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Data and Analysis Methods for Metropolitan-Level Environmental Justice Assessment

Data and Analysis Methods for Metropolitan-Level Environmental Justice Assessment. Chuck Purvis, MTC January 2001. Policy Context. Civil Rights Act (1964) – Title VI Executive Order 12898 (1994) USDOT Orders, Actions (1997-99) Proposed Metro Planning Regulations (May 2000)

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Data and Analysis Methods for Metropolitan-Level Environmental Justice Assessment

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  1. Data and Analysis Methods forMetropolitan-LevelEnvironmental Justice Assessment Chuck Purvis, MTC January 2001

  2. 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)

  3. 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

  4. 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

  5. Federal Data: Census Bureau • Decennial Census – Census 2000 data available in 2001/2003 • Small Area Income & Poverty Estimates (SAIPE) • Population Projections Program

  6. 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

  7. 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.

  8. 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

  9. Example of Geo-DemographicAnalysis The Ethnic Quilt:Population Diversity in Southern Californiaby CSUN GeographersJim Allen & Eugene Turner

  10. SF Bay Area Race/Ethnicity Projections

  11. Disadvantaged PopulationSF Bay Area, 1990 Census (PUMS)

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

  13. Minority Share of Total Population, 1990 Census

  14. Leading Racial/Ethnic Group1990 Census

  15. Racial/Ethnic Diversity1990 Census

  16. 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.

  17. 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

  18. Average Jobs (000s) within XX Minutes by Mode

  19. Average Jobs Within 60 MinutesTransit Time

  20. Average Jobs Within 60 MinutesDrive Time

  21. Change in Transit AccessibilityRTP Project vs No-Project

  22. Future Data • Annual Data for Large Areas (65K+ Pop.) After 2003 • Rolling Average, 5 year data for Very Small Areas, After 2007 Census Transportation Planning Package • Includes cross-tabulations of Interest for EJ Analysis • Available 2002/03

  23. 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

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