environmental justice toolkit, volume 2

8/14/2019 Environmental Justice Toolkit, Volume 2

http://slidepdf.com/reader/full/environmental-justice-toolkit-volume-2 1/82



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Research Team

Glenn Robinson, Morgan State University, School of Engineering and Institute for UrbanResearchAaron Golub, Arizona State UniversityTim Buckley, OHU, Department of Environmental Health Studies

Brendan Nee, ITYohannes Hailu, Michigan State University, Land Policy InstituteJackie Grinshaw, Center for Neighborhood Technology

National Experts

Center for Community Change, Rich StolzSmart Growth of America, Don ChenUniversity of Utah, Tom Sanchez

Transportation Equity Network, Laura Barrett

Outreach Team

Wallace Watson, PIINSarah Mullin, IsaiahPamela Hardway, MosesLisa, Hussain, Urban Habitat

Acknowledgements

Baltimore Region Environmental Justice and Transportation Project Team and the Low

Income and Minority Communities of Baltimore

Disclaimer

The opinions, findings, and conclusions expressed in this publication are those of theTransportation Equity Cooperative Research Program project team who are responsible for thefacts and accuracy of the data presented herein. This report does not constitute a standard,specification, or regulation.



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Steps Followed to Generate Estimations (Results).......................................................57

Regional Impact Analysis ....................................................................................................58

Low Income and Minority Community Area Analysis ..............................................60

I-95 Corridor Analysis ......................................................................................................69

Summary ............................................................................................................................73

Phase 6 - Being Heard (Communicating) Policy Implications .......................................74Conclusions................................................................................................................................76

Appendix 1: Bottom up Categorization and Discussion of EJT Issues .............................77

Appendix 2: Performance Measures, Analytic Tools, and Distributive Impacts ............82

Appendix 3: Look Up Guide to Support Application of an EJT Analysis ........................83

Figures

Figure 1: Cherry Hill Community/Land Cover (2000) .......................................................19

Figure 2 Overall structure of accessibility calculator and interface...................................30

Figure 3 Basic Accessibility Analysis Calculation................................................................31Figure 4. Menu to select specific neighborhood for analysis..............................................32

Figure 5 Example output from specific neighborhood analysis ........................................33

Figure 6 Tabular output for particular neighborhood accessibility analysis ...................34

Figure 7 Output for analysis of neighborhoods with at least 40% African-Americanhouseholds .................................................................................................................................37

Figure 8: Table of accessibility to “Manufacturing” jobs for neighborhoods with at least40% African-American households........................................................................................38

Figure 9 Table of accessibility to “Manufacturing” jobs for neighborhoods with at least50% households with incomes below 200% federal poverty line ......................................39

Figure 10 Personal Information Entry Page ..........................................................................43Figure 11 Underserved Comunities .......................................................................................45

Figure 12. Risk index applied to Cherry Hill .......................................................................49

Figure 13 Risk index applied to Federal Hill .......................................................................50

Figure 14. Risk index applied to Kirk Ave ...........................................................................51

Figure 15. Risk Index applied to "Hightway to Nowhere..................................................52

Figure 16. Scatter plot of vehicle miles within 200 feet of Baltimore residences inrelation to median household income. ..................................................................................53

Figure 17. Scatter plot of ordinal transformation of transportation and socio economicstatus indicators for Baltimore households...........................................................................53

Illustrations

Illustration 1: I-95 Corridor Transit Access ...........................................................................22

Illustration 2: African Americans and Hispanic/Latino Communities ........................................ 23

Illustration 3: Median Household Income and Household Density .................................24

Illustration 4: Change Median Household Income and Number of Office Workers......25



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Illustration 5: Change in Other Workers and Population ...................................................26

Illustration 6: Change in Worker Density and Total Number of Workers .......................27

Tables

Table 1: EJ Impact Analysis Tools...........................................................................................10

Table 2: Lessons Learned ..........................................................................................................12

Table 3: I-95 EJ Impact Analysis Tools....................................................................................12

Table 4 : Major Low Income Low Income and Minority Issue...........................................15

Table 5: Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000 ..20

Table 6 Sample report that pulls profile and trip data from the database .......................44

Table 7: Total Vehicle Miles Traveled ........................................................................................54Table 8: Baltimore Area VMT and Emission Summary by Functional Class.............................55

Table 9: Regional Vehicle Miles Traveled Analysis .............................................................59

Table 10 Regional Analysis with Income Classes.................................................................60

Table 11: All Case Study Areas vs. Region............................................................................61Table 12: Cherry Hill Study Area Growth Patterns .............................................................62

Table 13 Econometric Analysis Results for Cherry Hill ......................................................62

Table 14 Kirk Ave. Bus Depot Study Area Growth patterns:.............................................63

Table 15 Econometric Analysis for Kirk Ave ........................................................................64

Table 16: Lexington Market Study Area Growth Patterns..................................................65

Table 17 Econometric Analysis for Lexington Market ........................................................65

Table 18: Highway-to-Nowhere Study Area Growth Patterns ..........................................67

Table 19 Econometric Analysis Lexington Market ..............................................................67

Table 20: Sensitivity Analysis Changes in Socioeconomic Factors on VMT (2005-2030)69

Table 21: Regression Results – I-95 Corridor ........................................................................70Table 22: I-95 Corridor Study Area Growth Patters ............................................................71

Table 23: Measuring Equity .....................................................................................................83

Table 24: Performance Measures by Planning Goal Area...................................................84



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The tools used in the BREJT project four previous case studies, MTA Bus Depot, Cherry Hill,Lexington Market and Highway to Nowhere combine lessons learned and several levels ofanalysis techniques to demonstrate how issues associated with EJT issues can be systematicallyevaluated using a store of traditional transportation planning impact measures and analyticaltools.

As such, in the Kirk Ave. case study the MTA Bus Depot concerns focus on the location andoperation of a bus depot in an older, inner-city working-class neighborhood along KirkAvenue.

The Cherry Hill case study evaluates a history of public transit service changes, reductions andpoor service delivery in a predominately African American, and low-income community witha large number of residents who living in public housing.

While the Lexington Market case study analyzes the reaction to changes in transit service in anhistoric shopping destination frequented by lower-income residents from surrounding

communities in central Baltimore.

The Highway to Nowhere studies the concerns of communities in the U.S. Route 40 Corridorthrough West Baltimore regarding plans for a proposed Red Line and efforts to create transit-oriented development around an existing commuter rail station (West Baltimore MARC),fearing community disruption, destruction and dislocation as occurred in the partiallyabandoned I-170 which divided West Baltimore in the 1960s. Important lessons were learnedfrom using the above tools to evaluate the concerns of local residents, low income AfricanAmericans, transit dependant populations and community leaders in the BREJT project. Thekey lessons (Table 3) from the BREJT project informed the formulation of a fifth case study (I-95 Corridor)



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Hypothesis

As a companion to the initial case studies the e I-95 corridors case study broadens the scope ofanalysis by addressing the impact of future growth and offers an opportunity to add to thecollective experience by broadening the range of EJ issues studied in BREJT. The impact area

for Defense Base Closure and Realignment Commission (BRAC) is the county of Harford,Howard, Ann Arundel and Baltimore City. Within the impact zone of Aberdeen and FortMead are low income and minority sub populations of Middle Branch, Cherry Hill andWestport as well as other middle class communities. The hypotheses upon which this researchstudy is based are as follows:

Low income and minority households concentrated in central-city Baltimore futureaccess to jobs, education and will challenged by regional growth and development.

The public health risk of low income and minority residents n Baltimore is negativelyimpacted by proximity to traffic congestion.

The majority of jobs created by BRAC will be located outside the central city, and willbe less accessible for the transit dependant.

In some cases development pressures take advantage of disadvantaged neighborhoodvulnerabilities and often result in pressures that lead to abandonment, dismay andgentrification.

Underserved population groups seek ways to create healthier living environments seeenvironmental justice planning as an opportunity to address what they perceive to be alack of universally accepted definitions and procedures for analysis and evaluation oflocal issues.

Analysis Framework: Public Participation, Accessibility and PublicHealth

The community voices described above is used to help develop the public participationframework model presented below in the next section and is one of three core environmental

justice-planning components. The other two components are performance measures andanalytical tools. These components combine to provide environmental justice evaluationmethods and procedures that may be used to confirm or negate issues identified in low-incomecommunities. In particular the public participation framework is designed as the starting pointfor vetting issues and developing analysis strategies for interrogating environmental justiceand transportation issues through a triage-type activity that has multiple screening levels andexplicit feedback loops.

The feedback from various experts and our sponsors in the Baltimore Region Environmental Justice and Transportation was that a credible, systematic, comprehensive approach to addressTransportation Decision Making in transportation had not yet been developed. Based on thisfeedback and an extensive literature review, we concurred that this goal had not yet been

http://www.brac.gov/






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Environmental Justice needs and concerns in the context of both metropolitan planning as wellas more perfunctory or topical issues and concerns.

The issues and concerns elicited from the Baltimore community during Phase I of the TCERPproject speak to this breadth of coverage and specificity that will be required of the Toolkit andthe tools and performance measures it contains. A perusal of the concerns summarized in

Exhibit 1 suggests an abundance of concern in the following major areas:

Delivery of transit service: Frequency, proximity, reliability, quality,professionalism

Access and mobility: ability to reach jobs, health care, other needs, particularly bytransit

Funding parity: priorities in poor vs. affluent areas, bus vs. rail transit, condition oftransportation infrastructure, inclusion in decision making

Environmental: Exposure to traffic, noise, air pollution Quality of Life: Community health, individual health, safety

A reflection on these issues also suggests a spectrum of factors that may be contributing to theconcerns that could occur at all levels of planning, funding or operations. Many of the voicedconcerns may simply be the result of a change in operating policy that had more deeplyreaching effects than anticipated or recognized; in this case it may be sufficient to simplyreestablish the communications link between the community and the agency.

In other cases, however, the Problems may not be simple in nature or source, and a higher levelof assessment and intervention may be required, particularly if the problem is widespreadand/or is the result of shifted funding or program priorities. In such a case, it may likely benecessary to deepen the assessment and intervention to better understand the nature of theproblem or to investigate alternative solutions.

Given this “hierarchy” of issues, their causes, and the potential responses, the analysis toolsand the measures in the toolkit must have enough dexterity to permit an analysis which isappropriate and credible for the issue at hand, but which leaves open the option to “digdeeper” if the problem proves to be more complex or difficult to resolve with simplisticmethods. Ultimately, the Toolkit will attempt to provide its users with the ability to identifythe most appropriate measures and analyses to address a particular issue. Thus, thesimultaneous development of the measures of performance along with the analytic tooloptions in the context of addressing specific issues in a case study context is the preferredvehicle for understanding the salient impacts of environmental justice issues.

Environmental Justice Impact Analysis

Phase 1 - Community-Driven Intergovernmental Engagement and

Cooperation - I-95 Corridor Community Voices



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For the 2007 – 2008 period community meetings were was called by the BCDP and the CherryHill Trust members with community residents. The meeting participants provided writtencomments to the BCDP on the draft of the Middle Branch Master Plan, rezoning issues and thefinalization of the Cherry Hill Master Plan.

Issues

There primary concerns are listed below:

Middle Branch Master Plan proposalNotice of the Cherry Hill Master Plan submittal deadlineArea RezoningWhat will be the impact of the projected growth impact traffic congestion?What additional transportation services will be needed?How will other low income and minority communities be impacted?Will transportation accessibility be improved?What are the potential direct/indirect impacts on the Cherry Hill community?

Phase 2 - Community Assessment and Citizen Input Investigations(Identify the Local Problems)

Resident’s Concerns

In response to the Middle Branch Master Plan, the community noted its intention to review the

plan and tailor its master plan accordingly. The participants rejected the deadline date tocomplete its master plan. The BCDP noted a future date to meet with the community to extendthe community’s input and support. The Cherry Hill Trust and the community residentsagreed to meet immediately to formulate a plan to take before the Planning Commission on therezoning issue (BREJT, research assistant, 2007).

Where will the workers come from to fill these jobs, what are their socioeconomiccharacteristics, and what travel time/cost burden will they have to bear to commute to thecorridor? Is there sufficient current or committed transportation capacity to support this levelof development and the anticipated travel patterns? What level of new investment will beneeded to maintain adequate levels of service? What was the planning and programming

process that led to the regional decision to support this growth concept in the regional long-range plan and the Transportation Improvement Program (TIP)? What planning discussionsand provisions have occurred to either provide housing and transportation to meet thesefuture worker needs and/or what concurrent plans/investments have been considered toencourage new job creation nearer to the lower-income/minority worker base? How wasaccess to jobs by minority/low-income workers addressed by this plan?



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Phase 3 - Information Gathering and Analysis (Due Diligence)

The BCDP hosted a meeting to elicit community comments for the Cherry Hill Master Plan andthe community’s Capital Budget Program. This collaborative task force meeting held inpartnership with the Cherry Hill Trust organization presented viewpoints and ideas with the

community perspective to the forefront. The Cherry Hill Master Plan is a working documentwith the BCDP as an active partner in the development and implementation of the plan(BREJT, research assistant, 2007). The Cherry Hill community voiced disappointment of thelack of information provided prior the meeting. The rezoning issued was only handled withthe participants of the Middle Branch community. The participants agreed to meet later tobrain-storm a strategy to take before the Planning Commission on the appointed date (BREJT,research assistant, 2007).

Short term recommendations by the community were made and put into the budget to addressexcessive loitering in front of the community’s Town Center area. Further, the BCDP providedfunding for the distribution of fliers for community safety initiatives, and for the efforts of

promoting shopping in the Town Center by working in partnership with the local businessassociations. The BCDP acknowledged the community’s recommendations, informed the teamthat further investigations for program feasibility will be required. An emergency communitymeeting was called by the BCDP and the Cherry Hill Trust members with communityresidents. A majority of the community voiced opposition towards the Cherry Hill MasterPlan deadline date, stating that they were not forewarned or allowed time to respond.Secondly, a request for review of the Middle Branch plan is also needed time to allowcomments to be incorporated in Cherry Hill’s plan if necessary.

The meeting participants provided written comments to the BCDP on the draft of the MiddleBranch Master Plan, rezoning issues and the finalization of the Cherry Hill Master Plan. Inresponse to the Middle Branch Master Plan, the community noted its intention to review theplan and tailor its master plan accordingly. The participants rejected the deadline date tocomplete its master plan. The BCDP noted a future date to meet with the community to extendthe community’s input and support.

Phase 4 - Developing a Community Profile (Analytical) Mapping Analysis

As in any analysis this evaluation begins by framing a series of drill down questions that onceanswer defines to what extent low-income residents are impacted by dynamic growth and

development of an expanding urban area. The residents of West Port and Middle Branch likemany other low – income and minority residents fear that they will be displaced andunconvinced by growth. With the understanding that transport enables societal objectives tobe pursued, such as access to various sorts of opportunities the EJ question then become notonly which but how societal objectives are pursued and how the distribution of services dictatethe nature and level of services that are provided to assist disadvantaged persons. Thequestion then becomes are these fears justified or unjustified. Our approach to answering this



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question is to first attempt to reasonably determine understand whether minority/low-incomehouseholds will continue to be concentrated in central-city Baltimore and if so will their futureaccess to jobs, education, health care and other opportunities will be as limited, or even morelimited, than they are today.

Figure 1: Cherry Hill Community/Land Cover (2000)



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Table 5: Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000



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Typical Demographic Profile for I-95 Low Income Residents, 1990 and 2000

*Note: (Census Tracts should be updated to only include only 250207,250230, 250204) which indicate the 1990 population at10,897 with 49 Caucasians. In 2000 the population dropped to 7,664 and the number of Caucasians grew to 131.)



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Illustration 1: I-95 Corridor Transit Access



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Illustration 2: African Americans and Hispanic/Latino Communities



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Illustration 3: Median Household Income and Household Density



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Illustration 4: Change Median Household Income and Number of Office Workers



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Illustration 5: Change in Other Workers and Population



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Illustration 6: Change in Worker Density and Total Number of Workers



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Phase 5 - Drill Down to Evaluate the EJT Issues (Evaluation)

Accessibility Calculator

The Environmental Justice in Transportation Project is developing an integrated approach for calculating and analyzing accessibility and travel that will enable impacted groups, MPO’s,CBO’s citizens and city planners to conduct a preliminary analysis of current problems andfuture plans and offer some evaluative insights. The first phase of the toolkit includes two basicmodules: an Accessibility Calculator and a Community Travel Survey. Beta versions of both of these tools have been completed, and are presented in this paper with the hope of updating theenvironmental justice community about the progress of this project and to facilitate feedback and discussion about its direction.

Task I-3D of the Environmental Justice in Transportation Project is underway, and what hasemerged is an integrated approach for calculating and analyzing accessibility and travel that will

enable impacted groups, MPO’s, CBO’s citizens and city planners to conduct a preliminaryanalysis of current problems and future plans and offer some evaluative insights. The tool will be developed in three phases: a first phase will generate cumulative opportunity measures fromselected neighborhoods or for certain demographic groups, combined with community self-survey capabilities. A second phase will add further details, such as access measures to certaintransportation infrastructure, etc. A third phase will combine travel data with environmentalinformation to measure public health risks and exposure from travel. This document deals withthe development of the first phase. The first phase of the toolkit includes two basic modules:

Accessibility Calculator Understand accessibility to certain land uses (jobs, schools, education) from a given

neighborhood or all neighborhoods with a certain demographic characteristics (e. g.low-income) and compare that accessibility to other neighborhoods

Community Travel SurveyAllow individuals or communities to record and map their own travel patterns andcompare this to the regional transportation investment plans

An introduction to these two modules is covered in this report. For more detailed informationabout the development of the tools, see: [TERP Technical Docs]

Motivation

The calculator can be accessed at: http://www.brejtp.com/travel-diary .

Accessibility measures the ability to reach a desired destination within a time, distance, or costlimit. Threshold measures analyze who can reach a desired destination within some thresholdof time, distance, or cost. The prototype tool will take the Cumulative-Opportunity approachand will measure the number of essential destinations reachable within various times ordistance bands by transit and automobile. This is particularly useful in describing how well the



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transportation network works in relation to the distribution of transportation improvementsand how they serve total transportation needs for particular sub-groups. It is also perhaps themost simple and direct to set-up with the typical datasets available from city or regionalplanning agencies.

There are two basic accessibility measures included in the first phase of the tool development:

1. For a specific neighborhood:a. Calculate the total number of important destinations (jobs, jobs of certain types,

schools, medical facilities, etc) reachable within certain time bands (15, 30, 45minutes) by public transit and automobile.

b. Compare this measure with all other neighborhoodsc. Compare this measure for the various future regional transportation scenarios

2. For a specific Demographic group:

a. Calculate the total, and average, number of important destinations (jobs, jobs ofcertain types, schools, medical facilities, etc) reachable within certain time bands(15, 30, 45 minutes) by public transit and automobile from all neighborhoodswith the specified representation of the demographic under study (e.g. >50%Low-Income)

b. Compare this measure with the balance of the neighborhoods and allneighborhoods

c. Compare this measure for the various future regional transportation scenarios

Accessibility Calculator Structure

The accessibility calculator will use transportation and demographic data and outputaccessibility measures for each neighborhood. These outputs will be assembled in a databasethat will allow users to look at the accessibility of different neighborhoods (using the TAZs). Itwill be accessed with a front-end interface that supports user query. The interface will thendisplay output through result tables and statistical comparisons. Figure 1 shows the overall

layout of the accessibility calculator. The sections which follow will present the basic structureof the databases, the four sections of the accessibility calculator, and the overall web interface.



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Basic

Accessibility

Analysis (AA):

Accessibility for

Particular TAZ ‘I’

Accessibility for a

Particular

Demographic

Accessibility for

Particular

Neighborhood

(TAZ ‘I’)

Web-based interface

Basic Tabular and Statistical Output

Accessibility Calculations

Figure 2 Overall structure of accessibility calculator and interface

Data Requirements

For simplicity, the urban area is divided into traffic analysis zones (TAZs), which thereforebecomes the basic unit of analysis in the tool. The data requirements fall into two types: thedemographic and land-use data for each TAZ (called TAZ data) and the TAZ to TAZ traveltimes for Transit and Automobiles, called Transit and Auto Skims.

Each of the these databases are needed for each future planning scenario. The TAZ Datacontains all of the information concerning demographics and numbers and types of land-uses

contained in each TAZ. The Skims data set is a square set of size TAZ by TAZ with the TAZ toTAZ travel time for every TAZ to TAZ O-D pair.

Basic Accessibility Calculation

At the core of the tool is the measure of accessibility to destinations from each reachable TAZwithin a given time, T. Those reachable TAZs contain certain numbers of jobs, etc (desirabledestinations). The core accessibility calculator will generate a database of the reachabledestinations within the time bands specified (15, 30, 45 minutes) for each TAZ. The basicaccessibility analysis calculations steps are shown in Figure 2 below.



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Figure 3 Basic Accessibility Analysis Calculation

The output from the Basic Accessibility Analysis Calculation is a dataset for each scenario,showing the total destinations reachable for each TAZ. This output is used in the latercalculations in the tool. From there, the calculation will depend on whether the analysis is donefor a particular neighborhood or for neighborhoods with a particular demographic. We look atthese two options now.

Accessibility for Particular Neighborhood (TAZ ‘I’)

To analyze the accessibility for a particular neighborhood, the accessibility database generatedfrom the basic calculator will be queried for the accessibility measures from the selected TAZ,and all TAZs. Comparisons and maps can then be produced. When the TAZ number is shown,it can be entered into the menu shown circled in Figure 3.



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Figure 4. Menu to select specific neighborhood for analysis

After you click “Go” the output page is given, an example of which is shown below in Figure 4.Several different kinds of tabular and graphical outputs are presented from top to bottom, andeach is discussed in the following sections.



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Tabular Accessibility Output

Graphical Accessibility Output

Tabular Accessibility Output

Graphical Accessibility Output

Figure 5 Example output from specific neighborhood analysis



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1

2 3 4

5

6

Figure 6 Tabular output for particular neighborhood accessibility analysis

Figure 5 shows the top of the output with numbers pointing to particular parts. Eachnumbered part will be described now. Item 1 refers to the tabs across the top of the output. Thedatabase of accessibility includes various types of “essential destinations.” These differenttypes of destinations correspond to these tabs. So, in any analysis, one can look at theaccessibility to Food Stores, Heath facilities, Social Services, Elementary Schools, etc. Some ofthese “essential destinations” are actual facilities, such as Elementary Schools, and others, suchas “Trade” are types of jobs. For facilities, two kinds of outputs are given – the number of jobsin those facilities, and the number of facilities.a

Item 2 is pointing out the title of the section: “Food Stores – Number of Jobs.” If youscroll down, there is another section below with the same output for “Food Stores – Number ofFacilities.”

Item 3 is referring to the columns of accessibilities for the selected neighborhood, TAZ#10. The top row of numbers: 5,028 , 14,724 , 26,306 , etc. refer to the number of jobs in foodstores accessible by automobile within 15, 30 and 45 minutes, respectively. This is for the 2000

Base scenario, to be explained in a moment.

a Both are important because the number of facilities shows the availability of the service, but the number of jobsindicates the size of the facilities. For example, the number of food stores indicates availability, but doesn’t indicatewhether they are corner “beer and wine” stores or larger full service groceries. That is better understood by thenumber of jobs in food stores, as larger facilities have more jobs. In communities of concern, there is often alacking of larger full service groceries, the inclusion of both measures is important. The same measures are produced for schools and other services.



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Item 4 refers to the columns of accessibilities for all 1400 neighborhoods in the region, in orderto facilitate a comparison with the selected neighborhood. The top column refers to the samemeasures, averaged for all neighborhoods.

Item 5 is the five “scenarios” included in the database. These are five differenttransportation plans, each of which has different characteristics. The 2000 Base scenario is the

present day (at the time the travel model was made) and includes existing projects, includinghighway, road and public transit. The Financially Constrained scenario is the regionaltransportation plan for 2025 with a set of new investments (including highway, road andpublic transit) included up to a projected budget (i.e. “Financially Constrained”). The “NoProject” scenario is a projection for 2025 which assumes certain population and job growthwith no new investments in any transportation projects (including highway, road and publictransit). The “Project” scenario is the regional transportation plan for 2025 with a set of newinvestments (including highway, road and public transit) included up to a larger, more liberal,projected budget than the Financially Constrained scenario. Finally, the Transdef, is a regionaltransportation plan for 2025 with a different set of new investments (including highway, roadand public transit) which places more emphasis on “smart growth” options and public transit

investments than the Project scenario.

Item 6 is showing how the results of a particular scenario are presented in terms ofauto, transit and “composite” accessibility. These are the numbers of destinations reachablewithin the different time bands by these travel modes. The composite accessibility takes intoaccount a neighborhood’s automobile availability. The database of accessibility for eachneighborhood is stored in two ways – the number of opportunities reachable by public transitand the number reachable by automobile. This is done because of the amount of time it takes apublic transit users to reach a destination will often differ from the amount of time anautomobile user will take. The composite number takes into account the ownership level ofautomobiles in a particular neighborhood and computes one accessibility number from a

combination of the automobile and public transit numbers. The more households withautomobiles in a neighborhood, the closer the neighborhood’s accessibility number will be tothe automobile number. The fewer automobiles in a neighborhood, the closer theneighborhood’s accessibility will be to the public transit number. For example, for aneighborhood where all households have automobiles, the composite would equal the numberof reachable destinations by automobile, while for a neighborhood where half of householdshave automobiles; the composite would be an average of the number of reachable destinationsby automobile and by public transit.

Below the table and the eight graphs described thus far, are one more table and eight moregraphs presenting the accessibility information for neighborhood #10 for food stores by

number of facilities. The destinations which have both number of facilities and number of jobswill have this “double-length” output. The ones with only number of jobs, will only have oneset.

Analyzing a Selection of Neighborhoods by Characteristics

To analyze the accessibility for a particular demographic, the TAZ database is used to generatea list of TAZs which meet the required demographic, such as “>50% Low-Income” or “<25%



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1

2 3 4 5 6

7

8

Figure 7 Output for analysis of neighborhoods with at least 40% African-Americanhouseholds

Item 3 refers to the column of average accessibility measures for the 68 selected neighborhoods.These numbers have the same meanings as those discussed above. For example, for the 2000Base scenario, the 68 neighborhoods in the selection have an average of 2,315 food store jobsreachable to them within 15 minutes by automobile. Item #4 refers to the column ofaccessibility averages for all of the neighborhoods not in the selection. There are 1454-68 = 1386such neighborhoods. Item #5 refers to the column of accessibility averages for all of the 1454neighborhoods in the Bay Area. This is similar to the column shown in the individualneighborhood analysis.

Item 6 refers to a column of “T-scores,” which show the statistical significance of thedifferences between the accessibility measures for the neighborhoods in the selection and allneighborhoods. The statistical test is performed to see if the selected group of neighborhoods issignificantly different from all of the neighborhoods in the Bay Area. The details of how the T-

score is generated are unimportant. What is important, is that the larger the absolute value ofthe T-score (positive or negative), the more the difference between the group of neighborhoodsis significant. A “*” is placed next to T-scores which are significant at the 90% level, meaningthey are significant. Two “*” s are placed next to T-scores which are significant at the 95% level,meaning they are even more significant. Three “*” s are placed next to T-scores which aresignificant at the 99% level, meaning they are extremely significant. T-scores with no starsmean that the differences between the selected neighborhoods and all neighborhoods are not



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very great. A positive T-score means that the selected neighborhoods enjoy a higheraccessibility to the destinations than all neighborhoods. Item 7 and 8 show again the samescenarios and travel modes which were explained in the last section. Scrolling down the pageshows the table for the Food Stores, by number of facilities instead of by jobs. The output isread in the same manner as above.

Going to the “Manufacturing” tab (shown in Figure 8), shows the table for accessibility to thenumber of “Manufacturing” jobs. For example, – Item 1 points to the T-score of -3.04 for thedifference in “composite” accessibility within 45 minutes between the selected neighborhoodsand all neighborhoods, for the 2000 Base scenario. Item 2 points to the T-score of -2.90 for thedifference in “composite” accessibility within 45 minutes between the selected neighborhoodsand all neighborhoods, for the Project scenario. Both scores show that the 45-minute“composite” accessibility to “Manufacturing” jobs is significantly lower for the selectedneighborhoods than all neighborhoods.

1

2

Figure 8: Table of accessibility to “Manufacturing” jobs for neighborhoods with at least 40%African-American households

Returning to the main accessibility calculator menu, we can also chose neighborhoods byincome levels and automobile-ownership levels. These menus work in the same way that thedemographic characteristic choice menus work. Again, the percent of households is chosenfirst, followed by the characteristic. In this example, we seek to analyze all neighborhoods withat least 50% of households below 200% of the federal poverty line.



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Figure 10 Personal Information Entry Page

Results: Sample Report

Sample report that pulls profile and trip data from the database. It sorts by create data indescending order so you can use it to monitor new data as it comes in. There is also a link to



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download it to Excel. The report is set up to show 20 rows at a time, but when you click thedownload to Excel link, you’ll get all the data.

Table 6 Sample report that pulls profile and trip data from the database

Variable

Starting Location: ,Departure Time: 8:00 AMFinal Destination: ,Arrival Time: 8:00 AMTrip Cost: $0.00Trip Distance: 0 milesMethod of Travel: trainTrip Purpose: workMobility Limitations:Traveling Alone?

Intermediate Stops:Is auto travel cost affecting travel? Were transfers required? Was your travel time reasonable?Do you have any health related issues that affectyour travel?Did the conditions of the streets and roads causeany problems?Is the transit usually on time and reliable?Are you able to reach essential services andrecreational activities?

Auto, Public Transportation,Land Use and Public HealthPerformance Measures

Public Health Rationale as a Transportation Decision-Making Factor

A primary consideration to be addressed by the toolkit is public health risk impacts thatinclude accessibility and includes the recurring issue of service by transportation serviceproviders including a discussion of services dedicated by medical condition vs. pooling oftransportation resources and reservations/dispatching. This is a first and particularlyimportant step in improving participation among traditionally underserved populations and itis equally important in helping to determining who these populations are, where they live andtravel, how best to communicate with them and how best to serve them. We argue through the

demonstration below that:

•Public health has evolved out of a history recognizing and remedying health disparity andenvironmental justice. Some of the approaches and strategies used in public health are likelyrelevant here.



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•Transportation has a significant impact on public health and there is strong evidence that theimpact is disproportionate among communities of color that are socio economicallydisadvantaged.

As summarized in the literature there is extensive proof which: 1) delineates the significantimpact of traffic on community health and 2) provides evidence that the health burden is

disproportionately born by communities of color that are socio-economically disadvantaged.Therefore, we can argue that publichealth is one of a couple primaryconsiderations to be addressed in thetoolkit. Other considerations include theimpact of accessibility underservedcitizens. Appropriately, these interestsand views are represented bothupstream down stream of transportationdecisions.

Figure 11 Underserved Comunities

The goal of the current project is to present a practical approach, i.e. a toolkit, for providingunderserved populations with greater influence over transportation decisions so that thereinterests are better served by those decisions (Figure 11). This is important since it is thispopulation that is both disenfranchised from, and most strongly impacted by, the decisionmaking process.

Background and Rationale

Traffic-related air pollution has been implicated as a serious public health threat by a growingand increasingly convincing body of epidemiologic literature, which has linked trafficpollutant exposure with non-specific mortality (Friedman et al. 2001), cancer (Pearson et al.2000; Knox 2005), and a variety of cardiovascular (Bigert et al. 2003) and respiratory effects(Friedman et al. 2001; Brunekreef et al. 1997; Wjst et al. 1993; Weiland et al. 1994). In addition,risks from this exposure are disproportionately borne by racial minority and socio-economically disadvantaged subpopulations (Green et al. 2004; Apelberg et al. 2005; Gunier etal. 2003). While the adverse health consequences, epidemiology, and social disparities arealready compelling, it is clear that further elucidation is necessary of the magnitude, chemicalcomposition, and variability of human exposure, and source-to-effect mechanisms.Community exposure to a complex array of traffic-related pollutants is determined by vehicle

volume, as well as varied emissions characteristics of vehicles, such as differing tailpipeemissions, heat soak, tire and brake wear, and road dust re-suspension. This underlyingvariability in emissions drives highly dynamic concentrations of traffic-related pollutants,which are further modified by meteorology, source proximity, and human time-activitypatterns.



46

Automobiles and trucks are a major source of air pollution including such toxins and irritantsas carbon monoxide (CO), nitrogen oxides (NO), volatile organic compounds (VOCs),particulate matter, and particle-bound polycyclic aromatic hydrocarbons (PAHs). In the urbanenvironment, high-density traffic is brought in close proximity to densely populatedcommunities. This is particularly true in some older East Coast cities like Baltimore where row-house neighborhoods are within a couple of meters of heavily traveled urban corridors.

Environmental justice is a term used to describe the movement concerned with inequities inthe distribution of adverse environmental and health consequences of industrial activities andenvironmental policies (U.S.EPA 2004a). The movement grew from early observations that aseemingly unequal burden of pollution fell on disenfranchised and disadvantagedcommunities, often characterized by lower incomes and high proportions of minorities (Brown1995). With the issuance of Presidential Executive Order 12898 in 1994, achieving“environmental justice” was integrated into the missions of all federal agencies (Clinton 1994).The U.S. Environmental Protection Agency (EPA) defines environmental justice to mean that“no group of people, including a racial, ethnic, or a socioeconomic group” should bedisproportionately affected by “industrial, municipal, and commercial operations or the

execution of federal, state, local, and tribal programs and policies” (U.S.EPA 2004a).

There is ample evidence that minority and low-income communities bear a disproportionateburden of exposure to many environmental contaminants (Brown 1995; Institute of Medicine1999), including air pollution (Samet et al. 2001; Schweitzer and Valenzuela 2004). Becausenationwide ambient monitoring data are available for the criteria air pollutants (carbonmonoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide), we have somemeans for assessing exposure and risk in disadvantaged and minority communities. However,considerably less is known about the distribution of exposure to and risk from the wide rangeof hazardous air pollutants (HAPs or “air toxics”) identified by Congress in the Clean Air ActAmendments (1990), because nationwide ambient monitoring is not possible due to the sheer

number of pollutants and their diverse chemical properties (Caldwell et al. 1998; Morello-Frosch et al. 2000; Woodruff et al. 1998).

A recent analysis of modeled national estimates suggests that ambient concentrations of HAPsexceed benchmark risk levels for cancer and non-cancer endpoints in many areas of thecountry (Caldwell et al. 1998; Woodruff et al. 1998; Woodruff et al. 2000). Furthermore, severalrecent studies have documented a disproportionate burden of air toxics exposure and/or riskfalling on minority and low-income populations. These studies have included varying sourcesof exposure, including high traffic density (Green et al. 2004; Gunier et al. 2003), location ofToxic Release Inventory (TRI) and other treatment, storage, and disposal facilities (Morello-Frosch et al. 2002; Pastor et al. 2001; Perlin et al. 2001), and modeled estimates from EPA’s CEP

(Lopez 2002; Morello-Frosch et al. 2002).

Given the compelling evidence of a health threat that is exacerbated by environmentalinjustice, we have developed a strategy for identifying communities at risk using availablepublic data. The identification of such communities is a necessary first step to empowercommunities, design epidemiological studies to further elucidate the threat, and implementintervention studies to address the threat.



47

Assessing Transportation-Related Health Risk in Baltimore, Maryland

As discussed earlier, the impact of transportation on public health is a growing concern inmany metropolitan areas world-wide. Here we focus on Baltimore, Maryland a majormetropolitan area in the United States to illustrate how existing information can be used in

identifying communities at risk. Baltimore is typical of many old large east coast cities with ahousing stock that is dominated by row homes built in close proximity to busy urban arterialroadways. In this section we describe existing relevant data and their analysis for the purposeof identifying communities at risk from transportation-related air pollution. Using theseexisting data, we have developed a risk index to identify communities at risk due both tosocio-economic status and proximity to traffic emissions.

Data

Central to identifying communities at risk is the acquisition and analysis of data sources thatcan be used to derive indicators known to influence community exposure to transportationactivities. Several such indicators include proximity of residential locations to transportation

infrastructure (and related activity) as well as some measure of socio-economic status.Increased proximity to transportation activity is thought to increase health risk posed bytransportation while decreased socio economic status is thought to increase risk of healthimpacts to effected populations. Several types of geographic information are required forcalculation of these risk indicators. First, one must account for the location of residentiallocations as well as how they are to be represented in the analysis. Residential locations can berepresented as either the sites of individual buildings (provided the availability of this data) oras some aggregation of residential locations such as a zip code area, census block group or atransportation analysis zone (TAZ). In this application, a geographic information system (GIS)database containing building footprints for all areas within the city of Baltimore was obtainedfrom (2000).

To compliment the building footprints, another GIS dataset detailing the location, extent, andland use for land parcels in the city was also obtained from (2000). Second, geospatialinformation on the location and usage of transportation infrastructure is needed. In this case,the regional planning transportation network from (2005) was used to facilitate this task. Thisplanning network was developed to model and assign transportation activity between TAZs inthe region and as such, various types of inter-TAZ traffic for the year 2005 had been alreadyassigned to road segments in the network. That is, the volume of cars and trucks using eachroad segment in the network had already been estimated and attributed to each segment by(2005). Finally, socio-economic information (e.g., race, median income, education, etc.) on thestudy area’s population must be known to characterize socio-economic status. Here, year 2000median income for each U.S. Census block group was used as a proxy for socio-economicstatus. Block group data was selected since it is the smallest spatial unit for which censustabulations of household income data are available.

Methodology

Two indicator variables are used to characterize community health risk due to traffic exposure:1) proximity and 2) socio-economic status. One way of assessing proximity to traffic is tocompute the total vehicle miles (# vehicles on road segment*length of road segment) within a



48

threshold distance (e.g., 300 feet) of a residential structure or within a tabulation area (e.g.,TAZ). As discussed earlier, socio-economic status can be represented in a variety of ways (e.g.,median household income). Thus, using these two basic indicators of health risk associatedwith traffic exposure, a simple index (RIi) can be computed representing the level of riskassociated with each residence or a tabulation area (i.)

iii SES TVMT RI (1)Where,

i = unit of analysis (e.g., building or tabulation area) RI i = Risk index for unit i

TVMT i = Total Vehicle Miles Traveled (# of vehicles * length of road segment) within some proximity threshold of unit iSES i = Socio-economic status for unit i

Obviously, an issue with this index is that both variables are of a ratio nature, thus it is

necessary to convert them to ordinal measurements to facilitate their integration andcomparison. One way to accomplish this is to consider the range associated with each variableand split the range into bins of equal intervals. Thus, both TVMT and SES variables can besplit into 10 intervals of equal size indexed 1 through 10, such that a value of 1 indicates theleast risk and a value of 10 indicates the greatest level of risk. For example, those residenceswith the lowest levels of TVMT would be assigned a value of 1 while those with the highestTVMT would be assigned a value of 10. Similarly, those residences with the highest levels ofSES would be assigned a value of 1 while those with the lowest SES would be assigned a valueof 10. Therefore, computing RI i using the transformed ordinal variables results in a RI i withvalues ranging between 2 (lowest TVMT and highest income) and 20 (highest TVMT andlowest income).

TransCAD, a GIS specifically oriented toward the analysis of transportation data was used tofacilitate analyses of the datasets discussed above to compute the components of the risk index.Although TransCAD is primarily oriented toward transportation analysis, it is also well suitedas a general purpose GIS and, hence, has proven useful in addressing broad and diverseresearch questions such as those involved in deriving meaningful discoveries related to traffic,health, and environmental justice.

First, TransCAD was used to derive a variable indicating proximity to transportation activity.Proximity for each residential building is defined here as all transportation activity fallingwithin 200 feet of the building. Proximity to transportation was derived using the followingGIS methodology:

a. Select buildings falling within city parcels denoted as residential.b. Calculate vehicle miles traveled (VMT) for each road segment in the transportation

network. Here, VMT relates to daily vehicle (cars and trucks) use of a roadsegment.

c. Generate a 200 foot buffer for each residential building polygon.



49

d. Overlay buffer polygons with transportation network to compute the total VMT(TVMT) falling within 200 feet of each residential building.

Again, it should be noted that while residential buildings were used in this analysis, largerspatial units of analysis could also be used as well. For instance, one could use TAZs andcompute an overlay with the transportation planning network as is done above with each

building and compute total VMT for each TAZ.

Next, TransCAD was used to attribute each residential building in the building footprintdataset with the median income of the census block group the building falls within. Thismeasure of socio-economic status was integrated with the building data by first attributingeach residential building polygon with the median income of the census block group in whichthe building’s centroid is located.

Results

The environmental justice risk modeling was applied to four Baltimore communities (CherryHill, Federal Hill, Kirk Avenue, and Highway to Nowhere) to exemplify it as a tool as a part ofthe Baltimore Region Environmental Justice in Transportation Project (BREJTP).

Figure 12. Risk index applied to Cherry Hill



51

Figure 14. Risk index applied to Kirk Ave



57

VMT Reduction – Econometric Model

To address the goals specified in section 1, it is important to develop an econometric modelthat helps answer the 5 indicated questions. To do so, a model that relates VMT at the regionaland case study level with other relevant socioeconomic factors at the transpiration zone level isimportant. For the regional analysis, the following model can be specified:

ii Ind Off t IncMed D Inc Low DTPOP VMT 6543210 Re) _ () _ ( (1)

VMT refers to the vehicle mile travels for communities in the region (i), the parameters

0 to 6

are values to be estimated to indicate the relationship between variables and thedependent variable (VMT), TPOP refer to zonal total population, Low_Inc and Med_Inc referto low income transportation zone (if median income is below $25,000) and medium incometransportation zone (if median income is between $25,000 and $75,000), Ret refers to zonalretail employment, Off refers to zonal office employment, and Ind. refers to zonal industrialemployment. Note that the D indicates a dummy variable, i.e., the D for low income, forinstance, identifies transportation zones that are identified only as low income (in that case willhave a value of 1), otherwise will carry a value of 0 for zones that are not classified as lowincome.

An econometric model that investigates the relationship of case study VMT patterns with theregional VMT patterns can be specified as:

i

i

NW to Hwy DMkt Lex D Kirk D Hill Ch D

Ind Off t IncMed D Inc Low DTPOP VMT

) _ _ () _ ()() _ (

Re) _ () _ (

10987

6543210

(2)

Where all variables remain as defined previously, and D(Ch_Hill) refers to transportation zones

in Cherry Hill study area, Kirk refers to transportation zones in Kirk study area, D(Lex_Mkt)refers to transportation zones in Lexington Market study area, and D(Hwy_to_NW) refers toHighway-to-Nowhere study area.

Estimation of equations (1) and (2) provides answers to questions (1) through (5) in theprevious section. The above two models are estimated by using an Ordinary Least Squaresmethod. In estimating these models, econometric tests are conducted, and corrected whenneeded, for basic econometric problems in cross-sectional data.

Steps Followed to Generate Estimations (Results)

Section 3 provides a model that can help understand the relationships between zonal VMT andother socioeconomic factors, such as zonal median household income, number of workers, andpopulation (households). Such analysis would provide information on the relationshipbetween these factors and VMT generation, and consequently potential policy implications tomanage regional and local VMT.



58

To conduct the analysis based on the model indicated in section 3, the following steps arefollowed:

First, data on VMT by transportation zone is collected. Then, data on socioeconomicfactors, such as income, population and employment levels are matched with VMT datafor each transportation zone.

Next, data is identified both for the entire transportation region and for specific casestudy areas through an identification variable, called a dummy variable. A dummyvariable is a 0 and 1 code that labels 1 if the data is for a specific case study area and 0otherwise. This helps identify regional and case study data.

Next, the data is checked for completeness and consistency. This data is then brought toLIMDEP econometric software to estimate the relationship between VMT and othersocioeconomic variables (factors).

Finally, the result is summarized and presented for analysis.

Based on the model in section 3 and the steps outlined in this section, the results from theanalysis are provided in section 4. Note that section 4 also includes the following additionalanalysis:

Detailed examination of the relationship between income and VMT generation. First,the relationship between general income and VMT is considered. Next, to examine theimpact of different income classes on VMT, median zonal income is categorized intolow income, medium income and high income categories. Low income zone is definedas one where the medium income is below $20,000 per year. Medium income zones aredefined as on where income is between $20,000 and $75,000 per year. High income

zones are defined as those with income above $75,000 per year. This helps analyze therole of income in VMT determination by income class.

Detailed examination of shifts in VMT generation by case study area compared to theoverall region. To analyze whether case study areas have a distinct VMT generationpattern compared to the overall transportation region, a case study comparison withthe region is conducted.

Analysis of results is presented next in section 4.

Regional Impact Analysis

Focusing first on regional transportation zones, the analysis considered 1,123 transportationzones in the region. Consider, first, a basis analysis that explains differences in VMT generationacross transportation zones based on zonal differences in total population, median income, andretail, office, and industrial employments. Econometric results are provided in the table below.



59

The following key results are observed:

Transportation zones with more population generate significant amount of VMT. Theresult was highly statistically significant. For every 1 person difference across zone,VMT generation differs by 529.

Median income has a statistically significant impact on VMT generation. Zones withhigher median income generate more VMT. For every $1 rise in median income in azone, zonal VMT is expected to increase by 8.68.

In terms of employment types, the result suggests that retail businesses contributesignificantly higher VMT, followed by office employment. However, zones withpredominantly industrial employment have a lower VMT compared to non-industrialemployment zones. Additional retail job difference across zones is related to acorresponding VMT difference of 2,835. The number is 2.592 for office jobs.

Table 9: Regional Vehicle Miles Traveled Analysis

Regression Results – Regional Analysis

Variable Description of Variable Coefficient P-Value(% of error)

Constant Intercept of the model -2,154,054.75 0.00TPOP Total Population 528.97 0.00MEDINC Medina Income 8.68 0.00RETAIL Retail workers 2,834.83 0.00OFF Office Workers 2,591.62 0.00IND Industrial Workers -2,894.17 0.00

R-Squared 66%

The model’s R2 (predictive ability) is 66%. For limited number of variables considered in theanalysis, the result is robust.

One important question at this juncture is whether low income communities contribute higheror lower VMT. This questions has significant implications to environmental justice concerns.By estimating equation (1) with further categorized income data, the result from theeconometric estimation is provided in the table below. The results can be summarized asfollows:

• As discussed in the previous result, transportation zones with higher population and retailand office jobs have significantly higher VMT generation.

• Focusing on the impact of income on VMT generation, the result is interesting. Two incomegroups are included in the estimation (low and medium income) and high income group isexcluded to hold it as a comparison group. The result suggests that while low incometransportation zones have significantly lower VMT generation, there is no much difference



60

between medium and high income zones. The implication of this finding at the regional level isthat low income areas generate low VMT, but if they are located close to medium or highincome areas, they will experience more VMT generated from outside their region. Theenvironmental justice implication across income classes is thus clear from this finding.

Table 10 Regional Analysis with Income Classes

Regression Results – Regional Analysis with Income ClassesVariable Description of Variable Coefficient P-Value

Constant Intercept of the model -1,419,003.71 0.00TPOP Total Population 519.86 0.00

Low_Inc Low Income Zones -877,516.29 0.00Med_Inc Middle Income Zones -113,556.01 0.50RETAIL Retail workers 2,800.33 0.00OFF Office Workers 2,596.57 0.00IND Industrial Workers -2,918.59 0.00R-Squared 66%

The model’s R2 (predictive ability) is 66%. Again for limited number of variables considered inthe analysis, the result is robust.

Low Income and Minority Community Area Analysis

This section focuses on analyzing the VMT generation by zone by specifically separatingattributes of the following case study areas: Cheryl Hill (TAZ codes 871, 875, 877, 864, and 335),Kirk (TAZs 432, 477, and 935), Lexington Market (TAZs 970, 969, and 968) and Highway-to-nowhere (TAZs 122, 117, 116, 133, 1005, 1038, 1061, 1072, 1050, 1027, 1016, 1127, 1116 and 1105).For the purpose of identifying VMT generation comparison between the region and the casestudy areas, model (2) is utilized. The econometric analysis results for case study specific areasare provided in the table below.



61

Table 11: All Case Study Areas vs. Region.

Regression Results – All Case Study Areas Compared to the RegionVariable Description of Variable Coefficient P-Value

Constant Intercept of the model -13,837,666.98 0.00CH-HILLD Cherry Hill 246,559.85 0.81KIRK_D Kirk 107,785.10 0.94LEX_D Lexington Market 156,236.96 0.91HTONW_D Highway-to-Nowhere -204,399.57 0.01TPOP Total Population 514.88 0.00Low_Inc Low Income Zones -892,417.26 0.00Med_Inc Middle Income Zones -127,104.20 0.45RETAIL Retail workers 2,790.67 0.00OFF Office Workers 2,611.99 0.00IND Industrial Workers -2,949.56 0.00R-Squared 67%

The results suggest the following:

All of the previous regional analysis still holds when the data is separated between theregion and case study areas. The fact that transportation zones that have morepopulation, more retail and office jobs, and middle and higher income generate moreVMT is still confirmed in this model.

In terms of VMT differences by geographic location, the result suggests that CherryHill, Kirk, and Lexington Market do not have remaining systematic difference withregional VMT once we account for income and population differences across zones.However, even after accounting such factors, the Highway-to-Nowhere case study areastill has significantly lower zonal VMT generation.

The results overall suggest that socioeconomic factors have more explanatory powerthan location differences. Thus, low income and low population areas have lower VMTgeneration, but if they are located close to high income and high population areas, theymay experience larger VMT from outside their zone.

The model’s R2 (predictive ability) is 67%. Again for limited number of variables considered inthe analysis, the result is robust.

The analysis is repeated by testing each case study area individually. The results were the same

as above. The results are provided below:



63

Median income is expected to increase by $3,601 between 2005 and 2015 and by $14,924from 2015 to 2030.

The number of retail workers is expected to increase by 52 between 2005 and 2015 andby 228 from 2015 to 2030.

The number of office workers is expected to increase by 343 between 2005 and 2015 and

by 897 from 2015 to 2030. The number of industrial workers is expected to increase by 124 between 2005 and 2015

and by 228 from 2015 to 2030.

Based on the above scenarios, the projected impacts on VMT can be generated by using theregional regression estimates. These estimates provide the following relationships:

VMT = 529*POPULATION + 9*INCOME + 2,835*RETAIL + 2,592*OFFICE –2,894*INDUSTRY

By utilizing the above estimation equation and utilizing the average estimated impact for

growth of workers, the following VMT scenario can be predicted for Cherry Hill:

From 2005 to 2015: VMT is expected to rise by 287,229.

From 2015 to 2030: VMT is expected to rise by 901,172.

Table 14 Kirk Ave. Bus Depot Study Area Growth patterns:

Kirk Study Area Growth Patterns

0 20000 40000 60000 80000 100000 120000 140000 160000

TAZ 432

TAZ 477

TAZ 935

T A Z s

TPOP

TPOP1

TPOP2

HH

HH1

HH2

MEDINC

MEDINC1

MEDINC2

WORKERS

WORKERS1

WORKERS2



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Table 22: I-95 Corridor Study Area Growth Patters

I-95 Corridor Study Area Growth Patterns

0 200 00 4 000 0 600 00 8 00 00 10 000 0 1 200 00 14 000 0

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

346

347

348

349

350

351

846

847

850

852

853

855

856

857

859

860

872

873

RETAIL2

RETAIL1

RETAIL

IND2

IND1

IND

OFFICE

OFF1

OFF

MEDINC2

MEDINC1

MEDINC

TPOP2

TPOP1

TPOP

TAZ



72

I-95 Corridor Stuy Area Growth Patterns

0 50000 100000 150000 200000

874

877

878

879

881

883

886

890

891

892

893

894

895

896

897

924

950

951

952

953

955

956

973

978

1006

1007

1008

1009

1010

1011

1012

1013

1014

1015

1019

1020

1021

1022

1106

RETAIL2

RETAIL1

RETAIL

IND2

IND1

IND

OFFICE

OFF1

OFF

MEDINC2

MEDINC1

MEDINC

TPOP2

TPOP1

TPOP

TAZ



74

face higher VMT generated from outside their zone. This can have seriousenvironmental justice implications to low income zones since outside zone VMT risescan cause higher pollution, health problems and property value impacts.

3. Transportation zones with high office and retail jobs generate higher VMT. However,more industrial job based zones have lower comparative VMT.

4. The higher R square along the i95 corridor provides some evidence that the 4-stepgravity model is less accurate when it comes to non-corridor travel impacts.

5. The models are probably understating and possibly not representing travel impacts inlow income communities accurately which raises some interesting possibilities.

Phase 6 - Being Heard (Communicating) Policy Implications

The focus of this component of the toolkit is on potential environmental justice implications ofour finds. Based on the summary of results in the previous section, the potential policyconsiderations and implications can be summarized as follows:

1. High population and high population growth areas are likely to cause majority of theVMT increases over the coming years. A comprehensive integrative transportation andgrowth management policy will be needed. Attempts at managing growth at the localand regional level, through such instruments as land use regulation, transportationinvestment, and urban and regional planning need to consider the VMT implications ofalternative growth scenarios for optima growth and VMT management. As suchenvironmental problems as pollution, environmental health, and quality of life ingeneral have increasingly become relevant, such comprehensive growth and

transportation integrated management schemes seem very important.

2. Transportation and travel behavior is significantly tied to income profile. Particularly,high income communities are more likely to generate significantly higher VMT andconsequent pollution, while low income communities generate lower VMT but could beaffected by high VMT of neighboring high population and high income communities.Since sprawl particularly created an environment where low income-low, VMTcommunities absorb high VMT from the demand of sprawling residents, the potentialenvironmental justice implications are numerous. Low income communities that arenext to high population or population growth, or that are next to middle and upperincome communities are particularly at risk as such zones are likely to generate higher

VMT that can be passed to lower VMT zones. Since higher VMT means more pollution,the potential health and property value impacts could be significant.

3. Places with growth retail and office employment are likely to generate higher VMT.Thus, low income community zones that are in the way of major local and regionalretail and office job opportunities are likely to be impacted by VMT increases from



75

outside their zone. As such, the impact of regional job growth and job markets on VMTof other low income communities need to be carefully considered.

4. The findings in this report overall suggest that higher increases in VMT are expectedfrom high population growth, high income, and high job growth transportation zones.As such, effective long-term VMT reduction policies will be most effective if

implemented with a focus in such communities. However, the cost to low-income andlow VMT communities should be gauged in assessing the potential cost-saving andother benefits of such policies.



76

Conclusions

Reported in this summary document are the key research components of the communityguide, EJ Toolkit and its technical documentation. The TCERP Project was very successful in

gathering and analyzing information from low-income and minority communities ontransportation and environmental justice in a “bottom up” manner. The challenge now facingtransportation planners in the Baltimore region will be how best to incorporate the anecdotalinformation gathered during the TCERP Project into the transportation planning process. TheTCERP Project Team has proposed that the public participation process should be expandedand a quantitative, data driven analytic tool be developed to accomplish that goal.

Building upon the findings in Phase I, Phase II of the project developed and organized the EJ &Transportation Toolkit through selected case studies of the most relevant accessibility and airquality related issues in highly impacted and representative environmental justicecommunities in the Baltimore region. The overall methodology and approach to each case

study will involve a series of steps that will revolve around the cooperation and participationof community stakeholders and various agencies.

All of the anecdotal information and data from the Listening Sessions and the CommunityDialogue indicate that low-income and minority communities in Baltimore share theperception that: 1) transportation resources and services are not equitably distributedthroughout the Baltimore region; 2) the public participation process for transportationplanning needs to be improved; 3) transportation problems (air quality, access to jobs andhealth care, etc.) have a direct impact on low-income and minority communities; and 4) moreinformation should be available to the general public on how transportation planners decidewhere resources and services should be targeted.

Two of the “lessons learned” from the Public Dialogue project were that: 1) developing agood database of community leaders, local ministers and community activists is timeconsuming; and 2) conducting sustained outreach to community leaders, local ministers andcommunity activists is essential for achieving a good turnout for public hearings like theListening Sessions, 3) coordinate meetings with the community at times and locations advantageous

to the community and 4) continue efforts such as this one to enlightening low-income and minoritycommunities through a process of education.

The higher R square along the i95 corridor provides some evidence that the 4-step gravitymodel is less accurate when it comes to non-corridor travel impacts. The models are probably

understating and possibly misrepresenting travel impacts in low income communities.



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Appendix 1: Bottom up Categorization and Discussion of EJT Issues

Kirk Ave Bus Depot

The Midway Community is one in which residential and industrial uses collide. The KirkAvenue bus yard has been a point of contention between the surrounding community and theMTA for some time. The primary complaint has to do with noise and emissions impacts fromoperations at the yard on the Community. The bus lot sits in a traditional setting betweenindustrial land to the north and east, and residential neighborhoods that seem to havesomewhat receded over time on the west and south. What is not clear is the extent to whichthe operations at the Kirk Avenue have directly caused the decline of the neighborhood.

Problems

- Residents suffer from respiratory ill nesses and some have died- Residents have a significant increase in asthma- Negative impact on resurgence of neighborhood- Residents complain of physiological health impacts and respiratory health impacts- Enhancements as alternate land use scenarios that strengthen rather than continue the cycleof decline- impact upon the community and has had no aesthetic improvement in the last 50 yrs.- Adjacent to a bus yard- Noise and air pollution Problems- Depot is perceived as having a neg.- Residents have appealed to the MTA on numerous occasions to address these conditionsand nature of bus operations

Analysis and Findings

- Kirk Ave. Depot is the 2nd largest facility in terms of daily bus pullouts. - All 4 four busdepots have had a significant decrease in bus pullouts in 97-2007, however -Kirk Ave. hashad the largest decrease (22.5%)

- Routes from Kirk Ave. are primarily suburban commuter routes and do not serve the localcommunity. - Lack of accessibility options for the Kirk Ave. residential community.

-Noise pollution from Depot: 1) Announcement over loudspeakers 2) Engines run all day 3)Repairs and servicing -research by Hopkins found that noise levels exceeded the ordinancelevel during day and night, nearly every day. This could result in loss of sleep, high levels of

stress, affecting the health of the resident population. Although the daily average of airpollution didn’t exceed the USEPA standard, the 2 week average provides indication that theannual federal health standard may be exceeded-Effects of air pollution put residents at an increased risk for adverse health effects. -measuredair pollutants were: Black Carbon (BC) Polycyclic Aromatic Hydrocarbons (PAHs)



78

Assessment & Recommendations

-Community should have structured negotiations with MTA regarding near-term and long-term strategies that will begin to provide some relief from the impacts which are substantiallyattributed to the bus depot. -MTA has attempted to respond to the community’s concerns withmitigation measures: *new operational procedures in response to idling *suggestion of newer

and cleaner alternative fuel busses

-Community should pressure the MTA to get a clear statement of the likely impacts of the newfacility, and to obtain meaningful mitigation in the time between -Burdens created by the KirkAve. Depot *property values are lower *busses at Kirk Ave. provide little benefit to residents*noise levels (documented cases of psycho- logical impacts *emissions from bus pullouts andidling

Cherry Hill

The Cherry Hill community geographically is located in the southern section of Baltimore City,

south of the Inner Harbor/Central Business District of Baltimore City. The Cherry Hillcommunity was established in the late 1940’s when the Housing Authority of Baltimore Citychose it as a site of a federal project for African American war workers migrating from theSouth. In those days of segregated housing, no neighborhood in the city was available for aninflux of African Americans. Today, Cherry Hill is a mostly residential area with apartmentcomplexes, row houses, and public housing projects. Some of the public housing has beendemolished leaving large tracts of land in the middle of the community that can beredeveloped in the future

Problems

-Residents fee there are too few busses-The busses do not run on schedule-Bus stops, shelters, sidewalks are poorly-Para transit buses are poorly equipped-Poor community depends on transit-People miss appointments or are left stranded-Employers see Cherry Hill residents as unreliable

Analysis & Findings

Impact of Changes on Regional Accessibility

-Decreased transit access overall-Major areas of E. Baltimore inaccessible with 1 hour of travel time.-BWI corridor has major improvements in travel time.-Overall access to jobs for transit dependent households in Cherry Hill has declined.



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- Commercial interests were given preference over community well-being*parking lots

- Shoppers complained they had to walk longer distances to connect with busses- Public are exposed to walk to connect to busses- Public are exposed to vehicle exhaust as they walk to connect with busses- Public has to navigate busy street traffic, and street activity whilst carrying shopping bags

and shepherding small childrenAnalysis & Findings

- Historically, a large number of the city’s minority and low-income residents have traveledto the market by public transportation.- The net effect of the added rail services seem to have improved transit access to the market.- Improvements in transit access to the market are improved in the communities to the northand west of the market, along Liberty and Reisterstown RoadsChanges in Regional Transit Access

- The northeastern corridor shows a decline in transit service between 1999 and 2000.

- Absence of rail services and reductions in bus services in this corridor that are mainly AfricanAmerican residents

Chronology of events and Transportation Statistics

-On June 12, 2001 Fayette Street (farside) stop was disconnected-On January 23, 2002 the Marion Street (farside) stop was established-On March 4, 2002, the Lexington Street (nearside) stop was disconnected *Marion St. is half-ablock south of the Lexington Market Eutaw St. Entrance-Pedestrians are in substantial numbers at all hours along both sides of Eutaw Street-Main crosswalks are located on the north at Saratoga Street and south of the Market Entrance

at

Lexington Street

-The crosswalk at Lexington Street is not signalized. This crosswalk supports major pedestriantraffic ade up from visitors and transit users-1991 City of Baltimore traffic reported revealed a combined vehicle volume of 601 vehicles perhour. *amounts to one vehicle a second–making crossing without a signal difficult-Saratoga Street carries a combined vehicle volume of 525 vehicles per hour in the A.M. and884 vehicles per hour in the P.M. *pedestrian volumes counted at the same times indicated 443persons attempting to cross Saratoga Street along North Eutaw going north in the A.M. peak

hour, only 68 crossing to go south, 141 crossing Eutaw going east, and 392 heading west

Assessment & Recommendations

-Some hardship may have been visited upon riders to Lexington Market as a result of themovement of bus stops. *further information is needed to access the actual impacts -What isevident is the community was not included in the decision process of moving the bus stops.



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-These “issues of process” are a concern from an environmental justice perspective than themovement of the stops themselves. -While bus services have been reduced over the pastdecade, overall transit access to Lexington Market appears to have actually improved.-Bus riders are being forced to walk away from the front of the market to reach relocated busstops. *streets are crowded and street intersections are busy

Highway to Non-where

The “Highway to Nowhere” is a massive section of roadway that begins on the western edgeof Baltimore and heads due west out of the city as part of US Route 40 through neighbor-hoods of Poppleton, Harlem Park, Lafayette Square and Rosemont. Once the starting point ofan ambitious plan to connect I-95, as it passes through Baltimore, with I-70, which terminatesat the Baltimore Beltway (I-695) in the west, the highway would have been badged as I-170.However, the plan ran out of momentum and support before it could proceed beyond therailway line, and thus it remains to this day–almost 30 years after it was opened to traffic– agrade-separated superhighway that is only 1.4 miles long.

Problems

•The Highway separated and isolated neighborhoods•Increase in drug-related crime.•The creation of the Highway lead to a decline in property values•A continued and systemic lack of political willingness at both the city and state levels toinvest in these neighborhoods.•Substantial white population decline between 1950 and 2000 with the highest total populationdecrease after 1980.•Substantial black population increase between 1950 and 1960.•Negative impact on all neighborhoods

•Residents feel isolated and neglected.Analysis and Findings

•US/40 Highway to Nowhere corridor is comprised of minority; low-to-moderate ateincomes•Residents within a quarter of a mile of Highway are predominantly (more than 80%)African American•Low Median Household Incomes that surround Highway are between, $15,000 to $45,000 peryear•48% of persons over 25 with less than a high school education•21% unemployment rate

•13% married couple households•43.3% of persons below the poverty level•15% homes are owner occupied•57% homes are renter occupied•28% housing are vacant congestion, air quality, usage•The Highway to Nowhere is congested at peak weekday times.



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Appendix 3: Look Up Guide to Support Application of an EJT Analysis

In general, transportation projects are evaluated using performance metrics for their effectiveness(how well a proposal meets its objectives), efficiency (the cost of a project relative to its benefits)and equality (how equal are the burdens and benefits spread across geographic, income, racialand ethnic lines). Table 5 provides guides for using performance measures to evaluate equity.

To better understand and frame community-based issues and to inform the decision-makingprocess we recommend that goal-oriented performance measures be used to focus howinvestments in the transportation system impact on low-income and minority communities andthat objective - oriented metric measures show how improvement plans enhance transportationsystem performance in terms of accessibility gains associated community strategies.

Table 23: Measuring EquityCommunity

Issues Community Driven Public Participation

Goal Objectives Performance Measures

Job Access Economic Vitality andCompetitiveness

Encourage EmploymentOpportunities UrbanCommunities

Work Opportunities within 15, 30 and 45 minutescar and transit door-to-door. Percent of transit-dependent riders who can access jobs with 45 min by fixed route of transit

Maintenance Safety and Security(Motorized and Non)

Stop the Use of OldEquipment in LowIncome Neighborhoods

Percent and characteristic of out of service busescoming into and area.Pedestrian/bicycle injuries & fatalitiesVehicle Crashes, Age of Fleet

IncreasedAccessibility

Increase Accessibilityand Mobility Options

Access to Jobs Proximity to transitLevel of ServiceAccessibility to health care facilitiesAccessibility to educational facilities

Reduce Air and

Noise Pollution

Protect Environment,

Conserve Energy andImprove Quality of Life

Clean Environment Air pollution Concentrations, Incidence rates of

Respiratory disorders, Number of Households expto noise. Asthma rates in communities adjacent tolarge transportation facilities,

Improved TransitRoute Structure

Enhance Connectivityand Integration AcrossModes for People andFreight

Access to Shopping andServices

Number of fatalitieslocations improved per million passenger miles

Need Assessment Manage and PreserveExistingTransportation System

Advocate for projectfunding to improve localconditions.

Condition of roads and streetsCondition of side walksRatio of uncontested travel time between origins adestinations

Funding Equity LocalRegionalStatewide

Fairness in TransitFunding

Per Capita Transportation expendituresPer Capita Operating Expenses Number of fatalitiesIdentity of user who benefitLocations improved per million passenger miles

environmental justice toolkit, volume 2

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