Data and Analysis Methods forData and Analysis Methods forMetropolitan-LevelMetropolitan-Level

Environmental Justice Environmental Justice AssessmentAssessment

Chuck Purvis, MTCChuck Purvis, MTC

January 2001January 2001

Policy Context

• Civil Rights Act (1964) – Title VI• Executive Order 12898 (1994)• USDOT Orders, Actions (1997-99)• Proposed Metro Planning Regulations (May 2000)• Other Federal Discrimination Laws

– ADA (1990)– Disabilities Act (1978)– Age Discrimination Act (1975)– Rehabilitation Act (1973)

MPO Challenges

• Measuring both process and outcomes.

• EJ has traditionally been project-oriented, not program-oriented.

• Major uncertainty in long-range forecasting of population characteristics at the “very small area” (neighborhood, TAZ) level

Components of Discrimination Analysis (Proposed Regs)

• Geographic/Demographic Profiles of Region – low-income, minority, elderly, disabled

• Description of Transport Service Available and Planned

• Description of Disproportionately High and Adverse Environmental Impacts, or a Reduction in Benefits

Federal Data: Census Bureau

• Decennial Census – Census 2000 data available in 2001/2003

• Small Area Income & Poverty Estimates (SAIPE)

• Population Projections Program

State Data: California

• State Department of Finance (DOF) – State Data Center (SDC) for California– Annual Estimates Program– Population Projections Program

• Race/Ethnicity, by County, to year 2040

Race/Ethnic Forecasting Issues

• Immigration

• Residential Mobility (between states, metro areas, neighborhoods)

• Measurement in Decennial Census

• Patterns of Ethnic Intermarriage

• Self-Identification uncertainties

• See Hirschman, Univ. of Wash.

Local Data (SF Bay Area)

• Assoc. of Bay Area Governments (ABAG)– Subarea Projections Model (SAM) used to

predict households by Income Level– Model to predict very small area population by

age group (0-4, 5-19, 20-44, 45-64, 65+)

• Metropolitan Transport. Comm. (MTC)– Household Auto Ownership Forecasts

Example of Geo-DemographicAnalysis

The Ethnic Quilt:The Ethnic Quilt:Population Diversity Population Diversity

in Southern Californiain Southern California

by

CSUN GeographersJim Allen & Eugene Turner

SF Bay Area Race/Ethnicity Projections

GroupGroup 19901990 20002000 20102010 20202020

WhiteWhite 61%61% 53%53% 46%46% 41%41%

LatinoLatino 1515 1919 2121 2424

AsianAsian 1515 2020 2424 2626

African Amer.African Amer. 99 88 88 88

Amer. Ind.Amer. Ind. < 1< 1 < 1< 1 < 1< 1 < 1< 1

DiversityDiversity .691.691 .748.748 .781.781 .802.802

Disadvantaged PopulationSF Bay Area, 1990 Census (PUMS)

CategoryCategory Total Pop Total Pop (000s)(000s)

Percent of Percent of TotalTotal

MinorityMinority 2,3512,351 39.0%39.0%

Low IncomeLow Income 503503 8.48.4

ElderlyElderly 664664 11.011.0

DisabledDisabled 522522 8.78.7

TOTALTOTAL 6,0206,020 100.0100.0

DisadvantagedDisadvantaged 3,1793,179 52.852.8

ElderlyOnly

Disabled

Not Disadvantaged Minority Only Only

Poverty Only

Minority+Poverty

Elderly + Disabled

Other Two-Way

3-4-Way Category

Dimensions of Disadvantaged PopulationSF Bay Area, 1990 Census (PUMS)

Minority Share of Total Population> 70 Percent Minority50 - 70 Percent Minority25 - 50 Percent Minority< 25 Percent MinorityNo Population

Minority Share of Total Population, 1990 Census

Leading Racial/Ethnic Group (Plurality)Not PopulatedWhite, Non-HispanicAfrican AmericanHispanic, Any RaceAsian / Pacific Islander

Leading Racial/Ethnic Group1990 Census

Ethnic Diversity Index< 0.2 (Least Diverse)0.2 - 0.40.4 - 0.50.5 - 0.650.65 - 1.0 (Most Diverse)

Racial/Ethnic Diversity1990 Census

MTC Equity Analysis

• Evaluate changes in auto and transit accessibility to “disadvantaged” and “not disadvantaged” neighborhoods

• 38 Neighborhoods defined by non-profit organization, based on 1990 Census data.– Low income neighborhoods where median

income is 80% or less of county median.

MTC Accessibility Analysis

• “Isochron” analysis (line-of-equal time)– Total jobs within 30, 45, 60, 75 minutes of

neighborhood of residence by auto, transit

• Weighted analysis (gravity model)– Less tractable, but more sensitive to small

changes in accessibility

Average Jobs (000s) within XX Minutes by Mode

IsochronIsochron TransitTransit

Drive Drive AloneAlone CarpoolCarpool

30 minutes30 minutes 7272 702702 796796

45 minutes45 minutes 257257 13781378 16891689

60 minutes60 minutes 535535 22412241 26572657

75 minutes75 minutes 885885 30423042 34493449

Average Jobs Within 60 Minutes Transit Time< 200,000200,000 - 500,000500,000 - 750,000750,000 - 1,000,000< 1,000,000

Average Jobs Within 60 Minutes

Transit Time

Average Jobs Within 60 Minutes Drive Time< 1,000,0001,000,000 - 2,000,0002,000,000 - 2,500,0002,500,000 - 3,000,000> 3,000,000

Average Jobs Within 60 Minutes

Drive Time

Change in Transit Accessibility, RTP Project v No-ProjectLower Accessibility in ProjectNo DifferenceMinor Accessibility Improvements in ProjectModerate Accessibility Improvements in ProjectHigh Accessibility Improvements in Project

Change in Transit AccessibilityRTP Project vs No-Project

Future DataFuture Data

Census Transportation Planning Package Census Transportation Planning Package • Includes cross-tabulations of Interest for EJ Analysis• Available 2002/03

• Annual Data for Large Areas (65K+ Pop.) After 2003• Rolling Average, 5 year data for Very Small Areas, After 2007

Other Ongoing Research

• NCHRP Project 8-36, Task 11 – Technical Methods to Support Analysis of Environmental Justice Issues

• NCHRP Project 8-41 – Development of Technical Methods for E.J. Analyses

• USDOT training & technical assistance