UNDERGRADUATE THESIS

The Implications of Improved Access to Public Transportation: The Green Line Extension

11.ThU | May 21, 2009 Student Name: Margot Spiller

Thesis Advisor: Professor Joseph Ferreira

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Table of Contents

INTRODUCTION 5

Background 5 Purpose of this Thesis 8

LITERATURE REVIEW 10

Transit Development Theories 10 Defining and Measuring Access 13

Impacts of Transit Stations on Surrounding Neighborhoods 14

RESEARCH QUESTION 19

METHODOLOGY 21

Choosing the Station 21 Collecting the Data 22

Application to the Green Line Extension 23 Evaluating the Analysis 23

RESULTS 24

The Green Line Extension in Context 24 Variables Used to Evaluate Station Areas 25

The Existing Conditions of the Green Line Extension Station Areas 26 Comparison of the Green Line Extension to an Existing Station 30

Results of Davis Square Analysis 33

DISCUSSION 48

Discussion of the Davis Square Analysis Results 48 Application to the Green Line Extension 52

CONCLUSIONS 56

BIBLIOGRAPHY 58

APPENDIX 60

LIST OF TABLES

Table 1 – Travel Impacts of Land Use Design Features 15 Table 2 – Census Variables Used to Test for Gentrification 16 Table 3 – Census Variables Used to Track Transit Impacts 26

Table 4 – Population Density of Green Line Extension Station Areas 28 Table 5 – Socioeconomic Characteristics of Green Line Extension Station Areas 29

Table 6 – Travel Behavior of Green Line Extension Station Areas 30 Table 7 – Characteristics of the Davis Square Area in 1980 32

Table 8 – Population Density from 1980-2000 for Davis Square and Control Areas 35

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Table 9 – Percentage of Vacant Housing Units from 1980-2000 for Davis Square and Control Areas 36 Table 10 – Median Non-Condo Housing Value from 1980-2000 for Davis Square and Control Areas 37

Table 11 – Median Rent from 1980-2000 for Davis Square and Control Areas 38 Table 12 – Unemployment Rate from 1980-2000 for Davis Square and Control Areas 39

Table 13 – Workers with White Collar Jobs from 1980-2000 for Davis Square and Control Areas 40 Table 14 – College Graduates from 1980-2000 for Davis Square and Control Areas 41

Table 15 – Median Household Income from 1980-2000 for Davis Square and Control Areas 42 Table 16 – Median Family Income from 1980-2000 for Davis Square and Control Areas 43

Table 17 – Percentage of Commutes via Automobile from 1980-2000 for Davis Square and Control Areas 44

Table 18 – Percentage of Commutes via Public Transportation from 1980-2000 for Davis Square and Control Areas 45

Table 19 – Percentage of Commutes Longer than 30 Minutes from 1980-2000 for Davis Square and Control Areas 46

LIST OF FIGURES

Figure 1 – Distance Criterion for Public Transport Access 14 Figure 2 – Transit Proximity Impacts on Travel Behavior 15

Figure 3 – Inner Core Towns and Existing Transit Services 25 Figure 4 – Proposed Green Line Extension Stations Accessible by Somerville 26

Figure 5 – Land Use Categories of Somerville Parcels for 2005 28 Figure 6 – Land Use of Somerville in 1971 32

Figure 7 – Population Density from 1980-2000 for Davis Square and Control Areas 35 Figure 8 – Percentage of Vacant Housing Units from 1980-2000 for Davis Square and Control Areas 36 Figure 9 – Median Non-Condo Housing Value from 1980-2000 for Davis Square and Control Areas 37

Figure 10 – Median Rent from 1980-2000 for Davis Square and Control Areas 38 Figure 11 – Unemployment Rate from 1980-2000 for Davis Square and Control Areas 39

Figure 12 – Workers with White Collar Jobs from 1980-2000 for Davis Square and Control Areas 40 Figure 13 – College Graduates from 1980-2000 for Davis Square and Control Areas 41

Figure 14 – Median Household Income from 1980-2000 for Davis Square and Control Areas 42 Figure 15 – Median Household Income from 1980-2000 for Davis Square and Control Areas 43

Figure 16 – Percentage of Commutes via Automobile from 1980-2000 for Davis Square and Control Areas 44

Figure 17 – Percentage of Commutes via Public Transportation from 1980-2000 for Davis Square and Control Areas 45

Figure 18 – Percentage of Commutes Longer than 30 Minutes from 1980-2000 for Davis Square and Control Areas 46

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Introduction

Background Densely populated metropolitan areas present unique challenges to urban planners in terms of

economic development, land use patterns and transportation infrastructure, among other considerations.

The Metropolitan Area Planning Council has defined an “Inner Core” region of twenty cities and towns in

the Boston area that exhibit these characteristics because of their urban and populous nature. Among

these communities is the town of Somerville, which is the target of a proposed transit infrastructure

extension.

Somerville is located in Middlesex County, Massachusetts, two miles north of Boston. With

77,000 residents in 4.1 square miles, Somerville is the densest city in New England. Almost one third of

the population is foreign born, and certain areas of the town have high concentrations of moderate and

low-income residents. As of the 2000 census, the median income for a household in the city is $46,315,

and the median income for a family is $51,243 (U.S. Census Bureau 2000). Twelve and one-half percent

of the population and 8.4 percent of families are below the poverty line, and out of the total population,

14.3 percent of those under the age of 18 and 13.6 percent of those 65 and older are living in poverty.

Furthermore, more than one fourth of the town’s households do not own a car (STEP 2008). Because of

these factors related to median income and minority populations, much of Somerville has been designated

by the State as environmental justice population areas1. Environmental justice regulations are in place to

ensure that the programs and activities of the Federal government do not disproportionately affect the

human health or environment for minority and low-income populations in the United States.

1 Executive Order 12898, “Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations”, requires that each Federal agency shall, to the greatest extent practicable and permitted by law, “make achieving environmental justice part of its mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its programs, policies, and activities on minority populations and low-income populations” (Center for Environmental Excellence).

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Despite these considerations, the majority of Somerville does not have adequate access to public

transportation. With only one transit station in Davis Square, many residents resort to buses and

automobiles to reach their destinations. This constrains the already congested roadway network, leading

to unreasonably long travel times for the relatively short distances of many trips within the Inner Core

(Vanasse Hangen Brustlin (VHB), 2-2). Bus service is further hindered by overcrowding, unreliability,

and the fact that transit patrons often have to make multiple connections to reach their destinations. These

factors lead to inadequate access to Boston and other destinations, hampering economic development and

employment opportunities.

Beyond inadequate access, Somerville has suffered major environmental impacts from the 46-acre

MBTA Commuter Rail Facility located in its boundaries, as well as the infrastructure of I-93, Route 28

and other major arterials that bring with them large volumes of regional traffic. Somerville is located in

an area designated non-attainment for ozone by the U.S. Environmental Protection Agency (EPA), with a

classification of “serious” (VHB 2-3). The leading sources of ozone precursor emissions within the area

are motor vehicles. The community’s concern of air pollution is furthered by the area’s increased rates of

chronic diseases such as cancer death, asthma and obstructive pulmonary diseases.

In the early 1990’s, the Commonwealth of Massachusetts committed to implementing a number of

Boston region transit improvements as mitigation measures for the Central Artery/Tunnel Project. This

commitment was embedded in the State Implementation Plan to ensure air quality conformity. Among the

proposed improvements was an extension of the Green Line through Somerville and Medford (Green Line

Extension 2008). The Green Line Extension would have multiple stations within or within walking

distance of Somerville, including Union Square, Washington Street, Gilman Square, Lowell Street, Ball

Square and College Avenue.

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The goals of the extension are to improve mobility and regional access for residents in Cambridge,

Somerville and Medford. Improved mobility in the area will allow residents to more easily travel through

the study area and to downtown Boston (VHB 2-4). In turn, residents would benefit from greater

employment access and reduced commuting times. The Extension also plans to improve the efficiency

and effectiveness of the transportation system by expanding the viability of multiple mode choice options.

Residents will benefit from multimodal connections between commuter rail, bus services and light rail that

will improve mobility and flexibility in route choice.

By improving access and mobility, the project also aims to improve air quality and minimize

environmental impacts. Mobility improvements will help to move the area towards compliance with

National Ambient Air Quality Standards, as well as improve the overall environmental conditions in the

study area.

Advocates of the extension believe that train service is a crucial component to the city’s future

economic viability, as it will help businesses grow and bridge critical tax dollars for the city to pay for

needed services. Often cited is the transformation that has occurred in Davis Square, an area of

Somerville that changed dramatically after the Red Line was extended into Davis Square in 1984.

Beginning in the 1950s, the area fell into decline, and by the early 1970s the area was

characterized by empty storefronts and deteriorating buildings and infrastructure (MassGov). Today,

Davis Square is known as a vibrant urban center that boasts a mix of retail, office, institutional, residential

and entertainment uses. The City successfully integrated redevelopment of existing structures with new

development of over 170,000 square feet of office and retail space, as well as new multi-family housing,

making it a model for transit-oriented development. Many believe that similar changes can occur in

Union Square and other proposed transit station locations in Somerville once the Extension is completed.

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Purpose of the Thesis The City of Somerville has long advocated for increased public transportation routes within its

boundaries, as the City believes that increased access to public transportation will improve economic,

environmental and social conditions of the area for its residents. To justify the public funds of such a

project, it is important to have an accurate idea of the impacts on Somerville by the implementation of the

Green Line Extension, not only in terms of access, but of changes in land use, population characteristics

and other factors. While the Davis Square area may have had increased economic potential after the

station was implemented, what other effects did the transit line have on the nearby residents? Having an

accurate and complete picture of the repercussions of public transportation service on the surrounding

community is crucial so that the potential undesirable impacts can be addressed.

While there are a number of questions to ask of the Green Line Extension’s effects on Somerville,

the intention of this thesis is to quantify the likely changes in access resulting from the proposed Extension

and, using these results, determine the possible implications that increased access may have on

surrounding populations. Establishing which areas will have newly-acquired access to transit services will

provide an accurate picture of the existing population in terms of land use, socioeconomic characteristics

and travel behavior. These existing characteristics will be used to predict the potential changes that the

Green Line Extension will have on the community.

While predicting the effects of a publicly funded project is worthwhile, the data and technology to

do so has not been available to non-professionals until recently. Census data, which is now largely

available online and through library resources, provides a wide array of information about population

characteristics throughout the past forty years. Furthermore, within the past decade, Geographic

Information Systems (GIS) has provided an accessible means for analyzing this data. This thesis, then,

will investigate the extent to which these technologies can be used to study the impacts of public

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transportation projects. The results of this investigation will establish what analyses can be done using

commonly available resources and what should still be left to professional analysis.

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Literature Review

The literature review is broken up into four sections. The first section discusses the existing theories

about the effects of transit on surrounding communities, transit-oriented development and the debate over

compact development versus sprawl. The second section discusses both the definition of access with

respect to transportation and the various methods traditionally used to measure an accessible level of

transit. The third section reviews the observed implications of transit stations on the surrounding

neighborhood, and the fourth discusses the expected impacts of the proposed Green Line Extension on the

residents of Somerville.

Transit Development Theories

There are several popular theories in the planning world regarding the potential effects of locating

a transit station in a neighborhood. First, location theory will be reviewed, which addresses the effect of

transit accessibility on the cost of movement, in terms of money and/or time. Secondly, the degree to

which urban form can affect the realizing of transit benefits will be discussed by reviewing transit-related

development and transit-oriented development. Finally, the debate between light rail and bus rapid transit

will be reviewed.

Location Theory The basic concept underlying the land-use and transportation relationship is accessibility.

According to Giuliano (1986), as movement becomes less costly (in terms of money and/or time),

accessibility increases. The introduction of a rail line leads to greater accessibility for the area lying

within the transit corridor, giving it a relative advantage over those areas not served by the line. With all

else being equal, activities will shift towards those places that have become more accessible.

Traditional location theory, as devised by Alonso (1964), assumes a monocentric city where

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employment is in the central business district (CBD). As such, commuting prices increase with distance

from the CBD. In exchange for the lower commuting costs associated with being located near the center,

an individual is willing to pay higher rents. Following this theory, rents will be the highest at the center

where transportation costs are lowest.

As a result of these land value increases, accessible areas will see greater development densities

(Huang 2002). If land value is a factor in the production of a good or service, then its price will be higher

and it will be used proportionally less than other factors. It follows that land will be used more intensively

to account for its higher per-unit cost.

Location theory suggests that accessibility to activity centers will determine where an individual or

business chooses to reside (Giuliano 1986). An individual will live at the location at which the marginal

savings in rent from living further from the CBD equals the marginal cost of commuting. Similarly,

businesses locate in the area that maximizes accessibility to employees, clients and customers or best

facilitates the shipment of product inputs and outputs. From these guidelines, the theory predicts that the

CBD will be the most intensely developed area.

With these principles in mind, location theory must be used in the context of the modern

transportation network. In most urban areas, new rail lines will only marginally improve the accessibility

to the region, since there is already a vast network of streets and highways (Huang 2002). The effects that

a new transit line will have on regional development will be marginal; instead, it will redistribute

development that would take place elsewhere in the region to the immediate vicinity of the station.

The Urban Form and Transportation Connection

The extent to which constructing a transit line will affect the surrounding neighborhood’s

characteristics in terms of land use, travel behavior and socioeconomic factors depends largely on the type

of development around the station. Planners have long advocated for transit-oriented development (TOD)

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as a way to reverse the urban sprawl development patterns that have dominated growth throughout the 21st

century in the United States, which are seen as unsustainable and inequitable. TOD focuses on compact,

walkable communities centered around public transportation systems, with the goal of achieving a higher

quality life without dependence on a car for mobility and survival. Key components of TOD include a

walkable community design, a prominent train station in the town center, mixed land use, high density

development and reduced parking in the town center (Center for Transit Oriented Development).

Unfortunately, many past attempts at maximizing transit benefits to the community have not lived

up to expectations. Belzer and Autler (2002) term these past development attempts as transit-related

development, which narrowly defines the relationship between transit and development. Transit-related

development arises when transit agencies and the federal government seek to capture the value created by

rail systems by creating large-scale real estate developments on transit agency owned property. Instead of

maximizing the community benefits of the system and focusing on how transit should be integrated with

the larger area, this type of development focuses on the maximization of financial benefits for the parties

involved. Because of this decision to focus on revenue-maximizing real estate, these projects have not

experienced the transit ridership increases promoted by TOD advocates.

Light Rail versus Bus Rapid Transit

When designing a transit system to maximize the benefit-cost ratio of the investment, there are

differing opinions about whether light rail or bus rapid transit (BRT) is the best approach. In terms of

cost, the capital costs of a BRT system are generally lower than light rail capital costs when compared on

a cost-per-mile basis, and the operating cost differences of the two are not conclusive (GAO 2001). With

respect to performance, the two systems have also shown to have similar ridership and operating speeds.

Beyond cost and performance measures, BRT and Light Rail each have a variety of advantages and

disadvantages. BRT generally has the advantage of (1) having more flexibility than light rail, (2)

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having the ability to phase in service rather than having to wait for an entire system to be built, and

(3) being used as an interim system until light rail is constructed. According to transit operators

with experience in BRT, one of the challenges faced by BRT is the negative stigma that potential

riders attach to buses regarding their noise, pollution, and quality of ride. Light rail has advantages,

according to transit officials, associated with increased economic development and improved

community image. On the negative side, building a light rail system can have a tendency to provide a

bias toward building additional rail lines in the future.

As shown above, both BRT and light rail are compelling options for a transit project.

Choosing which system is more appropriate has to be made on a case-by-case basis considering

multiple factors such as cost, ridership, environmental impacts and community needs (GAO 2001).

Defining and Measuring Access

In developing a public transportation system, the interrelated issues of access and accessibility

should be addressed. Access is the opportunity for system use based upon proximity to the service and its

cost (Murray 1998). If distances or barriers to access a service are too great at the origin or destination of

a trip, the service is not likely to be the utilized as a mode of travel. Similarly, if the cost is too expensive

because there are cheaper services, the service is also not likely to be utilized.

Accessibility in the transportation field is defined as the ability of the public transport system to

allow individuals to reach desired goods, services, activities and destinations in a reasonable amount of

time (Litman 2008, Murray 1998). Access and accessibility are related through the fact that access to

transit allows for greater service accessibility, since the time needed to reach a transit service affects the

overall ability of an individual to reach a desired location through that transit service. With these

definitions, the distinction between access and accessibility of the public transport system is clear. While

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both access and accessibility are both important for a successful and well-utilized public transport system,

this thesis will focus on access through proximity to services.

Ensuring service coverage to public transport services is an important point since the time needed

to reach a public transport station or stop has a significant impact on total travel time, which affects

ridership (Murray 1998). Pedestrians are most likely to use transit services if the beginning and end of

their trip is close to a transit facility. As a general rule, 400 meters represents the distance of a

comfortable walk for most people under normal conditions (Calgary Transit Division 2006). Therefore,

residents who can reach a transit facility within this distance have adequate access to public transportation.

Figure 1 – Distance Criterion for Public Transport Access (CTD 2006)

Impacts of Transit Stations on Surrounding Neighborhoods Vehicle Miles Traveled Proximity to transit stations has also been shown to decrease vehicle miles traveled in households.

Research by the San Francisco Bay area Metropolitan Transportation Commission indicates that

households living near rail and ferry terminals drive significantly less and rely significantly more on

public transit than households further from transit. Arrington, et al. (2008), found that Transit-Oriented

Developments generate much less (about half) of the automobile trips as conventional, automobile-

oriented development.

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Looking at the reduced vehicle trip generation rates for various development schemes around the

station can further break down the impact of rail stations on vehicle miles traveled. Table 1 below

illustrates that the reduction in vehicle miles traveled can span from five to twenty percent depending on

the development in the area (Dagang 1995).

Figure 2 – Transit Proximity Impacts on Travel Behavior (MTC 2006)

Table 1 – Travel Impacts of Land Use Design Features (Dagang 1995)

Gentrification While there are many positive outcomes linked to locating a transit station in a neighborhood,

including higher transit ridership, new business, and added tax revenues, some critics have suggested that

transit-oriented development (TOD) can lead to gentrification. Jeffrey Lin, in “Gentrification and Transit

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in Northwestern Chicago” (2002), found that “the presence of transit, in combination with declining

automobile costs, leads to gentrification of inner-city, transit-served neighborhoods” (Lin 2002, 175).

Lin stated that gentrification can be measured in two ways, including the measurement of changes in

housing market activity, such as price changes, renovations permits and sales, or changes in household

status, such as household size, structure, income and education. Using only the measurement of housing

market activity, Lin concluded that a premium in land value closer to transit stations results in

gentrification. “The key variable, distance to transit, had a significant and strong negative effect on

property value change… This finding is consistent with the hypothesis that transit was a spur to

gentrification,” (p. 184).

While Lin relied on housing market activity to measure gentrification, other scholars have used a

variety of census variables to measure changes in household status. Hammel and Wyly (1996) created a

model using the census variables, shown in Table 2 below, to test for gentrification using socioeconomic

status, housing and total population variables.

Table 2 – Census Variables Used to Test for Gentrification

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Residential Property Values

Many transportation studies have investigated the effect of rail transit stations on surrounding

property values (Cervero et al. 2001, Diaz 1999, Lin 2002). There are four factors identified which may

account for changes in property values – two which may increase property values around transit stations

and two which may decrease values (Bowes et al. 2001). The first factor that may increase property

values around rail stations is the access advantage provided by the rail station. Commuters will be willing

to pay for properties close to rail stations if the rail offers a better transportation alternative to driving in

terms of reduced commuting time or less tangible benefits such as a less hectic commute. The second

positive factor is the neighborhood commercial services, such as retail establishments, that may move into

the area. These additional services may benefit nearby residents regardless of whether or not they use the

rail service, even though they may be primarily meant to service customers coming through the transit

station.

Countering these factors are the negative externality effects of the station, including noise or

pollution, as well as the potential increased crime rates due to the improved access to the neighborhood

provided to outsiders (Bowes et al. 2001, Debrezion et al. 2007). The overall impact on property values

due to rail station proximity is based on the relative impacts of these positive and negative factors.

Other Effects Research has shown correlations between proximity to a transit station to impacts in land use and

population density. According to the Rappaport Institute, which looked into the impacts of Commuter

Rail service on the Greater Boston area, areas that gained commuter rail stations saw a higher-than-

average increase in medium-density housing and commercial buildings between the years of 1971 and

1999 (Beaton 2006).

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With respect to population density, the same report by the Rappaport Institute found that, through

the 1990s, areas near active commuter rail stations generally saw slightly greater increases in population

density than the region as a whole. The study also found that the densest areas were most likely to have a

higher share of people using transit to get to work, which is corroborated in transit-oriented development

theories linking transit use to high population densities (Dunphy et al. 1996).

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Research Question

As shown by the literature review, the construction of a transit line using public funds is a debated

topic. The use of public funds is approved by policymakers and accepted by the public under the

assumption that the transit line will generate a number of benefits for the immediate surrounding area and

the region as a whole. These benefits, generated by the increased accessibility to and from the area around

the station, include decreased travel times and costs, increased land value and employment benefits.

With these considerations in mind, the purpose of this thesis is to explore the extent to which these

benefits can be measured using available data and technology, using the proposed Green Line Extension

as an example. Because the transit benefits are a product of the increased accessibility to and from the

region, this question will be answered in a two-step process:

• How will residents’ access to public transportation change with the Green Line Extension in

Somerville?

• What are the likely implications of increased access to public transportation for residents near

the proposed transit stations?

In order to accurately answer these questions, there are several other factors that must be addressed. First,

how can you measure the benefits of a project that hasn’t yet been implemented? Although a myriad of

data is available for the past forty years, there is no data that tells how the community will change in the

future. Therefore, determining the potential benefits of this project is not simply compiling information,

but instead a puzzle to be put together and projected into the future. Secondly, given the available data

and technology, what are the appropriate variables and tools to measure both the improved access and the

subsequent benefits? Choosing the inappropriate variable or manipulating the correct variables in the

wrong way both have the potential to yield inaccurate results.

After the implications of the Green Line Extension are estimated, this thesis will also aim to

separate out the intended outcomes, such as better access to public transportation, from the unintended

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outcomes, such as increased housing prices. Finally, a question which will be briefly touched upon, but is

important to keep in mind throughout this thesis: Which of these factors are desirable, and to whom?

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Methodology

Because the Green Line Extension has not yet been implemented, it is not possible to do a direct

analysis on the impacts the system has on the surrounding areas. Instead, to predict these effects, the

proposed stations will be compared to a similar recently implemented station.

Choosing the Station

In order for the results of an analysis on a different station to be projected onto the proposed Green

Line stations, the transit lines that connect to the stations in each project should have a similar level of

service, and the initial population and land use characteristics of the stations’ surrounding areas should be

comparable.

The level of service of the transit system is important because it will influence the degree to which

accessibility changes, and hence the degree to which the community benefits from the infrastructure. A

heavy rail line, which requires a much larger capital investment and has a higher level of service than, say,

a bus line, will allow residents living or working around the area to reach more destinations. A heavy rail

will also allow more travelers from outside the community to reach the area by transit, thus allowing for

more economic benefits to the immediate vicinity. Because the level of service has a direct impact on the

accessibility improvements to the area, comparing transit lines with different service levels will not yield

the most accurate results.

In addition to level of service, the comparison station should have similar initial population and

land use characteristics as the proposed stations. If an area around a station already has mixed land use or

a relatively high population density, then one would not expect as great an impact on these variables as

would occur in a single use or low density area. Similarly, an area whose population has a median

income below the regional average would likely see a higher shift in socioeconomic variables than an area

with residents that are already well off. Therefore, in order to have the results of the comparison be

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transferable with respect to land use and socioeconomic factors, the conditions before the station was

implemented should be similar to the existing conditions around the proposed Green Line Extension

stations.

Collecting the Data As mentioned in the research question, this thesis aims to analyze the effects of transit using

commonly available data and tools. More specifically, the characteristics of the area surrounding the

already-implemented station will be tracked using time-series census data and analyzed using GIS. The

data will be collected for years both before and after the station was implemented so that the effect of the

station can be easily determined. In order to get a complete picture of how the neighborhood has changed,

the data collected should include these elements:

• Travel Behavior – The purpose of the transit line is to increase accessibility, which should affect the travel behavior of the surrounding population. By tracking variables such as commute time and travel mode, the magnitude of the transit’s effect can be determined.

• Land Use – The population density and commercial density of the area is of interest, since theory suggests that activities will cluster around the station.

• Socioeconomic Trends – Because the addition of a transit station is expected to increase the land values in the immediate vicinity, the median rents and housing values in the area should be monitored. Furthermore, because these increased values may potentially change the demographics of the population, the socioeconomic status of the residents is also of interest.

To effectively separate the effects of increased accessibility from general economic trends, this

data must be compared to a control area that has not had an increase in accessibility during the time period

at hand. The first control that should be used is a regional average for the variables being analyzed. In

order for this average to give an accurate picture of the general trends that would be occurring in the

analysis area, the region should be confined enough to only include areas with similar environments as the

one being studied, but large enough to have a sufficient number of data points. Beyond the regional

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average, a second type of control should be used that looks at an area with well-established transit

services. This will ensure that there is not a trend occurring in all areas with transit access that is different

from the metropolitan average.

Application to the Green Line Extension Once the analysis on the existing transit station has been completed, the results can be projected on

to the areas surrounding the Green Line Extension. Under the assumption that the two transit lines are

comparable, the effects of the implemented line should be similar to the effects of the proposed line.

Therefore, a general estimate of the effects of the proposed line on the surrounding communities can be

given. If certain stations within the extension differ from the compared station in terms of land use or

existing population, the results will be modified to account for these differences.

Evaluating the Analysis At the end of this process, a review of the analysis will be conducted to determine the extent to

which readily available data and tools can be used to evaluate the effects of transit on the surrounding

area. The results of the previous investigation will be coupled with knowledge about how the transit

station changed the area around it in terms of travel behavior, land use and socioeconomic characteristics.

This will determine which effects were easily identified using census data and which went undetected

during this analysis.

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Results

The results section of the report will be divided into five parts. First, the location of the Green

Line Extension in context of the Boston area and existing transit services will be reviewed. Secondly, the

variables that will be used to represent land use, socioeconomic characteristics and travel behavior will be

given. These variables will then be used to present the existing conditions of the areas around the

proposed Green Line Extension. The fourth section discusses the selection of the existing station and the

control areas used for the comparison. Finally, the results of this comparison will be presented.

The Green Line Extension in Context Before delving into an analysis of the proposed Green Line Extension, it is appropriate to review

the line in the context of existing transit services and the larger urban area. The Green Line Extension is

an addition to the already extensive system of rail lines serving the Boston area. Figure 3, shown below,

shows the existing transit lines, the proposed line and their location within the towns of the Inner Core2.

Of these lines, three – the Red, Blue and Orange Lines – are heavy rail, the Green Line is light rail and the

Silver Line is bus rapid transit. As the map shows, the existing lines do not serve the majority of

Somerville. Davis Square, a Red Line station located on the Somerville-Cambridge border, is the only

existing station within the town boundary. Several other stations are close to the town and are within

access to some of its residents, including Porter Square in Cambridge and Sullivan Square in Charlestown.

The proposed Green Line Extension, highlighted in the map, which runs through the center of the town,

will service areas of Somerville that do not have access to the current transit system.

2 The Inner Core consists of twenty cities and towns within the metropolitan Boston area that are considered the most urban and populous within the Metropolitan Area Planning Commission (MAPC) planning area.

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Figure 3 - Inner Core Towns and Existing Transit Services

Variables Used to Evaluate Station Areas In order to conduct a complete analysis on the effects of transit, the data should cover three areas:

land use, socioeconomic characteristics and travel behavior. Because a goal of this project is to determine

the extent to which commonly available data can detect these effects, all data will be from the U.S. Census

or MassGIS that is available through the Internet or library resources. The variables used in this analysis

are presented in Table 33.

3 Land Use data is provided by MassGIS for the years 1971, 1985 and 1999. Further land use information for Somerville is provided by Joseph Ferreira on a parcel level for the year 2005. All other variables are available from the U.S. Census Bureau for the years 1970, 1980, 1990 and 2000.

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Table 3 - Census variables used to track transit impacts (U.S. Census, MassGIS)

Land Use Density

Land Use Category

Socioeconomic Percentage of Persons 25+ years of age with 4+ years of college

Percentage of unemployed persons in the workforce

Percentage of workers in managerial, professional or technical occupations

Median family income

Median household income

Median rent

Median non-condominium housing value

Percentage of vacant housing units

Travel Behavior Percentage of workers who drive alone to work

Percentage of workers who commute via public transportation

Percentage of workers who travel 30+ minutes to work

The Existing Conditions of the Green Line Extension Station Areas As mentioned in the methodology portion of the report, an accurate comparison between the Green

Line Extension project and an implemented transit station requires a similar level of service and initial

population and land use characteristics. In this section, the expected level of service of the Green Line

Extension will be reviewed. Additionally, the population and land use characteristics of the transit

corridor as a whole will be discussed and station areas with special considerations will be highlighted.

Level of Service of the Green Line Extension The Green Line Extension, as a portion of the MBTA Green Line, is a light rail service. Light rail

transit often has a lower level of service than heavy rail because it has lower capacity and lower travel

speeds. However, the Green Line Extension will have a right of way through the travel corridor and thus

will not have to interact with automobile or bus traffic on the street grid. This layout makes the service

27

faster and more reliable than a bus or even other portions of the Green Line, thus giving the Extension a

higher level of service than other at-grade transit systems. It is important to note that some trips which

begin on the Extension will continue through portions of the Green Line which do not have a right of way.

Thus, the level of service will depend on the destination location.

Characteristics of the Transit Corridor The focus of this thesis, as stated in the introduction, is the town of Somerville. Therefore, the

scope of the analysis on the Green Line Extension will be limited to those stations that will be accessible

to Somerville residents, where accessibility is defined as the maximum walking distance of 400 meters.

The stations that meet this criterion are shown below in Figure 3.

Figure 4 – Proposed Green Line Extension Stations Accessible by Somerville (MBTA)

The majority of the transit corridor is characteristic of Somerville in terms of land use and

population characteristics. As a metropolitan area, much of the land is already densely built out. Most of

the station locations are surrounded by dense residential housing mix of mostly single- or multi-family

28

housing with a portion of apartment units and condominiums. The population densities of each station

area as of 2000 are shown below. In addition to these figures, a density average of the Boston

metropolitan area is also included to view the stations in the context of the larger region. As the figures

show, all of the station areas have a higher density than the metropolitan average.

Table 4 - Population Density of Green Line Extension Station Areas (U.S. Census for 2000)

Area Population Density (Persons/Acre)

College Avenue 33.99

Ball Square 32.57

Lowell Street 30.64

Gilman Square 43.16

Washington Street 26.60

Union Square 29.40

Inner Core Average 26.79

The stations have a varying amount of non-residential parcels in the immediate area, including

commercial, industrial and tax-exempt buildings3. The land use classes of the parcels in Somerville, based

on 2005 data, are shown in Figure 4.

Figure 5 - Land Use Categories of Somerville Parcels for 2005 (provided by Joseph Ferreira)

29

As is visible by this map, the areas immediately surrounding each station are largely residential.

Washington Street Station and Union Square Station differ slightly from the others, with more commercial

and industrial parcels within the 400 meter buffers, which may account for the reduced population density

compared to other stations.

In terms of socioeconomic characteristics, there is a wide range of values between the station

areas. The values for each station area, as well as the Inner Core average, are shown in Table 5.

Table 5 - Socioeconomic Characteristics of Green Line Extension Station Areas (U.S. Census for 2000)

Variable

Area

Percentage of persons 25+

years of age with 4+ years

of college (%)

Percentage of

unemployed persons in

the workforce (%)

Percentage of workers in

managerial, professional

or technical occupations

(%)

Median Family Income

($)

Median Household

Income ($)

Median Rent ($)

Median Non-Condo

Housing Value ($)

Percentage of Vacant

Housing Units (%)

College Avenue 65.00 2.41 83.80 64,100 40,500 1470 255,000 4.42

Ball Square 40.80 2.31 72.60 63,400 56,700 980 284,000 2.67

Lowell Street 36.20 2.57 71.40 51,300 49,200 871 227,000 3.33

Gilman Square 30.10 2.46 66.40 49,300 44,300 823 231,000 3.10

Washington Street 23.50 2.10 67.40 35,500 30,900 762 198,000 5.38

Union Square 31.60 2.47 72.90 54,900 46,000 936 204,000 1.89

Metropolitan Average 37.50 3.28 74.00 61,900 51,500 858 240,000 3.61

The area around College Avenue Station has a noticeably higher median rent value, median family

income and percentage of college graduates, most likely due to its proximity to Tufts University.

Similarly, Ball Square Station has higher income and land value figures above the metropolitan average.

On the other end of the spectrum, the area surrounding Washington Street Station has noticeably low land

values and incomes. As a general trend, the remaining stations have socioeconomic characteristics which

are at or below the Inner Core average.

30

Finally, in terms of travel behavior, residents who live near the proposed station areas have similar

commutes, and thus similar levels accessibility, to the Inner Core average. Each station area has

approximately 60 percent of residents driving to work. The areas also have approximately 20-20 percent

of workers using public transportation and 40-50 percent of workers with a 30+ minute commute. The

only station which does not exhibit these trends is College Avenue Station, since many residents work at

the nearby Tufts University.

Table 6 – Travel Behavior of Green Line Extension Station Areas (U.S. Census for 2000)

Variable

Area

Percentage of workers

who drive alone to work

(%)

Percentage of workers

who commute via public

transportation (%)

Percentage of workers

who commute more

than 30 minutes (%)

College Avenue 27.50 19.20 25.80

Ball Square 62.00 28.50 45.90

Lowell Street 65.90 25.10 49.40

Gilman Square 63.60 26.50 50.00

Washington Street 63.90 19.80 46.82

Union Square 52.70 23.90 41.20

Metropolitan

Average 65.00 23.10 45.30

While this level of accessibility may at first seem acceptable, Somerville’s density and close

proximity to Boston are both factors that make the town a more viable candidate for transit-oriented

development than other towns in the metropolitan area. Therefore, the fact that over half of workers drive

alone to work is due to the fact that there is a lack of accessible and reliable transit services in those areas.

Comparison of the Green Line Extension to an Existing Station

With a more complete picture of the line’s level of service and the profile of the population that

will be affected by the service, a comparison between the Green Line Extension stations and an already

implemented station can be conducted. The station chosen for this analysis is the only existing subway

station in Somerville, Davis Square Station.

31

The Red Line Extension

In 1984, the Red Line was extended through Davis Square, Somerville, connecting the Square to

downtown Boston. The extension, like the rest of the Red Line, is a heavy rail service that offers travel

speeds similar to or greater than that of the proposed Green Line Extension. Although the level of service

of heavy rail is notably higher than light rail, this thesis treats the service of the Green Line Extension as

comparable to the Red Line due to the fact that both services have a designated right of way.

Characteristics of the Transit Corridor

Before the area had access to transit services, Davis Square was similar to the Green Line

Extension station areas in the context of the larger metropolitan region. The center of the square was

made up of commercial buildings, surrounded by dense residential housing. The density of the square in

1980 was 33.00 persons/acre, a significant jump from the Inner Core average of 25.50 persons/acre. The

land use of Somerville in 1971, with a 400 meter buffer around the Davis Square station indicating the

areas within walking distance of the service, is shown in Figure 5 below.

The initial land use and population characteristics of the Davis Square area in 1980, four years

before the station was implemented, are shown in Table 7, along with the characteristics of the region

during the same time period.

The socioeconomic conditions of Davis Square are similar to those of the proposed Green Line

Extension stations with income, employment and land value figures at or below the metropolitan average.

The travel behavior of the Davis Square residents denotes a level of accessibility that is slightly higher

than that of the region, since there is a higher rate of public transportation use. However, the low

percentage of commutes longer than 30 minutes suggests that workers may be employed close to their

residence due to the lack of access to employment opportunities further away. With socioeconomic and

32

travel behavior variables similar to those of the Green Line Extension, the effects of the Red Line on

Davis Square’s residents can be used to predict Green Line’s effects on the other areas of Somerville.

Figure 6 - Land Use of Somerville in 1971 (MassGIS)

Table 7 - Characteristics of the Davis Square Area in 1980 (U.S. Census)

Area

Variable Davis

Square

Metropolitan

Average

Population Density (Persons/Acre) 33.00 25.50

Percentage of persons 25+ years of age with 4+ years of college (%) 21.40 21.30

Percentage of unemployed persons in the workforce (%) 3.10 3.20

Percentage of workers in managerial, professional or technical

occupations (%) 59.90 61.00

Median Family Income ($) 19,500 20,200

Median Household Income ($) 15,600 16,500

Median Rent ($) 283 284

Median Non-Condo Housing Value ($) 45,400 50,200

Percentage of Vacant Housing Units (%) 4.48 5.71

Percentage of workers who drive alone to work (%) 41.50 45.50

Percentage of workers who commute via public transportation (%) 30.30 24.60

Percentage of workers who commute more than 30 minutes (%) 31.20 37.00

33

Controlling for General Economic Conditions

In order to accurately measure the effects of transit access on Davis Square, there must be a control

for general economic conditions. This is necessary to ensure that the trends seen in land use,

socioeconomic and travel behavior variables are caused by increased access and not other factors.

Incorrectly attributing these trends to accessibility will yield inaccurate results, overestimating (or

underestimating) the effects of the transit service. This thesis will use two controls to ensure that this

miscalculation does not occur – a regional average of the Boston metropolitan area and an area with transit

access throughout the study period.

• Inner Core Average – The area used to track the trends of the region is the Inner Core, as defined by the Metropolitan Area Planning Commission (MAPC). The Inner Core consists of 20 cities and towns, including Somerville, which exhibit the unique characteristics and challenges of urban areas. This area is the correct scope of control, since a smaller region may not provide enough data points to be statistically accurate and a larger region would include suburban towns that may exhibit different trends than the study area.

• Transit Area Average – In order to ensure that areas with transit access evolved in the same manner as areas without transit access, neighborhoods around transit stations that were implemented significantly before 1980 will also be used as a control. This thesis uses three Green Line stations for this purpose – Summit Avenue Station in Brookline, Warren Street Station in Brighton and Babcock Street Station in Allston.

� Summit Avenue Station – This station is a part of the Green Line “C” Branch, which

was first opened in 1889.

� Warren Street Station and Babcock Street Station – These stations are both part of the Green Line “B” Branch, which was opened in 1932.

It is important to note that the “B” and “C” branches of the Green Line have a lower level of service from the Green Line Extension because the lines do not have a right of way. Despite this difference, this thesis treats these stations as an accurate control for transit-accessible areas since the stations are so well established in their neighborhoods.

Results of Davis Square Analysis In order to fully capture the effects of the Red Line Extension on Davis Square, data was collected

before and after the implementation of the transit station. For the Davis Square station, which was

34

implemented in 1984, this was accomplished by collecting U.S. Census data from 1980, 1990 and 2000.

By monitoring neighborhood characteristics up to 16 years after implementation, the analysis hoped to

capture gradual effects, such as a turnover of population or a difference in housing densities, as well as

immediate effects, such as changes in commuting behavior. The results of the Davis Square analysis are

presented in three sections: land use, socioeconomic characteristics and travel behavior.

Land Use

As mentioned before, changes in land use are accounted for through density changes. Since census

data is residential based, land use changes can only be detected through changes in housing, as opposed to

changes in commercial buildings. According to location theory, population density should increase with

transit access due to the increased land value. Contrary to this expectation, the population density of

Davis Square decreased after 1984. The Davis Square results, as well as the control figures, are shown in

Table 8 and graphically in Figure 7. As the data shows, Davis Square is the only area which shows a

decreasing trend through the 20 year time span. Although some areas have a slight decrease in 1990, the

Inner Core and the Green Line stations have a higher density in 2000 than in 1980. Davis Square, on the

other hand, gradually decreases from 33.00 to 29.80 persons/acre during the same time.

35

Table 8 - Population Density from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Population Density Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 33.00 25.50 38.20 62.20 42.90

1990 31.70 25.70 36.30 70.70 44.40

2000 29.80 26.80 41.20 70.60 46.10

20.00

30.00

40.00

50.00

60.00

70.00

80.00

1970 1980 1990 2000 2010

Year

Population Density (Persons/Acre)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 7 - Population Density from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Socioeconomic Characteristics

Changes in the socioeconomic status of residents are measured by land values and employment

statistics. According to previous research, access to transit leads to gentrification in the immediately

surrounding areas. This trend should manifest in housing statistics through increased non-condo housing

values and rents. In terms of employment, gentrification should increase the median income of the

neighborhood, lower the unemployment rate and increase the percentage of workers with white-collar

jobs. The results of this portion of the analysis are shown below, with housing variables presented first,

followed by employment variables.

36

Table 9 - Percentage of Vacant Housing Units from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Vacant Housing

Units (%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 4.48 5.71 3.02 7.91 3.72

1990 4.23 6.10 4.40 5.50 3.68

2000 3.41 3.61 3.09 1.14 3.82

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

1970 1980 1990 2000 2010

Year

Vacant Housing Units (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 8 – Percentage of Vacant Housing Units from 1980-2000 for Davis Square and Control Areas (U.S. Census)

37

Table 10 - Median Non-Condo Housing Value from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Median Non-Condo

Housing Value ($)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 45,400 50,200 89,400 47,200 68,800

1990 186,000 175,000 360,000 194,000 300,000

2000 330,000 240,000 344,000 196,000 295,000

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

$350,000

$400,000

1970 1980 1990 2000 2010

Year

Median Non-Condo Housing Value ($)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 9 - Median Non-Condo Housing Value from 1980-2000 for Davis Square and Control Areas (U.S. Census)

38

Table 11- Median Rent from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Median Rent ($) Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 283 284 357 275 356

1990 696 652 598 703 813

2000 1,000 858 1,220 915 1,260

$0

$250

$500

$750

$1,000

$1,250

$1,500

1970 1980 1990 2000 2010

Year

Median Rent ($)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 10 - Median Rent from 1980-2000 for Davis Square and Control Areas (U.S. Census)

These results confirm that the housing characteristics of Davis Square changed significantly after

the area gained access to transit. However, the data is less clear about whether these changes were caused

by the increased transit access, or whether they were the product of general economic trends. For two of

the three variables, percentage of vacant housing units and median rent, the trends that suggested

gentrification in Davis Square were also seen in the control areas. For the third variable, median non-

condo housing value, Davis Square did considerably deviate from the control areas. The median value of

the area increased over 700% from 1980 to 2000, whereas the Inner Core and the three transit control

areas increased less than 500%. Therefore, the housing statistics suggest that access to transit did, to some

extent, affect land values in Davis Square.

39

Table 12 - Unemployment Rate from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Unemployment Rate

(%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 3.05 3.21 1.75 2.96 2.96

1990 3.95 4.83 2.95 5.43 5.57

2000 1.12 3.28 0.95 1.51 8.45

0%

2%

4%

6%

8%

10%

1970 1980 1990 2000 2010

Year

Unemployment Rate (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 11 – Unemployment Rate from 1980-2000 for Davis Square and Control Areas (U.S. Census)

40

Table 13 - Workers with White Collar Jobs from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Workers with White

Collar Jobs (%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 59.90 61.00 85.10 71.20 77.50

1990 76.30 67.20 87.60 64.00 77.60

2000 82.60 74.00 87.30 78.40 80.50

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

1970 1980 1990 2000 2010

Year

White Collar Jobs (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 12 – Workers with White Collar Jobs from 1980-2000 for Davis Square and Control Areas (U.S. Census)

41

Table 14 - College Graduates from 1980-2000 for Davis Square and Control Areas (U.S. Census) Area

Workers with White

Collar Jobs (%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 21.40 21.30 47.60 39.90 44.30

1990 46.10 30.80 60.60 44.60 71.30

2000 48.90 37.50 78.70 60.00 52.70

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

1970 1980 1990 2000 2010

Year

College Graduates (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 13 - College Graduates from 1980-2000 for Davis Square and Control Areas (U.S. Census)

42

Table 15 – Median Household Income from 1980-2000 for Davis Square and Control Areas (U.S. Census) Area

Median Household

Income ($)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 15,600 16,500 16,900 10,700 14,900

1990 35,800 36,900 37,100 20,800 35,700

2000 55,900 51,500 58,600 34,400 43,200

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

1970 1980 1990 2000 2010

Year

Median Household Income ($)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 14 – Median Household Income from 1980-2000 for Davis Square and control areas (U.S. Census)

43

Table 16 - Median Family Income from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Median Family

Income ($)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 19,500 20,200 24,200 14,000 18,400

1990 44,300 44,200 63,300 31,300 39,900

2000 67,500 61,800 90,300 41,800 49,800

$0

$20,000

$40,000

$60,000

$80,000

$100,000

1970 1980 1990 2000 2010

Year

Median Family Income ($)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 15 - Median Family Income from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Similar to housing trends during the same time period, employment trends in Davis Square from

1980 to 2000 suggest that the socioeconomic status of the area’s residents increased slightly more than

those of the control areas. The unemployment rate of Davis Square, which was similar to the Inner Core

rate in 1980, decreased 64% over the next 20 years, whereas the Inner Core rate remained the same. Both

the percentage of residents with a college degree and the percentage of workers with managerial, technical

or professional jobs increased also, suggesting a shift in the type of residents that live in Davis Square.

This shift in the education and job type is accompanied by an increase in income, which is similar to or

greater than shifts in the Inner Core and other transit areas.

44

Travel Behavior The final set of results for the Davis Square analysis includes changes in travel behavior due to

increased access to transit. Increased access to the public transportation system is expected to shift travel

mode from single occupancy vehicles to transit, which will be monitored in the commuting patterns of

residents. Finally, commuting times may either increase or decrease with this mode shift; increases may

occur due to the fact that residents can access employment opportunities further away from their homes,

whereas decreases may occur if employment destinations stay the same, but commuters avoid road

congestion by using transit.

Table 167 - Percentage of Commutes via Automobile from 1980-2000 for Davis Square and Control Areas (U.S. Census)

Area

Percentage of commutes

via automobile (%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 41.50 45.50 37.10 26.20 34.10

1990 45.60 59.20 47.60 34.30 43.70

2000 42.40 65.00 46.80 46.60 31.90

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

1970 1980 1990 2000 2010

Year

Commutes via Automobile (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 16 - Percentage of Commutes via Automobile from 1980-2000 for Davis Square and Control Areas (U.S. Census)

45

Table 178 - Percentage of Commutes via Public Transportation from 1980-2000 for Davis Square and Control Areas

(U.S. Census)

Area

Percentage of commutes

via public transportation

(%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 30.30 24.60 31.40 48.00 32.40

1990 36.80 15.10 26.00 42.80 21.60

2000 45.80 23.10 35.80 42.00 34.30

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

1970 1980 1990 2000 2010

Year

Commutes via Public Transportation (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 17 - Percentage of Commutes via Public Transportation from 1980-2000 for Davis Square and Control Areas

(U.S. Census)

46

Table 189 - Percentage of Commutes Longer than 30 minutes from 1980-2000 for Davis Square and Control Areas (U.S.

Census)

Area

Percentage of commutes

longer than 30 minutes

(%)

Davis Square Inner Core Summit Avenue Warren Street Babcock Street

1980 31.20 37.00 47.70 51.20 37.70

1990 43.50 38.50 40.00 50.20 32.50

2000 48.90 45.30 47.60 59.10 40.20

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1970 1980 1990 2000 2010

Year

Commutes longer than 30 m

inutes (%)

Davis Square

Inner Core

Summit Avenue

Warren Street

Babcock Street

Figure 18 - Percentage of Commutes Longer than 30 minutes from 1980-2000 for Davis Square and Control Areas (U.S.

Census) These results confirm the assumption that the Red Line Extension into Davis Square significantly

changed commuting patterns after 1984. The area has had a steady percentage of workers who commute

via automobile, as opposed to the Inner Core, which has seen a 20% increase in automobile commutes

over the study period. More striking is the percentage of Davis Square residents who commute via public

transportation, which has increased from 30% in 1980 to over 45% in 2000 while Inner Core commutes by

transit have actually decreased. This increased access to transit led to longer commuting times for Davis

47

Square residents, where the increase in the percentage of commutes longer than 30 minutes is significantly

greater than the control areas. From 1980 to 2000, the figures for Davis Square rose from 31% to 48%,

whereas the next biggest increase was from 37% to 45% for the Inner Core.

48

Discussion

This portion of the report will discuss the significance of the results from the Davis Square

analysis and attempt to use these results to project the effects of the Green Line Extension on the proposed

station areas. As the data suggests, the effects of transit access on Davis Square, and how these effects

apply to the proposed project, are not obvious; therefore, a more extensive look at what these data trends

mean for the affected residents in the community is in order.

Discussion of the Davis Square Analysis Results

The analysis of Davis Square from the years 1980 to 2000 shows that increased access to transit

affected the lives of the area’s residents far beyond the scope of transportation – in fact, the results suggest

that the transportation project may have caused a shift in the type of people that live in Davis Square. A

discussion of the results is presented below, with land use effects first, followed by socioeconomic

characteristics and travel behavior.

Land Use

As mentioned in the Land Use portion of the Results section, the density changes in Davis Square

since the Red Line Extension are not as expected. The data show a decreasing population density in the

area since 1980, dropping from 33.00 persons/acre to 29.80 persons/acre. Furthermore, this was not a

regional shift in density, as all of the control areas’ densities increased during the same time period.

However, this result is contrary to location theory, as the benefits of transit access should attract residents

to the area.

Before discussing the possible reasons for which the analysis produced results contrary to the

expected results, an overview of the policies guiding residential and commercial density is warranted.

Throughout the study period, Somerville kept a fairly low floor area ratio (FAR) of 2.0 and a building

height restriction of 50 feet, which caused development in Davis Square to be gradual (City of

49

Somerville). These policy decisions have capped the extent to which interest in profiting off of the

benefits of transit access can affect development, preventing projects such as high-tower apartment

complexes from significantly increasing the density of the area.

With this context in mind, this thesis proposes that the unexpected result in the analysis may be

due to insufficient land use data. In order to conduct a time-series analysis on the population density of

Davis Square from 1980 to 2000, one needs both the population for each decade, as well as the size of the

residential area in which this population resides. Population density can fluctuate due to either 1) a

change in the number of people living in the area or 2) a change in the area of land designated as

residential. The U.S. Census on a census block level provided the population data for this analysis and

MassGIS provided the land use data with a minimum mapping unit of one acre. Because MassGIS

created the land use file using 1:25,000 to 1:40,000 aerial photography, the categorization in the map is

fairly low resolution and does not pick up subtle land use changes. Under this low resolution, the Davis

Square area was shown to have no land use changes during the study period. Accordingly, the area used

to calculate the population density remained the same.

This set up may have led to an inaccurate calculation of the population density of the area because

the analysis did not account for subtle changes in land use that could have occurred in Davis Square after

the Red Line Extension. Once the station was constructed, portions of areas that were once residential

may have become mixed use or commercial, as businesses were drawn to the accessibility of the Davis

Square area. Therefore, the area in which residents could live decreased and the types of housing in the

area changed from single- or multi-family houses to denser residential housing, such as apartments or

condominiums. With this larger context, it is possible that while the actual population of the Davis Square

region decreased, the density at which this population resided may have increased.

50

Socioeconomic Characteristics

The results of the analysis confirm the assumption that, since Davis Square gained access to the

Red Line, the socioeconomic status of the area’s residents has changed dramatically. However, the

analysis does not confirm how these changes in land value and income correspond to the turnover rate of

residents. While it’s possible that the socioeconomic status of pre-1984 residents increased during this

time period, it’s more likely that pre-1984 residents with low-socioeconomic status were replaced with

new residents who could afford the increasing land values of the area.

Once the transit station was constructed in Davis Square, the land values of the area increased due

to the benefits of living in a transit-accessible area. As shown in the results section, the land values of the

Davis Square area were equal to the Inner Core median values in 1980, and then rose above the regional

average by 1990 (See Tables 10 and 11). If the cost of renting an apartment or owning a house increased

disproportionately more than cost of transportation decreased, the net cost of living in Davis Square

increased after 1984. The residents living in the area before this change had a median income below the

Inner Core average (See Table 7) and were potentially unable to afford this increase in living costs. As

the results show, over the next decade, the median socioeconomic status of residents in the area increased

greater than did the regional average. Cherie Abbanat, a resident of the area, notes that the area has seen a

shift from blue-collar workers to white collar workers living in the area since the implementation of the

station (2009). As these blue-collar workers moved out of the area, the number of kids living in the area

also decreased as many of the workers had families. A possible reason for this shift is that, gradually, the

pre-1984 residents may have been replaced by new residents with higher incomes who could afford the

higher living expenses associated with living in Davis Square. In this scenario, the socioeconomic

changes recorded in Davis Square would not have happened simultaneously; instead, the land values

51

increased first, followed by increased income levels and employment statistics. However, because data

for the study period gave only one figure per decade, it is difficult to determine the sequence of events.

It is also important to note that this analysis did not include the effects of increased accessibility on

commercial property values, since this analysis focused on Census data. Access to a commercial or office

building is beneficial for a business, because of the ease at which an employee, client or consumer can

reach the location. Therefore, it is likely that commercial property values increased along with residential

property values. This calculation is important to include in a cost-benefit analysis for a transit project, as

increases in commercial property values will generate tax revenue for the government.

Travel Behavior

As expected, the trends in travel behavior denote an increased level of access for residents in Davis

Square with the implementation of the transit station. Throughout the study period, the number of

residents who chose to drive alone to work did not increase, whereas the Inner Core increased 20%.

Furthermore, almost half of Davis Square residents chose to use public transportation for their daily

commute. These figures confirm the assumption that after 1984, residents of Davis Square had easy

access to public transportation and made use of this access in their day-to-day travels.

This embrace of public transportation is seen indirectly through the increase of commutes longer

than 30 minutes. The two potential reasons for this increased commuting time are either 1) that it takes

longer to get to a certain location via transit than automobile, or 2) residents chose to work further away

from their homes due to the increased access to other Inner Core towns. Because census data does not

provide information on where residents are employed, it is not possible to rule out either of these

scenarios. However, due to the constant traffic congestion on the major roads throughout the Inner Core,

it is unlikely that trips via transit (with right-of-way) take significantly longer than those made via

automobile.

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Finally, it is important to note that this analysis was not able to account for increased access to

Davis Square by non-residents. As mentioned in the literature review, locating a transit station in a

neighborhood produces both source and sink benefits – that is, residents can more easily travel from the

area and non-residents can more easily travel to the area. Because the census is residential-based, there

was not data available to account for the “sink” effects. Travel time savings for non-residents is an

important benefit of any transit project and should be included in a cost-benefit analysis when justifying

the public funds needed for the investment.

Application to the Green Line Extension

With a clear picture of how the residents of Davis Square were affected by the Red Line

Extension, one can more accurately forecast how the Green Line Extension will affect those living in

proposed station areas. The degree to which land use, socioeconomic characteristics and travel behavior

change will depend upon numerous factors, including land use and zoning policies set by the Somerville

government, the level or service of the Green Line Extension and the socioeconomic make-up of residents

currently living in the station areas.

Land Use

As mentioned in the Discussion section of this report, the analysis of Davis Square may not have

accurately portrayed the land use changes that occurred in the area after the Red Line Extension.

However, through research on the area and the applicable land use policies, it is clear that the low FAR

limits and height restrictions on buildings in the area led to a gradual increase in residential density and

commercial development in the decades after the station was put in place. It follows that the degree to

which density increases near the Green Line Extension stations areas will depend upon the land use

policies set in place by the Somerville government. If Somerville enacts FAR limits and height

restrictions similar to those in Davis Square, it is likely that development will be gradual.

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However, if Somerville plans to enact high FAR limits and increased height restrictions,

development is likely to occur more abruptly. This is true for Union Square, where the Somerville

government is proposing an FAR limit of 5.0 and general height restriction of 70 feet4 (Somerville

Government), as opposed to Davis Square’s 2.0 FAR and height restriction of 50 feet. From the

perspective of the government, this will create revenue that will compensate for the costs of the project.

Many residents, however, believe that this decision will result in unmanageable growth that will

overwhelm the people and the businesses that are currently there.

The degree to which land use will change also depends on the current mixes of land use in the

station areas. As mentioned in the Results section, the station areas have different mixes of commercial

and residential zoning. Those areas that are predominantly residential will likely see an influx of

commercial uses, whereas those that are already fairly mixed use will stay relatively the same. Below is a

summary of the likely land use changes for each station:

• College Avenue – The majority of residents around this station are associated with Tufts University. It is likely that a large portion of the station area will remain residential, as these residents will not be able to relocate away from the University. The station area also has a large portion of tax-exempt land, which may be turned into commercial or residential use so that the government can benefit from increased land values.

• Ball Square – This station area is largely residential with a few commercial buildings. It is likely that transit access will promote more commercial uses in the area.

• Lowell Street – Like Ball Square, Lowell Street is largely residential with some commercial and industrial buildings. The Extension will likely lead to more commercial and industrial uses in the area.

• Gilman Square – This area is mainly made up of tax-exempt uses. It is possible that the current owner of this land will sell it to a business or real estate company in order to benefit from the accessibility of the area. However, if it is a longstanding building, such as a religious center, the owner may keep the property.

4 The city’s plan has recently reduced its proposed maximum height to 70 feet, but still allows 85-foot height limit along Prospect Street in the square and 100 feet for “green construction”.

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• Washington Street – This area is fairly mixed use, with many commercial buildings and a dense residential housing. Therefore, the land use will not be greatly affected by the project.

• Union Square – As mentioned above, the Somerville government has big plans for Union Square. If the proposed zoning policies go through, the area will see an increase in commercial and high-density residential buildings.

Socioeconomic Characteristics

As the Davis Square analysis showed, the socioeconomic makeup of Davis Square residents

changed significantly after the area gained access to transit. The degree to which the Green Line

Extension will affect the socioeconomic status of the station areas’ residents will depend on the level of

service of the line.

As the literature review discussed previously, accessibility to transit services can lower a resident’s

transportation costs if the service removes the need for a car. The Red Line, as a heavy rail service, offers

the highest level of transit service, due to its reliability and right of way. Furthermore, the Red Line

travels through the heart of downtown Boston, with a direct route to Park Street and Downtown Crossing.

The Green Line Extension, although a light rail service, has the potential to offer a comparable level of

service. Because the Extension has a right of way up until downtown Boston, all trips made to and from

the central business district will have travel speeds similar to heavy rail. It is possible that delays may

occur if cars are held up from other portions of the Green Line that do not have a right of way. Portions of

the line are notorious for having slow travel speeds and heavy delays, so this effect should be taken into

account. Furthermore, the Green Line does not have a station at such a central location as does the Red

Line. However, the line does stop at North Station, which allows transfers to the Red Line. Therefore, the

Green Line is not significantly less convenient than the Red Line.

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With this level of service in mind, it is likely that land values around Green Line Extension

stations will increase as much as, or slightly less than, did those around Davis Square. Similarly, income

and employment impacts will likely be similar to or less than those in Davis Square.

Travel Behavior

The results of the Davis Square analysis confirmed that the Red Line Extension drastically

changed residents’ travel behaviors, increasing the percentage of commutes via public transportation while

keeping the percentage of commutes via automobile constant. The extent to which transit access will

affect travel behavior is dependent on the line’s level of service. If commuters view the service as reliable

and quick, they will be more willing to use it to while traveling to their places of employment. However,

if the service is unreliable or have slow travel speeds, commuters will likely continue to use their cars to

go to work.

As mentioned above, the Green Line Extension has the potential to have a level of service

comparable to that of the Red Line. It follows that the effects on travel behavior caused by Red Line

Extension into Davis Square will be comparable to those which will be caused by the Green Line

Extension.

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Conclusions

Throughout the world, public funds are being invested into transit projects with the hopes of

increasing access and mobility while improving the environment. The Green Line Extension, currently

under consideration, is no exception – the project aims to increase regional mobility and access to the

residents of Somerville, Cambridge and Medford, Massachusetts, while improving air quality and

reducing greenhouse gas emissions. With these issues in mind, the goal of this thesis was to explore the

extent to which the benefits of transit projects can be measured using commonly available data.

In order to investigate the impacts of a line that is not yet constructed, a comparison of an already

implemented transit station was completed using data available from the U.S. Census Bureau and

MassGIS. Davis Square, a transit station in Somerville implemented in 1984, was chosen due to its

similarities to the Green Line Extension corridor in terms of initial population characteristics, land use and

level of service. Characteristics of the station area and its residents were compiled before and after the

implementation of the station and compared to control areas to determine the effects of increased transit

access on land use, socioeconomic characteristics and travel behavior.

The results of the analysis confirmed that transit access increased land values in the area, which led

to an influx of wealthier residents who could afford to pay these higher rents and housing prices. The

data also showed that the addition of a reliable and fast transit service in the area allowed many

commuters living in Davis Square to use public transportation to travel to work as opposed to their

automobiles.

While these results were conclusive and agreed with firsthand knowledge of how the area changed,

certain aspects of the analysis were misleading or incomplete due to insufficient data. The analysis results

suggested that population density in Davis Square decreased over the study period, which is contrary to

location theory, as well as firsthand knowledge of the area. Furthermore, the analysis of the Red Line’s

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impacts on land values was limited to residential impacts, excluding changes in rent for commercial

buildings. Finally, accessibility benefits from the Extension were limited to those benefits seen by

residents of Davis Square and did not include benefits for travelers who are coming from other areas of

the Inner Core.

This exercise has shown that, through commonly available data and analytical software, the

potential for non-professionals to investigate the impacts of transit projects on surrounding communities

has expanded greatly over the past decade. Census data is particularly useful in tracking changes in

residential characteristics, such as housing, employment and travel behavior. However, these data are far

from comprehensive. Important information regarding subtle land use changes, commercial land values

and “sink” accessibility effects are overlooked when conducting an analysis based on residential data and

high-level land use data.

While this analysis was not able to comprehensively capture the effects of the Red Line Extension,

the types of data which are available are still expanding. Increased employment and trip destination data

has the potential to capture the “sink” effects of transit projects. Furthermore, commercial property data

can allow a future analysis to include transit’s impact on commercial land values. These new data will

allow a future analysis to cover regional and local effects that were not included in this project.

Although the amount of publicly available information is continuously expanding, an accurate and

contextual analysis requires experience and knowledge of the study area. Raw data must be coupled with

regional information, such as land use policies and the level of transit service, in order to fit into the larger

context. Therefore, despite the availability of data, a professional analysis is still, and may always be,

necessary to create a complete picture of how a transit project will affect the land in the immediate

vicinity of the station, as well as the larger region.

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Bibliography

Abbanat, Cherie. Personal Interview. May 3, 2009. Alonso, John G. “Public-Private Joint Development at Rapid Transit Stations.” Transportation Quarterly

40, 3: 317-31. 1986. Beaton, Eric. “The Impacts of Commuter Rail on Greater Boston.” Rappaport Institute of Greater Boston. PB-2006-4. September 2006. Belzer, Dena and Autler, Gerald. “Transit-Oriented Development: From Rhetoric to Reality.” The

Brookings Institute Center on Urban and Metropolitan Policy. June 2002. Bowes, David et al. “Identifying the Impacts of Rail Transit Stations on Residential Property Values.” Journal of Urban Economics 50(1) July 2001, Pages 1-25. Calgary Transit Division. “Transit Friendly Design Guide.” Calgary City Council. April 2006. Center for Environmental Excellence. “Environmental Justice – Overview.” <http://environment.transportation.org/environmental_issues/environmental_justice/#bookmarkTheEnvironmentalJusticeExecutiveOrderDOTOrderandFHWAOrder>. Last accessed May 17, 2009. Cervero, Robert et al. “Transit’s Value-Added: Effects of Light and Commuter Rail Services on Commercial Land Values.” Transportation Research Board. November 2001. City of Somerville, Massachusetts. Code of Ordinances. <http://www.municode.com/Resources/gateway.asp?pid=11580&sid=21>. Last accessed April 26, 2009. Dagang, Deborah. “Transportation Impact Factors – Quantifiable Relationships Found in the Literature.” JHK & Associates for Oregon DOT. 1995. Debrezion, Ghebreegziabiher et al. “Impact of Railway Station on Dutch Residential Housing Market.” European Regional Science Association, Number ersa05p748. August 2008. Diaz, Roderick. “Impacts of Rail Transit on Property Values.” APTA 1999 Rapid Transit Conference.

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Hammel, Daniel and Wyly, Elvin. "A Model for Identifying Gentrified Areas with Census Data" Urban

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Appendix

This appendix reviews the resources used to collect residential and land use data for the time-series

analysis, as well as the methodology used to analyze this data. In the first section, information about the

data layers used in the Davis Square analysis will be presented. The second section discusses the

geographic boundaries of the analysis. Finally, the methodology for processing this data will be

reviewed.

Data Collection

As mentioned throughout the thesis, the purpose of this project was to determine the extent to

which commonly accessible data can be used to analyze transit projects. Therefore, the analysis of Davis

Square relied on data available from the U.S. Census Bureau and MassGIS, the Commonwealth’s Office

of Geographic and Environmental Information.

Land Use Data

Information regarding the land use of Inner Core towns was obtained through MassGIS’s website.

The MassGIS Land Use data layers have both 21 and 37 land use classifications (depending on the desired

level of detail), which was created from 1:25,000 aerial photography. The layer has statewide coverage

for 1971, 1985 and 1999. Finally, the Somerville government provided a parcel-level layer of Somerville

land use.

Residential-Based Data

The U.S. Census Bureau provided all data used in the analysis regarding changes in residential

characteristics. Although portions of the 2000 census are available on the Bureau’s website, Geolytics

was used to access long-form census data from 1980, 1990 and 2000. Geolytics provides census

demographics for social researching and is commonly available through library resources.

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Geographic Boundaries

In order to accurately measure the changes in land use and residential characteristics in Davis

Square and the control transit station areas, the geographic boundaries of the analysis should be as detailed

as possible. Due to the various sources of data, variables used for the analysis were not all available on

the same level of detail. Therefore, an explanation of the level of analysis for each area is provided.

Land Use Data

In the case of land use, data would ideally include all land within walking distance from the transit

station and be clipped where this access ends, which is 400 meters away from the station. However, the

MassGIS land use layers are fairly high level, with a minimum mapping unit of one acre. Therefore, the

analysis included all land use areas that overlapped at all with the 400-meter buffer of the station area.

Residential-Based Data

For residential-based data, the ideal geographic limits would be residential areas within the 400-

meter buffer of the transit station.

Data Processing

This analysis was performed using ArcMap, Microsoft Access and Microsoft Excel. All census

data used in this project was collected from Geolytics on a blockgroup level and imported into ArcMap.

This data was then joined to the census blockgroup layer in ArcMap. As mentioned above, the buffer

boundary for each transit station was used to create ratios for each block group denoting the portion of the

block group with access to the facilities.

Data for each blockgroup was then imported into Access. In Access, a variable’s value for each

blockgroup was multiplied by the appropriate ratio; the variables were then aggregated into two tables

(one for socioeconomic characteristics and one for travel behavior) to be imported back into ArcMap.

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With these tables, a preliminary median value for each variable was determined using ArcMap’s statistical

tools.

These preliminary median values were brought into Excel, where they were divided by the sum of the

ratios used in Access. These final values were aggregated into tables in Excel, which were used to

measure the changes in each transit area over the study period.