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Feasibility Study on Mixed-Use Transit-Joint Development in Growing Urban Areas, Using Meaningful Urban-Form and Nonurba...

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Permanent Link: http://ufdc.ufl.edu/UFE0010862/00001

Material Information

Title: Feasibility Study on Mixed-Use Transit-Joint Development in Growing Urban Areas, Using Meaningful Urban-Form and Nonurban-Form Variables
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0010862:00001

Permanent Link: http://ufdc.ufl.edu/UFE0010862/00001

Material Information

Title: Feasibility Study on Mixed-Use Transit-Joint Development in Growing Urban Areas, Using Meaningful Urban-Form and Nonurban-Form Variables
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0010862:00001


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Full Text











FEASIBILITY STUDY ON MIXED-USE TRANSIT-JOINT DEVELOPMENT IN
GROWING URBAN AREAS, USING MEANINGFUL URBAN-FORM AND
NONURBAN-FORM VARIABLES















By

SHAO-MING ZHANG


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

UNIVERSITY OF FLORIDA


2005



























Copyright 2005

by

Shao-Ming Zhang





























This document is dedicated to my parents.
















ACKNOWLEDGMENTS

I would first like to thank my family and friends for their constant encouragement

and support. Next, I would like to thank my advisor, Dr. Ruth Lorraine Steiner. Her

enthusiasm for solving urban problems with a multi-disciplinary perspective and her

profound knowledge of transit-oriented development has greatly influenced me. I would

also like to thank my graduate committee, Dr. James Nicholas and Dr. Paul Zwick, for

their help and guidance.




















TABLE OF CONTENTS


IM Le

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ................. ................. vii........ ....


LIST OF FIGURES ................. ..............viii...............


AB STRAC T ................ .............. xi


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Background and Motivation for This Research ................. ............... ......... ...1
Problem Statement ................. ...............8.................
Research Question .............. ...............9.....
Organization of the Thesis ................. ...............10................

2 HOW DO PEOPLE DEFINE TOD AND TJD? ................ ............................11


Defining TOD ............ .......__ ...............11..
History of TOD .............. .. .... .. ...............11..
Various Definitions of TOD ....._ .....___ .........__ ............1
Peter Calthorpe's "TOD Idealism" ................ ...............14........... ...
"From Rhetoric to Reality" ............ ...............22.....
Experiencing T JD .............. ...............25....
Defining T JD ............. ... ....._ ...._ .............2
Effects of Rail TJDs on House Prices ............ ...... ._ ........_........2
Financial Initiatives for T JD ....._ .....___ .........__ ............2
Conclusions............... .... ... ...................2
Choosing Rail T JD for the Study .............. ...............29....
Defining the Study Area .............. ........ .._ ........_ ...........3
Choosing Two Regression Models for the Study ............................_ ........._30
Introducing the Household Income Level as a Variable into the Regression
Analy si s............... ...............3

3 HOW DO PEOPLE MEASURE THE LEVEL OF LAND-USE MIX? ....................32


Frank & Pivo's Entropy Index............... ...............32.












Cervero & Kockelman's Dissimilarity Index ................. .....___............... ....33
Conclusions............... ..............3


4 RESEARCH METHODOLOGY .............. ...............35....


Key Research Questions and Overview of Research Approach ................. ...............3 5
Conceptual M odel ........._.__....... .__ ...............36....
Data S ource............... ...............3 8

Data Analysis................. ...............3
Urban-Form Variables............... ...............3

D ensity .............. .... ...............38.
Level of Land-Use Mix ......__....._.__._ ......._._. ............3
Non-Urban-Form Variables............... ...............3

Property Information .............. ...............41....

5 DESCRIPTION OF CASE STUDY AREA ....._.__._ ...... ._._. ....._._..........4


Describing the Case Study Area--Atlanta metropolitan Area .........._.... ..............43
Atlanta Overview. .............. .. ........__ .... ._ .............4
Socio-Economic Characteristics of the Region ............... .......................46
MARTA and TJDs in the Region ...._.._.._ ........._.._......_ ...........5


6 RESULTS AND DISCUSSIONS............... ..............6


Re sults........._..... ...._... ...............60.....
Discussions .............. ...............63....


7 CONCLUSIONS AND AREAS FOR FURTHER RESEARCH. ........._.._... .............69


Conclusions............... .... ...........6
Areas for Further Research ............. ...... ._ ...............71...


APPENDIX


A CALCULATING THE LEVEL OF LAND-USE MIX............... ..................7


B APPLYING THE VALUES FROM THE US CENSUS DATA TO THE
PARCEL DATA ................. ...............78.................


LI ST OF REFERENCE S ................. ...............8.. 1......... ....


BIOGRAPHICAL SKETCH .............. ...............84....

















LIST OF TABLES


Table pg

6-1 Variables used in the first regression model .............. ...............60....

6-2 "Taxable value of the property" as dependent variable ................. ............... .....61

6-3 Variables used in the second regression model ................. ................. ........ 62

6-4 Coefficients of the independent variables with the "number of workers using
transit for traveling" as its dependent variable............... ...............63

6-5 Percentage of population in each age category (Source: Census 2000
Supplementary Survey) ........... ........... ...............66....

6-6 Percentage of different types of household (Source: Census 2000 Supplementary
Survey) .............. ...............66....

6-7 Percentage of different types of housing units (Source: Census 2000
Supplementary Survey) ........... ........... ...............67....


















LIST OF FIGURES


figure pg

2-1 Calthorpe' s diagram of TOD .............. .....................15

2-2 TODs with different core commercial areas sizes and locations ................... ........16

2-3 Different housing types with various residential densities .............. ..................17

2-4 Different housing types for TOD .............. ...............17....

2-5 Various combinations of housing types with the same average residential
density (Source: Calthorpe, 1993) .............. ...............17....

2-6 Diagram of second areas in TOD .............. ...............18....

2-7 Minimum amount of public, core commercial and residential uses in TODs ......18

2-8 Diagram of TODs' relationship to transit and circulation ............. ....................19

2-9 Diagram of urban TOD .............. ...............19....

2-10 Diagram of neighborhood TOD ........._.._. ....._... ........._. ... ....__. ...20

2-11 TODs in three types of locations ............. ...............20.....

2-12 Example of TOD design in the redevelopable sites .............. .....................2

2-13 Example of TOD design in the infill sites ............. ...............21.....

2-14 Example of TOD design in the new growth areas ............. .....................2

2-15 Design features of TODs .............. ...............21....

4-1 Conceptual M odel One .............. ...............37....

4-2 Conceptual Model Two............... ...............37..

4-3 A map showing the parcel data overlaying with the US Census data ...................41

5-1 The location of the Atlanta metropolitan area in the United States ................... ....44











5-2 Transportation access to the Atlanta metropolitan area ................. ................ ...45

5-3 Exciting nightlife, Atlanta, Georgia. ......___ ... ................_ ...........4

5-4 Evening falls over Atlanta, Georgia............... ...............46

5-5 Age distribution of people in the Atlanta metropolitan area. .............. ..............47

5-6 Types of households in the Atlanta metropolitan area............._.._ .........._..__....48

5-7 Geographic mobility of residents of the Atlanta metropolitan area..............._..__....49

5-8 Poverty rates............... ...............50.

5-9 Types of Housing Units ..........._.._ ....... ...............51....

5-10 Occupants with housing cost burden .............. ...............51....

5-11 Time-lapse photo showing congestion and brake lights on freeway heading
downtown ..........._._ ....._. ._ ...............53.....

5-12 Rail map of MARTA ........... ......__ ...............54..

5-13 MARTA train moving in front of the Atlanta Skyline ............_.. ........._.....55

5-14 Rail operator in front of his train ............_.....__ ....__ ...............55

5-15 Site of Lindberg City Center ........._...... ...............57.._.__. ....

5-16 Master plan of Lindberg City Center ................. .....___.....___..........5

5-17 How retail shops, restaurants, offices, and multifamily residential space can be
connected through the Main Street in Lindberg City Center ........._.._... .............58

6-1 Difference between the expected cumulative probability and the observed
cumulative probability in the first regression model for the case study ..............68

A-1 Selecting parcels in each buffer ........._.._.. .....__. ...._.. ...........7

A-2 Map showing overlapped buffers............... ...............74

A-3 Square footages of buildings on selected parcels ................. .....___.............75

A-4 Statistical tools used to get total square footage of the office space inside every
buffer ................. ...............75.................

A-5 Applying the entropy index values to the parcels inside each buffer ....................76

A-6 Joining the tables for the level of land use mix .............. ...............76....











A-7 Area around Five Points Station with all the GIS layers .............. ....................77

B-1 One census block group being selected .............. ...............78....

B-2 Selecting parcels with their centers in the selected census block ........................79

B-3 Census block group ID number being applied to the selected parcels inside the
census block group ................. ...............79...__._......

B-4 Joining the tables for the nonurban-form variables .............. .....................8
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Arts in Urban and Regional Planning

FEASIBILITY STUDY ON MIXED-USE TRANSIT-JOINT DEVELOPMENT IN
THE GROWING URBAN AREAS, USING MEANINGFUL URBAN-FORM AND
NON-URBAN-FORM VARIABLES

Shao-Ming Zhang

August 2005

Chair: Ruth Steiner
Major Department: Urban and Regional Planning

Transit-Oriented Development (TOD), known as a form of walkable, mixed-used,

location-efficient development with convenient transit service, has been a buzzword in

the fields of urban planning, architecture and real estate development for over a decade in

the U.S. Although separately confronted with their individual Einancial barriers, public-

private partnerships between the transit agencies and the private developers are emerging

as a promising way to implement TODs. Often seen in the Mixed-Used Transit-Joint

Development (TJD), a subset of TOD, the public-private partnerships can be further

categorized based on their Einancial agreements, such as ground lease, air-rights lease,

operational cost sharing, construction coast sharing, and station connection-fee programs.

In order to create partnerships that allow the Einancial benefits to translate into

profitability for both the private developer and the transit agencies, the financial benefits

that TJDs' maj or characteristics, including high-density and mixed land-use, would bring

to the proj ect need to be understood first.









In this research, two multiple regression model were constructed to examine the

effects of TJD proj ects' maj or characteristics on real estate values and the transit

ridership, controlling for proximity to transit. When searching the meaningful variables

for the regression analysis, the author improves the current model in measuring the level

of land-use mix at the parcel level based on Frank & Pivo's Entropy Index model.

The results from the case study in the Atlanta Metropolitan area challenge the

hypothesis that higher density and greater level of land-use mix would increase the

financial return from the TJD proj ects.















CHAPTER 1
INTTRODUCTION

Background and Motivation for This Research

The 2000 U.S. Census revealed that the country is growing much faster than

expected. If this surprising trend continues, by 2050 the United States will have more

than half a billion people -double its current size (Half a billion 2002). The rapid

population growth, urban sprawl, suburbanization and traffic congestion cost people

more and more time on the road. After housing, transportation is now the second largest

expense for American families (Candy 2003). Inefficient transportation in the urban

areas of the United States has been effectively restricting many American families'

access to the resources they need to increase their wealth. In many areas of the United

States, traffic woes have generated a cohort of individuals who are drawn to the idea of

living near public transit and enjoying a less stressful commute to work (Transit

Cooperative Research Program (TRCP) 2002). While the American love affair with the

automobile is not yet over, concerns about the negative effects of automobiles -on the

physical environment, on public health, on the quality of life, and on the relationships

among people in the community -have led to policies and proj ects that promote more

public transit use, including heavy and light rail, bicycle paths and public walkways. A

greater federal investment in non-highway-related proj ects, such as public transit,

intercity rail, bicycling, walking, and inter-modal connections, has also been reported

(STPP 2003). The renewed interest in transit use and transit investment, together with

the resurgence of investment in America's downtown areas, calls for a diversification of









real estate projects and a type of development know as transit-oriented development

(TOD), a form of pedestrian-friendly, mixed-used, and location-efficient development

with convenient transit service (Belzer and Autler 2002).

The Intermodal Surface Transportation Efficiency Act (ISTEA) in 1991 and the

Transportation Equity Act for the 21st Century (TEA -21) in 1998 laid the groundwork

for building safe, environmentally-sound, transit-oriented communities. Several federal

initiatives, such as a more permissive interpretation of the federal common-grant rules for

new mixed-used transit-j oint development (TJD) and the location efficient mortgage

(LEM) program, underwritten by Fannie Mae, have also sought to leverage TOD. The

shrinking household size and the increasing share of foreign immigrants in the United

States have contributed to the rising popularity of TOD. Growing numbers of singles and

single-parent families, childless couples, and "empty-nesters" seeking to downsize their

living quarters, along with influxes of foreign immigrants coming from countries with a

heritage of transit -oriented living, have created ready-made consumer markets for TODs

(Calthorpe 1993; Transit Cooperative Research Program (TRCP) 2002). Furthermore, a

receptive policy environment, together with changing demographic characteristics in

favor of TOD, have heightened public interest in TOD as a means of redressing a number

of urban problems, such as traffic congestion, affordable housing shortages, air pollution,

and incessant urban sprawl (Transit Cooperative Research Program (TRCP) 2002).

A review of international trends also uncovers latent factors that challenge the

conventional strategy of building more highways, and which may further catalyze the

United States' transition from an auto-dominated country to a more transit-dependent

society. In China and India, together home to 2.3 billion people, motor vehicle use is










skyrocketing. Car sales in China increased seventy three percent in 2003 alone, and by

2030 China is proj ected to have more motor vehicles than the U. S. (INFORM Reports

2004). As a consequence of such enormous transportation growth, China and India are

vying with the United States for rapidly dwindling oil reserves. The world's second and

sixth largest oil-consuming nations respectively, they are both expected to see annual

growth rates of about four percent in oil consumption-- the fastest rates in the world -

over the next two decades (INFORM Reports 2004). Most energy supply experts

conclude that oil supplies are likely to peak within the next ten to thirty years. However,

none of these estimations factor in the unexpected increases from new oil-based

transportation systems in China and India. The foreseeable shortfall of oil supply in the

near future, and the resultant significant rise in gasoline prices, would likely change

people' s view of public transit as an alternative transportation mode in the United States,

a shift that could create a vast market worldwide for TODs in the long run.

Despite the nuances in defining TOD by different transit authorities, successful

pilot cases in Portland, Oregon, San Francisco, California, Dallas, Texas, Denver,

Colorado, and Atlanta, Georgia, have illustrated various implementations of TOD

principles throughout the country. Noticeably, financial support from the government at

various levels has played a very important role in guaranteeing the actual completions of

these projects. However, the nation's switch from highway-dominated development to

transit-oriented development comes at a time when financial resources at every level -

federal, state, and local are shrinking. Looming deficits and unanticipated

expenditures brought on by the September 11Ith attacks threaten the Einancial stability of

most domestic programs. In spite of the appealing aspects of TOD, critics raise doubts









about the financial feasibility of putting the "idealistic" theory into practice, further

j eopardizing the possibility of widespread implementation of TOD principles. To date,

record has shown some failed TOD cases. Laguna West, for instance, was originally

touted as a TOD prototype for the suburbs of Sacramento, but a downturn in the real

estate market led to eventual bankruptcy (Transit Cooperative Research Program (TRCP)

2002). Anticipated TODs in some parts of the United States have failed to break ground

because of unrealistic market expectations (Transit Cooperative Research Program

(TRCP) 2002). Harsher critiques concerning the financial barriers to TOD

implementation even reprobate the idea of using transit systems in most parts of the

country. Andrew Jakes once said in his article ("Transit success? It's the real estate,

stupid"): "The San Francisco cable car, the Las Vegas bus system and the existing Seattle

monorail transit systems make money. But the overwhelming maj ority of U. S. transit

systems, particularly newly installed West Coast systems are big financial losers" (Jakes

1998).

On one side, concerns about financial loss from "risky" TODs discourage

governments and public transit authorities from providing more funding for such

development. Locational liability of the areas covered by existing transit system, delay

of station-area development, non-supportive government policies including exclusionary

zoning and suburban-like building codes, difficulties in cross-jurisdictional cooperation,

insufficient consideration of the real estate market, and lack of market feasibility studies,

all contribute to hesitant public involvement in spurring TODs (Porter 1997). On the

other side, private developers, another major financial source for transit-oriented

developments, make decisions based primarily on the real estate market rather than the









presence of transit. Even if there appears to be a burgeoning demand for transit-oriented

development, private developers, lenders, and investment groups must be sufficiently

convinced that this demand is real and sustainable before they risk capital creating a TOD

(Transit Cooperative Research Program (TRCP) 2002). Although separately confronted

with their individual financial barriers, public-private partnerships are emerging as a

promising way of implementing TODs. The public sector has the power to resolve land-

assembly problems, ensure that the site is development-ready, ease the entitlement

process, and even contribute land and infrastructure costs, all of which bring developers

not only financial resources, but also the confidence of being grounded in the reality of

the local market (Dunphy, Myerson, Pawlukiewicz 2003). Together with the real estate

savvy, contacts with end users, and understanding of financial resources contributed by

private developers, these assets can help engender public- private partnerships that

provide opportunities to set mutual expectations and to share risks, costs, and rewards

(Dunphy, Myerson, Pawlukiewicz 2003). Financial outcomes could be further enhanced

through tax increment financing, and government and foundation grant funding. The

formation of public-private partnerships is described in the literature as the best solution

to the problem of successfully implementing TODs (Transit Cooperative Research

Program (TRCP) 2004).

Mixed-used transit-j oint development (TJD), defined in the recent literature as a

subset of TOD, is a form that is proj ect-specific and takes place either on or adj acent to

transit-agency land, and represents the most frequently discussed scenario for public-

private partnership in TOD proj ects (Transit Cooperative Research Program (TRCP)

2004). In spite of the varying definitions of both TOD and TJD by different institutions,









the distinction between the two closely related terms is so ambiguous that quite often

transit agencies (and other stakeholder groups) use the terms interchangeably (Transit

Cooperative Research Program (TRCP) 2004). In general, their differences are in regards

to scale: TOD usually encompasses multiple city blocks, representing a neighborhood in

size and character, while TJD tends to be proj ect-specific, often occurring within a city

block and tied to a specific real estate development (Transit Cooperative Research

Program (TRCP) 2002).

Public transit agencies have been given the flexibility of operating TJD proj ects by

FTA' s Circular 9300.1~: "FTA encourages incidental uses of real property that can raise

additional revenues for the transit system or, at a reasonable cost, enhance system

ridership. FTA approval is required for these incidental uses of real property which must

be compatible with the original purposes of the grant" (FTA 1997). This provision has

been interpreted as empowering transit agencies to sell land holdings financed by federal

grants without having to return proceeds as long as the funds are used to shape

communities being served by transit (Federal Register Notice 1997). In addition to the

pioneering TJD practice of the Washington metropolitan Area Transit Authority

(WMATA) and San Francisco's Bay Area Rapid Transit District (BART), various other

transit agencies in the United States have also been implementing TJD proj ects.

Often occurring on a transit agency's property, these TJDs can be further

categorized based on their lease types and financial agreements, including ground lease,

air-rights lease, operational cost sharing, construction coast sharing, station connection-

fee programs, or other initiatives that promote real-estate development at or near transit

stations to the mutual benefit of public and private interests (Transit Cooperative









Research Program (TRCP) 2004). Whether in the form of ground or air rights leasing,

station connection fees, or the sharing of capital-construction costs, experience shows

that successful TJDs typically involve carefully crafted collaborations among many

individuals, organizations, and institutions with vested interests in the outcomes,

including developers, lenders, transit agencies, local and regional planning organizations,

and public interest groups (Knight and Trygg 1977; Porter 1997).

An early review of TJD identified two of the main obstacles from the point of the

view of elected officials: doubts about the profitability of TJD, and shortages of qualified

staff members who can package TJD deals and produce financial pro forma (Keefer

1984). Involvement of different interest groups and various lease types and financial

agreements makes it even more difficult for the related agencies to prepare financial pro

forma, which is used to prove the profitability and the feasibility of a TJD. A necessary

feasibility study on such development would provide practitioners both in public

institutions and private sectors a more sound foundation for further cooperation in TOD

and TJD proj ects.

In addition to serving this purpose, this research is also the author' s attempt to

integrate knowledge from different disciplines for solutions to the problems of

communities. Creative and feasible solutions are more likely to arise from

comprehensive knowledge and philosophical thinking in various realms including

politics, economics, ecology, sociology, technology, and aesthetics. All of these

concerns should be viewed holistically for a successful project. In reality architects,

planners, landscape architects, traffic engineers, civil engineers, biologists, developers,

environmentalists, bankers, and even neighborhood groups too often seek to optimize









only a specific aspect of a proj ect, without looking beyond their individual issues, which

are in fact interconnected (Calthorpe 1993).

Problem Statement

A growing body of literature shows the financial benefits of TJDs. The challenge

that remains is how to create partnerships that allow those benefits to translate into

profitability for both the private sector (the developer), and the public sector (the transit

agencies) that are often the landowners of such developments. There are several keys to

the negotiation of a successful private-public partnership for TJD projects:

* Quantification of the increases in property values, resulting from both the transit
connection and TJD arrangements.

* Calculation of the financial benefits from increased ridership associated with TJDs.

* Consideration of the increased costs of such proj ects, because of their high-density
and mixed-used (Dunphy, Myerson, Pawlukiewicz 2003).

Theory maintains that the savings in travel-time and enhanced accessibility

conferred by TJD should get capitalized into higher land values and market rents.

Numerous studies have also demonstrated that all else being equal, being adj acent to rail

stations raises property values, though to varying degrees (Transit Cooperative Research

Program (TRCP) 2002).

On the other hand, the effects of TJDs themselves and their maj or characteristics

(particularly high-density and mixed land-use) on real estate values, controlling for

proximity to transit, has been examined only sparingly (Transit Cooperative Research

Program (TRCP) 2002). To understand the risks and profits of a development, real estate

market analysis is often used to identify the demand and supply, usually for a particular

property type (e.g., apartment buildings, or offices). Involving more than the sum of the

parts of different property types, mixed-use development is complex for both developers










and public agencies. Compounded with the difficulties of measuring the complicated

impacts of a transit system on developments, quantifying the increase in property values

for TJD arrangements under particular market conditions using a universally applicable

equation is almost an impossible task. Normally multiple regression models that partial

out the unique effect of proximity to transit, as well as the presence of TJD

characteristics, are used in explaining increases in property values, thus approximating

the reality.

In order to quantify the increase in transit ridership, the impacts of TJD on

people's transportation modal choice also has to be studied. The results of such a study

would provide calculated Einancial benefits from increased ridership within a certain

market frame, which could be an important factor in the decisions of transit agencies.

Because the synergy among different uses and functions required for TJD is

extremely difficult to achieve, TJDs almost always involve more complexity and higher

costs than other forms of infill development (Belzer and Autler 2002). Therefore,

balancing the financial benefits and increased costs of TJD proj ects presents another

important concern in creating successful partnerships.

Research Question

This paper answers the following general question: What financial benefits would

TJDs' maj or characteristics, specifically high-density and mixed land-use, bring to a

project? This broad question is answered by considering the following related questions:

* To what extent will high-density and mixed land-use increase the property values
of TJDs?

* Will an increase in the density and the level of land-use mix of TJDs bring a higher
transit ridership?










*What is the impact of other factors, including the household income level of the
neighborhood around TJDs, on the Einancial return?

These questions are addressed through two regression analyses. One multiple

regression model was used to examine the effects of TJD proj ects' maj or characteristics,

high-density and mixed-use, on real estate values, controlling for proximity to transit.

Household income level was also included in this regression model to understand its role

in the feasibility of TJD proj ects.

Another multiple regression model was developed to understand the relationship

between transit ridership and TJD arrangements under certain socio-economic situations.

Organization of the Thesis

This thesis is organized into seven chapters including this introduction. The first

three chapters present the background of the study and review previous work on the

topic. Chapter 2 reviews the literature on the definitions and theories of TOD and TJD.

Chapter 3 reviews previous work on measuring the level of land-use mix.

In Chapter 4 and 5, the framework for this research is outlined. Chapter 4 defines

the research methodology and Chapter 5 describes the case study area.

The results of the research are presented and discussed in Chapter 6. Chapter 7 is a

summary of the research and a discussion of areas for further research.















CHAPTER 2
HOW DO PEOPLE DEFINE TOD AND TJD?

Although varying in scope and specificity, most TOD and TJD definitions share

several common elements, including high-density and mixed land-use. In this chapter,

different definitions and theories of TOD and TJD are reviewed.

Defining TOD

Before the definitions and the theories of TJD, a subset of TOD, are discussed, the

history of TOD and various definitions of TOD must first be introduced. Based on

Calthorpe's framework of TOD theory, Belzer and Autler define TOD with both

adaptiveness and flexibility.

History of TOD

By putting TOD in its historical context, Belzer and Autler depict evolving

definitions and obj ectives of TOD through each historical TOD phase, namely,

Development-Oriented Transit in the early twentieth century, Auto-Oriented Transit

during the post-war years, today's Transit-Related Development, and tomorrow' s

Transit-Oriented Development (Belzer and Autler 2002).

By the early 1900s, electric streetcar systems had emerged in many cities like New

York and San Francisco, contributing to the growth of America' s suburbs (Middleton

1967). With typical features such as a transit depot and public space in the center of the

neighborhood, small cottage-type houses, and a street pattern and scale that allowed

convenient walking distances to transit, the early street suburbs were successful examples

of pedestrian access to transit service that connects to downtown j obs and neighborhood










services (Cervero 1993). Although some may date transit-oriented development projects

in the United States back to the late nineteenth and early twentieth centuries, Belzer and

Autler described those railroad and streetcar suburbs more aptly as "development-

oriented transit" than "transit-oriented development" because private developers built

transit serving and adding value to their development rather than vice-versa.

Starting in the 1930s, American's love affair with the automobile and highways,

however, broke the interdependence of housing, jobs and transit inherent in early

streetcar suburbs (Belzer and Autler 2002). Precipitous declines in transit use and

abandonment of many rail systems characterized the post-war auto-oriented society in the

United States. With the exception of some commuter suburbs around older cities, most

transit became a last resort rather than a reliable transportation option tied to development

(Belzer and Autler 2002).

Many proj ects today are named transit-related development merely to acknowledge

the physical connection between transit and development. "Transit Adjacent

Development" (TAD) has emerged in much of the written literature to characterize such

development that lacks any functional connectivity to transit, whether in terms of land-

use composition, means of station access, or site design (Parsons, Brinckerhoff, Quade

and Douglas, Inc. 2001).

Various Definitions of TOD

Besides Belzer and Autler 's bona fide TOD as tomorrow's new paradigm of

development, various terms have surfaced to convey the idea of TOD, like "transit

villages," "transit-supportive development," and "transit-friendly design" (Transit

Cooperative Research Program (TRCP) 2002). A sample of TOD definitions can be

found in the literature:









Development within a specified geographical area around a transit station with a
variety of land uses and a multiplicity of landowners. (Salvesen 1996, p.37)

A mixed-use community that encourages people to live near transit services and to
decrease their dependence on driving. (Still 2002, p.44)

A compact, mixed-use community, centered around a transit station that, by design,
invites residents, workers, and shoppers to drive their cars less and ride mass transit
more. The transit village extends roughly a quarter mile from a transit station, a
distance that can be covered in about 5 minutes by foot. The centerpiece of the
transit village is the transit station itself and the civic and public spaces that
surround it. The transit station is what connects village residents to the rest of the
region... The surrounding public space serves the important function of being a
community gathering spot, a site for special events, and a place for celebrations--a
modern-day version of the Greek agora. (Bernick and Cervero 1996, p. 5)

A mix of residential, retail and office uses and a supporting network of roads,
bicycle and pedestrian ways focused on a maj or transit stop designed to support a
high level of transit use. The key features of TOD include (a) a mixed-use center at
the transit stop, oriented principally to transit riders and pedestrian and bicycle
travel from the surrounding area; (b) high density of residential development
proximate to the transit stop sufficient to support transit operations and
neighborhood commercial uses within the TOD; and (c) a network of roads, and
bicycle and pedestrian paths to support high levels of pedestrian access within the
TOD and high levels of transit use. (Oregon 1995)

Although varying in scope and specificity, most TOD definitions share several

common elements, such as mixed land use and functional connection to the transit

system. Traits that are less universal in the definitions of TOD relate to the notions of

"New Urbanism" and "Smart Growth," including compactness and pedestrian-friendly

environment. Admitting the existence of different definitions, interpretations, and

implementations of TOD, some observers have sought to categorize TOD proj ects.

White and McDaniel (1999) have identified six forms of TOD proj ects based on their

physical design characteristics: (1) Single-Use Corridors: concentrations of single transit-

intensive uses in transit corridors; (2) Mixed-Use Corridors: concentrations of a variety of

land uses on a single parcel or group of parcels within a transit corridor; (3) Neo-

Traditional Development: development that primarily focuses on design features that









reproduce traditional town or village settings with small lots, narrow streets, detached

parking behind houses, reduced setbacks, and front porches; (4) Transit-Oriented

Development: compact, mixed-used development concentrated near transit stops; (5)

Hamlet or Village Concept: single-family homes clustered around a central green area or

commons; and (6) Purlieu: A development of approximately 150 acres and 7,000

residents, with comprehensive and detailed design regulations.

Lacking quantitative interpretations of different TOD implementations,

practitioners generally find it difficult to analyze and evaluate ambiguously defined TOD

projects. One person's TOD can be another person's TAD. For developers, architects,

designers and planners, quantifiable guidelines and references are necessary in order to

rigorously implement TOD proj ects.

Peter Calthorpe's "TOD Idealism"

Calthorpe frames modern design theory of TOD with its practical implications in The

Next American M~etropolis: Ecology, Community, and the American Dream, by providing

both quantifiable guidelines and quality urban design proj ects as examples of TOD.

Based on a well-defined TOD concept (Figure 2-1),1 Calthorpe further delineated TOD as

a mixed-use (residential, retail, office, open space, and public uses) community within an

average 2,000-foot walking distance of a transit stop and core commercial area.

Calthorpe' s principles of implementing TOD successfully link his definitions to his

guidelines of urban design:

*To organize growth on a regional level to be compact and transit-supportive;



STOD concept is simple: moderate and high-density housing, along with complementary
public uses, jobs, retail and services, are concentrated in mixed-use developments at
strategic points along the regional transit system.









Secondary Area


Figure 2-1: Calthorpe's diagram of TOD (Source: Calthorpe 1993)

* To place commercial, housing, jobs, parks, and civic uses within walking distance
of transit stops;

* To create pedestrian-friendly street networks that connect directly to local
destinations;

* To provide a mix of housing types, densities, and costs;

* To preserve sensitive habitat, riparian zones, and high quality open space;

* To make public spaces the focus of building orientation and neighborhood activity;
and

* To encourage infill and redevelopment alone transit corridors within existing
neighborhoods (Calthorpe 1993).

Commercial areas, residential areas, public spaces, and secondary areas are the four

major required components of Calthorpe's TOD design. A mixed-used core commercial

area located adj acent to a transit stop is required to provide at a minimum convenience

retail and local offices at a minimum, while larger core areas could have more flexible

combinations including major supermarkets, restaurants, service commercial,

entertainment uses, comparison retail, second-floor residential, and employment-

intensive office and light industrial uses. Although noting the variances in the size and











location of TOD projects, Calthorpe strictly defines the ratio of the commercial core areas

to the total area of the project. Whether this area functions as community center,


neighborhood center, or convenience center, at least ten percent of the total TOD site area

and a minimum of 10,000 sq-ft of retail space adj acent to the transit stop are designated


as the commercial core area according to Calthorpe' s design guidelines (Figure 2-2).

TOD
Residenllal







COMMUNITY CENTER
TOD







NEI~llBORHIOOD CENTER
TOD1






CONVENIENCE CENTER

Figure 2-2: TODs with different core commercial areas sizes and locations (Source:
Calthorpe 1993)

Housing within a convenient walking distance from core commercial areas and

transit stops is designed to meet with the residential density requirement of eighteen


dwelling units per acre, averaged by densities of a mix of housing types. By listing a

matrix of housing types (Figure 2-3) including small lot single-family, townhouses,


duplexes, apartments and condominiums (Figure 2-4), Calthorpe points out the flexibility

in housing design and combinations that can satisfy the density requirement of TODs


(Figure 2-5).














14.18 10-14 12-16 18-20 1522 27-30 IS20 36-40 14-.18 4065 30-5 20-30
dulac dulac drrlac dulac dulac du/la dulac dulac dulac duhla dulRe dulac

Figure 2-3: Different housing types with various residential densities (Source: Calthorpe
1993)





Small-Lot Single-Family Townhouses



Duplexes Aprtments & Condominiums
FigureL.C; 2-4 Diffren hosn yesfrTD(Suc:Cltop 93






Figure 2-5: Vafrioscobnainso housing types with the same average residential

desiy Surcf~: Calthorpe 1993)durt2





Fgresident and orkes cminTiOns and nihboring areas ast the "Publi Usraees" omoent.a




A "Secondary Area," according to Calthorpe' s TOD theory, is the area adj acent to

TODs, which may include areas across an arterial and no further than one mile from the

core commercial area. Although it may have lower density land uses, the "Secondary

Area" is expected to provide multiple direct street and bicycle connections to the transit

stop and core commercial area, with a minimum of arterial crossings (Calthorpe 1993)

(Figure 2-6).


















Snondary Aru

Fiur -6 iara f eon rea in TO (Suce athre93









Pb icr 10% I5 5'YU 15









Memmum Corpe, Aral Maxmumt Coe Arxea-s Mm snai mmum CeAe armomn CoreAe


Fubigur 2-7: Minmumec amount of public, core commercial and residential uses bin TOgs


Br nc aesde s tesate gouidttelinesi of enitytand mixed-use the urelantiosi ewe

the ~ ~ ~ ~ S deeomn n rni sas eglatedHOO qatifibl iBn tha h iemutb oae

on n xitig r lane tunk rasi leor, on a% fede bsrotewthn emnue

trasi trve tie rolmastp omn th trunklne% (Fgue -8).


























Figure 2-8: Diagram of TODs' relationship to transit and circulation (Source: Calthorpe
1993)

Within these strict definitions and guidelines, Calthorpe provide various

alternatives and some flexibility based on the location of a development. According to

regional conditions, Calthorpe's TODs fall into two categories:

*Urban TOD: located directly on the trunk line transit network: at light rail, heavy
rail, or express bus stops. They are suggested to be developed with high
commercial intensities, job clusters, and moderate to high residential densities
(Figure 2-9).

Secondary Area ,;











suggstedto hve modrae rdensiyrsdntil evce ealntranet



civicSeonr an rceaioa ue











Secondary Ae





Secondry Are

FigureI 2-0 iga fnihoho TD(ore atop 93






development ,I

on~/2 the---Ir city'sia peihr (Clhop 193)








Figure 2-11: TDsin tre tpe of legbrocations (Source: Calthorpe 1993)


Besidores prdesctripin nwrs xmle fdsgsfrec scenarios andOs Fgre21)

archietrl destign guideveoalne proided by althcorpe greiatly nhned our e mr


uderstandingtowrssc eeomn Fgr -2,Fgr -3 iue21,Fgr

2-15): i e rwt ra ha r agrudvlpe rprie yial





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Figure 2-12: Example of TOD design in the redevelopable sites (Source: Calthorpe 1993)








RI E K F~

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Figure 2-14: Example of TOD design in the new growth areas (Source: Calthorpe 1993)


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:-ri


Figure 2-15: Design features of TODs (Source: Calthorpe 1993)










Together, Calthorpe's written works and design practice provide a positive example

of quantifiable interpretation and detailed guidelines of TOD, backed up with systematic

analysis on different levels. However, the gulf between Calthorpe's appealing theoretical

models and the widespread practice of TOD under various and changing situations

remains imposing. Laguna West, a master-planned community in southwest Sacramento

County, is an example of Calthorpe's application of TOD. It was originally advertised as

a TOD prototype for the suburbs of Sacramento, but a downturn in the real estate market

led to eventual bankruptcy (Transit Cooperative Research Program (TRCP) 2002). The

ever-changing nature of the real estate market and the diversity of locational constraints

demand a more adaptive definition of TOD and newer quantitative approaches to gauge

the determinants of successful TOD proj ects.

"From Rhetoric to Reality"

By describing TOD as a continuum process under various circumstances with

different obj ectives, Belzer and Autler' s TOD definition achieves a new theoretical

understanding incorporating both adaptiveness and flexibility. It provides practitioners

with the freedom to devise their own quantifiable guidelines, tied to both the desired

functional outcomes of a proj ect and localized situations within a certain timeframe out

of a continuous timeline. Belzer and Autler wisely avoid both the pitfalls of rigidness

and ambiguity by discussing TOD within a thoughtful framework:

1. A focus on the desired functional outcomes of TOD, not just physical
characteristics. Although appropriate physical qualities (e.g. density, distance, and
urban form) are essential for making TOD work, an exclusive focus on these
characteristics can obscure the main goal of transit-oriented development, which is
not to create a particular physical form but to create places that function differently
from traditional development. TOD projects should capitalize on the synergy that
results from a functional integration of land use and transit, such as reduced auto
dependency, which in turn leads to other benefits. Physical characteristics are a
means of achieving those desired ends, not ends in and of themselves.









2. Acknowledgement of a continuum of success. The degree to which a TOD
proj ect achieves desired functional outcomes can vary depending on the quality of
the proj ect and the characteristics of the place. This provides criteria that can be
used as performance measures to assess how well proj ects fulfill certain goals. A
high-density development within one quarter mile of a transit station may fail to
take advantage of the full range of synergies made possible by TOD, even if it is
better in some ways (e.g. mode split) than more conventional development.
Focusing on functional outcomes allows such a proj ect to be labeled a partial
success rather than wholly labeling it TOD on the basis of physical characteristics.

3. Adaptation to different locations and situations. Transit systems and locations
vary greatly in their characteristics and their suitability for TOD. We should not
expect the same results from a proj ect in the core of a metropolitan area and one in
the distant suburbs, just as we cannot necessarily hope for the same outcome in
Dallas as in Chicago. Focusing on quantifiable functional outcomes accounts for
both different degrees of success and the uniqueness of individual places. Just as a
proj ect can be judged as more or less successful TOD, so two proj ects with the
same functional outcomes in very different places can be assessed within the
context of those places. (Belzer and Autler 2002, p. 12)

Partly because of the gap between the complexity of the interrelated obj ects in the

open system and the quantitative process approximating the phenomenon in an assumed

isolated system within a certain timeframe, it is as paradoxical to believe Socratic

Wisdom as to understand that strictly-defined quantitative guidelines are sometimes

harder to be implemented than flexible guidelines providing a right perspective and a

proper way of thinking towards a suitable solution for various situations. Coming back to

the original motivation behind TOD efforts, which is to improve our quality of life

through the powerful combination of transit stations and development, Belzer and Autler

's pioneering way of interpreting and evaluating TOD transforms a rhetorical "must be"

into a realistic "could be." Informed by earlier emphasis on the specific components and

elements of TOD rather than the holistic scrutiny of their functional outcomes in the

complex reality, definitions of transit-oriented development often focus on built form.

For example, Bernick and Cervero (1996) emphasize the role of the "three Ds" (density,

diversity, and design) in the success of TOD (Belzer and Autler 2002). Experience










proves that proper built form alone, although a necessary element, is not sufficient for

achieving all the benefits of TOD. For example, units per acre, a measure of physical

form, gives very little information about the way a place functions: a high-density area

can easily be less pedestrian-friendly than a low-density one (Belzer and Autler, 2002).

Because most definitions and guidelines of TOD, such as those developed by Calthorpe,

focus on built form, many proj ects that are billed as successful TODs don't function very

well in reality (Belzer and Autler 2002). Self-limited by the "must-be"s in their

definitions, these proj ects may have overcome an important barrier by creating dense

mixed-use development next to a transit station, but they fall short when measured by

performance rather than physical characteristics. In contrast, a focus on outcomes allows

a better benchmark of success and a better measure of the tradeoffs that most proj ects

must make (Belzer and Autler 2002).

Belzer and Autler present a definition in the form of six interrelated performance

criteria that can be used to evaluate proj ect function and outcomes:

* Location efficiency, which converts driving from a necessity into an option,

* Value recapture for the individuals and households,

* Livability, including improved air quality, increased mobility choices, decreased
congestion, improved access to retail, services, recreational, and cultural
opportunities, etc,

* Financial return for both private developers and transit agencies,

* Choice of a diversity of housing types, retail types, and transportation,

* Efficient regional land-use patterns resulting in less loss of farmland and open
space, more suitable regional and sub-regional balance between j obs and housing,
shorter commutes, and less traffic and air pollution, etc.

This new definition provides not only the space for people to ponder the reasons for

those "must-be"s in previous definitions and under different circumstances, but also the









flexibility of implementing the proj ect through consecutive stages and evolving phases in

an ever-changing process. For example, Belzer and Autler discuss financial return by

providing both reasons for mixed-use and flexibility in thinking of the ways to Einance

and implement such proj ects, instead of saying simply that transit-oriented development

must be mixed-use and location-efficient:

Assuming that each use within the program yields an acceptable rate of return, a
mixed-use strategy can be more advantageous for the developer than a single-use
proj ect because it allows for greater flexibility in responding to various market
cycles, protects against market volatility, and holds value over time. In addition, it
may be easier to Einance smaller increments of different development products than
one large single use because the proj ect risk is spread among a wider variety of
lenders and equity investors.

While TOD proj ects may require more complex Einancing strategies, the potential
exists for increased return, particularly if proj ects are designed to take advantage of
the benefits provided by location efficiency. (Belzer and Autler, 2002, Page 13)

Experiencing TJD

Defining TJD

Although the distinction between TOD and TJD is not always clear, mixed-used

transit-j oint development (TJD) is treated as a subset of TOD. Most TJDs in the United

States are commercial in nature. Rail agencies tend to focus on large-scale TJD projects,

most commonly commercial office-retail developments and mixed residential-retail

developments, while the j oint development proj ects of bus operators are more likely to be

single or specialized uses like sports facilities, entertainment centers, or daycare facilities

(Transit Cooperative Research Program (TRCP) 2004).

TJD is distinguished from other forms of TOD mainly by being tied to a specific

real-estate proj ect, venture, or brokered deal and involving the direct participation of a

public entity, often a transit agency, in revenue streams and sometimes ownership

(Transit Cooperative Research Program (TRCP) 2004). Existing TJD projects often









occur on a transit agency's property or in its air rights, although they can also be built on

nearby private land if an improvement is physically or functionally integrated with a

transit facility (Transit Cooperative Research Program (TRCP) 2004). Various

initiatives, including air-rights development, ground-lease arrangements and station

connection-fee programs, are used to promote TJD proj ects for the mutual benefit of

public and private interests (Transit Cooperative Research Program (TRCP) 2004).

The Washington metropolitan Area Transit Authority (WMATA) is a pioneer in the

practice of TJD. WMATA defines joint development as: "a creative program through

which property interests owned and/or controlled by WMATA are marketed to office,

retai l/comm erci al, recreati onal/entertai nm ent, and re si denti al devel op ers with the

obj ective of developing transit-oriented development proj ects" (Transit Cooperative

Research Program (TRCP) 2004). Between 1970 and 2002, WMATA formally entered

into thirty-eight j oint development proj ects in the District of Columbia and the bi-state

area. The sum value of these ventures has exceeded $2.5 billion (Transit Cooperative

Research Program (TRCP) 2004). Collectively, these projects, including everything from

air-rights leases and land rents to station connection fees, yield the agency some $6

million in annual revenues (Transit Cooperative Research Program (TRCP) 2004).

San Francisco' s Bay Area Rapid Transit (BART) District, also active in joint

development, entered into eight j oint development agreements between 1984 and 2003

(Transit Cooperative Research Program (TRCP) 2004).

Although rail systems have no monopoly on TOD and TJD, most cases of TJD

projects are initiated by rail agencies. Some successful TJD projects in the world over

the past decade have occurred in and around bus-way stations most notably in Ottawa,









Canada, and Curitiba, Brazil (Cervero 1998; Parsons, Brinckerhoff, Quade and Douglas

Inc. et al. 1995). Bus-based TODs are currently manifesting in San Diego's mixed-use

Uptown District, and are also being aggressively planned in North Carolina for

Charlotte's northeast and north side BRT corridors (Transit Cooperative Research

Program (TRCP) 2002). Because of rail agencies' greater institutional capacities and

planning resources, they are however more likely to negotiate monetary contributions

with private developers than are bus agencies (Transit Cooperative Research Program

(TRCP) 2004).

Effects of Rail TJDs on House Prices

Existing studies have demonstrated that adj acency to rail stops has a much more

significant impact on property values than adjacency to bus stops (Transit Cooperative

Research Program (TRCP) 2002). Adj acency to rail transit stations no doubt improves

access of neighborhood residents to commercial activity centers. At the same time,

however, rail transit stations also bring noise, traffic, and other nuisances to

neighborhoods. Some researchers speculate that transit stations may have a positive

effect on the values of homes in lower-income neighborhoods because the benefits of rail

transit station accessibility more than offset any nuisances, and that transit stations may

have a negative effect on values of homes in high-income neighborhoods because the

nuisance of transit stations more than offsets the benefits. Based on Nelson' s study in

Atlanta, Georgia, it appears that elevated transit stations have positive price effects on

homes in lower income neighborhoods and negative price effects on homes in higher

income neighborhoods within a region approximately 2.7 mi east to west by 1.7 mi north

to south in DeKalb County (Nelson 1992).









Financial Initiatives for TJD

Transit agencies in fast-growing areas like greater Washington D.C., Atlanta,

Dallas, San Diego, and the San Francisco Bay Area have been aggressive in pursuing

TJD projects. Experience shows that, if they are to be successful, TJD projects must be

proactively championed by the public sector (Transit Cooperative Research Program

(TRCP) 2004). For most TJD proj ects, transit agencies use a variety of tools to spread

risks and rewards, the most common type of which is leasing of ground space (Transit

Cooperative Research Program (TRCP) 2004). There are also other Einancial tools for

available to partnerships, such as air-rights leases, sharing of operation costs, sharing of

construction costs and station connection fees.

There are notable examples of air-rights leases (mostly office space) above rail

stations: Bethesda Station mixed-use project, Ballston in Arlington County, Great

American Plaza in San Diego, Union Station in Los Angeles, Datran Center at the South

Dadeland Station in Miami, and Resurgens Plaza at Atlanta' s Lenox Square metropolitan

Atlanta Rapid Transit Authority (MARTA) Station (Transit Cooperative Research

Program (TRCP) 2004).

Some TJD proj ects involve the sharing of operation costs between transit agencies

and the private sectors (e.g., ventilation systems, utilities, and parking facilities).

"WMATA's Farragut West Station, for example, taps into the International Square office

and retail project's heating and air conditioning system. At the Bethesda Station, heat

generated by the transit system is being recycled into an integrated mixed-use office-

retail-housing proj ect" (Transit Cooperative Research Program (TRCP) 2004). Some

TJD proj ects involve the sharing of construction costs (e.g., building foundations, parking

facilities, and construction staging areas). "Developer-financed bus bays and drop-off









spaces at the Van Ness and Bethesda Stations, for example, saved WMATA an estimated

$2.1 million (1982 dollars) in construction costs. Still, rail agencies have been far more

aggressive in seeking out cost-sharing deals, especially east coast transit agencies like

WMATA and New York City's metropolitan Transportation Authority" (Transit

Cooperative Research Program (TRCP) 2004).

Station connection fees, another common form of financial initiative, likewise

tend to fall within the province of rail agencies (Transit Cooperative Research Program

(TRCP) 2004). They are especially popular with retail developers since they can deliver

potential shoppers to the ground floors of connecting buildings. In the case of the

Friendship Heights Station, a maj or retailer paid WMATA a one-time fee of $300,000

(1982 currency) for the right to connect to the station rotunda and also paid for the design

and construction of the tunnel. They were followed by two other retail developers who

paid tie-in fees of $737,000 and $775,000, respectively, plus annual rents, for their own

connections to Friendship Heights (Transit Cooperative Research Program (TRCP)

2004).

Conclusions

Findings from the literature review about how people define TJD informed the

choices of study area and related variables for the regression analysis in this research:

Choosing Rail TJD for the Study

Because of rail agencies' greater institutional capacities and planning resources,

most TJD cases available for scrutiny are initiated by rail agencies (Transit Cooperative

Research Program (TRCP) 2004). Another reason for choosing properties around rail

systems for this study is that existing studies have demonstrated that adjacency to rail










stops has a much more significant impact on property values than adjacency to bus stops

(Transit Cooperative Research Program (TRCP) 2002).

Defining the Study Area

Calthorpe delineated TOD as a mixed-use community within an average 2,000-foot

walking distance of a transit stop and core commercial area. As a result, parcels within a

2,000-foot distance from rail stations were selected for this research.

Choosing Two Regression Models for the Study

The two main analytical approaches that were used to measure transit' s impacts on

real estate values are: (1) hedonic price models-- normally multiple regression models

that partial out the unique effect of proximity in transit, and the presence of TJD

characteristics, in explaining property values; and (2) matched-pair comparisons--

comparisons of effective contract rents and per-square-foot land values between station

areas and control sites (Transit Cooperative Research Program (TRCP) 2002). In this

research, one multiple regression model was set up to understand the effects of TJD

arrangements on the property value.

Most existing feasibility analyses focus merely on studying the financial return

from the increase in real estate values of TJD properties. As a result, proj ects were

predicated on a purely financial rationale rather than a broad vision of how transit could

work in tandem with surrounding development (Belzer and Autler 2002). Increasing

evidence, however, shows that TJD can yield more benefits than merely increased land

value, including growing transit ridership, an important factor for transit agencies to

consider when exploring partnerships with private developers.









Therefore, both the increase in real estate values of TJD properties and the ridership

increase produced by TJD proj ects were calculated in the study using two multiple

regression models.

Introducing the Household Income Level as a Variable into the Regression Analysis

Belzer and Autler point out that the residential proj ects fail to include units targeted

at a mix of income groups or household sizes, but focus solely on one particular market

segment, be it subsidized dwellings targeted at lower income households or luxury units

for young singles and empty nesters (Belzer and Autler 2002). As a result, the different

roles played by household income level also composed an important component in this

feasibility study.

Nelson's study in Atlanta, Georgia, further shows that elevated transit stations have

positive price effects on homes in lower income neighborhoods and negative price effects

on homes in higher income neighborhoods within a region approximately 2.7 mi east to

west by 1.7 mi north to south in DeKalb County (Nelson 1992). Therefore, the

household income level and the housing information of the neighborhoods surrounding

rail transit stations were introduced into the model as its non-urban-form variables.















CHAPTER 3
HOW DO PEOPLE MEASURE THE LEVEL OF LAND-USE MIX?

Multiple regression models that partial out the unique effect of proximity in transit

and the presence of TJD characteristics were used to measure transit' s impacts on real

estate values in explaining property values.

Mixed land-use is a very important characteristic of TJD proj ects according to most

TOD and TJD theories. Calthorpe defines TOD as a mixed-use (residential, retail, office,

open space, and public uses) community within an average 2,000-foot walking distance

of a transit stop and core commercial area (Calthorpe 1993). In order to understand the

relationship between mixed land-use and the financial return from TJDs including the

increases in the property values and in the transit ridership, the level of land-use mix of a

TJD community needs to be measured quantitatively. The two most common ways of

measuring level of land-use mix (land use heterogeneity) are Frank & Pivo' s Entropy

Index and Cervero & Kockelman' s Dissimilarity Index.

Frank & Pivo's Entropy Index

Frank and Pivo developed an entropy index to describe the evenness of the

distribution of built square footage among seven land-use categories. According to their

paper in "Transportation Research Record 1466," the entropy index was based on the

following equation (Frank and Pivo 1994):

Level of land-use mix (entropy value)

= [single family- log (single family)] + [multifamily- log (multifamily)]

+[retail and service- log (retail and service)] + [office- log (office)]









+[entertainment- log (entertainment)] + [institutional- log (institutional)]

+ [i ndu stri al/m anufacturi ng- l og (i ndu stri al/manufacturi ng)]

Cervero & Kockelman's Dissimilarity Index

The land-use mix index developed by Cervero and Kockelman may also be called a

"dissimilarity index" since it is based on "points" awarded to each actively developed

hectare based on the dissimilarity of its land use from those of the eight adj acent hectares.

The average of these point accumulations across all active hectares in a tract is the

dissimilarity or mix index for the tract (Kockelman 1997):


Dissimilar try Inder =Mix Index=Z I t Xk
K S
whereu K = Numbertr of A1ctj~ivel Develo0ped Hct~ares inr Tractr and
and Xi = I if Cent~ral Active H'ectares Use Typedg~ers fomar
that of a Neighboring Bac~tare (Xa =r 0 othiseuF).


Conclusions

The Dissimilarity Index, developed by Cervero and Kockelman using the data

consist of dominant land uses assigned to 1-hectare squares of land, has its limitation in

measuring land use heterogeneity below the scale of a hectare. In order to understand the

relationship between the level of land-use mix and increases in property values at the

parcel level, Frank & Pivo' s Entropy Index, was deemed more suitable for this research.

In Frank and Pivo's paper, their equation resulted in the development of a

normalized value between a minimum of 0 and 0.845 (Frank and Pivo 1994). However,

proportions of each land use among all the land uses are always smaller than 1, which

may result in a negative value for the entropy value according to calculation of Frank and

Pivo' s Entropy Index. Therefore, an improved calculation of the level of land-use mix is

developed based on Frank and Pivo's Entropy Index for this research:






34


Level of land-use mix = -A / LN (N)

Where :

A= E {b(n)/a LN [b(n)/a]}

With b(n)/a = proportion of building floor area of each land use among total square

feet of all the land uses present in a buffer (when building floor area of one specific land

use equals to 0, value of 0.01 will be given for its calculation);

N = Number of land use categories used in the research.















CHAPTER 4
RESEARCH METHODOLOGY

In this chapter, the key research questions are reviewed and the methodology used

to conduct this research is described. The data quality of each variable for the regression

analysis is also discussed in this chapter. The findings of the literature on the different

definitions of TJD as well as various measurements of the level of land-use mix both

refined the approach of this research. Based on the conceptual model, US Census 2000

data and parcel data were examined for the case study.

Key Research Questions and Overview of Research Approach

This research tried to quantify the financial benefits brought from TJDs' major

characteristics, specifically high-density and mixed land-use. Specific questions

included :

* To what extent will high-density and mixed land-use increase the property values
of TJDs?

* Will an increase in the density and the level of land-use mix of TJDs bring a higher
transit ridership?

* What is the impact of other factors, including the household income level of the
neighborhood around TJDs, on the financial return from TJDs?

These questions were addressed through two regression analysis. One multiple

regression model was used to examine the effects of TJD proj ects' maj or characteristics,

high-density and mixed-use, on real estate values, controlling for proximity to transit. In

addition, household income level was also included in this regression model to

understand its role on the feasibility of TJD proj ects.










Another multiple regression model was developed to understand the relationship

between transit ridership and the TJD arrangements under certain socio-economic

situations.

A case study of the parcels within a 2,000-foot distance from MARTA' s rail

stations in the Atlanta metropolitan area utilized meaningful urban-form and non-urban-

form variables for the regression analysis.

Conceptual Model

In this research, "Population Density," "Average Floor Area Ratio," "Level of

Land-Use Mix," "Median Household Income," "Per Capita Income," "Total Housing

Units inside the Census Block Group," "Median Number of Rooms per Housing Unit,"

"Number of the Workers Using Transit for Traveling," and "Average Travel Time to

Work" were calculated from the census data at the block-group scale. "Square Footage

of the Property," "Acreage" and "The Year When the Building was built" were obtained

from the parcel data. All these independent variables were used to develop the first

multiple regression model to understand the impacts of TJD's characteristics on the

property value under certain socio-economic conditions (Figure 4-1).

Similarly, "Population Density," "Average Floor Area Ratio," "Level of Land-Use

Mix," "Median Household Income," "Per Capita Income," "Total Housing Units inside

the Census Block Group," "Median Number of Rooms per Housing Unit," "Average

Travel Time to Work," "Square Footage of the Property," "Acreage" and "The Year

When the Building was built" were used as independent variables to understand their

relationship with "Number of the Workers Using Transit for Traveling" (Figure 4-2).
































Figure 4-1: Conceptual Model One


Figure 4-2: Conceptual Model Two









Data Source

Data for each variable were obtained or calculated from the census 2000 data at

the block-group level and the parcel data.

Data Analysis

Urban-Form Variables

Land use, density and design are all crucial to the success of TJD projects. Land

use and density of proj ects are more often used to identify TOD and TJD proj ects, as it is

almost impossible to quantify various community designs. Density and level of land-use

mix were therefore introduced as the urban-form variables in this research.

Density

Density can be defined in at least two ways: how dense the built areas are clustered

within an area, and how the population who use the space is clustered within an area.

Therefore both "average floor area ratio" calculated from parcel data and "population

density" calculated from US Census data were introduced to the regression analysis.

Level of Land-Use Mix

An improved calculation of the level of land-use mix was developed based on

Frank and Pivo's Entropy Index for this research:

Level of land-use mix = -A / LN (N)

Where :

A= E {b(n)/a LN [b(n)/a]}

With b(n)/a = proportion of building floor area of each land use among total square

feet of all the land uses present in a buffer (when building floor area of one specific land

use equals to 0, value of 0.01 will be given for its calculation);

N = Number of land use categories used in the research.










When calculating the level of land-use mix of the parcels inside each 2000 feet

buffer around transit stations using the Entropy Index, various land uses of the parcels

were grouped into 11 categories for this research: "Office," "Institutional,"

"Recreational," "Single Family," "Multifamily," "Parking/TCU," "Commercial," "Park/

Open Space," "Industrial," "Vacant," "Unknown/ Other."

While many of the buffers were isolated from each other, some were overlapped

with each other, producing many parcels selected within more than 2 buffers. In order to

apply one level of land-use mix value to each parcel, the maximum entropy index value

among all the values calculated from the related buffers was applied to the parcels

contained by more than 2 buffers. Arc GIS 9.0 was used to calculate the level of land-use

mix for this research (See Appendix A).

Non-Urban-Form Variables

Previous study suggests that other factors than the urban-form variables affect the

financial return from TJDs. Based on Nelson's study in Atlanta, Georgia, it appears that

elevated transit stations have positive price effects on homes in lower income

neighborhoods and negative price effects on homes in higher income neighborhoods

within a region approximately 2.7 mi east to west by 1.7 mi north to south in DeKalb

County (Nelson 1992).

The income level and housing information of the neighborhoods surrounding

transit stations were therefore introduced into the model as its non-urban-form variables.

Information on "Median Household Income," "Per Capita Income" obtained from US

Census data were used for regression analysis as income-related variables. Information

on "Total Housing Units" and "Median Number of Rooms per Housing Unit" were

introduced into the regression analysis as housing-related variables.









To MARTA, increases in ridership mean increases in its operating revenue, which

is an important component in MARTA' s decision-making for TJD proj ects in Atlanta.

Information on "Number of Workers Using Transit for Traveling" was obtained from US

Census data. The "Average Travel Time to Work" was calculated from the information

provided by the US Census data, using the following equation:

Total travel time to work = ([PO31003]*2.5 + [PO31004]*7.5 + [PO31005] *12.5 +

[PO31006] *17.5 + [PO31007] *22.5 + [PO31008] *27.5 + [PO31009] *32.5 + [PO31010]

*37.5 + [PO31011] *42.5 + [PO31012] *52 + [PO31013] *74.5 + [PO31014] *90) /

[PO31002]

Where :

PO3 1002 = Number of workers traveling to work;

PO3 1003 = Number of workers traveling to work for less than 5 minutes;

PO3 1004 = Number of workers traveling to work for 5 to 9 minutes;

PO3 1005 = Number of workers traveling to work for 10 to 14 minutes;

PO3 1006 = Number of workers traveling to work for 15 to 19 minutes;

PO3 1007 = Number of workers traveling to work for 20 to 24 minutes;

PO3 1008 = Number of workers traveling to work for 25 to 29 minutes;

PO3 1009 = Number of workers traveling to work for 30 to 34 minutes;

PO3 1010 = Number of workers traveling to work for 3 5 to 39 minutes;

PO3 101 1 = Number of workers traveling to work for 40 to 44 minutes;

PO3 1012 = Number of workers traveling to work for 45 to 59 minutes;

PO3 1013 = Number of workers traveling to work for 60 to 89 minutes;

PO3 1014 = Number of workers traveling to work for 90 or more minutes.









Most of the data for calculating these non-urban-form variables was obtained from

the US Census data. However, the parcel data containing all the property information

was at a much smaller scale than the US Census data (Figure 4-3). In order to overlay the

different datasets at these different scales, Arc GIS 9.0 was used for the analysis (See

Appendix B).


Figure 4-3: A map showing the parcel data overlaying with the US Census data

Property Information

Several variables providing property information were also introduced into the

regression analysis. Information of the acreage of the parcel, square footage of the






42


buildings, and the year when the buildings were built are obtained from the parcel data.

Taxable values of the property were used as the property values of parcels.















CHAPTER 5
DESCRIPTION OF CASE STUDY AREA

Previous researches on TJDs have done many case studies in the San Francisco Bay

area, southern California, Washington, D.C. and New York City (Bernick & Cervero,

1996). The Atlanta metropolitan area in Georgia is one of the fastest growing areas in the

southeastern United States. The metropolitan Atlanta Rapid Transit Authority

(MARTA), formed as a j oint public instrumentality of the City of Atlanta and the

counties of Fulton, DeKalb, Cobb, Clayton, and Gwinnett, operates a bus and rapid rail

transportation system in the region. As one of the TJD proj ects around MARTA' s rail

stations, Lindbergh City Center has been the subj ect of several earlier studies (Dumbaugh

2004). A region-wide feasibility study on the TJD projects around MARTA's rail

stations, however, has not been done before.

In this research, a case study in the Atlanta metropolitan area was used to

understand the financial benefits of TJDs' maj or characteristics, specifically high-density

and mixed-land use, in the setting of the southeastern United States.

Describing the Case Study Area--Atlanta metropolitan Area

Atlanta Overview

With a current population of more than 369,000, Atlanta, the capital of the state of

Georgia was first named Marthasville in honor of the then-governor's daughter,

nicknamed Terminus for its rail location, and then changed soon after to Atlanta, the

feminine of Atlantic. Covering a much bigger area than the City of Atlanta, the Atlanta











metropolitan area has a population of more than four million, which makes it one of the


largest metropolitan areas in the southeastern United States (Figure 5-1).




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movemnt.Amon cacther man aolvn impishments of wiche Atlantas African-Americaetni

cromunity ish pmiroud s thed Noe Peacoe Pize thacty' Martin Luthery KingJr woninh 1964.

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during President Jimmy Carter's administration.

In contrast with the stereotyped romantic versions of the south as moving at a

leisurely pace, the hectic building and rebuilding that characterized the period after the

Civil War continues unabated (Concierge.com). Still viewed by die-hard Southerners as









the heart of the Old Confederacy, Atlanta has presented itself as a fast-paced modern city

proud of its heritage (Concierge.com). In the past two decades, Atlanta has experienced

unprecedented growth. Since the late 1970s dozens of dazzling skyscrapers (Figure 5-3,

Figure 5-4) designed by such luminaries as Philip Johnson, I. M. Pei, and Marcel Breuer

have reshaped the city's profile (Concierge.com).


Figure 5-3: Exciting nightlife, Atlanta, Georgia (Source: Concierge.com)


Figure 5-4: Evening falls over Atlanta, Georgia (Source: Concierge.com)

Socio-Economic Characteristics of the Region

According to US Census data, the Atlanta metropolitan area in the year of 2000 had

a household population of 4.0 million, fifty-one percent of whom were females and forty-










nigh percent of whom were males. The median age was 32.9 years. Twenty-seven

percent of the population was under eighteen years and seven percent were sixty-five

years and older (U.S. Census Bureau) (Figure 5-5).



65 and over 7%


4 5 to 6 4 2 10%


25 to 44 36%


1 8to 24 9%


Under ls 87%

0 10 20 30 40 50 60 70 80 90 100

Percent of population


Figure 5-5: Age distribution of people in the Atlanta metropolitan area (Source: Census
2000 Supplementary Survey)

In the Atlanta metropolitan area, for people reporting one race alone, sixty-four

percent were White; twenty-nine percent were Black or African American; less than half

a percent were American Indian and Alaska Native; three percent were Asians; less than

half a percent were Native Hawaiian or Other Pacific Islander, and three percent were

Some other race. One percent reported Two or more races (U.S. Census Bureau).

In the City of Atlanta itself, however, the breakdown is dramatically different. For

people reporting one race alone, sixty-five percent were Black or African American in the

City of Atlanta (U. S. Census Bureau).










In 2000, there were 1.5 million households in the Atlanta metropolitan area. The

average household size was 2.69 people. Families made up sixty seven percent of the

households, including both married-couple families (forty-eight percent) and other

families (nineteen percent) (Figure 5-6). Non-family households made up thirty-three

percent of all households in the Atlanta metropolitan area. Most of the non-family

households were people living alone, but some were comprised of people living in

households in which no one was related to the householder (U. S. Census Bureau).




Marriecl-couple families 48%


Other families 19%


m People living alone 25s%


Other nonfamily households 7%


0 10 20 30 40 50 60 70 80 90 100
Percent of households

Figure 5-6: Types of households in the Atlanta metropolitan area (Source: Census 2000
Supplementary Survey)

In 2000, eighty-one percent of the people at least one year old living in the Atlanta

metropolitan area were staying in the same residence one year earlier; nine percent had

moved during the past year from another residence in the same county, five percent from

another county in the same state, four percent from another state, and one percent from

abroad (U. S. Census Bureau) (Figure 5-7).











Same residence 81%o


Different residence, same county 9%


SDifferent county, same state 5%


SDifferent state 4%,/


Abroad 1%l~


0 10 20 30 40 50 60 70 80 90 100

Percent of people 1 year and over

Figure 5-7: Geographic mobility of residents of the Atlanta metropolitan area (Source:
Census 2000 Supplementary Survey)

In the year of 2000, eighty four percent of people twenty-five years and older had at

least graduated from high school and thirty-two percent had a bachelor's degree or higher.

Among people sixteen to nineteen years old, fifteen percent were dropouts; they were not

enrolled in school and had not graduated from high school (U.S. Census Bureau).

In the year 2000, for the employed population sixteen years and older, the leading

industries in the Atlanta metropolitan area were Educational, health, and social services,

fourteen percent, and Retail trade, twelve percent (U. S. Census Bureau).

Seventy-nine percent of the workers in the Atlanta metropolitan area drove to work

alone in the year of 2000, twelve percent carpooled, four percent took public

transportation, and two percent used other means. The remaining four percent worked at

home. It took those who commuted to work an average of thirty minutes to get to work

(U.S. Census Bureau).










In 2000, the median income of households in the Atlanta metropolitan area was

$50,237. Ten percent of people were in poverty. Thirteen percent of related children

under eighteen were below the poverty level, compared with ten percent of people sixty-

five years old and over. Eight percent of all families and twenty percent of families with a

female householder and no husband present had incomes below the poverty level (U. S.

Census Bureau) (Figure 5-8).




People age 65 and over 10%



S Related children under l 8 3



All families 8 %



Female householder families 20%


0 10 20 30 40 50 60 70 80 90 100

Percent: below poverty level

Figure 5-8: Poverty rates (Source: Census 2000 Supplementary Survey)

In 2000, the Atlanta metropolitan area had a total of 1.6 million housing units, 5.8

percent of which were vacant. Of the total housing units, sixty-six percent were in

single-unit structures, twenty-nine percent were in multi-unit structures, and four percent

were mobile homes(U.S. Census Bureau) (Figure 5-9). Thirty-four percent of the

housing units were built since 1990 (U.S. Census Bureau).












SSingle-unit structures 66%


h In multi-unit structures 29%


~-" Mobile homes 4%


0 10 20 30 40 50 60 70 80 90 100

Percent of housing units


Figure 5-9: Types of Housing Units (Source: Census 2000 Supplementary Survey)

In the year of 2000, the Atlanta metropolitan area had 1.5 million occupied housing

units 956,000 owner occupied and 542,000 renter occupied. Three percent of the

households did not have telephone service and seven percent of the households did not

have access to a car, truck, or van for private use (U.S. Census Bureau).

Twenty-six percent of owners with mortgages, nine percent of owners without

mortgages, and forty-three percent of renters in the Atlanta metropolitan area spent thirty

percent or more of household income on housing (U. S. Census Bureau) (Figure 5-10).




Owners with mortgage 26%


D Owners without mortgage 9%


Renters 43%/'


O 10 20 30 40 50 60 70 80 90 100
Percent paying 30 percent or more of income for housing


Figure 5-10: Occupants with housing cost burden (Source: Census 2000 Supplementary
Survey)









MARTA and TJDs in the Region

In 1996, the U. S. Environmental Protection Agency (EPA), as executor of the

Clean Air Act (CAA) amendments of 1990, declared the Atlanta metropolitan region to

have entered a "conformity lapse," which meant planners at the Atlanta Regional

Commission (ARC) had to come up with a long-term transportation plan to bring Atlanta

into conformity with CAA standards by 1998 (Feigon, Hoyt, and Ohland 2004). At the

root of Atlanta' s multiple problems, including traffic congestion and air pollution, were

at least two decades of unplanned growth and road building. The Atlanta metropolitan

area sprawls over several counties in the region, including the counties of Fulton,

DeKalb, Cobb, Clayton, and Gwinnett. Commentators despaired of Atlanta's dozens of

balkanized municipalities ever being able to work out a passable transportation

improvement plan under the guidance of the beleaguered ARC (Feigon, Hoyt, and

Ohland 2004). In the absence of an effective regional planning body, MARTA emerged

as a key player in the campaign to relieve the city's traffic congestion.

MARTA was formed as a j oint public instrumentality of the City of Atlanta and the

counties of Fulton, DeKalb, Cobb, Clayton, and Gwinnett by action of the General

Assembly of the State of Georgia (the MARTA Act) to design and implement a rapid

transit system for the Atlanta metropolitan area (MARTA). MARTA now operates both

bus and rapid rail transportation system in the region. Making transit more attractive to

Atlanta residents has become an important priority in the region's effort to combat both

congestion and poor air quality (Feigon, Hoyt, and Ohland 2004) (Figure 5-11).













































Figure 5-11: Time-lapse photo showing congestion and brake lights on freeway heading
downtown (Source: MARTA)

MARTA' s rail system consists of 47.6 miles of operational double track and thirty-

eight fully-functioning stations (MARTA). The rail system has lines running both east-

west and north-south. The main lines intersect at the Five Points Station, located in

Atlanta' s Downtown Business District (Figure 5-12).













































END
NOrth-SOuth Rail Line
East-W~est Rail Line
Iritrstates
Ridestores
Statiolrs with free daily perking
Stations with long-term and
freea daily parking


Rail Map



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r81 Garnert

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) 87 Arponrt O
if


Figure 5-12: Rail map of MARTA (Source: MARTA)

The fixed rail system, which consists of steel-wheel trains, operates at speeds up to

seventy miles per hour on steel rails using an electrified "third rail" as the power source


(Figure 5-13). The rail transit system consist of approximately 338 air-conditioned

vehicles, which operate in combinations of two to eight vehicle trains (Figure 5-14).



































Figure 5-13: MARTA train moving in front of the Atlanta Skyline

(Source: MARTA)


Figure 5-14: Rail operator in front of his train (Source: MARTA)









MARTA operates under severe financial constraints, as it is the only large

metropolitan transit system in the nation that receives no money from state government

(Feigon, Hoyt, and Ohland 2004). Any increase in ridership means an increase in its

operating revenue. As young professionals began returning to live in the city core in the

1990's, MARTA recognized that there was a market for TOD around its rail stations

(Feigon, Hoyt, and Ohland 2004).

MARTA has completed several transit-j oint development proj ects, leasing the air-

rights over several stations. Following the downturn of the real estate market in the

1980s', MARTA' s various parking lots and unused parcels became tremendously

valuable with the return of bullish Atlanta real estate market in the 1990s'(Feigon, Hoyt,

and Ohland 2004).

Despite the predominance of rail technologies, they do not have a monopoly on

TOD and TJD. MARTA also operates 158 bus routes in the region besides its rail

system. Nevertheless, most TJD activities have been observed around its rail stations.

Atlanta' s Lindberg Station, in particular has become one of the most well-known TOD

and TJD proj ects in the nation.

The theory of TJD were able to be put into practice at Lindbergh City Center due

largely to FTA' s joint development policy ruling that enables land purchased using

federal funds to be leased to the private sector as long as the resulting development is

transit supportive. Using a competitive-bid process, MARTA selected a master

developer, Carter and Associates, in 1997 to implement a large-scale mixed-use proj ect at

a forty-seven-acre site surrounding Lindbergh Station. Located along Piedmont Avenue

between Atlanta' s rapidly growing Midtown and Buckhead districts, and with great









access to the region's downtown and Perimeter Center employment hubs, the site seemed

ideal for the proj ect (Figure 5-15).


Figure 5-15: Site of Lindberg City Center (Source: http://www. carterusa.com/)

MARTA' s final plan for the site includes about 2.5 million square feet of

commercial and office space, 2.2 million of which was reserved for BellSouth, 300,000

square feet of retail, roughly 1,300 residential units, as well as a 160-room hotel

(Dumbaugh 2004) (Figure 5-16). A pedestrian-friendly Main Street, featuring retail

shops, restaurants and offices, will bridge over the rail station into a multifamily

residential -office district (Figure 5-17).






















~ r
I
lr
Figure 5-16. Master plan of Lindberg City Center (Source: http:~www. carterusa.com/)

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Figure 5-17. How retail shops, restaurants, offices, and multifamily residential space can
be connected through the Main Street in Lindberg City Center (Source:
http:~www. carterusa. com/)











BellSouth, one of Atlanta' s largest companies, will be the proj ect' s anchor tenant:

BellSouth's move to the Lindbergh site reflects a corporate decision to relocate
scattered suburban offices to a central-city transit node in response to growing
employee frustration over traffic congestion and a perception that quality of life
was eroding. The consolidation of its offices into three new centers will mean that
eighty percent of company employees in metropolitan Atlanta will work near a
MARTA station, compared with thirty percent today. BellSouth hopes to lure its
employees to MARTA by providing free or discounted transit passes and free
private parking at outlying MARTA stations. (Transit Cooperative Research
Program (TRCP) 2004, page 50)















CHAPTER 6
RESULTS AND DISCUSSIONS

Results

In this research, two separate linear regression models were used in order to

understand the roles the urban-form and the non-urban-form variables plays in deciding

the feasibility of TJD projects. Those urban-form and non-urban-form variables, together

with related property information (Table 6-1), were used as independent variables in the

first multiple regression model to understand the impacts of TJD arrangements on the

property value (taxable value of the property) under certain socio-economic conditions.

Table 6-1: Variables used in the first regression model
Mean Std. Deviation N
Taxable Value 421035.56527 33 82489.5618903 4688

Acreage .348795 .9923584 4688
Average Travel Time 31.115570 8.3806150 4688
Square Footae 10310.802901 50654.2334957 4688
Total Housing Units 496.231655 328.8233493 4688
Median Num of Rooms per 4.538289 1.3 922763 4688
Housing Units
Level of Land-Use Mix .59093 5 .1351577 4688

Population Densit 7373.928775 7597.4111458 4688
Averg Floor Area Ratio (AR) 22088.698166 58819.4452637 4688
Median HH Income 30117.067193 25713.4279892 4688
Per Capita Income 18677.457765 17288.5931482 4688
Year Built 1940.415316 46.1487802 4688
Number of workers using transit 77.512799 47.6354418 4688
for traveling


In SPSS 12.0, step-wise regression method was used to eradicate the multi-co

linearity of the model. Four independent variables were selected out as the variables










playing a significant role in deciding the property value (taxable value of the property) of

the parcels in the study areas in the Atlanta metropolitan area: "square footage of the

buildings on the property," "acreage of the parcel," "population density," "total housing

units in the census group block to which specific parcels belong." A model with an

adjusted R-Square value of 0.394 was constructed.

Contrary to the hypothesis of most TOD theories, the level of land-use mix and the

average floor area ratio, two of the most commonly used identifying variables, were

excluded from the model due to their insignificant impact on property value. Population

density, another identifier of density, played a significant role in deciding the property

value in this model, although its coefficient calculated from the linear regression model

was negative, and thus different than expected (Table 6-2).

Table 6-2: "Taxable value of the property" as dependent variable
Independent Unstandardized
Variables Coefficients
(Constant) 76509.977
Square Fooae 46.431
Acreage -506030.460
Population -14.868
Density
Total Housing 306.133
Units

The second multiple regression model was used to understand the impacts of the

urban-form and non-urban-form variables, and related property information on the transit

ridership within the study region including TJD proj ects and potential TJD sites in the

Atlanta metropolitan area (Table 6-3).










Table 6-3: Variables used in the second regression model
Mean Std. Deviation N
Number of workers 77.512799 47.6354418 4688
using transit for
traveling
ACREAGE .348795 .9923584 4688
Averg Travel Time 31.115570 8.3806150 4688
Square Fooae 10310.802901 50654.2334957 4688
Total Housing Units 496.231655 328.8233493 4688
Median Num of 4.538289 1.3922763 4688
Rooms per Housing
Units
Taxable Value 421035.56527 33 82489.5618903 4688

Level of Land-Use .59093 5 .1351577 4688
Mix
Population Densit 7373.928775 7597.4111458 4688
FAR 22088.698166 58819.4452637 4688
Median HH Income 30117.067193 25713.4279892 4688
Per Capita Income 18677.457765 17288.5931482 4688
Year Built 1940.415316 46.1487802 4688


In SPSS 12.0, step-wise regression method was used to eradicate the multi-co

linearity of the model. Ten independent variables were selected out as the variables

playing an important role in deciding the transit ridership in the study areas in the Atlanta

metropolitan area: "average travel time," "median number of rooms per housing unit,"

"total housing units in the census group block specific parcels belong to," "per capital

income," "level of land-use mix," "population density," "average floor area ratio,"

"property value (taxable value of the property) of the parcels," "acreage of the parcel,"

and "square footage of the buildings on the parcel." "Property value (taxable value of the

property) of the parcels" was then removed because of multi-co linearity. And a model

with an adjusted R-Square value of 0.519 was constructed.

Although the variables indicating the density ("population density" and "average

area floor ratio") and the level of land-use mix played significant roles in deciding the










number of workers using transit for traveling in the linear regression model, level of land-

use mix was negatively related to the "number of workers using transit for traveling,"

partly rej ecting some of the TOD theories that increasing the level of land-use mix would

result in an increase of number of workers using transit for traveling (Table 6-4). A

higher population density in this regression model resulted in an increase of number of

workers using transit for traveling, which supports most of the hypotheses for TOD/ TJD

research and the TOD/TJD theories.

Table 6-4: Coefficients of the independent variables with the "number of workers using
transit for traveling" as its dependent variable
Independent Variables Unstandardized
Coefficients: B

(Constant) 38.563
Average Travel Time 1.860
Median Num of Rooms per -1.810
Housing Units
Total Housing Units .071
Per Capita Income 1999 -.001
Level of land-use Mixed -49.632
Population Density .001
FAR -5.710E-05
Acreae -2.328
Square Footae 4.760E-05

Discussions

This research was carried out to understand the financial benefits brought by TJDs'

major characteristics, specifically high-density and mixed land-use. Financial benefits,

including increases in property values and in transit ridership, are often assumed for TJDs

with a higher density and a greater level of land-use mix in previous studies (Belzer and

Autler 2002).









The results of this research, however partly rej ect the hypothesis that a higher

density and a greater level of land-use mix bring a higher Einancial return, particularly in

the case of the TJD proj ects in the Atlanta metropolitan area, Georgia, a growing urban

area in the United States. On one hand, "population density" played a significant role in

determining the property value of the parcels in the study region, while the "level of land-

use mix" played an insignificant role in determining property value. On the other hand,

two indicators of density, "population density" and "average area floor ratio," also had

significant impacts on the number of workers using transit for traveling in the study

region. An increase in the level of land-use mix, however, resulted in a decrease in the

number of workers using transit for traveling, an important factor in the success of TJD

proj ects. Contrary to Nelson's results on the relationship between the income level and

the property value of the parcels around the transit stations in DeKalb County, Georgia

(Nelson 1992), variables indicating income level did not play a significant role in

determining property value in the regression analysis for this research.

Based on the results of the case study in the Atlanta metropolitan area, a higher

level of land-use mix is unlikely to make TJD projects more financially successful. It is

also difficult to reach the conclusion that higher densities would bring a higher Einancial

return to the TJD proj ects in the study region, because a higher population density results

in a decrease in the property value and an increase in the numbers of workers using

transit for traveling at the same time. Furthermore, the numbers of workers using transit

for traveling is just one component of the transit ridership, for people may take transit

with other purposes than merely going to work. Previous study also indicate that

population density may be a proxy to other locational factors (Steiner 1997). In another










word, changes in property value associated with changes in population density may be

caused by locational differences among the transit stations in the study region.

But before drawing the conclusion that increases in the level of land-use mix and

density will not necessarily make TJD proj ects more feasible in the Atlanta metropolitan

area, Georgia which is contrary to the related theories several factors must be

considered.

First, mixed land use in the TOD theory stands for well-connected mixed land use

with quality urban design (Calthorpe 1993). Parcels with various land uses and densities

in the study region may just be juxtaposed without a considerate design for TJD

purposes. It is accepted that land use, density and designs are all crucial to the success of

TJD proj ects. Difficulties in quantifying various unique designs of TJD proj ects has

resulted in a lack of discussion of design elements and their interrelation with land use

and density in most quantitative TJD and TOD researches. Emphasis on land use and

density may have obscured the potentially prominent role of actual design in deciding the

success and feasibility of TJD proj ects.

Second, TJD or TOD is not the panacea for all urban problems in the United States.

Employment and other economic factors in many senses play a much more important role

in shaping urban development than transit systems. Although the Atlanta metropolitan

area, with seven percent of its households in the year of 2000 having no access to a car,

truck, or van for private use according to the US census data, has a market for TJD

proj ects, decades of unplanned urban sprawl have created an urban pattern that makes the

change towards mixed-used development expensive and difficult.










Third, by comparing several socio-economic characteristics of the study region

with other parts of the United States, some unique factors of the study region may make

the implementation of TJDs in the Atlanta metropolitan area more difficult than in some

other parts of the United States:

* The Atlanta metropolitan area has a smaller age cohort of over sixty-five years old,
who are possibly more likely to be the transit riders (Table 6-5).

Table 6-5: Percentage of population in each age category (Source: Census 2000
Supplementr Survey
Age Category Atlanta The United San Washington,
metropolitan States Francisco D.C.
area County,
California

65 and over 7% 12% 13% 12%
45 to 64 21% 22% 22% 23%
25 to 44 36% 30% 41% 34%
18 to 24 9% 9% 9% 10%
Under 18 27% 26% 15% 21%

* Both San Francisco County and Washington, D.C., where most successful TJDs
have been reported, have lower percentages of married-couple families and a higher
percentages of people living alone than the Atlanta metropolitan area (Table 6-6).

Table 6-6: Percentage of different types of household (Source: Census 2000
Supplementr Survey
Type of Atlanta The United San Washington,
household metropolitan States Francisco D.C.
area County,
California

Married-couple 48% 51% 31% 21%
families
Other families 19% 17% 14% 23%
People living 25% 26% 38% 47%
alone
Other non- 7% 6% 17% 9%
family
households










*Both San Francisco County and Washington, D.C., where most successful TJDs
have been reported, have lower percentages of single-unit structures and a higher
percentages of multi-unit structures than the Atlanta metropolitan area (Table 6-7).

Table 6-7: Percentage of different types of housing units (Source: Census 2000
Supplementr Survey
Type of housing Atlanta The United San Washington,
unit metropolitan States Francisco D.C.
area County,
California

Single-unit 66% 66% 33% 39%
structures
In multi-unit 29% 27% 67% 60%
structures
Mobile homes 4% 8% 0% 0%


Fourth, a linear relationship between the dependent and independent variables is

assumed in the regression models. In reality, the relationships among them are much

more complicated than the model. Besides, many other factors like the interactive items

among the variables are not considered in the model either. All of these factors together

create a gap between the regression models and reality (Figure 6-1).


















E 0.6-


O 0.4-



0.2-




0.0 0.2 0.4 0.6 0.8 1 .0
Observed Cum Prob

Figure 6-1: Difference between the expected cumulative probability and the observed
cumulative probability in the first regression model for the case study

Finally, the new formula used to calculate the level of land-use mix in this research

still leaves room for further discussions and improvements:

* Some researchers calculated "N" in the equation as the number of different land
uses that are not vacant. In order to calculate the level of land-use mix for all the
parcels inside all the buffers in the study region, a same "N" value (the maximum
number of different land uses) is applied to the calculation. There are some values
for the parcels inside some of the buffers are zero, for some groups of parcels
around certain stations do not have all the categories of the land uses. Zero then is
treated as 0.01 to let it working for LN function. Some but insignificant differences
are created from doing this.

* The equation uses the square footage of the buildings on the parcels instead of the
acreage of the parcels for the calculation. However, there are some land uses that
are functional even if there is no building on them, including the parking space,
open space and parks, which actually are important components according to
Calthorpe' s theory of TOD (Calthorpe 1993).

* The vacant lands designated for some specific land use are not included in the
calculation of the level of land-use mix. But in a rapidly changing and growing
urban area like the Atlanta metropolitan area, those lands may have some hidden
impacts on the overall performance of the TJD proj ects.















CHAPTER 7
CONCLUSIONS AND AREAS FOR FURTHER RESEARCH

Conclusions

Advocates of TJD assume that increases in the density and level of land-use mix

will bring greater financial return to both private developers and transit agencies. This

research was carried out to understand the financial benefits brought from TJDs' major

characteristics, specifically high-density and mixed land-use.

Findings in the literature review identified the increases of the property values and

in the transit ridership as two of the maj or financial benefits of TJDs. A case study in the

Atlanta metropolitan area selected parcels inside a 2,000 foot buffer around MARTA' s

transit stations for regression analyses. Two regression models were constructed for the

study. One multiple regression model was used to examine the effects of TJD proj ects'

maj or characteristics, specifically high-density and mixed-use, on real estate values,

controlling for proximity to transit. Another multiple regression model was developed to

understand the relationship between transit ridership and the TJD arrangements under

certain socio-economic situations.

Using meaningful urban-form and non-urban-form variables, these two regression

models were used to address several questions:

* To what extent will high-density and mixed land-use increase the property values
of TJDs?

* Will an increase in the density and the level of land-use mix of TJDs bring a higher
transit ridership?










* What is the impact of other factors, including the household income level of the
neighborhood around TJDs, on the financial return from TJDs?

However, the results of this research partly rej ect the hypothesis that a higher

density and a greater level of land-use mix bring a higher financial return to TJD proj ects,

specifically in the Atlanta metropolitan area, Georgia, a growing urban area in the United

States. Based on the results of the case study, the level of land-use mix played an

insignificant role in determining the property value. Furthermore, an increase in the level

of land-use mix resulted in a decrease in the number of workers using transit for

traveling. It is also difficult to reach the conclusion that a higher density would bring a

higher financial return to the TJD proj ects in the study region, because a higher

population density results in a decrease in the property value and an increase in the

numbers of workers using transit for traveling at the same time.

Different from Nelson's results on the relationship between the income and the

property level of the parcels around the transit stations in DeKalb County, Georgia

(Nelson 1992), variables indicating the income level do not play a significant role in

deciding the property value in the regression analysis for this research.

Before any conclusion that increases in the level of land-use mix and density will

not necessarily make TJD proj ects more feasible in the Atlanta metropolitan area can be

drawn, several factors were discussed:

* Mixed land use in the TOD theory stands for well-connected mixed land use with
quality urban design (Calthorpe 1993). Parcels with various land uses and densities
in the study region may just be juxtaposed without a considerate design for TJD
purposes.

* TJD or TOD is not the panacea for all the urban problems of the United States.
Employment and other economic factors in many senses play a much more
important role in shaping urban development than transit systems. Decades of
unplanned urban sprawl have created an urban pattern that makes the change
towards mixed-used development costly.










* There may be several unique social-economic characteristics of the study region
making the implementation of TJDs in the Atlanta metropolitan area more difficult
than in some other parts of the United States.

* A linear relationship between the dependent and independent variables is assumed
in the regression models. In reality, the relationships among them are much more
complicated than the model.

* The formula used to calculate the level of land-use mix in the research still has
several points for further discussions and improvements.

At last, it is also noticeable that reasons for pursuing TJD projects with growing

concerns over environmental, economical, and social sustainability goes far beyond the

calculation of the financial returns from TJD projects. As a way of smart growth, TJD

proj ects offer the hopes, if only to a modest degree, of relieving traffic congestion, air

pollution, energy depletion, and social disintegration of the neighborhoods.

Areas for Further Research

Land use, density and designs are all crucial to the success of TJD proj ects.

However, it is often mistakenly assumed that mixed land use and high-density alone

enable higher financial return from TOD and TJD projects. While greater financial return

is probably more likely if proj ects are designed to take advantage of the benefits provided

by these other two characteristics, mixed-use and high density are not determinative in

and of themselves. Land use and density of proj ects are more often used to identify TOD

and TJD proj ects rather than design, but experience shows that actual design of projects

may have played a major role in deciding their success.

Identified as America' s prototypical TJD proj ect because of its mixed land use and

high density, the Lindbergh Station proj ect raised several questions about the roles of

density and mixed land use in a real TJD proj ect:









Research shows that too much parking has a deleterious effect on transit ridership,
aggravates traffic congestion, and drives up the cost of proj ects. Too much density
is just as bad. Construction parking at Lindbergh cost about $10,000 per space,
driving up project costs. One of the results is that Lindbergh's apartments and
condos will be priced above what many Atlanta residents can afford. As long as
parking is convenient, and taking transit and walking around Atlanta is not,
Lindbergh residents with the means to choose are not likely to choose transit. Too
much density exacerbates the problem, because density combined with the transit
access and increased mobility causes the value of the property to inflate, which
creates pressure to create monocultures of offices or retail. At $40 a square foot,
for example, space may be too expensive for any use other than high-end offices or
a concentration of commercial activity that needs to draw patrons from a vast
market area that is not serviceable by transit. (Feigon, Hoyt, and Ohland 2004)

On the contrary, some bedroom communities around the San Francisco Bay area

exemplify successful transit-oriented developments without mixed land use. And

homogenous developments of student apartments thrive along the bus routes in the

university town of Gainesville.

The roles that land uses, density and designs, together with the interactive effects

among them, are playing still need further research towards a better and comprehensive


understanding.



















APPENDIX A
CALCULATING THE LEVEL OF LAND-USE MIX


In order to understand and calculate the level of land-use mix using the new


equation, several steps were carried out in Arc GIS 9.0 as follows:


1. Based on the land use codes provided in the parcel data, various land uses are


grouped into 11 categories: "Office," "Institutional," "Recreational," "Single Family,"


"Multifamily," "Parking/TCU," "Commercial," "Park/ Open Space," "Industrial,"


"Vacant," "Unknown/ Other."


2. Individual layers containing the parcels inside each 2000 feet buffer around


every transit stations are created to calculate the level of land-use mix for each buffer,


which will be applied to the parcels inside each buffer (Figure A -1).



Lets you select features from one or more layers based on w..am .4e are located
in relation to the features in another layer
I want to.
Select features from ~
the following layer(s]:
0RefinedStudy_Parcles_1
Study parcels
[selected parcel2
r Only show selectable layers in this list
that.
are, contained by ~
the features in this layer:

R Use selected features [1 features selected)
r Appb a buffer to the features in buffer
of l00D0000 Centimeters T
The red features represent the features in buffer.
The highlighted cpan features are selected because they
are contained by the red features.


Figure A-1: Selecting parcels in each buffer









While many of the buffers are isolated from each other, some are overlapped with

each other, producing many parcels selected within more than 2 buffers (Figure A-2). In

order to apply one level of land-use mix value to each parcel, the maximum entropy

index value among all the values calculated from the related buffers is applied to the

parcels contained by more than 2 buffers.


Figure A-2: Map showing overlapped buffers

3. Within every buffer, parcels with each land use category are selected. Square

footages of the buildings on the selected parcels are then obtained for the calculations

(Figure A-3).













Sizelndust FTVacant FTOffie FTUnknown FTlnstitut FTRereati
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0


0 0 87658 O 0
0 0 328946 O 0
0 0 84354 O 0
0 0 137736 O 0


0 0 245360 0 0
0 0 0 0 0
0 0 0 0 0




Record: 4iil Show: 5 SeL. Reords (2660utor18398Seleted.] Oiptions -


Figure A-3: Square footages of buildings on selected parcels

4. Individual tables are created for each buffer to calculate the level of land-use mix


for each buffer. Statistic tools provided by Arc GIS 9.0 are used to get total square


footage of all the parcels inside every buffer with each land use category (Figure A-4).


Field
ISumFTOffice I
Statistics:


Frequency Distribution
040k


Count: 367
Minimum: 0.00000030
Maximum: 2402.000000
Sum: 3529.000000
Mean: 9.61 5804 200
Standard Deviation: 138.164287
100

0 I .
0 351 702 1053 1404 1755 2106



Figure A-4: Statistical tools used to get total square footage of the office space inside
every buffer


5. Entropy index values (level of land-use mix), calculated using the new equation,


are applied to the parcels inside each buffers (Figure A-5).













~011~2


What do you want to join to this layer?
IJoan attributes from a table I

1. Choose the field in this layer that the join will be based on:
IPIN


2. Choose the table to ioin to this layer. or load the table from disk:
ICollegPark ~
F Show the attribute tables of layers in this Ilst


3. Choose the field in the table to base the join on.
IPIN I


Type: Functions:

Stnh N u m b e r A b s 1 (
Exp[ ]
0 Date F1X i
Log [ ]





r Advanced


FTLandUse CntPIN
FT Land UselDID
FT Land Use PIN
FT Land Use.Sum FTComm
FT_Land_Use.Sum_F~indu
FTLandUse SumFTlnst
FT Land Use.Sum FTMFam
FTLandUse SumFTOffi
FT_Land_Use Sum_FT~pen
FT Land Use Sum FTPark
FTLandUse SumFTRect
FT Land Use Sum FTSFam
ParcelCollegepark.Mixed_Use -
10709734927


Figure A-5: Applying the entropy index values to the parcels inside each buffer


6. Tables containing the information of each parcel's level of land-use mix are



joined to the database containing the information of all the other urban-from variables


and non-urban-form variables (Figure A-6).


About Joining Data Cne



Figure A-6: Joining the tables for the level of land use mix


7. A dataset with the information of all the urban-form and non-urban-form



variables for the research is created (Figure A-7).



































Figure A-7: Area around Five Points Station with all the GIS layers






















APPENDIX B

APPLYING THE VALUES FROM THE US CENSUS DATA TO THE PARCEL

DATA


Several steps are carried out in Arc GIS 9.0 to give the values of the all the non-


urban-form variables obtained from the US Census data to the individual parcels inside



every census block groups:


1. Select one census block group (Figure B-1);



E lle Edit Iew insert/eto lools Vndow tlelp

i Select By locaton...
E dita Se/ B lecByYaphics v Target: inderibergStatonCI



SBE martasta Set Selectable Layers...
01
08 martarall~ lear Selected Features

O @k:lurpproject CMin~~~li~~
S0 fultongpc ptins.., Mdd o curent Selection
8 0 klb~p emove From Current Selection
O Select From Current Selection
8 0 fultonsparcel
8 0 dekalbgparcel
O 0Inverse Dstance
Predicton Ma
[Fultongparce
Filled Contour
0.000000 9:
9302.145508
33372.71484
M 256831.7812:
W 673889.3750(
1 1753062.750(
M 4545642.5001
M 11771772.00(
88 30470364.00(
O @~ k:\urpprojects~acso~s
O 2 buffer






Figure B-1: One census block group being selected


2. Select the parcels with the study region that have their centers in the selected


census block group at the same time (Figure B-2);















Lets you select features from one or more layers based on where they are located
in relation to the features in another layer.
I want to:
Iselect features from
the following layerss:
10martarail~sp
study parcels
[]selected parcel2 1
r Only show selectable layers in this list


the features in this layer:
Igrp00_Project
P Use selected features (1 features selected)
r Apply a buffer to the features in grp00_Project
of: 0 000000 ICentimeters I

The red features represent the features in grp00_Project.
The highlighted cyan features are selected because they
have their center in the red features.







Points Lines Polygons





Figure B-2: Selecting parcels with their centers in the selected census block


3. Find the census block group ID in its attribute table;



4. Apply the census block group ID number to the selected parcels inside the


census block group (Figure B-3);



Fields Type: Frncuons
ACREAGE (Number
ACREAGE 1 An
B ocklD 0 Sting
CITY 1 Date
CITYID Lo
COUNTY
COUNTY I1
COUNTY ID
ESTSQFT I
BlocklD- = FAdvanced











1] O~? Show AIIeetd Records [21 0utof 1839B Selected.]



Figure B-3: Census block group ID number being applied to the selected parcels inside
the census block group








80



5. After each parcel has been given its census block group ID number, the parcel


data is joined with the database containing the information of all the non-urban-form


variables for this research (Figure B-4);












B Muprolcts~sas29mhedtlo l etanio soi llalat h


BB aketiorsp Clote hail leidn et~lheoas n la C iro l
O~tya ne




BIdksbarcoaiel oLsl~







6. A new file at the parcel level thenl Ii~i~olsli creae wihteifraio faltenn
urban-form variables f~~~~or this eerc rvde yoeraig ie t h esu lc

grou level.a
















LIST OF REFERENCES


Belzer, D. and Autler, G. 2002. A Discussion Paper Prepared for the Brookings
Institution Center on Urban and metropolitan Policy and The Great American
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Bernick, M. and Cervero, R. 1996. Transit Villages for the 21st Century. New York:
McGraw-Hill.

Calthorpe, P. 1993. The Next American Metropolis: Ecology, Community, and the
American Dream. New York: Princeton Architectural Press.

Candy, A. 2003. Affordable Housing and Transportation: Creating New Linkages
Benefiting Low-Income Families. Housing Facts & Findings, Vol. 5 No. 2:
FannieMae Foundation. Online reference. April, 2005.


Cervero, R. 1993. Transit-Supportive Development in the U. S. Washington, DC: Federal
Transit Administration.

Concierge.com. April, 2005.

Dumbaugh, E. 2004. Overcoming Financial and Institutional Barriers to TOD:
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Dunphy, R. Myerson, D. and Pawlukiewicz, M. 2003. Ten Principle for Successful
Development Around Transit. Washington, DC: The Urban Land Institute.

Federal Register Notice, 1997. April, 2005.
.

Feigon, S. Hoyt, D. and Ohland, G. 2004. The Atlanta Case Study: Lindbergh City
Center in The New Transit Town: Best Practices in Transit-Oriented
Development, Edited by Dittmar H. and Ohland G. 2004. Washington, D.C.:
Island Press.

Frank, L. and Pivo, G. 1994. Impacts of Mixed Use and Density on Utilization of Three
Modes of Travel: Single-Occupant Vehicle, Transit, and Walking. Transportation
Research Record # 1466. Washington, D.C.: Transportation Research Board.










Federal Transit Authority (FTA) 1997. FTA' s Circular 9300. April, 2005.
.

Half a Billion Americans? The Economist. April, 2005.
.

INFORM Reports 2004. INFORM. April, 2005.


Jakes, A. 1998. Transit Success? It's The Real Estate, Stupid. The Seattle Daily Journal
of Commerce. Online reference. April, 2005. < http://www.djc.com>

Keefer, L. 1984. Profit Implications of Joint Development: Three Institutional
Approaches. Washington, D.C.: U.S. Department of Transportation, Urban Mass
Transportation Administration.

Knight, R. and Trygg, L. 1977. Land Use Impacts of Rapid Transit: Implications of
Recent Experiences. Washington, D.C.: U.S. Department of Transportation,
DOT-TPI-1 0-77-29.

Kockelman, K. 1997. The Effects of Location Elements on Home Purchase Prices and
Rents: Evidence from the San Francisco Bay Area. Transportation Research
Record # 1606. Washington, D.C.: Transportation Research Board.

Metropolitan Atlanta Rapid Transit Authority (MARTA). 2005. April, 2005.


Middleton, W. 1967. The Time of the Trolley. Milwaukee: Kalmback Publishing.

Nelson, A. 1992. Effects of Elevated Heavy-Rail Transit Stations on House Prices with
Respect to Neighborhood Income. Transportation Research Record # 1359.
Washington, DC: Transportation Research Board.

Oregon Revised Statutes, Section 307-600-1. 1995. April, 2005.
.

Parsons, Brinckerhoff, Quade and Douglas, Inc. 2001. Transit Oriented Development in
America: A Working Paper. Sacramento: California Department of
Transportation Statewide TOD Study, draft report.

Porter, D. 1997. Transit Focused Development: A Synthesis of Practice. Washington,
DC: Transportation Research Board, National Research Council.

Salvesen, D. 1996. Promoting Transit-Oriented Development. Urban Land, July. April,
2005.
ofm&ContentlD=33 87>.










Steiner, R. 1997. Traditional Neighborhood Shopping Districts: Patterns of Use and
Modes of Access. Ph.D. dissertation. University of California, Berkeley.

Still, T. 2002. Transit-Oriented Development: Reshaping America's metropolitan
Landscape. On Common Ground, Winter, Pages 44-47.

Surface Transportation Policy Project (STPP) 2003. Stay The Course: How To Make
TEA-21 Even Better, Surface Transportation Policy Project Report. Washington,
DC.

Transit Cooperative Research Program (TRCP) 2002. Transit-Oriented Development and
Joint Development in the United States: A Literature Review. Washington DC:
Transit Cooperative Research Program (TRCP), Research Results Digest, Number
52.

Transit Cooperative Research Program (TRCP) 2004. Transit-Oriented Development in
the United States: Experiences, Challenges, and Prospects. Washington DC:
Transit Cooperative Research Program (TRCP), Report 102.

U.S. Census Bureau. April, 2005.

















BIOGRAPHICAL SKETCH

Shao-Ming Zhang has been a master' s student of urban planning at the University

of Florida since fall 2002. He was born in Hangzhou, China, and studied architecture at

Zhejiang University. During his study in China, he interned at the East China Architecture

Design Research Institute in Shanghai. After graduation he came to the United States to

study for his first master' s degree in urban planning with a graduate assistantship from the

Department of Urban and Regional Planning. Focusing on the study of urban design, he

also finished all the courses required for an Interdisciplinary Concentration in Geographic

Information System (ICGIS) at the University of Florida. Besides, he also got enrolled in

the Department of Civil and Coastal Engineering at the University of Florida for his

second master' s deg~ree.

He had his first internship in the United States at Regional Transit System (RTS) in

the City of Gainesville, Florida. After working as an urban planner under architect and

planner Dennis A. Smeltz in 2004, he started his career in Orlando, Florida, as an urban

designer in the summer of 2005.