, etc.) may cause urban

develops discontinuously. Besides, road networks also can shape urban development footprints.

A number of studies has explored the impacts of road networks and physical terrain conditions

on urban sprawling development (Lo and Yang 2002; Tsou et al. 2015). Harvey and Clark (1965)

mentioned that over developing highways and expressways makes urban grow faster outwards.

Road networks make people freely move within city, between city and the countryside, and

among different cities, which makes people can live far away from city therefore increases urban

sprawl.

1.1.1.3 Economics factors

Rapid economic growth (such as higher salary, more employment opportunity, etc.)

needs more housing, working places and associated other types of infrastructure to be built up.

The construction process inevitably produces various unorganized, discontinuous developments.

Poor coordination between developers and governments even makes this situation to be much

worse (Bhatta 2009b). Cheaper land price far away from urban center encourages most

industrials choose to build single-story, low-density industrial parks on the urban fringe, which

causes sprawling development. Land investments from developers and individuals make large

amount of urban lands vacant within the core city area, which causes the city grows more sprawl

outward (Harvey and Clark 1965).

1.1.1.4 Other factors

Personal preferences, such purchasing single-family home, buying large lot size, needing more living space, etc., determine urban sprawling growth patterns to some degree (Bhatta 2010).

With the decrease of household size and chasing high quality living environment than before

(Perry 2014), more people choose to live in single-family homes, which provide more privacy and more living space. The vast single-family residents boost low-density residential development on the city edge.

Harvey and Clark (1965) pointed out that places outside of city boundary is less controlled and loosely regulated, therefore they are better choices for developers and individuals for new housing construction. Either poor land use policies or failure to implement planning policies may cause urban spread out development. For example, inappropriate planning policies may cause separation of uses, and people have to take long-distance travel among residential,

commercial and working places. Urban land uses invade into nature/agriculture land which

causes sprawl is also related with failed implementation of land use policies ((Bhatta 2010)).

Urban sprawling development introduces a city with many harmful impacts (Johnson

2001; Ewing et al. 2003; Streutker 2003). The major common negative effects caused by urban

sprawl include climate change, impairing natural environment, society inequity, arising

individual health issues etc. and can be categorized as sprawl-caused directs impact and sprawl- caused indirect impacts.

1.1.1.5 Sprawl-caused direct impacts

Ewing et al. (2003) compared people’ average driving distances between top ten most sprawling metropolitan areas and ten least sprawling metropolitan areas, and found that people in relative more sprawling areas drive about 7 miles more than less sprawling areas. Long-distance driving consumes more gasoline, and increases more emission of CO2, also results in traffic congestion, and causes more traveling time on commuting between home and offices. Frunmkin

(2002) stated that urban sprawl encourages more cars being used, thereby producing more air pollutants, such as CO, CO2, ozone, Sulphur dioxide, nitrogen oxide, microscopic particles, etc., which create smog, acid rain, impairing air quality. There are existing studies showing that temperature increases in the sprawled area (Weng et al. 2007). Increased city size with increasing number of habitants causes increasing urban temperature.

Zhao (2013) explored the impact of sprawl on social segregation in Beijing, China. The study showed that low-density sprawling urban development with uneven distributions of public facilities and transportation infrastructure in peri-urban area, can increase residential segregation between residents with different levels of income.

1.1.1.6 Sprawl-caused indirect impacts

Many studies pointed out that sprawl produce many negative impacts on public health

(Ewing et al. 2014; Frumkin 2002; Savitch 2003; Yanos 2007; Sturm and Cohen 2004). Sprawl- caused air pollution can cause severe breathing problems, skin diseases, etc. Sprawl-caused traffic congestion can increase divers’ anxiety and stress. Sprawl-caused city overheating can

make people become weakness, even incur severe illness. Ewing et al. (2014) conducted multiple

regression analysis to explore urban sprawl’s impact on human health. The study showed that

urban sprawl has strong positive relationships with obesity, high blood pressure, and other health

conditions.

1.2 Objectives

The first objective of my dissertation is to design new urban sprawl measures to identify urban sprawling pattern by making use of parcel data (a type of high spatial resolution GIS data).

This objective is completed by conducting two independent studies from different perspectives.

The two sets of sprawl measures are calculated at micro scale (i.e.1 km2 grid), then aggregated to macro scale (i.e. city/metropolitan area). The first set of new sprawl measures include distance of peak development (based on housing densities) and residential accessibility to urban economic centers. The two newly-created distance indices and two literature-based mixed land use indices are applied to measuring levels of urban sprawl in the three principle cities (including cities of

Orlando, Kissimmee and Sanford) in the Orlando Metropolitan Area. The second set of new urban sprawl indices include average living space per residential unit, land-use diversity, and accessibility to business hubs They are applied to measuring and comparing levels of urban

sprawl across major Metropolitan Statistical Areas in Florida, which include metropolitan

statistical areas of Jacksonville, Tampa, Orlando and Miami. The first set of sprawl indices are

based on the percentage of micro-scale units inside each macro study area with sprawling status,

which are the relative values of urban sprawl; while the second set of sprawl indices are achieved

through weighted averaging all sprawl values based on the micro-scale units inside each macro

study area, which are the actual sprawl values. Finally, we compare the sprawl levels of

cities/metropolitan areas by using these sprawl measures.

The second objective of my dissertation is to analyze the driving factors contributing to urban growth. A number of potential factors (e.g. population, topography, economy, policy, etc.) may have promoted or prohibited dispersed urban development. Our goal is to identify the dominant factors on sprawl in selected surveyed cities in Florida. We proposed two different research methods to explore urban growth driving factors’ influences on urban development, which include single-level linear regression model and multilevel linear regression model.

Single-level linear regression model is a common way to build the relationships between urban growth and associated driving factors. Different from the existing studies, in my dissertation,

Geographic Weighted Regression (GWR) analysis is being added to explore local variation of each driving factor’s influence on urban growth. Firstly, we construct a single-level linear

regression model frame to explore urban growth driving factors among the surveyed cities in

Florida, which participated in the statewide Florida Energy Sustainability Survey. Four variables

of sustainable urban development policies from the survey as well as five other variables from

the physical, demographic, socio-economic, and environmental are calculated at city scale to be

as the independent variables in the single-level linear regression model. These potential driving

factors are first examined by a global OLS (Ordinary Least Square) model to identify their global

influential trends on urban growth across the Florida cities. Then, a local GWR model is applied

to detect local variations of these urban growth drivers for cities at different locations. Compared

to the traditional single-level linear regression model, multilevel modeling is a pretty new method to be used in analyzing urban growth and the associated driving factors. Secondly, we construct a multilevel linear regression model frame to explore the impacts of urban growth driving factors on local urban development at both city scale and block-group scale. We use the same surveyed cites of Florida, and the associated block groups to build the multilevel model.

The associated driving factors as the independent variables in the multilevel regression model are partially directly from our previous single-level regression model at city scale, such as the variables of population, protected land, and the sustainable land use policies; others variables are newly built at block-group scale, such as local-scale urban development, topography, road density, median housing value, and city-scale unemployment status. First, we examine city effects on local urban development; and then we analyze block-group level variables’ effects on local urban development; lastly we analyze block-group and city level variables’ effects on local urban development. The proposed analysis will help to identify factors that city planners or decision makers need to consider when plan future urban development in a sustainable way.

1.3 Significance of the dissertation

Fast urban growth and, in particular, sprawling development inevitably impacts our environment, community and neighborhood health. It also contributes to changes in microclimate in urban area, increases the transportation burden, and brings excessive consumption of energy to support sprawled urban development. Therefore, research on urban growth and sprawl is of great importance to shed light on curbing undesired unsustainable urban

development. My dissertation provides new methods to quantitatively measure levels of urban sprawl. Besides, impacts of land-use regulation policies were examined and evaluated with empirical, fine-scale urban growth data. Both serve as direct knowledge base for understanding forces and status of urban growth/sprawl.

One of the intellectual merits of our research is that we use parcel data, a type of big data,

which is recently freely accessible to the public. Compared to commonly used census

population/housing and remote sensing land-cover data, parcel data at fine spatial scale can

provide exclusive attributes for each land parcel, (such as land-use type, total living space,

number of residential units, the actual year built, and etc.), and can be directly used to urban

sprawl calculation without any additional data source needed. Besides, these sprawl measures are

easily replicable and transferable for analysis across regions or along timeline. Another

intellectual merit is that these sprawl indices aggregated from individual housing unit to 1-km

spatial resolution (much finer that the traditional analyses based on Census and/or remote

sensing data) provide useful information on spatial heterogeneity of urban growth/sprawl within a neighborhood, city, county, or metropolis. These indices may be easily aggregated and, hence, comparable with other neighborhood- or city-level research. The advantage of using multilevel modeling approaches is that it enables effects of driving forces at different spatial scales to be considered and captured in one model of hierarchy.

In terms of the broader impacts of this research, the methods proposed in my dissertation can be extended to solve other research questions. For instance, the proposed sprawl indices can be used to compare sprawl levels of different cities/metropolitan areas or identify trends of sprawl in a city/metropolitan area. My current modeling of driving forces is for urban growth. In

the future, the multilevel model may be adapted to focus specifically on urban sprawl and determine relative importance of its driving factors. Therefore, it helps urban planners and decision makers to create or refine land use policies/regulations to promote urban sustainable development in future.

CHAPTER 2

BACKGROUND/LITERATURE REVIEW

2.1 Defining urban sprawl

Currently, there is no universal definition of urban sprawl. The definition of urban sprawl has many different versions. There are some classic definitions from our geographers and other interdisciplinary, such as urban planning and public policy. For example, urban sprawl is used to describe the phenomenon which combines the characteristics of “low density”, “ribbon or strip”,

“scattered”, “leapfrog” or “isolated” development (Nelson et al. 1995; Galster et al. 2001) defined sprawl as “a pattern of land use in an urbanized area that exhibits low levels of some combination of eight distinct dimensions: density, continuity, concentration, compactness, centrality, nuclearity, diversity, and proximity.” Ewing (1997) defined sprawl as “low density development, strip development, and/or scattered or leapfrog development.” Later, Ewing et al.

(1997) extended the sprawl development definition as “low density, poor mixed land use, lack of thriving activity centers, and poor accessibility.” Altshuler and Ibanez-Gomez (1993) defined that sprawl is continuous low-density residential development at the edge of the metropolitan areas, ribbon-style development along suburban highways, and leapfrog-pattern development with leaving isolated undeveloped area in urban areas. Urban sprawl has been defined as growth by the creation of new low-density suburbs with detached or semi-detached housing and large commercial strips (Schneider and Woodcock 2008; Schwarz 2010). Some people defined that urban sprawl is a special type of urban growth by creating new low-density suburbs with large- size-lot housing and large commercial strips (Inostroza et al. 2013; Schneider and Woodcock

2008; Schwarz 2010).

According to vast literature review, characterization of urban sprawl is still going on.

Torrens (2008) argued that defining urban sprawl in the literature is narrative and subjective, as well as measuring sprawl is data-driven. In most cases, sprawl is often defined based on its costs

(Torrens 2008; Benfield et al. 1999; Burchell et al. 1998). Rarely people define urban sprawl based on the benefits (Torrens 2008; Bae and Richardson 1994; Gordon and Richardson 1997).

When people define urban sprawl, some key words are frequently repeated in their literature, such as density, accessibility, fragmentation, green space, decentralization, aesthetic, and etc.

(Bhatta 2010). Bhatta (2010) claimed that the concept of urban sprawl is difficult to be defined, and is often discussed without any associated accurate definition. Yang (2011) said there are many versions of definitions of urban sprawl. How to define urban sprawl varies from person to person, and depends on people’s understanding about sprawl. Wilson et al. (2003) stated that more people would summarize urban sprawl phenomenon instead of define urban sprawl accurately.

Even though how to define urban sprawl more accurately and is controversial, it needs to be defined when people study how to measure it at some degree. Based on the previous literatures, in our study, we define urban sprawl as low-density, scattered development that usually accompanies low level of mixed land uses and excessive automobile transport.

2.2 Measuring urban sprawl

In terms of measuring sprawl, many academics published their works, which involves a wide range of studies. In this section, we mainly review urban sprawl measures based on different data types commonly used by people to measure urban sprawl degree, and discuss the methods of how people to measure for density, mixed land use and accessibility which are the

most typical sprawl characteristics defined in our study and frequently appeared in other researchers’ studies.

2.2.1 Remote-Sensing-data-based sprawl measure

Yeh and Li (2001) used Shannon’s entropy method to measure urban sprawl with the integration of RS and GIS, which showed the degree of urban sprawl regarding urban land use form. Bhatta (2009) identified urban growth pattern changes of Kolkata, the capital of the Indian, based on Landsat images. The method in the paper detected urban sprawl by dividing the study area into different zones. That helps identify the degree of urban sprawl in each zone instead of the entire study area. Also, Shannon’s entropy is one of the common used indexes to measure urban sprawl. However, from remote sensing images, methods of measuring urban sprawl are limited due to only being able to discern built-up area and non-built-up area (Bhatta 2009). It is hard to classify further, like residential, commercial, public infrastructures, etc. Besides, the accuracy of identifying urban area is doubtful due to current remote sensing classification techniques (Bhatta 2010).

Luck and Wu (2002) detected urban landscape pattern change by using gradient analysis with landscape metrics. The method well showed that how land uses were distributed with the changing distance to urban center. Also, the landscape metrics were helpful to identifying the degree of shape complication and patch fragmentation in the urban center. However, the way the authors classified the land use data seems not ideal. In the classification system, there were desert, agricultural, residential and urban. In common sense, urban land use should include residential. So again, using remote sensing images to classify land use is not an optimal choice.

2.2.2 GIS-data-based sprawl measure

Regarding taking use of GIS data to study urban sprawl, Torrens (2008) explored many metrics to measure sprawl, such as “the inverse power function and the negative exponential function” to measure density, calculating the level of scatter by using the number of housing units and the Euclidean distance to measure fragmentation degree, etc. Theobald (2005) explored urban growth pattern of the cities in the USA at both coarse scale (spatial resolution) and macro scale (spatial extent). He used U.S. Geological Survey’s National Land Cover data set (NLCD), which provides fine resolution (30 m) data. He described landscape patterns of exurban growth by estimating historical and current housing densities. Even though the data is at a fine resolution, the spatial distribution of housing units were estimated based on the density of major roads’ network. Therefore, the accuracy is still questionable.

In terms of taking use of GIS data to study urban sprawl at fine scale, there are a few scholars focusing on it (Hasse and Lathrop 2003; Frank et al. 2009). For example, Hasse and

Lathrop (2003) used LU/LC data from Department of Environmental Protection (DEP) plus county parcel map from Planning Department to produce a housing-unit-level measurement to characterizing residential sprawl. Frank (2009) created a walkability index based on transportation survey and census-based demographic data to assess the neighborhood quality of life in the Baltimore---Washington, DC region. Even though these research methods have

improved the way of measuring urban sprawl, which are able to identify the sprawl characteristic

of mixed land use and the results are of higher accuracy compared to the results based on remote

sensing data, the methods are not universally applicable and simple way of measuring sprawl:

they depend on multiple data sources, which make the applications to be complicated.

In conclusion, it is very necessary to put forward a number of innovative sprawl metrics to measure urban sprawl at a fine scale and obtain a relative accurate result. Also, the corresponding methods should be easy and applicable.

2.2.3 Density, mixed use and accessibility sprawl measures

In this section, we review those sprawl measures closely related with our definition framework, including three dimensions of density, mixed use and accessibility. Density is one of the most widely used urban sprawl measurement (Galster 2001; Black 1996; Burchell et al. 1997;

Torrens and Alberti 2000). People usually used low density settlements to as a metric to measure urban sprawl (Weilenmann 2017; Huanget al. 2007; Paulsen 2012; Pirotte and Madre

2011; Torrens 2008 ; Wassmer 2008). Density measure changes for varied sprawled studies, and it can be represented by population density, single family house density, residential space, etc.

Fulton et al. (2001) compared rates of land consumption (for urban uses) and population growth in metropolitan areas, and found significant sprawling in the US Northeast and Midwest between

1982 and 1997. Theobald (2005) examined changes in Census housing-unit densities throughout the US, and mapped excessive expansion of low-density exurban settlement between 1980 and

2000.

Salvati et al. (2012) studied urban sprawl development in the Mediterranean region by quantifying low-density settlements pattern. They believed that urban sprawl caused the pattern of rural lands changing, and made cropland and woodland became fragmented. Through investigating the long-term land cover changes from 1960 to 2000, and the variation in density of buildings from 1961 to 2001 in Rome urban area, the study results showed that low-density settlements’ sprawling growth affect these rural land patterns changing.

Voorde et al. (2011) created spatial metrics to map spatial distribution of impervious

surfaces in urban areas based on medium spatial resolution images of 1988 and 2001. From the

density map, it clearly showed the urban development with uneven built-up density pattern: the

city center was compact and dense, and the suburban areas were sprawled with low-density.

In the study titled as endless urban growth, Haase et al. (2013) pointed out that some

cities even though have both a declining population and a decreasing household number, the

urban land area of these cities continue spread outwards due to the increasing living space per

capita. According to their study, average housing space per residential unit should be a better

measure on urban sprawl instead of traditional measure using population/housing density (Haase

et al. 2013).

Bhatta (2010) in his study of “causes of urban growth and sprawl, also discussed that the

demand of more living space which forced rapid low-density development must be an indication

of urban sprawl. Weilenmann et al. (2017) included single households in their OLS model to

analyze how increased demand for residential space affected urban sprawl, and the results

showed the demand of more residential space were positively related to urban sprawl.

Mixed use/diverse land use has been recognized as a way to increasing residential walkability in the nearby neighborhood, which includes more small business (such as restaurants, grocery stores, etc.) and leisure destinations, to coexist with local residents within a given area (Brown et al. 2009). According to the literature review, there are vast existing methods to calculate mixed land use (Song et al. 2013; Manaugh and Kreider 2013). Song (2013) reviewed a complete

number of methods in measuring land use mix in recent years, and discussed the benefit and

drawback of each measure in details. In the review, they discussed the integral measures, such as

single land use based Percent/Proportion index, two types of land use based Balance Index, more than two types of land use based Entropy Index and Herfindahl-Hirschman Index, and these indices measure land use mix level from the overall distribution within the defined area (e.g. city boundary) or unit of analysis (e.g. 1x1 square kilometer). They discussed the divisional measures relative to the integral measures as well, such as Dissimilarity Index, Clustering Index, Exposure

Index, and Gini Index, and these measures are sensitive to variations of land use patterns within the analysis unit. Manaugh and Kreider (2013) claimed that current many researches on mixed land uses are focused on the presence and proportion of different uses.

Ewing and Hamidi (2014) proposed a mixed land use index based on the aspects of jobs and population balance, diversity of land uses, and residential uses to nonresidential uses, which includes countywide average job-population balance, county wide degree of job mixing, and countywide average walk score to represent sprawl degree. Brown et al. (2009) in their compared four types of diversity measures in relation to personal body health, including entropy scores, distances to walkable destinations, proxy measures of mixed use, and land use categories used in entropy scores.

Accessibility term is frequently used in urban sprawl and transportation or other related studies. The broad meaning of accessibility is the ability to access somewhere, and it involves either time or distance costs (Ewing 1997). Sohn et al. (2012) developed to accessibility-based sprawl indicators to understand the impact of urban development. They emphasized that the judgement on sprawl was more appropriate based on accessibility instead of purely based on morphology.

In Torrens’ study, he designed and calculated accessibility characteristics based on road distances including accessibility to the CBD (referring all hotels), to major employers, to schools, and to other educational opportunities to measure urban sprawl (2008). According to his analysis, accessibility boosts massive and thriving sprawl on the outskirts of the city because it provides auto-oriented access to central cores, which upgrades people’s ability to move freely more far.

Ewing et al. (200) measured urban development pattern based on accessibility of the street network. Compact development usually has the street network with shorter blocks which allow greater accessibility by pedestrians and cyclists. While for sprawled development pattern, the street network usually coexist with busy arterials and huge super-blocks, which limit people’s transportation behaviors such as walking and biking.

Tsou et al. (2015) took Taiwan as a case to explore the relationship between multilevel highway systems and local development patterns. They designed multilevel highway networks’ influences on local urban development patterns. In their study, two scales of highway network structure were measured to represent the accessibility at different levels.

2.3 Spatial/landscape metrics

People usually quantify urban sprawl level with the help of constructing spatial or landscape metrics (Bhatta 2010). O’Neill et al. (1988) defined that spatial or landscape metrics were numeric indices that measure spatial patterns of a landscape. These metrics have been used in landscape ecology for a long time (Bhatta 2010; Gardner and O’Neill 2001).

In landscape ecology study, construction and measure of landscape metrics are based on a designated spatial unit from multiple scales, such as landscape patches, classes or entire landscape in a study area (Bhatta 2010; McGarigal and Marks 1995). Patch metrics are for

measuring patches in the landscape, class metrics are for measuring class in the landscape and

landscape metrics are for measuring entire landscape. Measures of diversity of different land use

types include patch richness, patch richness density, relative patch richness, Shannon’s diversity

index, Simpson’s diversity index, Shannon’s evenness index, Simpson’s evenness index.

Measures of shape of patches, classes or landscape include perimeter-area ratio, shape index,

fractal dimension index, linearity index, perimeter-area fractal dimension, etc. Measures of

proximity of patches, classes or landscape include proximity index, similarity index, proximity index distribution, similarity index distribution, etc. (Bhatta 2010; Turner 2001).

Considering urban is a special landscape, urban growth and sprawl development can be measured by a number of spatial or landscape metrics, which can be based on patch, class, and landscape scales (McGarigal et al. 1995 and 2002; Luck and Wu 2002). Most of time, combinations of landscape metrics are used to measure urban growth features. For example,

Luck and Wu (2002) used the metrics of mean patch size, patch index and patch density together to explore the nature of the fragmentation of the residential development in the Phoenix in 1995.

Feng and Li (2012) analyzed urban sprawl spatial pattern in Nanjing, China. Based on Landsat

MSS/TM images from four time periods, they constructed six landscape metrics, including contagion index, fractal-dimension index, and shape index to detect fast urban sprawl with low density pattern along the urban fringe. Inostroza et al. (2013) measured urban sprawl and

fragmentation in Latin America. They borrowed three metrics including built-up area, density,

spatial configuration (Angel et al. 2005, 2010a; Schneider and Woodcock 2008; Schwarz 2010)

and created one called speed metric together as a dynamic measure to assess the trends of urban

sprawl and fragmentation for 10 Latin American cities over a period of 20 years. Voorde et al.

(2011) created spatial metrics to map spatial distribution of impervious surfaces in urban areas based on medium spatial resolution images of 1988 and 2001. From the density map, it clearly showed the urban development with uneven built-up density pattern: the city center was compact and dense, and the suburban areas were sprawled with low-density.

2.4 Urban growth driving forces

Analysis of driving factors associated with urban growth has started for a while. Early studies constructed the relationship between urban growth and associated factors from either physical, demographic, and economic aspects (Lo and Yang 2002; He et al. 2006). For example,

Lo and Yang (2002) analyze the drives of land use/land cover changes in Atlanta metropolitan area. They use remote sensing image combined social economic data to explore how population density, income, topographic elements (mean elevation and mean slope) influence high-density and low-density urban use.

With more awareness of the importance of protecting nature resource, animals, environment, etc., researchers start to treat environmental variables (e.g. protected lands) as urban growth related factors when they analyze urban growth (Theobald 2005; Kolb et al. 2013).

For instance, Theobald (2005) study exurban growth patterns in the USA from 1980 to 2020.

Forecasted urban growth patterns were generated by the Spatially Explicit Regional Growth

Model after protected lands being excluded, which are the prohibited development areas for human activities.

Some researchers think all physical distance related metrics can influence urban land use change. In the paper (Lin et al. 2011), the authors simulated land uses’ changes (including built-

up area) by using the distance related driving factors (e.g. distance to a major road, distance to a

river, distance to an urban planning area, etc.).

Recently, more and more researchers study on how land use policies and regulations

affect urban growth in either qualitative or quantitative way (Frenkel 2004; He et al. 2006). For example, Frenkel (2004) modeled the implementation of growth-management policy in restraining urban sprawl and depletion of open space. He claimed that policy variables (e.g. growth management policies of the Law of Return on population, spatial planning, and land

policy management) are necessary parts in determining land-development need. Also, they can

encourage the rejuvenation of urban areas. The examination addressed that growth management

policies have great potential effectiveness in preventing urban sprawl.

Obviously, few of the previous studies include a comprehensive set of potential driving

factors when analyzing their influences on urban growth, not to mention, exploring which type of

factors pose more influences on urban growth (e.g. physical topography limitations or

government policies?). In addition, according to the theory of spatial variation, these driving

factors are supposed to have varied influences on urban growth for different cities. Therefore, it

is necessary to explore this phenomenon visually and quantitatively.

2.5 Analysis/modeling of growth forces

Currently, there are plenty of studies which focus on land use change/urban growth

models, such as economic models (e.g. Irwin and Geoghegan 2001), statistical models, spatial

temporal models, cellular automata models (Yang et al., 2008), hybrid models, and multi-agent

models (e.g. Torrens 2006b). These models help to understand rapid urban growth associated

driving factors, and forecast future urban growth patterns (Alsharif and Pradhan 2014), which

help discourage urban sprawl process, and boost sustainable development. They provide us

professional knowledge about the causes and impacts of urban growth mechanisms, which

benefits the construction of new regulations and policies on sustainable development and smart

growth (Sakieh et al. 2015).

Most of urban growth models are developed from land use/cover change models. The

typical urban growth models include cellular automata, CA-markov model, logistic regression

model, ANN-based model and agent-based model. For example, simple cellular automata (CA)

model use a grid of cells to model the process of urban growth. Each cell’s development is based on a set of transition rules and neighborhood configuration. CA-markov model integrates the

Markov and CA approaches, and is a robust method to model urban growth in terms of quantity estimation as well as spatial and temporal characteristics. In the CA-markov model, the Markov chain process manages temporal dynamics among the land use/cover categories based on transition probabilities, which the spatial dynamics are controlled by local rules determined either by cellular automata spatial filter or transition potential maps (Maguire et al. 2005). The

CA Markov model begins allocating changes from the nearest cells to each land use type

(Pontius Jr. and Malanson 2005). Logistic regression model provides the probability of the presence/absence of each land use at each location based on their drivers. Therefore it takes use of the development probability to decide where urban grows (Verburg et al. 2004). Agent-based model can use agents to simulate urban system. Agent usually represents object with properties and behaviors. Agent can interact with other agents, and also can interact with the environment.

Agent-based model originates from CA model but it considers human’s decisions, which fits simulation of complicated system.

Logistic regression model has been used in modeling urban growth patterns and

examining the relationships between urban sprawl and its driving factors for a while. Alsharif and Pradhan (2014) applied multivariate logistic regression model and remote sensing data to analyze urban sprawl in the metropolitan city of Tripoli. They proposed 11 driving factors that may affect urban sprawl, which are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. The results showed that the logistic regression model was able to explain urban sprawl driving factors effectively. The results help us to understand urban sprawl as well as biophysical and social econometric factors and their relationships, and to forecast various scenarios of future urban growth.

Sakieh et al. (2015) executed the SLEUTH urban growth model to simulate urban growth

Karaj City. In the SLEUTH model, they included diffusion, breed, spread, slope and road gravity factors. According to multiple steps of calibrating model, the results showed that slope, spread and road gravity are the major driving forces of urban growth.

Lo and Yang (2002) analyzed the drivers of land use/land cover changes, and based on these drivers, simulated the future land use/land cover change of Atlanta from 1999 to 2050.

They separately modeled the physical and socioeconomic drivers’ different impacts’ on land use/land cover changes at different scales, i.e. the entire Atlanta metropolis, county scale and census tract scale by using multiple linear regression models.

There are some paralleled studies emerged in urban related studies, such as transportation and policy and regulation by using multilevel linear regression modelling techniques. For

example, Tsou et al. (2015) took Taiwan as a case to explore the relationship between multilevel

highway systems and local development patterns. They designed multilevel highway networks’

influences on local urban development patterns. In their study, two levels of highway network

structure were measured to represent the accessibility at different levels. In another example, Li et al. (2013) conducted a spatial multinomial logit model analysis to identify drivers of land use changes in China from 1988 to 2005. The drivers of land use changes included land use, weather conditions, land quality, topographic features, and economic variables. Lee et al. (2014) adopted a multilevel model to examine the relationships between the use of impact fees by Florida cities

and its associated impacts relating to governance and growth management at both local and

regional scales. Fontes et al. (2008) performed a multilevel hierarchical model to examine the

effects of urban scale and productive structure on individual wage disparities in Brazil in 1990

and 2000. Through multilevel model, the analysis is able to include the variables at different

levels (individual and urban).

CHAPTER 3

STUDY AREA AND DATA

3.1 Metro Orlando and its principal cities

Metro Orlando, best known as one of the most-visited cities in the United States, is located in central Florida (Figure 3.1). It is composed of Lake, Orange, Osceola, and Seminole counties according to the definition of the Orlando Metropolitan Statistical Area (U.S. Office of

Management and Budget 2009). Ranked by population, Metro Orlando is the third largest metropolitan area in the State of Florida and the 26th largest in the United States (U.S. Census

Bureau 2010). Its principal cities include Orlando, Kissimmee and Sanford.

According to census statistics (U.S. Census Bureau 2010), Orlando is the largest city by area (286.7 km²) and by population (239,425) among the three principal cities. Kissimmee and

Sanford are smaller than the city of Orlando in both spatial scale and population. Kissimmee encompasses 44.86 km² with a population of 59,704. Sanford has an area of 58.53 km² and its population is 53,679. Located close to the highway I-4 corridor, Kissimmee and Sanford play important roles in supporting economic and demographic development of Metro Orlando

(Oldakowski et al. 1997). For example, rapid expansion of Disney (near Kissimmee and Orlando) and related entertainment industries such as hotels, restaurants, and shopping centers have attracted a large number of tourists and provided great job opportunities for this area.

Establishments of air and naval military bases (to the east of Orlando and Sanford) and high-tech industries within the metropolis (e.g. ITT Corporation, AT&T, and McDonnell Douglas, etc.) brought a significant number of employments to this area. All of these prompt a relatively large number of residents and/or working commuters in the Metro Orlando. Therefore, it is reasonable

to use these three principal cities representing development of the Metro Orlando in order to satisfy its various economic and space growth needs.

Figure 3.1 Geographic location of Metro Orlando, and its three principal cities

In this study, we used parcel data from the Florida Department of Revenue (FDR) mainly

to calculate sprawl indices. According to FDR, parcel data were collected by property appraisers

individually for each of the 67 counties in Florida. FDR compiled all parcel data acquired from

each county into a database that has detailed attributes such as parcel’s land-use type, lot size, building size, actual year built, ownership, and sales and assessed values, etc. (Florida

Department of Revenue 2012). A check of parcel accuracy was conducted by the researchers through visually comparing a number of randomly selected parcels with ESRI World Imagery

Map (ESRI 2012). This imagery dataset presents land cover throughout the United States with a spatial resolution of 1 meter or better; therefore, land-uses such as single-family housing and commercial/industrial complex can be visually identified. Results indicated that land-use information from the FDR parcel data is trustworthy.

We took a subset of the FDR parcel data that geographically covers the entire Metro

Orlando area. A series of ancillary cartographic boundary (e.g. city limit and county boundary), obtained from the Census Bureau’s MAF/TIGER geographic database (U.S. Census Bureau

2010), were used to delimit the study area enclosing the three principal cities of the Metro

Orlando.

3.2 MSAs in Florida

Four Metropolitan Statistical Areas (MSAs) are included in our study (Figure 3.2). They are the largest metropolitan areas in the State of Florida. These are Jacksonville MSA (or Metro

Jacksonville), Orlando MSA (or Metro Orlando), Tampa MSA (or Metro Tampa), and Miami

MSA (or Metro Miami).

Metro Jacksonville (Table 3.1) is located in northeast Florida by the North Atlantic

Ocean. The central geographical location is at 30°19'50"N 81°39'43"W. It has only one principal

city, Jacksonville; but, it is composed of five counties, which are Nassau, Baker, Duval, Clay and

St. Johns County. The urban growth starts from the center of city of Jacksonville extends

outwards. According to the 2010 U.S. Census, the total population was 1,345,596 in this MSA;

and the 2015 estimate was 1,449,481. This metro area was projected to have a total of 1,514,720

population in 2020 (U.S. Census 2010). The Jacksonville metropolitan area is the 40th largest

(by population) in the U.S. and the 4th largest in the state of Florida.

Metro Orlando (Table 3.1), best known as one of the most-visited metropolitan areas in

the U.S., is located in central Florida. Its central geographical location is at 28°33'25"N

81°22'47"W. Its principal cities include Orlando, Kissimmee and Sanford. It is composed of

four counties, including Lake, Orange, Osceola, and Seminole County. According to the 2010

U.S. Census and 2015 Census estimates, the total population of this MSA increased from

2,139,565 in 2010 to 2,326,729 in 2015. This MSA was projected to have a total of 2,439,316

population in 2020. Ranked by population, Metro Orlando is the third largest metropolitan area

in the State of Florida and the 26th largest in the United States (U.S. Census 2010).

Metro Tampa (Table 3.1) is located in the central-west part of Florida along Tampa

Bay and the Gulf of Mexico. The central geographical location is at 28°07'48"N 82°23'02"W. It

incorporates Hernando, Hillsborough, Pasco, and Pinellas counties. It includes Tampa, St.

Petersburg, Clearwater, and Largo principal cities. According to the 2010 U.S. Census, the total population was 2,788,715 in this MSA, with an estimate of 2,917,813 in 2015. This MSA is

projected to have a total population of 2,989,688 in 2020. Metro Tampa is the 18th largest MSA in the US and the 2th largest in the state of Florida.

Metro Miami (Table 3.1) is located in southern Florida. The central geographical location is at 26°05'21"N 80°10'02"W. It includes area of Miami-Dade, Broward, Palm Beach counties.

Figure 3.2 Geographic locations of the studied metropolitan areas

Table 3.1 Description of physical and demographic characteristics of four MSAs in Florida

MSAs Location Principle cities Counties Area Population

(in 2010)

Jacksonville 30°19'50"N Jacksonville Nassau, Baker, 9,580 km2 1,345,596 MSA 81°39'43"W Duval, Clay and

St. Johns

Orlando 28°33'25"N Orlando, Lake, Orange, 10,390 km2 2,139,565 MSA 81°22'47"W Kissimmee and Osceola, and Sanford Seminole

Tampa MSA 28°07'48"N Tampa, Hernando, 6,616 km2 2,788,715 82°23'02"W St. Petersburg, Hillsborough, Clearwater, Pasco, and Largo and Pinellas

Miami MSA 26°05'21"N Boca Raton, Miami-Dade, 15,890 km2 5,585,605 80°10'02"W Deerfield Beach, Broward, Delray Beach, Palm Beach Fort Lauderdale, Jupiter, Kendall, Miami, Miami Beach, Pompano Beach, and West Palm Beach

Its principal cities include Boca Raton, Deerfield Beach, Delray Beach, Fort Lauderdale, Jupiter,

Kendall, Miami, Miami Beach, Pompano Beach, and West Palm Beach. According to the 2010

U.S. Census, its total population was 5,585,605, with an estimate of 5,937,100 in 2015. This

metro area is projected to have a total population of 6,246,056 in 2020. Metro Miami is the 8th

largest MSA in the US, the 2nd largest in the Southeastern US, and the largest in the state of

Florida. The largest Census places in Metro Miami by population are Miami, Hialeah and Fort

Lauderdale (DATAUSA).

In this study, we used parcel data from the Florida Department of Revenue (FDR) mainly

to calculate sprawl indices. According to FDR, parcel data were collected by property appraisers

individually for each of the 67 counties in Florida. FDR compiled all parcel data acquired from

each county into a database that has detailed attributes such as parcel’s land-use type, lot size,

building size, actual year built, ownership, and sales and assessed values, etc. (Florida

Department of Revenue 2012). A check of parcel accuracy was conducted by the researchers through visually comparing a number of randomly selected parcels with ESRI World Imagery

We took a subset of the FDR parcel data that geographically covers all four MSAs. A series of ancillary cartographic boundaries (e.g. MSA and county boundaries), obtained from

Census Bureau’s MAF/TIGER geographic database (U.S. Census 2010), were used to delimit the study areas.

As another important spatial dataset, the 1-km US National Grid (USNG) in ArcGIS

Shapefile format was obtained directly from the National Geospatial-Intelligence Agency (NGA).

The USNG is based on universally-defined coordinate and grid systems and can, therefore, be easily extended for use worldwide as a universal grid reference system. This grid is used to calculate urban sprawl indices for each 1 km2 cell across all four MSAs (NAPSG 2013).

The Florida state-wide road data were also acquired to perform distance-cost accessibility analysis. This dataset contains road segments prepared in the Florida Department of

Transportation Roads Characteristics inventory (RCI) dataset. In this study, we select major and second highways (including roads such as interstate, other freeways or expressways, other principal arterial, minor arterial, major collector, and minor collector) as our road network for calculation. Due to the high data volume, local roads are currently excluded from calculation.

3.3 Florida cities

3.3.1 Surveyed cities

Florida is located in the Southeastern U.S. According to the U.S. Census, its land area is approximately 53,625 square miles, with a population of 18,801,310. It is the 3rd most populous and 8th most densely populated state (U.S. Census Bureau 2010). There are 268 cities throughout the entire state. Among those, 124 cities are included in our study, which consist of

91 small cities and 33 medium/large cities (Figure 3.3). These cities correspond to those with land regulation policy data available in the statewide Florida Energy Sustainability Survey, which was conducted by the Sustainable Energy & Governance Center at Florida State

University. The survey was finished in the year 2009, with 165 completed questionnaires collected from government officials of Florida’s cities. We excluded the tiny cities (i.e.,

population lower than 2,500 people according to the 2010 U.S. Census) from our analysis given the consideration of the criteria created by the Census Bureau being used to separate urban and rural. For the remaining 124 cities, we used a population threshold of 50,000 to separate small cities (i.e., population ranger from 2,500 to 50,000) from the medium/large ones (i.e., population greater than 50,000).

Figure 3.3 Geographic location of Florida state and the surveyed cities

3.3.2 Data

In our analysis of driving factors for urban growth at multiple scales (including Chapters

4.3 and 4.4), multiple variables at different levels (including city level and census block-group

level) extracted from various datasets of different formats were integrated ( see Table 3.2). These

include:

• Florida Energy Sustainability Survey (2009), which is from the Sustainable Energy &

Governance Center, Florida State University. It is questionnaire-based survey. It investigates

Florida local governments’ policies and actions related to energy efficiency and climate change.

Four land-use polices closely related with sustainable urban development are selected from the survey in our studies (i.e. polices of compact development, mixed use development, transport- oriented development and infill development).

• National Land Cover Database (NLCD) (2006-2011), which is used to calculated urban growth rate at multiple scales (i.e. city-level and census block-group level) in our studies.

• Socioeconomic variables such as population, housing value, and employment from U.S.

Census and American Community Survey

• Environmental variables such as slope generated from Digital Elevation Model (DEM),

road data from Florida Department of Transportation, and protected land from Protected Areas

Database of the United States (PADUS)

• Census cartographic boundaries at block group and place scales, which helps us to

aggregate driving factors related with urban growth at multiple spatial levels

In the Chapter 4.3, urban growth drivers’ analysis is conducted at city level by using GWR

geospatial statistical method. The analysis includes 1 city-level urban growth rate dependent

variable and 9 city-level independent variables from physical, socioeconomic and policy features

(see Table 3.3). In the Chapter 4.4, urban growth drivers’ analysis is conducted at multiple scales

Table 3.2 Data sources used in the studies and their brief descriptions

Name Description Year Organization National Land It is a raster-format product and provides national 2006, U.S. Geological Cover Database land use/land cover classification across the 2011 Survey (NLCD) United States at a spatial resolution of 30 meter Protected Areas It is a vector-format product. It provides 2010 U.S. Geological Database of the conservation lands’ spatial distribution nationally, Survey United States and describes land ownership of each protected (PADUS) area, and etc. http://gapanalysis.usgs.gov/padus/data/download/ Florida They are shapefiles of various designated roads, 2011 Florida Department of such as U.S. highways, state roads, local roads, Department of Transportation etc. Transportation RCI Roads http://www.fdot.gov/planning/statistics/gis/road.sh tm Florida Energy It is questionnaire-based survey. It investigates 2009 Sustainable Sustainability Florida local governments’ policies and actions Energy & Survey related to energy efficiency and climate change. Governance Center, Florida State University Florida Digital Florida Digital Elevation Model (DEM) Mosaic at 2005 GeoPlan Center, Elevation a spatial resolution of 5 meter University of Model (DEM) Florida Place It is shapefile-based product from the Census 2010 U.S. Census Boundary Bureau’s MAF/TIGER geographic database, Bureau which represents geographic areas of places in the U.S. Demographic Provide statistical information about the nation’s 2006, U.S. Census Statistics people from decennial censuses, including 2011 Bureau population estimates for the United States, states, counties, cities, and towns, etc. Employment Obtained from ACS 5-Year Estimates, which 2006- American Statistics provide data for social, economic, housing and 2010 Community Housing demographic statistics Survey (ACS) Statistics https://www.census.gov/programs- surveys/acs/guidance/estimates.html Note: although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties.

Table 3.3 Description of urban growth related variables at multiple levels

Variable Unit Chapter 4.3 Chapter 4.4

Dependent variable

Urban growth rate (2006-2011) (%) Y/C Y/B

Independent variable

Slope (°) Y/C Y/B

2 Road density Y/B (m/m ) Median housing value (2010) ($) Y/C Y/B

Population growth rate (2006-2011) (%) Y/C Y/C

Unemployment rate (%) Y/C Y/C

Protected land percentage (%) Y/C Y/C

Compact development (binary) Y/C Y/C

Mixed use development (binary) Y/C Y/C

Transport-oriented development (binary) Y/C Y/C

Infill development (binary) Y/C Y/C Note: Y/C means the variable is used in the related study, and the variable is from city level; Y/B refers to the variable is used in the related study, and the variable is from census block-group level

by using multilevel modeling. The analysis includes 1 block-group-level urban growth rate dependent variable, 3 block-group-level independent variables (i.e. slope, road density, and median housing value) and 7 city-level independent variables from physical, socioeconomic and

policy features (see Table 3.3). More details of variables’ calculation will be discussed in the methodology section.

CHAPTER 4

METHODOLOGY

4.1 Measuring urban sprawl within a metropolis

4.1.1 Parcel data and land-use reclassification

In this study, we used parcel data from the Florida Department of Revenue (FDR) mainly to calculate sprawl indices. According to FDR, parcel data were collected by property appraisers individually for each of the 67 counties in Florida. FDR compiled all parcel data acquired from each county into a database that has detailed attributes such as parcel’s land-use type, lot size, building size, actual year built, ownership, and sales and assessed values, etc. (Florida

Department of Revenue 2012). A check of parcel accuracy was conducted by the researchers through visually comparing a number of randomly selected parcels with ESRI World Imagery

We took a subset of the FDR parcel data that geographically covers the entire Metro

Orlando area. A series of ancillary cartographic boundary (e.g. city limit and county boundary), obtained from the Census Bureau’s MAF/TIGER geographic database (U.S. Census Bureau

2010), were used to delimit the study area enclosing the three principal cities of the Metro

Orlando. In accordance with the goal of this study, we also reclassified the FDR-defined land-use types. The new land-use categories used for our analysis include residential, commercial, industrial, agricultural, public facility (including institutional and governmental land uses), and

other land uses. Within the residential land-use category, we reclassified the FDR land-uses into

single-family use, multi-family use, condominium or apartment, and other residential types.

4.1.2 Assigning land parcels to one of the three principal cities

All of the land parcels were assigned to one of the three principal cities in the Metro

Orlando area. To do that, first, the entire metropolitan region (about 10,400 km2) was divided into 19,872 1×1 km2 cells. Then, land parcels were allocated to these 1-km2 cells according to their spatial location. If a land parcel crosses multiple cells, it was split into multiple parts correspondingly and each part was assigned separately to the corresponding cell that contains it.

After that, we assigned each 1-km2 cell (with all its land parcels) to one of the three principal cities based on two factors, i.e. distance to city centers and cities’ size. The center of each principal city was determined according to the spatial location of its city hall. Each 1-km2 cell was assigned to one of the three principal cities by employing the classic Huff Model (Huff

1964), which was originally used to describe probabilities of stores’ attractiveness to customers.

We used this model to calculate attractiveness of each principle city in terms of residence and jobs. Each of the 1-km cells (and hence all parcels within this cell) was assigned to one of the cities, the probability of which is positively proportional to the cities’ size and inversely proportional to its distance to city centers (Equation 1).

Wi Dα P = ij ij n  Wi ∑Dα i=1 ij

Equation 1

where Pij stands for the probability of cell j being assigned to cityi ; Wi is the attractiveness of city

i measured by this city’s size; Dij is the Euclidean distance from cell j to cityi ;α is an exponent

applied to distance, usually ranging 1.5-2. In our studyα was set to 2.

4.1.3 Residential density and distance of peak development

Residential density was calculated as the total number of housing units per 1-km2 cell, where the total amount of housing units is the sum of single-family units and multi-

family/condominium /apartment units (calculated as number of buildings multiplied by units per

building, both retrieved from the parcel dataset). Then, based on the Southwest Florida Regional

Planning Council’s definition of urban areas (1994), we define “urban” density as greater than

1,483 residential units/ km2 to capture densely developed urbanized areas, “suburban” density as

249-1,483 residential units/km2 to capture moderately developed urban-suburban fringes,

“exurban” density as 51-248 residential units/km2 to capture sparsely developed residential areas

outside urban-suburban fringes, and “rural” density as equal or smaller than 50 residential

units/km2 where few residential land use are built up (Figure 5.1). And we classify the cells

associated with different residential densities into the four urban-rural categories we just defined.

This is a high priority reference classification scheme which is found to be more appropriate

used in our study.

We created a sprawl index called distance of peak development, which corresponds to the

distance measured from a city’s center to include 75% of this city’s urban residential units. The

greater value of this peak development distance indicates a higher level of residential sprawling.

The distance of peak development was calculated following the steps below: 1) For each

principal city, we drew a scatterplot of residential density by four urban-rural categories, with x- 40

axis representing a cell’s Euclidean distance to its city center and y-axis representing this cell’s residential density (Figure 5.2). 2) The urban densities, i.e., cells with the highest residential densities, were determined to calculate distance of peak residential development for each of the three principal cities. 3) Considering the fact that city of Orlando is much larger than cities of

Kissimmee and Sanford, the direct comparison of the actual peak development distance does not yield helpful results. Therefore, in our study, a relative distance was adopted for the calculation of residential peak development. The relative distance was calculated as Euclidean distance of each cell to its city’s center divided by radius of that city. The radius of a city was determined by its maximum housing development distance. 4) Based on the scatterplot of urban-density cells, we took the relative distance so that at least 75% of the urban cells are distributed from the city center to this distance (Figure 5.3).

4.1.4 Mixed land use indices-diversity and evenness

In this study we used diversity and evenness indices to calculate degree of mixed land use in each of the 1-km2 cell. At cell level, diversity index indicates how many land-use types exist per square meter (Figure 5.4). Evenness index indicates how equally these multiple land-uses coexist per square meter (Figure 5.5). The two indices used together can represent level of dominance in land uses.

The land-use types for all parcels included residence, commercial, industry, agriculture, public facility, and others. Therefore, diversity calculated as the total number of land-use types per cell (km2) has a value range from 1 to 6 (e.g. 1 corresponds to one land-use type and 6 corresponds to six land-use types existing per cell). Larger value represents higher degree of mixed land use. Evenness was calculated according to Shannon’s entropy (Equation 2),

= 𝑛𝑛 ( ) ′ 𝐻𝐻 𝑛𝑛 − � 𝑃𝑃𝑖𝑖𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑃𝑃𝑖𝑖 �𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑛𝑛 𝑖𝑖=1 Equation 2

where is the evenness, is the proportion of the th land-use type in a land-parcel cell, is the total𝐻𝐻′ number𝑛𝑛 of land-use𝑃𝑃 𝑖𝑖types in a land-parcel cell𝑖𝑖 (Thomas 1981). The evenness index𝑛𝑛 ranges from 0 to 1, with 0 indicating one dominant land use and 1 indicating six evenly distributed land uses in our study.

At city scale, we plotted histogram of high- vs. low- cells. High diversity cells correspond to those containing three or more land-use types; and low diversity cells refer to those with one or two land-use types. With regard to the evenness index, at city scale we separated high (>=0.5) and low (<0.5) evenness categories. Since lower diversity and higher evenness are commonly thought associated with higher level of urban sprawl, we used the percentage of cells that belong to the low-density categories as our measure for the city-scale diversity index. Similarly, we used

the percentage of cells that belong to the high-evenness category as our measure for the city-

scale evenness index.

4.1.5 Residential accessibility to urban economic center(s)

In this study, we developed a sprawl index to measure parcels accessibility to urban

economic centers. By our definition, this accessibility index refers to relative distance

(normalized by a city’s radius) that encloses 75% of the residential land uses served by a city’s

economic center. For a city, a large relative Euclidean distance of a parcel to economic centers

indicates poor accessibility to service and, hence, a higher degree of urban sprawl. This

accessibility index was calculated according to the following steps.

First, urban economic centers were determined by analyzing spatial clusters of service-

type land uses such as commerce, industry and public facility. A weighted count of service-type

land uses (i.e., commercial, industry, and public facility) was calculated for each 1-km2 cell,

where commercial land uses were assigned double weight as industrial or public land uses (i.e.,

commercial use weighs 2.0, industrial use weighs 1.0, and public facility weighs 1.0). Next,

Local Moran’s I, which identifies statistically significant spatial clusters of high (or low) values

(Anselin 1995), was adopted for the analysis of urban economic centers (Figure 5.8). From this

map, we manually allocated centroids of these high service parcel clusters as each city’s

economic centers.

We then calculated the actual Euclidean distance from each residential-dominated cell to

its nearest urban economic center. Here, residential-dominated cells were defined as cells where

single-family, multi-family, and condo/apartment parcels collectively occupied at least 0.5 km2 within a 1-km2 cell. In order to compare across cities of different sizes, we used the relative

Euclidean distance (calculated as corresponding actual Euclidean distance divided by the city’s radius) to denote the accessibility sprawl index.

Finally, we plotted the histogram of residential land uses, with x-axis standing for relative distance from a city’s economic center and y-axis representing percentage of residential- dominated cells at corresponding distance (Figure 5.9). The 75th percentile was applied as a cutoff threshold to determine the accessibility distance for a city, so that within this distance 75%

of the residential-dominated lands are served by at least one of the city’s economic centers. Thus, by our calculation the longer is this accessibility distance, the higher level of sprawl a city is.

4.1.6 An integrated sprawl index

An integrated sprawl index was created to evaluate the overall degree of sprawl for each of the three principle cities under investigation. It was calculated as a weighted average of distance of peak development (weighed by 1.0), mixed land use (diversity and evenness, each weighed as 0.5), and residential accessibility to urban economic centers (weighed as 1.0), respectively.

4.2 Measuring and comparing urban sprawl across metropolitan areas

In this study, a new set of sprawl indices was created, including average livable space per residential unit (ALS), land-use diversity, and accessibility to business hubs, to measure and compare levels of urban sprawl across the four Metropolitan Statistical Areas in Florida.

4.2.1 Average livable space per residential unit (ALS)

The index of average livable space per residential unit (ALS) measures the residential/housing size (in square feet) for each residential unit on average. In this study, residential unit includes three types, which are single-family residence, multi-family residence and condominium residence. The spatial unit of analysis is 1 km2 cell laid out in the 1 km USNG, i.e. we calculated the ALS index for each of the 1 km2 cells delineated by USNG across the four

MSAs. We constructed this index according to the Equation 3:

= 𝑁𝑁 𝑁𝑁

𝐴𝐴𝐴𝐴𝐴𝐴 � 𝐴𝐴𝑖𝑖�� 𝑈𝑈𝑖𝑖 𝑖𝑖=1 𝑖𝑖=1 Equation 3

where is total livable area of parcel , is the number of residential units on parcel , and N

𝑖𝑖 𝑖𝑖 is the number𝐴𝐴 of parcels in cell. The unit𝑖𝑖 𝑈𝑈of the ALS sprawl index is square feet per residential𝑖𝑖 unit.

At the MSA scale, the overall ALS sprawl measure is calculated as the mean of all ALS

values of all 1-km2 cells inside the metropolitan area.

4.2.2 Land use diversity

Mixed-use development is a term based on single-use development, which can be used to

indicate the level of urban sprawl (Houshmand 2012; Bhatta 2010; Manaugh and Kreider 2013).

As the name suggested, mixed-use development needs the land use types to be diverse including

as many as possible residentce, commericial, industrial, etc. in a settlement area (e.g.

neighborhood). So, in an area if most of land parcels are the same type of land use and very few

of them are other types of land use, this indicates monopolistic urban development pattern.

Land use diversity measures the degree of mixed land use, which by definition refers to

the pedestrian-friendly development that blends residential, commercial, industrial and/or other

types of land uses (Song et al. 2013; Manaugh and Kreider 2013). In our study, dominance of single category of land use was considered to be an indicator of a higher level of sprawling in a

neighborhood. We chose Herfindahl-Hirschman Index (denoted by HHI) to calculate land use

diversity (Equation 4). HHI first is applied in the economic study of market concentration, and

later introduced to the geography and urban planning fields to evaluate the level of mixed land

use (Song et al. 2013). The HHI sprawl index was calculated for each of the 1 km2 USNG cell.

= 𝑁𝑁 2 𝐻𝐻 � 𝑠𝑠𝑖𝑖 𝑖𝑖=1 Equation 4 where is the proportion of land use type i in cell, and N is the number of land use types in cell. H 𝑠𝑠ranges𝑖𝑖 from 1/N to 1;

The larger the HHI value is, the larger the proportion of individuals of land use in total is and the more monopolistic land use distribution is (Lu et al. 2017), and when H value higher than 0.25 indicates high monopoly (U.S. Department of Justice and the Federal Trade

Commission 2010; Bilgin et al. 2017; Brezina et al. 2016).

In this study, land use types were reclassified first into four categories (i.e. N equals 4), which includes residential, commercial, industrial, and public facility. We excluded agricultural, and other (such as waste land, marsh, sand dunes, swamps etc.) land uses.

The overall sprawl measurement at metropolitan scale is calculated as the mean of HHI values on the highly-monopolized cells (i.e. H > 0.25) inside each metropolitan area.

4.2.3 Accessibility to business hubs

An accessibility measure was devised to calculate the physical travel distance from a residential-dominated 1-km2 cell to its closest commercial center. In the study, the travel distance was calculated on road networks. Residential-dominated cell was defined as a 1-km2 cell where single-family, multi-family, and condo/apartment parcels collectively occupied at least 0.5 km2 within it. Business hubs, or commercial centers, refer to concentrations of commercial activities in MSAs, are defined as the centroids of commercial land use clusters with high-level density of commercial land use, which are determined by performing local Moran’s I spatial analysis which

identifies statistically significant spatial clusters of densely-developed commercial uses (Anselin

1995), (Anselin 1995). The analysis (Equation 5) was performed again on the 1 km2 USNG grids.

= min{ , ,… }

1 2 𝑁𝑁 𝐴𝐴𝐴𝐴𝐴𝐴 𝑁𝑁 𝑑𝑑 𝑑𝑑 𝑑𝑑 Equation 5 where ACC is the travel distance from a residential-dominated cell to its nearest business hub.

, ,… are the distances from the residential-dominated cell to each of its surrounding business𝑑𝑑1 𝑑𝑑2 hubs.𝑑𝑑𝑁𝑁 The unit of the ACC sprawl index is mile.

The sprawl measurement at metropolitan scale ( _ ) was calculated as the mean of the accessibility ( ) on all residential-dominated cells𝐴𝐴𝐴𝐴𝐴𝐴 (M)𝑀𝑀 inside the metropolitan area, weighted by the associated𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 number of residential units ( ) on the cells (refer to Equation 6).

𝑁𝑁𝑁𝑁𝑖𝑖

_ = 𝑀𝑀 𝑀𝑀

𝐴𝐴𝐴𝐴𝐴𝐴 𝑀𝑀 � 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑁𝑁𝑁𝑁𝑖𝑖�� 𝑁𝑁𝑁𝑁𝑖𝑖 𝑖𝑖=1 𝑖𝑖=1 Equation 6

4.2.4 Integrated sprawl index

We created a composite sprawl index based on three sprawl indices (i.e., average livable space per residential unit, land use diversity, and accessibility to business hubs) and used the integrated index to determine the overall sprawl levels for the four metropolitan areas. Referring to previous research methods (Ewing and Hamidi 2010; Galster et. al. 2001), we created three “Z scores” of three sprawl indices for each metro area. Z score is the number of standard deviations from the mean of the distribution for that dimension. Then we added these three Z scores, 47

assuming equal weights for each index, to obtain the overall sprawl Z score for each metro area.

A higher Z score indicates a higher level of sprawling in our study.

4.3 Global OLS and local GWR analyses of urban growth factors

In this section, we explored global and local variations of urban growth drivers and investigating sustainable land use policies’ impacts on urban growth across surveyed Florida

cities.

4.3.1 Variables of interest and calculation

According to previous literature and expert knowledge (Glaeser et al. 2006; Lo and Yang

2002; He et al. 2006; Kolb et al. 2013; Lin et al. 2011; Frenkel 2004), we select 9 potential

driving factors related with urban growth including four urban growth control policies variables

from the Florida sustainability survey data and five variables from the physical, demographic,

socio-economic, and environmental, which are population growth rate, unemployment rate,

median housing value, average slope, protected land percentage, and land use policy

implementations of compact development, mixed-use development, transport-oriented

development, and infill development.

Urban growth rate (%) is calculated based on the NLCD datasets of the years of 2006 and

2011. The NLCD provides 16 classes under Level II land use/land cover classification scheme.

The classes of developed low, medium, and high intensity, which are summed to represent urban

impervious surfaces of varying degrees, are used for estimation of urban area. Hence, urban

areas of the surveyed cities in the two years are calculated by counting the impervious surface

area inside the cities’ administrative boundary. Urban growth rate between 2006 and 2011 is

calculated by Equation 7.

( ) GR= × 100 𝑉𝑉𝑝𝑝𝑒𝑒 − 𝑉𝑉𝑝𝑝𝑝𝑝 𝑝𝑝𝑝𝑝 𝑉𝑉 Equation 7 where GR represents the 5-yr urban growth rate in percentage, represents latter urban area in

𝑝𝑝𝑒𝑒 2011, represents previous urban area in 2006. 𝑉𝑉

𝑝𝑝𝑝𝑝 𝑉𝑉Population variable is represented by population growth rate (%) between 2006 and 2011, which is calculated by the percentage of the population change in the two time periods divided by the number of population in the previous year in the city’s statistical level.

Based on the Florida DEM data (resampled to 30-meter pixels), the variable of average slope (º) inside the cities’ administrative boundary is derived for the surveyed cities.

Unemployment rate (%) and median housing value ($) for the surveyed cities are obtained directly from ACS 5-year survey data between 2006 and 2010.

Protected land percentage (%) is extracted from the PADUS data. Circular buffers of the surveyed cities are first created to calculate the protected land area inside the buffers. Then the protected land percentage is calculated by using the protected land area divided by the total land area (excluding water bodies, like ocean, lakes, etc.) inside the buffers. Here, the sizes of buffers of the surveyed cities are determined according to cities’ classification scheme (see Table 4.1).

We borrow this classification scheme defined by population range from U.S. Census Bureau,

Census of Population (2010). We decide the corresponding buffer radius based on the maximum

urban development radius of different sizes of cities from our sprawl study: part I (Chapter 4.1

and 5.1).

Table 4.1 Cities’ classification scheme by population range and buffer radius

Population range Buffer radius (mile)

250, 000 or more 80

100, 000-24, 9999 60

50, 000-99, 999 40

under 4, 9999 20

From the survey, four questions relevant to land use policy on sustainable urban

development are selected to derive policy variables, which ask if the surveyed cities have any policies of urban compact development, mixed use development, transport-oriented development and infill development. If the city has any of the four policies, they are coded 1; otherwise, they are 0 (1 means there is corresponding policy action in the city; 0 means there is no corresponding policy action in the city).

So far, 9 potential driving factors related with urban growth are extracted for the

surveyed Florida cities. Combined with the dependent variable of urban growth rate, we will use

them for further analysis of their impacts on urban growth at global and local scales.

4.3.2 Two-scale regression model frame

To analyze driving forces’ influences on urban growth globally and locally, 124 surveyed

cities in the Florida are selected in the study (refer to Chapter 3). In order to explore the impacts

of these driving factors on the cities of different sizes, we classify the surveyed cites into

different city groups. According to the demographics, a threshold of 50, 000 population is used

to separate small and medium/large cities. 91 out of them are categorized as small cities, and 33

out of them are categorized as medium/large cities. Therefore, three city groups are categorized:

1st city group includes all 124 surveyed cities (population >2, 500), 2nd city group includes 91 small surveyed cities (population between 2, 500 and 50, 000), and 3rd city group includes 33 medium/large surveyed cities (population >50, 000). This classification scheme based on population range is from the demographics from U.S. Census Bureau (2010).

In the following, we build two-scale regression model frame work, including static global

OLS model and nonstatic local GWR model to investigate the relationships between urban growth and its relevant potential driving factors at global scale and local scale.

To investigate the global impacts of these potential urban growth drivers for different city groups, we select ordinary least squares (OLS) to construct the global-trend model and both independent variables and dependent variable satisfy all the assumptions inherently required by this method, such as linear relationships, data outliers exclusion, non-multicollinearity, normal distribution, etc. Consider investigation of policies’ impacts on urban growth is very important in the study. In other words, we are interested to know whether policies’ implementation helps to discourage urban sprawl. First of all, we do t-test for the three city groups in terms of the four policy variables (i.e. compact development, mixed use development, transport-oriented development, and infill development). From t-test results, we can know whether these policies work or not on inhibiting urban sprawl. We will perform t test on 1st city group, 2nd city group and 3rd city group, respectively. Then, OLS is applied to explore the relationships between independent variables and dependent variable for the three city groups. For each city group, global trends of most significant variables’ impacts (either positive or negative) on urban growth

will be detected. In this way, it can be known that how physical, socio-economic, environmental and policy factors influence urban sprawl at a global scale.

Furthermore, we will explore local variations of these significant variables’ impacts on urban growth of cities at different locations. As is known, the closer the observations, the more

similarity of the relationships among variables they have. Therefore, relationships between variables might vary over geographic space and parameters’ estimation might exhibit significant spatial variation (Fotheringham 1997). Geographically Weighted Regression (GWR) model is a useful tool to explore these variations. GWR adds spatial location information into the traditional regression framework, and its general formula is written as (see Equation 8):

yai=0 (u ii , v )++∑ a kiiiki (u , v ) xε k

Equation 8

where (uii , v ) refers to the coordinates of the i th point in space and akii(u , v ) is a realization at point i (Fotheringham 1997). In the study, GWR model will be employed for each city group.

By doing so, it can be visually found that how the effect of each significant driving factor on

urban growth varies by cities at different locations locally.

In terms of each city at a specific spatial location, a set of parameters of driving factors

will be calculated, of which positive parameters mean that these factors urges urban sprawl, and

negative values mean the factors which inhibit urban sprawl. The larger the parameter is, the

greater the impact on urban growth.

4.4 Multilevel analysis of urban growth drivers

4.4.1 Variables’ determination

According to previous literature and expert knowledge (Glaeser et al. 2006; Lo and Yang

2002; He et al. 2006; Kolb et al. 2013; Lin et al. 2011; Frenkel 2004), we believe that local

urban development is to be affected by a series of physical, social, and economic environment at

multiple-scale geographic range. In the study, we suppose that local urban development is

restricted by topography, road density and local housing value, and it is also affected by

population, unemployment, and protected land at upper level.

In the study, we use block group unit (defined by US census 2010) as the bottom level,

and city unit (defined by US census 2010) as upper level to construct 11 potential driving factors

associated with urban development including block-group-level topography, road density and

local housing value; city-level sustainable urban development policies, population,

unemployment, and protected land. Urban development policies consist of land use policy

implementations of compact development, mixed-use development, transport-oriented

development, and infill development.

Out of these 11 variables, the variables of population growth rate, protected land

percentage, and four sustainable land use policies are the same as used in the study in Chapter

4.3, and can be directly applied in the multilevel model.

4.4.2 Variables’ calculation

Local urban development in this study is represented by urban growth rate (i.e. the

increased percentage of built-up area) during the years of 2006-2011 at census block group scale.

Urban growth rate is calculated based on the NLCD datasets of the years of 2006 and 2011. The

NLCD provides 16 classes under Level II land use/land cover classification scheme. The classes

of developed low, medium, and high intensity, which represent urban impervious surfaces of

varying degrees, are used for estimation of built-up area. Urban growth rate between 2006 and

2011 is the percentage of the difference of built-up areas in the two time periods divided by the

built-up area in the previous year.

Topography is represented by average slope derived from DEM at block group level.

Road density is calculated by the total length of roads (including US highways, State roads and

Interstate roads) divided by their associated census block size. Local housing value is represented

by 5-year median housing value at block group level between 2006 and 2010, which is directly

derived from American Community Survey.

Unemployment rate change during 2006-2011 is hard to be obtained directly from

American Community Survey. Two alternative unemployment variables are instead, which are

3-yr unemployment rate (2007-2009) and 3-yr unemployment rate (2009-2011) from American

Community Survey at city level. We anticipate unemployment in the economic recession of late

2007–2009 may have different impact on local urban development from the later time period

(U.S. Bureau of Labor Statistics 2012).

4.4.3 Examining driving factors’ influences on local urban development by multilevel model

4.4.3.1 Multilevel model framework on local urban development

Consider driving factors may affect urban growth at different levels (the chart in Figure

4.1.), we construct multilevel model to analyze these factors’ influences at both city level and block group level.

Multilevel analysis in our study includes several steps: first we build a “null” model

without explanatory variables to examine city effects; then we add block-group level variables

into the multilevel models to examine their influences on local urban development; finally, we

include both block-group level and city level variables in the multilevel model to examine their

influences on local urban development at multiple scales.

4.4.3.2 The overview of multilevel model structure

According to the variables we calculated at different scales in the previous section (see

Chapter 4.4.2), the multilevel model structure is designed in Figure 4.1. More details about the

multilevel variables are explained in Table 4.2.

Protected land

Unemployment Sustainable urban development policies (2007-2009) & (2009-2011) (2009) Compact development Mixed use development Population Transport-oriented development LEVEL 2 (2006-2011) Infill development City level

LEVEL 1 Local urban development Block group level (2006-2011)

Average slope Road density Median housing value

Figure 4.1 The multilevel model framework on local urban development

Table 4.2 Description of variables in multilevel model

Variable Unit Abbrev. Description

Dependent variable

Block-group-level

Urban growth rate urban growth rate between 2006 (%) UG (2006-2011) and 2011

Independent variables

Block-group-level

average slope within block group Average slope (°) ASlop boundary

Road density within block group Road density (m-1) RDen boundary

Median housing value Median housing value within ($) MHous (2010) block group boundary

City-level

Population growth rate Population growth rate between (%) PG (2006-2011) 2006 and 2011

Unemployment rate unemployment rate between (%) UEmp09b (2007-2009) 2007 and 2009

Table 4.2 - continued

Variable Unit Abbrev. Description

Unemployment rate unemployment rate between (%) UEmp09a (2009-2011) 2009 and 2011

protected land percentage within Protected land percentage (%) Protect city boundary

Compact development (binary) CmpDev policy of compact development (2009)

Mixed use development policy of mixed land use (binary) MxdDev (2009) development

Transport-oriented policy of transport-oriented (binary) TODev development (2009) development

Infill development (binary) IfDev policy of infill development (2009)

4.4.3.3 Build the “null” model in the multilevel model framework

Level 1: UGij = b0j + rij

Level 2: b0j = r00 + u0j

In the level-1 model, UGij, is the urban growth rate of the ith block group in the jth city, b0j is the mean of the urban growth rate for the jth city across all its block groups, and rij is the difference between city j’s average urban growth rate and local urban growth rate at block group

i. In the level-2 model, r00 is the overall mean across cities, u0j is the difference between j’s average urban growth rate and the overall mean across cities.

The mixed model produced by substituting the level-2 model into the level-1 model is

UGij = r00 + u0j + rij

The null model is a model without any predictors, and it is used to examine whether cities differ from each other, on average, on urban growth rate. It decomposes the total variance into between-city variance and within-city between-block-group variance . The significance of 𝟐𝟐 𝟐𝟐 city effects indicates the𝝈𝝈 𝐮𝐮𝟎𝟎multilevel model is needed to analyze the relationsh𝝈𝝈𝐫𝐫 ips between the independent variables and the dependent variable instead of single-level multiple linear regression model. The significance of city effects can be examined by a likelihood ratio test.

4.4.3.4 Adding block-group level variables (level 1) in the multilevel model

Level 1: UGij = b0j + b1jASlopij + b2jRDenij + b3jMHousij + rij

Level 2: b0j = r00 + u0j; b1j = r10; b2j = r20; b3j = r30

The mixed model produced by substituting the level-2 model into the level-1 model is

UGij = r00 + r10 ASlopij + r20 RDenij + r30MHousij+ u0j + rij

The newly added level-1 variables are average slope (ASlopij), road density (RDenij) and

median housing value (MHousij) at block group scale (refer Table 4.2). We will examine their

relationships with local urban development. For example, building more roads will increase or

decrease local urban growth rate by holding the other two variables of average slope and median

housing value constant? Is it a significant variable on local urban growth?

The addition of block-group level variables in the model is expected to reduce level 1 variance

. Besides, a reduction in level 2 between-city variance level variables is also possible if 𝟐𝟐 𝟐𝟐 the𝝈𝝈𝐫𝐫 composition of the cities regarding these variables is not𝝈𝝈𝐮𝐮𝟎𝟎 equal (HOX 2000; Fontes 2010).

4.4.3.5 Including both block-group level variables (level 1) and city level variables

(level 2) in the multilevel model

Level 1: UGij = b0j + b1jASlopij + b2jRDenij + b3jMHousij + b4jPGij + b5jUEmp09bij +

b6jUEmp09aij + b7jProtectij + b8jCmpDevij + b9jMxdDevij + b10jTODevij +

b11jIfDevij + rij

Level 2: b0j = r00 + u0j; b1j = r10; b2j = r20; b3j = r30; b4j = r40; b5j = r50; b6j = r60; b7j =

r70; b8j = r80; b9j = r90; b10j = r100; b11j = r110

The mixed model produced by substituting the level-2 model into the level-1 model is

UGij = r00+ r10 ASlopij + r20 RDenij + r30MHousij+ r40 PGij + r50 UEmp09bij +

r60 UEmp09aij + r70 Protectij + r80 CmpDevij + r90 MxdDevij +

r100 TODevij + r110 IfDevij + u0j + rij

The newly added level-2 variables are Population growth rate (PGij), unemployment rate

between 2007 and 2009 (UEmp09bij), unemployment rate between 2009 and 2011 (UEmp09aij),

Protected land percentage (Protectij), policy of compact development (CmpDevij), policy of

mixed land use development (MxdDevij), policy of transport-oriented development (TODevij),

and policy of infill development (IfDevij) at city scale (refer to Table 4.2). We will further

examine their relationships with local urban development together with level-1 variables. For

example, a city with compact development policy and without this policy has different effects on

local urban growth keeping other variables have the same values? If so, is this effect significant on local urban growth?

CHAPTER 5

RESULTS

5.1 Urban sprawl in Metro Orlando

5.1.1 Distance of peak development based on housing densities

A high spatial variation existed when mapping residential densities by four urban-rural categories for all three principal cities (Figure 5.1). The most densely developed residential areas, urban category by 1,483 and more units per km2, appeared located away from Orlando’s city center. A similar pattern was shown for the other two much smaller principle cities.

On statistical plots, low to medium densities appeared to stretch out through the entire city range for all of the principle cities under investigation (Figure 5.2). In contrast, places with highest residential densities (i.e., urban densities represented in red) are skewed right for Orlando while being scattered for the cities of Kissimmee and Sanford (Figure 5.2). This indicates high- density urban residential development is closer to city center in Orlando than in the other two cities.

Orlando was shown as the least sprawling city among the three cities under investigation, when determined by the cutoff distance threshold that encloses the majority (75%) of city’s urban residential units (Table 5.1). The relative distance of peak development for Orlando is 0.17, which indicates urban residential units (>1,483 units/km2) concentrated in a range less than 1/5 of Orlando’s city radius. Such range is above 1/3 of the city’s radius for Kissimmee (0.33) and

Sanford (0.37).

(b) Sanford

(a) Orlando

Figure 5.1 Spatial distribution of cell-based residential densities of four urban-rural categories for the cities of Orlando (a), Sanford (b), and Kissimmee (c)

(a).

(b).

(c).

Figure 5.2 Statistical pattern of four types of residential densities cells against relative distance to city center for the cities of Orlando (a), Kissimmee (b) and Sanford (c) 63

(a).

(b).

(c).

(b) Sanford

(a) Orlando

Figure 5.4 Land-use diversity was counted as the total number of land-use types within each 1-km2 cells.

(b) Sanford

(a) Orlando

Figure 5.5 Land-use evenness was calculated using Shannon’s entropy index, which ranges between 0 and 1 with a higher value indicating higher degree of mixed use.

Table 5.1 Summary of sprawl indices of density, mixed use and accessibility and overall sprawl index for the cities of Orlando, Kissimmee and Sanford

Housing Mixed Land Use Overall sprawl Accessibility City Density (weighted Diversity Evenness (W.=1.0) (W.=1.0) average) (W.=0.5) (W.=0.5)

Orlando 0.17 0.57 0.80 0.53 0.35

Kissimmee 0.33 0.35 0.66 0.66 0.37

Sanford 0.37 0.47 0.77 0.63 0.41

W. represents weight

5.1.2 Mixed land use

In our study, a city’s level of sprawling in terms of mixed land use is represented by diversity

and evenness indices. We used 3 (the median value of diversity) as a threshold to divide the

diversity measure into two categories, i.e., low diversity (<3) and high diversity (>=3). The

proportion of cells with low diversity was 0.57 for Orlando, 0.35 for Kissimmee, and 0.53 for

Sanford (Figure 5.6). In other words, Orlando was dominated by low-diversity land-use cells; therefore, it has the highest level of urban sprawl among the three investigated cities according to this diversity index (Table 5.1).

Similarly, we used 0.5 (the median value of evenness) as a threshold to divide the evenness measure into two categories, i.e., low evenness (<0.5) and high evenness (>=0.5). The proportion of cells with low evenness was 0.80 for Orlando, 0.66 for Kissimmee, and 0.77 for

Sanford (Figure 5.7). This indicates that, in terms of evenness, Orlando was dominated by high-

evenness land-use cells; therefore, it has the highest level of urban sprawl among the three cities

(Table 5.1).

Figure 5.6 Proportions of the cells with low and high diversities for the cities of Orlando, Kissimmee and Sanford

Figure 5.7 Proportions of the cells with low and high evenness for the cities of Orlando, Sanford, and Kissimmee

5.1.3. Accessibility to service areas

For city of Orlando, four economic centers were identified (Figure 5.8). One is close to the city’s center marked by city hall, and another two are located within 15 miles from the city center. The fourth one is located about 30 miles away from the city center. Only one economic center was identified for Kissimmee and for Sanford.

(b) Sanford

(a) Orlando

Figure 5.8 Economic centers of service parcels for the cities of Orlando (a), Sanford (b), and

Kissimmee (c) 69

The histogram of parcels’ location to their nearest urban economic centers is skewed

right in Orlando (Figure 5.9 (a)). Most of the residential-dominated land areas have the relative

accessible distances between 0.10 and 0.60. For the cities of Kissimmee (Figure 5.9 (b)) and

Sanford (Figure 5.9 (c)), most of the residential-dominated land areas have the relative accessible distances between 0.3 and 0.7. These indicate that, removing the factor that Orlando being a much larger city in size by area, Orlando’s urban residents are served by economic centers in a relatively closer distance than those lived in the other two cities.

Our residential accessibility to urban economic centers index (Table 5.1), i.e., the relative distance within which 75 percent of urban residents have been served by at least one city’s urban economic centers, indicates that city of Orlando (0.53) is less sprawling than Kissimmee (0.66) or Sanford (0.63). In other words, three quarters of residential-dominated land for Orlando are located within about half of its urban development radius, while those for Kissimmee are located

within about six tenths of its urban development radius and Sanford about seven tenths.

Therefore, among the three principal cities, Kissimmee is the most sprawled city, Sanford is in

the middle, and Orlando is the least sprawled city.

5.1.4 Integrated evaluation of urban sprawl

In our study, the integrated sprawl index was an average weighted equally based on

distance of peak residential development, mixed land use (separated into diversity and evenness)

and residential accessibility to urban economic centers (Table 5.1). According to this integrated index, Sanford is the most sprawled city and Orlando is the least sprawled city.

(a)

(b)

(c).

5.2 Urban sprawl across Florida’s four metropolitan statistical areas

5.2.1 Average livable space per residential unit

Metro Tampa had 4799 cells that were occupied by at least one housing unit (Table 5.2).

The average livable space per residential unit in Tampa metropolitan area is 2283.8 (sq.ft./res. unit), with a variation from 385 to 17182.2 and the standard deviation of 1008.77 (sq.ft./res. unit).

The largest ALS values, indicating bigger residential units on average, distributed in the north and middle part of Tampa metropolitan area (i.e., cells shown in the red color in Figure 5.10 (a)).

Most of these places were located away from the principal cities such as Tampa and St.

Petersburg, where the ALS values are much lower (i.e., cells with light blue color in Figure 5.10

(a)). Close to the Tampa Bay area the ALS values are lower than the MSA’s average, indicating smaller residential units on average.

Table 5.2 Means and standard deviations of average livable space per residential unit

Livable space # of cells Mean Std. Min. Max.

(sq. ft./res. unit) Dev.

Jacksonville MSA 3857 2159.16 1053.86 228 26811.54

Orlando MSA 4376 2138.69 1859.58 160 41707.77

Tampa MSA 4799 2283.80 1008.77 385 17182.22

Miami MSA 3745 2336.41 1388.89 306.9 26746

(a) (b

(c (d)

Figure 5.10 Sprawl index of average livable space per residential unit on 1-km2 USNG cells

Metro Orlando has 4376 ALS values at 1 km2 USNG cell scale. The sprawl index in

terms of residential/housing size for the entire Metro Orlando is 2138.69 (sq.ft./res. unit), and the

ALS values vary from 160 to 41707.77 (sq.ft./res. unit), with a standard deviation of 1859.58

(Table 5.2). Most of the Orlando MSA has low ALS values (lower than the average), such as in

the city of Orlando and nearby areas including cities of Kissimmee and Sanford, and the

northwest part of Orlando MSA (see the cells with light blue color in Figure 5.10 (b)). The most

of high ALS values are cluster distributed in the west part outside of Orlando MSA (see the cells

with red color in Figure 5.10 (b)). These areas include many resorts near Disney. There are also a

few small clusters with high ALS values, such as Alaqua Lakes luxury homes.

Metro Jacksonville has 3857 ALS values at 1 km2 USNG cell scale. The sprawl index in

terms of residential/housing size for the entire Metro Jacksonville is 2159.16 (sq.ft./res. unit),

and the ALS values vary from 228 to 26811.54 (sq.ft./res. unit), with a standard deviation of

1053.86 (Table 5.2). More than half of the cells with ALS values in the Jacksonville MSA have low ALS values (lower than the average), and in the center of principal city of Jacksonville, the

ALS values are smaller and away from the city center, the ALS values are higher(Figure 5.10

(c)). There is one notable large cluster with high ALS values in the Palm Valley. Also, there are some cells with high ALS values along the east shore of St Johns River (Figure 5.10 (c)).

Metro Miami has 3745 ALS values at 1 km2 USNG cell scale. The sprawl index in terms of residential/housing size for the entire Metro Miami is 2336.41 (sq.ft./res. unit), and the ALS values vary from 306.9 to 26746 (sq.ft./res. unit), with a standard deviation of 1388.89 (Table

5.2). Most of the principal cities in Miami MSA have low ALS values, such as cities of Miami,

Fort Lauderdale, West Palm Beach and so forth (see the cells with light blue color in Figure 5.10

(d)). Away from these principal cites, the ALS values are increased. There are two notable high

ALS values’ concentrations. One high ALS cluster is located in the south part of Miami MSA,

close to Biscayne Bay, and includes the villages of Pinecrest and Palmetto Bay. The other high

ALS cluster is located in the north west part of Miami MSA, and inside the Wellington village

(see the cells with red color in Figure 5.10 (d)).

Comparing the means of residential/housing size among the four metropolitan areas

(Table 5.2), Miami MSA has the largest average housing space. Tampa MSA ranks the second,

following by Jacksonville MSA and then Orlando MSA.

5.2.2 Land use diversity

Metro Tampa has 2613 HHI values at 1 km2 USNG cell scale. The average of HHI values for the entire Metro Tampa is 0.8819, with the lowest value of 0.2789, the highest value of 1 and a standard deviation of 0.1526 (Table 5.3).

Table 5.3 Means and standard deviations of land use diversity

Land use diversity # of cells Mean Std. Min. Max.

Dev.

Jacksonville MSA 2011 0.9034 0.1548 0.3099 1

Orlando MSA 2610 0.9062 0.1565 0.3025 1

Tampa MSA 2613 0.8819 0.1526 0.2789 1

Miami MSA 2894 0.8798 0.1597 0.2959 1

Metro Orlando has 2610 HHI values at 1 km2 USNG cell scale. The average of HHI values for the entire Metro Orlando is 0.9062, with the lowest value of 0.3025, the highest value of 1 and a standard deviation of 0.1565 (Table 5.3).

Metro Jacksonville has 2011 HHI values at 1 km2 USNG cell scale. The average of HHI values for the entire Metro Jacksonville is 0.9034, with the lowest value of 0.3099, the highest value of

1 and a standard deviation of 0.1548 (Table 5.3).

Metro Miami has 2894 HHI values at 1 km2 USNG cell scale. The average of HHI values

for the entire Metro Miami is 0.8798, with the lowest value of 0.2959, the highest value of 1 and

a standard deviation of 0.1597 (Table 5.3).

According to the statistical summary (Table 5.3), most cells in the four metropolitan

areas have very high values of monopoly (which is also indicated by cells with red color in

Figure 5.11 (a), (b), (c) and (d)). It means a cell is either dominated by one of the residential,

commercial, industry, or institutional uses. Our goal is to measure land use diversity among

urban areas, therefore, agricultural or development-prohibited areas were excluded from the

analysis.

Even though most of the cells have high monopoly, we can still see some places, such as

Tampa, Pinellas Park, and Largo have relative better mixed land uses in the Tampa metro area;

Leesburg, Kissimmee, Orlando, and Sanford have relative better mixed land uses in the Orlando

metro area; Jacksonville and St. Augustine have relative better mixed land uses in the

Jacksonville metro area; Miami, West Palm Beach and Fort Lauderdale have relative better

mixed land uses in the Miami metro area (see the cells with light green and orange color in

Figure 5.11 (a), (b), (c) and (d)).

(a) (b)

Figure 5.11 Sprawl index of land use diversity on 1-km2 USNG cells

5.2.3 Accessibility to business hubs

Metro Tampa has 833 residential accessibility values at 1 km2 USNG cell scale. The average travel distance per residential unit from the residential dominated places to their nearest business hubs for the entire Metro Tampa is 7.267 (mile), and the accessibility values vary from

0.233 to 23.431 (mile), with a standard deviation of 4.64 (Table 5.4).

Metro Orlando has 1291 residential accessibility values at 1 km2 USNG cell scale. The average travel distance per residential unit from the residential dominated places to their nearest business hubs for the entire Metro Orlando is 8.647 (mile), and the accessibility values vary from

0.755 to 32.842 (mile), with a standard deviation of 5.214 (Table 5.4).

Metro Jacksonville has 570 residential accessibility values at 1 km2 USNG cell scale. The average travel distance per residential unit from the residential dominated places to their nearest business hubs for the entire Metro Jacksonville metro area is 9.717 (mile), and the accessibility values vary from 0.735 to 53.136 (mile), with a standard deviation of 9.833 (Table 5.4).

Metro Miami has 733 residential accessibility values at 1 km2 USNG cell scale. The average travel distance per residential unit from the residential dominated places to their nearest business hubs for the entire Metro Miami metro area is 6.536 (mile), and the accessibility values vary from 0.414 to 20.211 (mile), with a standard deviation of 4.914 (Table 5.4).

For Tampa and Miami MSAs, most of the distance costs are within 20 miles (see the cells with blue colors in Figure 5.12 (a) and (d)); For Orlando MSA, most of the distance costs are within 20 miles, and a few of them are between 20 and 40 miles (see the cells with green and yellow colors in Figure 5.12 (b)); For Jacksonville MSA, the distance-cost range are extended to

60 miles. In the south west part away from city of Jacksonville, there are quite many residential- dominated places with large distance costs.

Table 5.4 Means and standard deviations of accessibility to business hubs

Accessibility # of cells Mean Std. Min. Max.

(miles/res. unit) Dev.

Jacksonville MSA 570 9.717 9.833 0.735 53.136

Orlando MSA 1291 8.647 5.214 0.755 32.842

Tampa MSA 833 7.267 4.64 0.233 23.431

Miami MSA 733 6.536 4.914 0.414 20.211

On average, based on the accessibility sprawl index, Jacksonville MSA has the largest

distance cost to the nearest economic centers, which shows the most sprawling characteristic.

Miami MSA has the least distance cost to access the nearest economic centers, which shows the

least sprawling characteristic. Orlando and Tampa MSAs have the moderate distance costs

between Metro Jacksonville and Miami, and Tampa MSA is less sprawling than Orlando MSA.

5.2.4 Composite sprawl index

Regarding the sprawl index of average livable space per residential unit (ALS), the Z

scores of the four metro areas are 1.115 in Miami MSA, 0.566 in Tampa MSA, -0.733 in

Jacksonville MSA, and -0.947 in Orlando MSA; Regarding the sprawl index of land use

(a (b)

Figure 5.12 Sprawl index of accessibility to business hubs on 1-km2 USNG cells

diversity, the Z scores of the four metro areas are 0.962 in Orlando MSA, 0.761 in Jacksonville

MSA, -0.786 in Tampa MSA and -0.937 in Miami MSA; Regarding the sprawl index of accessibility to business hubs, the Z scores of the four metro areas are 1.181 in Jacksonville

MSA, 0.426 in Orlando MSA, -0.545 in Tampa MSA and -1.061 in Miami MSA. The composite sprawl index for Jacksonville MSA is 1.207; for Orlando MSA, it is 0.441; for Tampa MSA, it is

-0.766; for Miami MSA, it is -0.883 (Table 5.5). Therefore, according to the composite sprawl index, Jacksonville MSA is the most sprawling metropolitan area, Orlando MSA is in the second,

Tampa MSA is in the third, and Miami MSA is the least sprawling metropolitan area.

Table 5.5 Sprawl Z scores and ranking

Livable Land use Accessibility Composite Rank space diversity (miles/res. index (sq. ft./res. unit) unit)

Jacksonville -0.733 0.761 1.181 1.207 1 MSA

Orlando -0.947 0.962 0.426 0.441 2 MSA

Tampa 0.566 -0.786 -0.545 -0.766 3 MSA

Miami 1.115 -0.937 -1.061 -0.883 4 MSA

5.3 Urban growth drivers across Florida cities: A study at city scale

5.3.1 Policy actions’ impacts on urban growth

In terms of four land use policy actions, Table 5.6 shows the t test results for the three city groups. We can see that different policy actions have different influences on urban growth for the three city groups. For the 1st city group (i.e. all-sized cities), urban growth of the cities with the policy action of infill development was significantly lower than the cities without this policy action, which indicates that the policy action of infill development effectively discourage urban sprawl. For the 2nd city group (i.e. small-sized cities), transport-oriented and infill development policy actions effectively discourage urban sprawl. For the 3rd city group (i.e. large/medium-sized cities), none of the land use policy actions are effective for controlling urban

sprawl. Therefore, based on t test results, it can be concluded that not all policy actions work on

fighting urban sprawl. In addition, it seems that performing land use polices to manage urban

sprawl is very difficult for large/medium-sized cities.

5.3.2 OLS global model

From the regression results (see Table 5.7.), we found that for all-sized Florida cities,

population growth, average slope, median housing value, infill development policy action are the

four variables to be included in the OLS regression model, associated with an Adjusted R-

Squared value of 0.37. In the model, with the increase of either population growth or average

slope, or both, urban growth goes up, while with the increase of median housing value or with

being implemented infill development policy, urban growth is decreased. The most significant

variables on urban growth are population growth rate with a coefficient of 0.19 and average

slope with a coefficient of 3.37.

Table 5.6 T test results for the three city groups in terms of four land use policy actions

Land use 1st city group: all-sized 2nd city group: small-sized 3rd city group: large/medium- policy actions cities cities sized cities (pop. > 50,000) (pop. > 2,500) (pop. 2,500 ~ 50,000) ugr0 ugr1 p-value ugr0 ugr1 p-value ugr0 ugr1 p-value Compact 0.08 0.10 0.64 0.09 0.11 0.62 0.07 0.06 0.29 development Mixed use 0.08 0.12 0.23 0.09 0.13 0.29 0.06 0.08 0.54 development Transport- 0.09 0.05 0.07 0.10 0.03 2.5e-05 0.06 0.07 0.82 oriented development Infill 0.09 0.04 5.6e-03 0.10 0.03 2.6e-06 0.07 0.06 0.88 development Table abbreviations: pop. is for population; ugr0 represents cities without policy actions; ugr1 means cities with policy actions

For small-sized cities, population growth, average slope, median housing value, transport-oriented and infill development policy actions are the five variables to construct the

OLS regression model, associated with an Adjusted R-Squared value of 0.39. In the model, with

the increase of either population growth or average slope, or both, urban growth goes up, while

with the increase of median housing value or with being implemented transport-oriented

development policy or infill development policy, urban growth is decreased. The most

significant variables on urban growth are the same as in the 1st city group, which are also

population growth rate with a coefficient of 0.18 and average slope with a coefficient of 3.88.

For large/medium-sized cities, population growth, protected area proportion, median housing

value are the three variables to construct the global model, associated with an Adjusted R-

Squared value of 0.36. In the model, with the increase of population growth, urban growth goes 83

Table 5.7 OLS global model results for the three city groups

1st city group: all cities 2nd city group: small cities 3rd city group: large/medium (pop. > 2,500) (pop. 2,500 ~ 50,000) cities (pop. > 50,000)

Variables Coef. Sig. AdjR2 Coef. Sig. AdjR2 Coef. Sig. AdjR2 0.37 0.39 0.36 Population growth 0.19 * 0.18 * 0.24 * rate Average slope 3.37 * 3.88 * ―― ――

Protected area ―― ―― ―― ―― -0.19 *

Unemployment ―― ―― ―― ―― ―― ―― rate Median housing -3.0e- -2.0e- -4.0e- value 06 06 06 Compact ―― ―― ―― ―― ―― ―― development Mixed use ―― ―― ―― ―― ―― ―― development Transport- ―― ―― -0.63 ―― ―― oriented development Infill development -1.82 -1.62 ―― ―― Table abbreviations: Coef. is for coefficient; Sig. is for variable significance; AdjR2 is for Adjusted R-Squared; * indicates a statistically significant p-value (p < 0.01)

up, while with the increase of protected area proportion or median housing value or both, urban growth is decreased. The most significant variables on urban growth are population growth rate with a coefficient of 0.24 and protected area proportion with a coefficient of -0.19.

The results support that population is the number one important driving factor to boost

urban sprawl, which is consistent with the conclusions from previous studies. The slope variable has a significantly positive relationship with urban growth, which means higher average slope is associated with faster urban growth. For medium/large-sized cities, protected land plays a significant role on discouraging urban sprawl compared to small-sized cities. Some policy actions (i.e. transport-oriented development and infill development) are helpful to inhibit urban growth, especially for small-sized cities but they are not significantly influential on controlling urban growth at all. In other words, policy implementation is, after all, a soft tool on controlling urban sprawl, which is not as effective as controlling population’s growth on fighting urban sprawl.

5.3.3 GWR results

OLS model provides an uniform impact on urban growth for each driving factor included in the model for the whole Florida, and each driving factor’s influence on urban growth do not vary with different cities. The most significant global driving factors related with urban growth are excavated by the proposed global OLS regression model. Global trend show that for both small-sized city group and all-sized city group, population growth rate and average slope are prominently influential on urban growth, and they encourage urban growth. For medium/large- sized city group, population growth rate and protected land are prominently influential on urban growth. Population growth rate boost urban growth while protected land discourage urban growth. GWR model can visually show the local variations of each driving factor at different locations, i.e. how a factor makes different contributions on affecting urban growth of varied cities. The model provides more details of each factor’s influences on urban growth, such as in

which cities population growth increase urban growth heavily and where protected land helps to

discourage urban growth most.

Regarding GWR results, factors with negative parameters should be paid attention, specifically with large negative values. They are helpful to discourage urban sprawl and promote urban sustainable development. Figure 5.13 shows GWR results on the most significant driving

factors for three city groups, i.e. all-sized city group, small-sized city group, and medium/large

city group. Each subfigure shows the varying parameter of each significant variable among cities

at different locations. In subfigure, cities with red color indicate the variable have greater impact

on urban growth. On the contrary, cities with yellow color indicate the variable have less impact

on urban growth.

For all-sized city group and small-sized city group, population growth rate play a greater

role on boosting urban growth among cities in the northern and middle part than those in the

southern part of Florida (see Figure 5.13 (a) and (c)). While for medium/large-sized city group,

population growth rate play a greater role on boosting urban growth among cities in the middle

part than those in the southern part of Florida (see Figure 5.13 (e)). For all-sized city group,

mean slope has a greater impact on urban growth among cities in the south than those in the

middle and north of Florida (see Figure 5.13 (b)). While for small-sized city group, the pattern

reverses (see Figure 5.13 (d)). For medium/large-sized city group, protected area percentage

plays a greater role on inhibiting urban growth in the south than other parts of Florida (Figure

5.13 (f)). Especially, the increase of protected area inhibits the increase of urban development in

Miami metropolitan area greatly.

(a) (b)

(e) (f)

Figure 5.13 GWR for all-sized cities, small-sized cities and medium/large-sized cities

5.4 Urban growth drivers examined with multilevel modeling approaches

5.4.1 Examine four urban growth control policies’ impacts at Census block-group scale

At Census block-group level, t test results for three city groups regarding four land use group control policies are showed in Table 5.8. The city-level’ policy implementations have varied impacts on urban development at census block-group units inside different groups of cities. Compact development policy has not significant impacts on urban development inside the block-group units for all of the three groups of cities. With the mixed use policy, the urban development inside the block-group units for all of the three groups of cities increased compared to the cities without this policy, which has significant positive influences on urban growth among cities. Regarding the policy actions of transport-oriented development and infill development, they are able to significantly decrease urban development for the small-sized cities, while the trends are reverse for the medium and large-sized cities.

5.4.2 Examining city effects

For each group of cities, a likelihood ratio test is performed comparing the null multilevel model with a null single-level model. The likelihood ratio (LR) is calculated as two times the difference in the log likelihood values for the two models. For all-sized cities, LR is 283.48. For small-size cities, LR is 142.596. For medium and large size cities, LR is 92.36. Since 5% point of a chi-squared distribution on 1 d.f. is 3.84, there is strong evidence of city effects on urban development for all the three groups of cities.

For all cities included, the overall mean urban growth rate across cities is estimated as 6.8%

(Table 5.9). The mean urban growth for city j is estimated as 6.8%+ u0j, where u0j is the random

Table 5.8 T test results at block group level for three city groups regarding four land use policies

Land use policies Urban growth rate (%)

All cities Small-sized Medium/large- included cities sized cities Compact development

with policy 6.0 6.72 5.18 (CmpDev =1) without policy 5.37 6.81 4.68 (CmpDev =0) t-test sig. 0.2707 0.9278 0.4695

Mixed use development with policy 7.27 8.37 6.2 (MxdDev =1) without policy 5.19 6.5 4.57 (MxdDev =0) t-test sig. 0.0006827*** 0.05862. 0.02747*

Transport-oriented development with policy 5.13 3.55 5.86 (TODev =1) without policy 5.45 7.09 4.59 (TODev =0) t-test sig. 0.5252 4.646e-08*** 0.06628.

Infill development

with policy 4.63 3.75 5.59 (IfDev =1) without policy 5.47 7.06 4.68 (IfDev =0) t-test sig. 0.1135 4.984e-07*** 0.2961

Note: Sig. levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

effect of city j on urban growth. A city with u0j >0 has a mean of urban growth rate that is

higher than average, while u0j<0 for a below-average city. From the result, it can be found that

the between-city variance is estimated as 25.87, and the within-city variance is estimated as

116.27, which indicates that 18.2% of the variance in urban growth can be attributed to differences between cities.

For small-sized cities, the overall mean urban growth rate across cities is estimated as 7.5%

(Table 5.9). It can be found that the between-city variance is estimated as 35.04, and the within-

city variance is estimated as 130.53, which indicates that 21.2% of the variance in urban growth

can be attributed to differences between cities. For medium and large size cities, the overall

mean urban growth rate across cities is estimated as 5.1% (Table 5.9). The between-city variance

is estimated as 7, and the within-city variance is estimated as 108.9, which indicates that 6% of

the variance in urban growth can be attributed to differences between cities. The statistical

summary in Table 5.9 indicates that overall mean urban growth is relative slow for medium and

large size cities during 2006-2011.

5.4.3 Analysis of block-group level variables’ effects on local urban development

According to Table 5.10, the variables of mean slope and road density at block-group level have significant effects (p <.0001) on local urban development for three groups of cities, while the variable of median housing value only moderate-significantly (p <0.1) impacts on urban growth rate for the medium and large size cities. For example of medium and large size cities, for every 1 standard deviation of increase in mean slope, urban growth rate increases by around 0.7 percent, controlling for the variables of road density and median housing value; for every 1 standard deviation of increase in road density, urban growth rate decreases by around 0.9

percent, controlling for the variables of mean slope and median housing value; for every 1

standard deviation of increase in median housing value, urban growth rate increases by around

0.4 percent, controlling for the variables of mean slope and road density. Compared with the

other two groups of cities, the urban growth rate that either increase or decreases with each

variable changes relatively slow in the medium and large size cities.

Besides, compared level-1 models (Table 5.10) with the null models (Table 5.9), we can

find that the addition of the variables of mean slope, road density and median housing value has

reduced the amount of variance at both the city level and the block group level. For example of

all size cities, the between-city variance has reduced from 25.87 to 22.79, and the within-city

variance has reduced from 116.27 to 115.03. After accounting for the effects of block-group

level variables, the proportion of unexplained variance that is due to differences between cities

decreased a lot from 18.2% to 16.5%.

5.4.4 Analysis of block-group and city level variables’ effects on local urban development

The two-level models (Table 5.11) show the results for final multilevel modelling for

three groups of cities. We can see that for the three groups of cities, the variables that have significant impacts on local urban development are slightly different.

In the three groups of cities, the variables of mean slope, road density (at block-group level), and population growth rate (at city level) have the same significant effects on local urban development. For example, the variables of mean slope and population growth rate have the positive effects on urban growth rate; the road density variable negatively impacts on local urban development. The influence of physical condition (topography and road density) on local urban growth is weaker than the spontaneous population growth. For the variable of median housing

Table 5.9 Testing for city variation effects of three groups of cities

All cities Small-sized cities Medium/large-sized included cities

Fixed Intercept only components (Intercept) 6.772*** 7.545*** 5.141*** (0.518) (0.714) (0.516) Variance of random components Between cities 25.87*** 35.04*** 6.987*** 𝟐𝟐 Within cities 𝝈𝝈𝐮𝐮𝟎𝟎 116.27 130.53 108.919 𝟐𝟐 Log likelihood 𝝈𝝈𝐫𝐫 -17729.3 -6181.6 -11528.1

AIC 35464.6 12369.2 23062.3

Number of 4640 at block 1585 at block group 3055 at block group obs: group level, 122 level, 89 at city level level, 33 at city at city level level

Note: Standard errors are given in parentheses Signif. levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

value, it only has significant influence on local urban development in the medium and large size cities, and the relationship is positive which may indicate that the higher housing price motivates to build more housing quickly in large cities.

At city level, the variable of unemployment rate has a moderate significance (p <0.1) on local urban development in all-size cites. For every 1 standard deviation of increase in unemployment rate, urban growth rate decreases by around 0.7 percent, controlling for other variables unchanged. The variable of protected land percentage has a strong significance 92

Table 5.10 Multilevel models with block-group level variables for three groups of cities

All cities included Small-sized cities Medium/large-sized cities

Fixed Level 1 components (Intercept) 6.579*** 7.21*** 5.089*** (0.492) (0.665) (0.515) Mean slope 0.868*** 1.038* 0.733*** (0.188) (0.375) (0.212) Road density -1.057*** -1.602*** -0.917*** (0.171) (0.39) (0.186)

Median housing 0.204 -0.198 0.367. value (0.167) (0.311) (0.196) Variance of random components Between cities 22.79*** 28.41*** 6.975*** 𝟐𝟐 𝝈𝝈𝐮𝐮𝟎𝟎 115.03 129.57 107.516 𝟐𝟐 Log likelihood 𝝈𝝈𝐫𝐫 -17698.9 -6169.0 -11508.5 AIC 35409.9 12350.1 23028.9

Number of Obs.: 4640 at block 1585 at block 3055 at block group group level, 122 at group level, 89 at level, 33 at city level city level city level Note: standard errors are given in parentheses Signif. levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 5.11 Multilevel models with both block-group level and city level variables for three groups of cities

All cities included Small-sized cities Medium/large- sized cities

Fixed components Level 1

(Intercept) 5.914*** 6.225*** 5.243*** (0.482) (1.003) (0.668)

Mean slope 0.816*** 0.85* 0.748*** (0.187) (0.371) (0.212)

Road density -1.049*** -1.559*** -0.924*** (0.17) (0.387) (0.185)

Median housing 0.211 -0.139 0.408* value (0.166) (0.309) (0.196)

Level 2

Population growth 1.749*** 1.656*** 2.655*** rate (0.249) (0.283) (0.726)

Unemployment rate -0.658. -0.751 0.961 (0.362) (0.578) (0.87)

Protected area -0.227 0.523 -1.036* percentage (0.391) (0.606) (0.413)

Compact -1.644 -1.307 -2.938. development (1.391) (1.862) (1.532)

Mixed land use 1.601 1.613 0.361 (1.423) (1.835) (2.001)

Transport-oriented -0.673 0.281 -0.427 development (2.031) (3.36) (1.94)

Infill development -1.169 -2.01 1.426 (1.983) (3.112) (1.737)

Table 5.11 - continued

All cities included Small-sized cities Medium/large- sized cities

Variance of random components

Between cities 12.05*** 15.84*** 2.402*** 𝟐𝟐 𝝈𝝈𝐮𝐮𝟎𝟎 115.10 129.58 107.509 𝟐𝟐 𝝈𝝈𝐫𝐫 Log likelihood -17672.3 -6151.3 -11496.1

AIC 35370.6 12328.6 23018.1

Number of Obs.: 4640 at block group 1585 at block group 3055 at block level, 122 at city level, 89 at city level group level, 33 at level city level

Note: standard errors are given in parentheses Signif. levels: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(p <0.05) on local urban development in medium and large cites. For every 1 standard deviation of increase in protected land percentage, urban growth rate decreases by around 1 percent, controlling for other variables unchanged.

Among the four city-level sustainable urban development policies, it is noticed that the policy of compact development plays a moderate significant role on local urban development (p

<0.1), while other land use policy variables are not significant (Table 5.11). Holding other 95

variables, we predict a 2.9 decrease in the urban growth rate in the cities with the compact development policy compared with the cities without this land use policy for the medium and large size cities.

Compared to the AIC values in the previous models (Table 5.9 and 5.10) for the three groups of cities, the final multilevel models are much better. The AIC values keep decrease gradually. In addition, both of the between-city variance and the within-city variance are decrease with introducing the block-group-level and city-level variables (Table 5.9, 5.10 and

5.11).

CHAPTER 6

DISCUSSION

6.1 The urban sprawl measures

Housing density is one of the commonly adopted sprawl indices to measure the degree of urban sprawl. It usually refers to the number of households or housing units per unit area (e.g., square kilometer or square mile). In most previous research examining levels of urban sprawl at the city scale, housing density indices were estimated based on population, household, or housing units at census scales, much coarser than parcel data. In this study, we adopted the traditional concept of housing units estimation but applied it to parcel data. In addition, we created a sprawl index, called distance of peak development based on housing densities. This index proposed from part I was used to characterize the distribution of residential development along the distance to urban center. Thus, the fine-scale spatially explicit parcel information has been transformed to measure urban sprawl at the aggregated city scale, with a potential to be used in other geographic scales (e.g., county, state, or other user-defined scales).

The determination of the peak development distance is based on urban residential units

(1,483 units per 1-km2) in the present analysis. We focused our analysis on high-density housing,

because such concentrated development may have less profound ecological and environmental

impacts than scattered low-density housing does (Gilbert 1996; Martinuzzi et al. 2007; Shen et al.

2013). Therefore, we believe peak development distance drawn based on high-density residents is a reasonable measure for sustainability. The shorter distance of peak development corresponds to more concentrated high-density development and, therefore, a lower level of urban residential sprawl.

The index of residential accessibility to urban economic center from part I borrowed concept of accessibility used in literature (Turner et al. 2003). The present estimation of accessibility is based on Euclidean distance. The determination of urban economic centers are based directly on parcel’s land use information, since according to literature a city may have one or several economic centers, depending on its history and complexity according to literature.

Previous studies showed that industry and commerce agglomerations are usually urban economic centers (Quigley 1998; Cuthbert and Anderson 2002).

In the study from part II, the sprawl index of average livable space per residential unit measures the housing space for each residential unit on average. Residential units with large space livable area style are commonly seen in single family homes on large size lots. This type of large-lot single-family residential consume more land and are defined as low-density type sprawl development in many scholars’ literature (Popenoe 1979; Galster et al. 2001; Hasse and Lathrop

2003; Chin 2002). In other words, for the same amount of urban developable land, it is no doubt urban developers can build up less single family houses with large size of livable space than those single family houses with compact size of livable space. According to the statistical report from Perry (2014), the living space per person in a new home keeps increasing from 1973 to

2013, and it has doubled (roughly 1800 to 3200 square feet per residential unit for standard 3 person in the average size home) over last 40 years, which confirms that using average livable space per residential unit as an indicator to compare sprawl level of low-density development among cities is an effective measurement. Weilenmann et al. (2017) studied how increased demand for residential space affected urban sprawl, and their results showed the demand of more residential space were positively related to urban sprawl. Bhatta (2010) in his study of “causes of

urban growth and sprawl,” also discussed that the demand of more living space which forced

rapid low-density development must be an indication of urban sprawl. In the study titled as

endless urban growth, Haase et al. (2013) pointed out that some cities even though have both a

declining population and a decreasing household number, the urban land area of these cities

continue spread outwards due to the increasing living space per capita. According to their study,

average housing space per residential unit should be a better measure on urban sprawl instead of

traditional measure using population/housing density

The sprawl index measuring residential/housing space is unique in the way that 1) no

Census or remote sensing or other aggregated data could provide such detailed information on

size of residential livable space like parcel data do; and 2) this also utilizes the precision data that

parcels provided in terms of type of residential units (such as single vs. multiple houses) as well

as number of units per building, which are also absent from aggregated data.

Continuing on the accessibility measurement from the sprawl measures in part I, the

sprawl index of residential accessibility to business hubs from part II has been improved, which apply the actual road network data to measure the travel distance instead of Euclidean distance.

The determination of business hubs is based directly on parcel’s commercial land use information according to the viewpoint that business centers are concentrations of activity that help businesses thrives (Ewing et al. 2002). Centering is measured by concentrations of development in or around historic central business districts (CBDs) of metropolitan areas, and makes a unique characterization of urban sprawl (Ewing and Hamidi 2014; Ewing et al. 2002).

A single-use residential area needs residents travel frequently by cars instead of walking or

biking to reach employment, commercial, or leisure destinations. Measuring the degree of mixed

land uses is an important indicator of sprawl (Masoumi 2012; Ewing et al. 2002). In terms of

measuring mixed land use to indicate the sprawling level, Ewing and Hamidi (2014) summarized

three types of mixed-use measures: 1) measuring the number of housing and jobs within subarea

of a region; 2) measuring the diversity of land uses within subareas of a region; and 3) measuring

proximity of residential uses to nonresidential uses. According to Song (2013), all of the methods

in measuring land use mix have the benefit and drawback. In the review, they stated that more

than two types of land use can apply Entropy Index and Herfindahl-Hirschman Index to calculate

diversity, and these indices measure land use mix level from the overall distribution within the

defined area (e.g. city boundary) or unit of analysis (e.g. 1x1 square kilometer). Existing sprawl related researches on measuring land use mix are rare to directly apply Entropy Index and

Herfindahl-Hirschman Index to calculate diversity at small neighborhood scale due to the limitation of fine-scale data availability for the large region, such as one or more than more metropolitan areas. Consider parcel data is especially able to directly and accurately provide the land use type on each parcel, and it is easily to calculate the number of each type of land use on the 1-km2 USNG grid, we used Shannon’s entropy measure from part I and HHI measure from part II to measure land use diversity. The Shannon’s entropy measurement has some problems even though it is a popular diversity index (Torrens 2008; Song et al. 2013; Manaugh and

Kreider 2013). Usually, the entropy value reaches zero when diversity is absent and increases as

diversity grows. However, it is not always the case. For example, in a subregion, the entropy

value for two types of land use and both occupying 50 percentage of total number of land uses is

1, which is larger than the entropy value for three types of land use and each of the three types

occupying 10, 40 and 50 percentage of total number of land uses. Therefore, in part I, our mixed

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land use measurement combined the entropy value and the land use types together to indicate the

degree of mixed land use. In part II, we used HHI index to measure land use monopoly degree,

and high monopoly indicates more sprawling. In theory, it is a better sprawl index compared to

the entropy index in measuring land use diversity.

By comparing the composite sprawl results among four metropolitan areas, Jacksonville metropolitan area has the most sprawling level with an overall Z score of 1.207, Orlando metropolitan area ranks in the second regarding the sprawling level with an overall Z score of

0.441, Orlando metropolitan area is in the third regarding the sprawling level with an overall Z score of -0.766, and Miami metropolitan area is the least sprawling metro area, with an overall Z

score of -0.883. This sprawling level ranking for the four metropolitan areas is exactly same as

that was derived by Ewing and Hamidi (2010) (see the comparison in Table 6.1.). In their

research, they used compactness Z score for sprawl ranking in 221 metropolitan areas in US in

the year 2010. Their research helps to verify the effectiveness of the sprawl measures being

proposed in our study. In addition, we compared the means of the three sprawl indices for the

four metropolitan areas using ANOVA and Tukey tests, and see if the means of each sprawl

index are significantly different among the four metropolitan areas. The analysis results showed

that overall the three sprawl measures are effectively to be represented as the sprawl levels of the

four metropolitan areas (ANOVA test p<.001). For the means of average livable space per

residential unit and land use diversity, Tukey test identifies that Jacksonville MSA is

significantly different from Tampa and Miami MSAs. While the differences between group

means in Orlando and Jacksonville MSAs are not statistically significant. Tampa and Miami

MSAs have the same situation. For the mean of accessibility to business hubs, Tukey test

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identifies that Jacksonville MSA is significantly different from Orlando, Tampa and Miami

MSAs. While the differences between group means in Tampa and Miami MSAs are not statistically significant.

Table 6.1 Comparison of our sprawl ranking (2012) with Ewing’s sprawl ranking (2010) for the four studied Florida metropolitan areas

Sprawl “Z score” in Sprawl “Z score” in Sprawl rank

our study (2012) Ewing’s (2010)

Jacksonville MSA 1.207 80.85 1

Orlando MSA 0.441 83.97 2

Tampa MSA -0.766 98.49 3

Miami MSA -0.883 144.12 4

Note: Sprawl ranking in Ewing’s (2010) is based on the overall compactness score with a mean of 100 and a standard deviation of 25. The more sprawling metropolitan area is associated with much smaller Z score. This is the opposite of way we define Z score for sprawl index in our study.

6.2 Parcel data

6.2.1 Description of parcel data, accuracy and availability

“Parcel/ cadastral data represent the geographic extent of the past, current, and future rights and interests for tax department (FGDC 1997).” Nationwide parcel/ cadastral data is

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important fine scale data, which is supported by FGDC for the development and maintenance.

The FGDC is taking efforts to provide the user community with annually updated parcel data for

the potential uses such as economic development, land use planning, etc.

Parcel data can be achieved from the Department of Revenue for tax purpose, and

includes both parcel’s geospatial information and tax related attribute produced and organized by

county. The parcel geometry file delineates land parcels’ geospatial locations and boundary, and

its attribute file provides tax-related information, including the local assessor’s information and

comparable attributes for public lands for the parcels.

According to the application in our study, the parcel data are good to be used in academic

research purposes. A check of parcel accuracy was conducted by the researchers through visually

comparing a number of randomly selected parcels with ESRI World Imagery Map (ESRI 2012).

This imagery dataset presents land cover throughout the United States with a spatial resolution of

1 meter or better; therefore, land-uses such as single-family housing and commercial/industrial

complex can be visually identified. Results indicated that land-use information from the FDR parcel data is trustworthy. The parcel data quality is also examined by the Bureau of Land

Management (Stage and Meyer 2006). The FGDC Subcommittee conducted an evaluation of seven state parcel management (Alabama, Arkansas, Florida, Montana, North Carolina,

Tennessee and Wisconsin).They claimed that Florida Suwannee Rive Water Management

District (SRWMD) contributed funds to the FL Department of Revenue’s corner densification to

improve the accuracy of the parcel data. In their studies, they evaluated seven states’ parcel data.

Yearly increasing number of applications that utilize parcel data for research purposes indicated parcel data are of important benefits and are valuable for usages in many fields.

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Stage and Meyer (2004) claimed that many recent projects have demonstrated the

feasibility of creating regional and statewide parcel databases. From the long run, parcel will

surpass Census data in many fine-scale land use related studies, such as urban sprawl,

environment and hazards studies.

6.2.2 Parcel data applications

The applying parcel data for various projects and academic researches become popular

and increased with more parcel data are nationwide standardized and high quality managed

(Stage and Meyer 2006). Nowadays, parcel/ cadastral data are frequent used in the studies related with population density since the parcel data are able to provide the detailed information such as the number of residential units, spatial location, the built year and land use type. Disaggregating population data at census scales (e.g. Census tracts) or other units is of importance. Therefore, many academic literatures focus on dasymetric mapping techniques to interpolate and disaggregate block group population counts to finer parcel unit. Maantay and Maroko (2009)

developed a new mapping method, the Cadastral-based Expert Dasymetric System (CEDS), to

calculate population in hyper-heterogeneous urban areas better than traditional mapping

techniques. In their case study, they used the proposed CEDS method to analyze population

impacted by 100-year flooding in New York City. Mitsova et al. (2012) conducted the study on

applying dasymetric mapping techniques to interpolate and disaggregate census block group

population counts to parcel unit. Their study visualized population density spatial distribution

from courser scale to finer scale, and was a more realistic representation of population

distribution in the study area of Miami-Dade, Florida. Due to the difficulties in identifying rural

developed areas from remote sensing data, which makes the classification of developed land is

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less accurate in rural areas than in urban areas, Leyk et al. (2014) used parcel data to model residential developed in rural areas. Usually, fine-scale residential estimation in rural areas is not available, or is not accurate in the traditional land cover datasets such as the NLCD. They applied parcel data in predictive models to improve residential estimation in rural areas, which showed the usefulness of parcel data. Besides, parcel data can be used for emergency response and support.

Stage and Meyer (2004) stated that parcel data are the most current and accurate data available for emergency response. In their study, they discussed that how important the parcel data was in Hurricane Isabel. Parcel data provides detailed information about land ownership, property values, structures, and land use, which are not available from Census data.

Stage and Meyer (2005) discussed how parcel data used in wildland fire management. Parcel data provides the location of properties and their proximity of structures to a fire and also provides property use such as residential, commercial, agriculture, etc. and value to assess the economic impact to a community.

Stage (2009) stated that parcel data quality are quite good and work well and adequate in numerous applications for many professional purposes. Recently, Florida Resources and

Environmental Analysis Center (FREAC) created Florida 2010 1km population data by combining 2010 census data with 2011 Florida Department of Revenue parcel data to estimate a population for each land parcel in Florida guided by information contained within the property appraiser database. The estimates were then aggregated to the 1-km2 USNG grid.

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6.2.3 Advantages of using parcel data for urban sprawl analysis

One of the advantages of using parcel data as we have seen in our study is that it allows

application of precision data in evaluating cities’ development. Traditional studies of urban

sprawl heavily depend on medium or coarse spatial resolution remote sensing data (such as

ETM+, ASTER, etc.) (Lu and Weng 2004; Pu et al. 2008), which are prone to various

uncertainties (such as classification accuracy, land-use type identification, spatial resolution

uncertainty, etc.). One of the most serious problems is the limited capability of identifying land

use types from remote sensing data. For example, remote sensing imagery shows a large

construction; however, in most cases the researchers are not able to tell whether that’s an office

building or for residential uses. Therefore, the interpretation of measured urban sprawl based on

remote sensing data can hardly be linked to evaluation of residence development or accessibility

analysis.

Parcel data also provides large amounts of spatial and information details compared to commonly used Census data. In parcel dataset, each land parcel is a single independent measure unit. It provides precision land use information at very fine spatial scale, which can produce countable and incredible results for urban sprawl analysis. For instance, each parcel has very detailed information, such as the land use type, the built-up date, the number of housing unit, etc.

Further, in terms of residential parcel, the dataset provides the refined land use type, such as single family, multiple family, etc. All of the information can be used for itemized studies of residential or commercial development. In addition, parcel information can be scaled up, whereas the scaling down of coarse Census data presents much more challenges. Therefore, parcel data has prominent advantages to be used for measuring urban sprawl.

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The acquisition of parcel data, however, takes researchers some efforts. At the time of

preparing this manuscript, we anticipate some painstaking processes for researchers to locate and

retrieve high accuracy parcel data especially in countries or areas where spatial data

infrastructure is not very well established. However, with the trending of big data, we expect to see more and more parcel data to be publicly available to scholars. Another issue with applying parcel data to urban sprawl analysis is the requirement of large data volume especially for research at state or national scale. Again, with the improvement of geographic data infrastructure, this drawback will become less and less significant in the long run.

6.3 Land use policies’ impacts on urban growth

In terms of four land use policies, we independently examined their effects on urban growth at both city level and census block-group level for three different groups of cities. The

“compare mean” analyses showed that cities differed when they implement certain policy. At city level, different policy variables have different influences on urban growth for the three city groups. For the all-sized cities group, urban growth of the cities with the policy of infill development was significantly lower than the cities without this policy action, which indicates that the policy action of infill development effectively discourage urban growth. For the small- sized cities group, transport-oriented and infill development policy actions effectively discourage

urban growth. For the large/medium-sized cities group, none of the land use policies are effective

for controlling urban growth. At Census block-group level, the city-level’ policy mplementations

have varied impacts on urban development at census block-group units inside different groups of

cities, too. For instance, compact development policy has not significant impacts on urban

development. With the mixed use policy, the urban development inside the block-group units for

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all of the three groups of cities increased compared to the cities without this policy, which has

significant positive influences on urban growth among cities. Regarding the policy actions of transport-oriented development and infill development, they are able to significantly decrease urban development for the small-sized cities, while the trends are reverse for the medium and large-sized cities. Therefore, based on t test analyses, for small-sized cities, transport-oriented development policy takes effect on decreasing urban growth well at both census block-group level and city level. This conclusion is consistent with the result in Ambarwati et al. (2014).

They proved that transport-oriented development strategy helped to control the settlement development. For the three groups of cities, compact development policy did not affect urban growth at all, which can be explained by Gordon and Richardson (1997). They discussed that the highly compact communities are much less affordable relative to the average house prices throughout the entire region of the city. For instance, the house prices are usually cheaper in the suburban communities. T test showed that mixed land use policy was positively related with

urban growth, which reflected that the effective of growth control policies may have apparent

influences on urban growth positively (Boarnet et al. 2011; Carruthers 2002b; Wassmer 2006).

Based on t test results, it can be concluded that not all policies work on decreasing urban growth.

Further, in the sections 4.3 and 5.3, a single-city-level model frame was proposed to

analyze global trends and local variations of driving factors on urban growth. The global OSL

model reveals the policy regulations (i.e. transport-oriented development and infill development) are helpful to decrease urban growth, especially for small-sized cities but they are not significantly influential on urban growth compared to other driving factors. Besides, land use policy variables of compact development and mix-use development did not appear influencing

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urban growth. In the Chapters 4.4 and 5.4, a multilevel model frame was created to examine land use policies’ influences on urban growth at Census block-group level. The results showed the land use policies had varied impacts on urban development, however most of the policy variables are not significant to increase or decrease urban growth. The police of compact development was the only significant variable helping to decrease local urban development for the medium/large- sized cities. We can conclude that overall the land use policies are not effective on urban growth control. As Conway and Lathrop (2005) claimed, it is unclear if the land use policies are effective on urban growth control or not. According to their study, they used policy-based simulation to project future land development in southeastern New Jersey, USA. The results showed that none of the land use policies are effective to alter future land development.

Similar researches (Boarnet et al. 2011; Glickfeld and Levine 1992) claimed that cities with growth control policies and cities without growth control policies have little differences on urban land consumption. For example, local growth control polices in California did not reduce urban construction (Glickfeld and Levine 1992).

While some researches (Boarnet et al. 2011; Carruthers 2002b; Wassmer 2006) stated that the effective of growth control policies have apparent influences on urban growth either positively or negatively. For example, Boarnet et al. (2011) used people and jobs per area of developed land as the dependent variables to examine whether Florida’s growth management program is associated with changes on urban growth spatial pattern. Their results indicated that growth management regulations are associated with less residential construction in urban areas and more residential construction in suburban areas at county level.

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Overall, land use policies can be used as a soft tool to aid controlling urban growth, while they are not as effective as physical factors on controlling population’s growth on discourage urban growth.

6.4 Multilevel modeling

According to the multilevel modelling results, it is known that physical factors, such as topography, road density and protected land percentage seem have strong significant influences on local urban development. The social economic factors such as local median housing price and city’s employment level seem have weaker effects on local urban development. Compared to environmental and socioeconomic variables, all land-use regulation variables appear not to be significant predictor for urban growth in our current study.

The separation of the surveyed cities into different groups based on their sizes make the study more deep and valuable. The same variables in terms of different size group of cites show varied effects on local urban development. For example, for all-size cities, for every 1 standard deviation of increase in road density, urban growth rate decreases by around 1 percent; for small- size cities, for every 1 standard deviation of increase in road density, urban growth rate decreases by around 1.5 percent; for all-size cities, for every 1 standard deviation of increase in road density, urban growth rate decreases by around 0.9 percent. This means that the variable of road density make urban built-up area to increase more slowly in medium and large cities than small cities. Besides, for different sizes of cities, the significant variables associated with urban growth rate are changed. For instance, the variable of unemployment rate is only significant to all size cities, and the variables of median housing value, protected land percentage and compact development policy only are significant to the medium and large cities.

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Our multi-level modeling analyses indicated that, while population growth is a significant driver of urban development (Table 5.11), urban growth varies greatly by cities throughout our study area. Such variance is accounted for by physical setting; for example, growth rate appears to be high in steeper slope and area with lower road density. This may reflect new development occurring in more remote area with less favorable terrain condition due to reduced availability of land in existing urban areas, which caused by massive population increase. The world’s urban residents have increased significantly since the 1950s and the population in 2009 was 3400 million and is expected to be doubled in 2050 (Megahed et al. 2015). This may also display that people’s low-residential-and-transit preference. Gordon and Richardson (1997) revisited several issues relevant to new urban development for cities in the United States. They stated that “low- density settlement is the overwhelming choice for residential living with the aid of automobiles, and low densities make high-capacity transportation networks less attractive.” The variance of urban growth among different cities in Florida may also be accounted for by housing values.

Model results indicate that new development tend to be located in higher-value neighborhood in medium or large cities (Table 5.11). Such pattern, however, is not holding true with statistical

significance for small cities.

6.5 Future works

In the next step, I plan to adopt the multilevel modeling approach and apply it specifically

to the evaluation of drivers of urban sprawl (as an alternative to the present research focusing on

urban growth in the third and fourth studies). In particular, the assessment may be conducted at

the 1-km (or Census block group) scale, so that the fine-scale urban sprawl measures (created in

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my first and second studies) may be examined in terms of their relationships to socioeconomic, environmental, and policy variables.

Research can also be expanded to forecast future urban growth spatially, that is, model future metropolitan development under a series of predefined scenarios for the assessment of urban growth sustainability. we will use urban growth spatial pattern variables (e.g. high-density housing development and accessibility to economic centers), environmental variables (e.g. protected land and open water) and/or human behavior interference variables (e.g. local housing market control and government regulation), some of which are derived from the results of the previous research results. The goal is to develop urban growth models that create different sustainability scenarios to simulate metropolitan urban development. The method will integrate

CA (Cellular Automata) models and MCE (Multi-Criteria Evaluation) techniques. These urban growth scenarios provide valuable information regarding urban sustainable development for decision makers and city planners.

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CHAPTER 7

CONCLUSION

This dissertation consists of four relative independent studies. First two are focused on using fine-scale parcel data to devise new measures of urban sprawl, which showed to be a promising way broadening the traditional analysis of sprawl indices. The third and fourth studies of the dissertation are focused on exploring the urban growth drivers at multiple spatial scales. We found:

In the Orlando Metropolitan Area (or Metro Orlando), three quarters of the urban residents lived within one fifth of the Orlando's city radius measured from its city hall, more concentrated than Kissimmee (one third) or Sanford (two fifths). This makes it the least sprawling city among the three, assessed by our distance of peak development index. We also found that Orlando was the most sprawling city among the three according to adapted mixed land use indices. In addition, according to our residential accessibility to urban economic center index, three quarters of Orlando’s urban residents are served by economic centers located within half of the city’s radius, shorter distance than Kissimmee or Sanford (both around two third of their city’s range). Evaluated by the three sprawling indices collectively, Orlando appeared to be the least sprawling cities among the three although it is the largest city among the three measured by its geographic range.

Among the four metropolitan statistical areas in Florida, Jacksonville MSA is the most sprawling by integrated index that takes into account of all three measures of urban sprawl, i.e. average livable space per residential unit (ALS), land use diversity, and accessibility to business hubs. Comparing the individual urban sprawl measures among the four metropolitan areas, the

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Miami MSA ranked the highest for average living space (i.e., the measure of average livable

space per residential unit); the Orlando MSA was the one with the highest level of land-use monopoly (according to our diversity measure based on the Herfindahl-Hirschman Index); and residents in Jacksonville MSA travel the longest distance to access nearest commercial centers

(according to our measure accessibility to business hubs). Each individual index provides additional in-depth understanding of different dimensions in urban sprawl phenomena.

Our two independent studies show that parcel data, though costly in terms of price or

labor at present, prove to be reliable dataset and provide tremendous flexibility for urban sprawl

analysis. The two indices created in this study, i.e., distance of peak development and

accessibility to urban economic center, are capable of capture parcel-scale patterns and transfer

the fine-scale pattern information to city or coarser geographic scales. The concept of using

relative distance is useful when comparing levels of urban sprawl among cities with different

sizes. Finally, methods presented in our research may be used to compare levels of urban sprawl

among different cities as well as characterize trends of a city’s development in temporal

sequence. With the increasing of parcel data’s accessibility to the public, using fine scale parcel

data will benefit more researchers, such as urban planner, geographers, etc. compared with

traditional US census population and housing data or remote sensing images. The aggregation of

parcel attributes onto uniform-sized grids makes spatial visualization and analysis more fast and

clear.

For the studies of urban growth drivers, according to the city-level GWR modeling on all

three groups of Florida cities(i.e. all cities, small cities and medium/large cities), the urban

growth drivers of population (+), slope (+), and protected land (-) are significant variables either

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encouraging or discouraging urban growth with varied impacts across the entire Florida. For all cities and small-sized cities, population variable played a greater role on increasing urban growth among the cities in the northern and middle part than those in the southern part of Florida.

Including all cities, slope variable had a greater impact on urban growth among the cities in the south than those in the middle and north of Florida, while for small-sized city group, the pattern reversed. For medium and large-sized cities, population and protected land are prominently influential on urban growth. Population increased urban growth while protected land decreased urban growth. Spatially, population variable played a greater role on boosting urban growth among the cities in the middle part than those in the southern part of Florida. Protected land played a greater role on inhibiting urban growth in the south than other parts of Florida.

Especially, the increase of protected land inhibited the increase of urban development in Miami metropolitan area greatly. Regarding specific land-use policy variables (from our present survey), none is significant on urban growth at the city level.

According to multilevel modeling on the same three groups of Florida cities, new variables significantly influencing on the urban growth at block-group scale are detected.

Besides the variables of population (+), slope (+), and protected land (-), road density (-) is significantly related with urban growth. New urban development appears to be high in steeper slope and area with lower road density. In addition, new development tends to be located in higher-value neighborhood in medium or large cities not in small cities. Compared to environmental and socioeconomic variables, all land-use regulation variables appear not to be significant predictors for urban growth. However, compact development appears to discourage urban growth, as presence of this policy is shown negatively related to growth rate for all models.

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The modelling methods adopted in our study help to explore more detailed relationships

between urban growth and its associated driving factors. GWR modeling can visually show the local variations of each driving factor at different locations, i.e. how a driving force makes different contributions on affecting urban growth of varied cities. Multilevel modeling can analyze the driving factors’ influences on urban growth by considering different level variables’ effects. In our case, it separates the total variance into between city variance and within city between block group variance, which makes the prediction of the urban growth drivers’ impacts on urban growth more accurate and valuable.

This study discovered a new perspective to spatially measure urban sprawl based on recently well-organized nationwide parcel data. In addition, our study conducted comprehensive analyses on understanding of the relationship between growth patterns of different cities and associated driving factors. Our study area covers many varying-scale places in Florida, such as

Florida cities with different sizes, Florida metropolitan areas, and principal cities inside the metropolitan area, and all of which are highly representative, and play important role. It is a good experience that technically and conceptually integrates multidisciplinary approaches in statistics,

GIS spatial analysis, socioeconomic science and policy regulations, and landscape ecology to explore the phenomenon in complex urban environment. The outcomes of this study can provide valuable inferences to for decision makers or urban planners.

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BIOGRAPHICAL SKETCH

Guang Xing was born on February 2, 1982 in Liaoning, China. She enrolled at Yunan University

in September, 2001 and earned the Bachelor of Geographic Information System in July, 2005.

Shortly thereafter he started working on his Master degree under Dr. Fabin Li at Institute of

Mountain Hazards and Environment, Chinese Academy of Science. She received the Master

degree in Geographic Information System in July, 2008. She worked in Tieling Urban Design

and Planning Institute, Liaoning, China as Assistant Planner during the years of 2008-2010. She

entered Florida State University in June, 2012, and started working on her Doctoral degree under

the supervision of Dr. Tingting Zhao. She will graduate with the Doctor of Philosophy degree in

Geography in August, 2017.

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