Abstract. [Map 1] [Map 2] [Map 3] [Figures 1, 2, and 3] Metropolitan Transportation Commission - Oakland, California. Defining Communities of Concern

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1 Environmental Justice for Long-Range Regional Transportation Plans: Using Census Data to Target Communities of Concern Presented By: Rachel Gossen Project Team: Therese Knudsen, Chuck Purvis, Rachel Gossen Introduction Key Points Abstract Defining Communities of Concern Access to Low-Income Jobs Urban and Suburban Densities [Map 1] [Map 2] [Map ] [Figures 1, 2, and ] [Table 1] Conclusion Metropolitan Transportation Commission - Oakland, California

2 Environmental Justice for Long-Range Regional Transportation Plans: Using Census Data to Target Communities of Concern Presenter: Rachel Gossen Project Team: Therese Knudsen, Chuck Purvis, Rachel Gossen ABSTRACT As the metropolitan planning organization for the nine-county San Francisco Bay Area, the Metropolitan Transportation Commission (MTC) is charged with measuring the distributional effects of the transportation investments outlined in the region s long-range transportation plan. One analysis MTC performed to satisfy this responsibility was to assess whether minority and low-income populations were disproportionately burdened by the region s planned transportation investments. MTC used three Census data sets to perform this analysis: Summary File (SF), the 5-percent Public Use Microdata Sample (PUMS), and the CTPP2000 package. SF and PUMS data were used to identify high concentrations of minority and low-income residents of the Bay Area. Additionally, these data were used to stratify residents by vehicle availability and means of transportation to work. Jobs held by low-income individuals were extracted from the CTPP2000 Part 2 data to explore the relationship between jobs and housing for low-income residents. Using Census data allowed MTC to identify target communities, or communities of concern. Once these communities were defined, indicator variables associated with access and travel time to jobs and essential destinations, user benefits, vehicle miles traveled, and emissions were extracted from MTC s travel forecasting system. A comparison was then made between communities of concern and the remainder of Bay Area communities (non-minority and nonlow-income areas). This allowed MTC to evaluate the different transportation investment alternatives and determine whether minority and low-income communities shared equitably in the benefits of the investment packages without bearing a disproportionate share of the burdens. Rachel Gossen Page 1 of

3 < 200% of Poverty Level 21% >= 200% of Poverty Level 79% Figure Bay Area Population by Poverty Level Source: Census 2000 Summary File, Table P88 American Black/African Indian/Alaska Native American 0.4% 7.2% Multi-racial/Other.8% Asian/Pacific Islander 19.% White 49.9% Hispanic/Latino 19.4% Figure Bay Area Population by Race/Ethnicity Source: Census 2000 Summary File, Table P7 Zero 1% Zero 27% Zero 10% One or More 87% One or More 7% One or More 90% Minority Low-Income All Bay Area Households Households Households Figure. Vehicle Availability for Low-Income, Minority, and Total Bay Area Households Source: Minority Households - Census 2000 Summary File, Tables H44 and HCTI Low-Income Households - Census 2000 PUMS 5% Sample Data

4 Table 1. Census 2000 Minority and Low Income Shares by Community of Concern Community of Concern Minority Low Income 1 SF Downtown / Chinatown / North Beach / Treasure Isl. 68% 44% 2 SF Tenderloin / Civic Center 60% 54% SF South of Market 65% 5% 4 SF Western Addition / Haight-Fillmore 54% 8% 5 SF Inner Mission / Potrero Hill 72% 41% 6 SF Bayview / Hunters Point / Bayshore 91% 4% 7 SF Outer Mission / Crocker-Amazon / OceanView 81% 26% 8 SM Daly City 84% 17% 9 SM South San Francisco / San Bruno 79% 29% 10 SM San Mateo 88% 4% 11 SM East Palo Alto / North Fair Oaks 86% 41% SC Stanford / Mountain View 58% 41% 1 SC Alviso / Shoreline / Sunnyvale 75% 20% 14 SC Santa Clara 57% 29% 15 SC Central San Jose 8% 29% 16 SC South San Jose / Morgan Hill 54% 0% 17 SC Gilroy 78% 42% SC Milpitas 78% 14% 19 Ala Fremont / Newark 75% 15% 20 Ala Hayward / Union City 79% 25% 21 Ala Ashland / Cherryland / San Leandro 70% 0% 22 Ala Fruitvale / East Oakland 92% 49% 2 Ala West / North Oakland 8% 52% Ala Alameda 67% 6% 25 Ala Berkeley / Albany 57% 46% 26 CC Richmond 88% 47% 27 CC San Pablo / North Richmond 85% 42% 28 CC Hercules / Rodeo / Crockett 69% 15% 29 CC Martinez 41% 8% 0 CC Concord 68% 45% 1 CC Baypoint / Pittsburg / Antioch 68% 8% 2 CC Brentwood 56% 1% Sol Vallejo 75% 2% 4 Sol Fairfield / Suisun City 58% 42% 5 Sol Vacaville 44% 1% 6 Sol Dixon 52% % 7 Nap Napa / American Canyon 44% 6% 8 Nap Calistoga 44% % 9 Son Central Sonoma Valley 45% 6% 40 Son Santa Rosa 51% 40% 41 Son Healdsburg 48% 41% 42 Son Guerneville / Monte Rio 17% 6% 4 Mar San Rafael Canal District 84% 59% 44 Mar Marin City 68% 8% Communities of Concern 77% 5% Remainder of Bay Area Communities 7% 14% Bay Area Total 50% 21%

5 Communities of Concern Lake Sonoma Napa Yolo Marin Solano 1 Sacramento San Francisco Contra Costa Alameda San Joaquin 19 San Mateo Santa Clara St Co 16 Santa Cruz µ0 6 Miles 0 6 Kilometers G:\_section\Planning\EJ\Arcmap_proj\Rachel\EJ_map_minor70_pov0_unicolor_pagesize_v9.mxd Cartography: MTC GIS (Garlynn Woodsong) April Street Base Map: 2004 Geographic Data Technology, Inc. All rights reserved 17 San Benito C

6 Low-Income Jobs - Year 2000 o Lake Sonoma Napa Yolo Solano Marin Sa San Francisco en am cr to Co t un y Alameda San Joa q u in C ounty Contra Costa San Mateo Santa Clara Low-Income Jobs and Communities of Concern - Year 2000 Communities of Concern, Clipped to Developed Areas < 100 Low-Income Jobs Low-Income Jobs Stanis Count > 500 Low-Income Jobs Source: CTPP 2000 Part 2 Data, Table 2-46 µ 0 0 Santa Cruz 6 6 Miles Kilometers C:\Census2000\Census_Applications_Conference\Low_Income_Jobs.mxd Cartography: MTC GIS (Rachel Gossen) April Street Base Map: 2004 Geographic Data Technology, Inc. All rights reserved San Benito Cou

7 Urban and Suburban Densities- Year 2000 o Lake Sonoma Napa Yolo Solano Marin Sa San Francisco en am cr to Co t un y Alameda San Joa q u in C ounty Contra Costa San Mateo Santa Clara Urban and Suburban Densities and Communities of Concern - Year 2000 Communities of Concern, Clipped to Developed Areas Stanis Count Urban ( > 10,000 persons/sq.mi.) Suburban ( <= 10,000 persons/sq.mi.) µ 0 0 Santa Cruz 6 6 Miles Kilometers C:\Census2000\Census_Applications_Conference\Urban_Suburban_Densities.mxd Cartography: MTC GIS (Rachel Gossen) April Street Base Map: 2004 Geographic Data Technology, Inc. All rights reserved San Benito Cou

8 INTRODUCTION The purpose of the equity analysis was to measure the benefits and burdens associated with the transportation investment packages outlined in the 2005 Regional Transportation Plan for the San Francisco Bay Area and to make sure that minority and low-income communities, or communities of concern, shared equitably in the benefits without bearing a disproportionate share of the burdens. The equity analysis measured Access and travel times to jobs, Access and travel times to essential destinations, User benefits, and Vehicle miles traveled and emissions. For each measurement, a comparison was made between the results for minority and low-income communities and the remainder of Bay Area communities (non-minority and non-low-income areas). Census 2000 data was used to identify high concentrations of minority and low-income communities in the Bay Area. Minority populations included Hispanic/Latino, Asian/Pacific Islander, Black/African American, American Indian, or Alaskan Native individuals and persons who reported two or more races. To account for the high cost of living in the Bay Area, an individual was considered low-income if his or her household earned less than 200% of the federal poverty level. Travel analysis zones, or TAZes, were flagged if 70% or more of the population was a minority or if 0% or more of the population was low-income. The flagged TAZes were then consolidated to form the 44 communities of concern used in the equity analysis. Once communities of concern were defined, MTC s travel forecasting system was used to extract the indicator variables outlined above. One of the key measures was access and travel times to jobs. Employment data from the Association of Bay Area Governments was used. However, an important consideration was to determine whether these were jobs for which low-income residents were qualified. To address this, the CTPP 2000 Part 2 data was used to identify Bay Area jobs held by low-income workers. Though this data was available only for workers earning less than 150% of the federal poverty level as opposed to the 200% threshold used to define low-income residents valuable information on the location of low-income jobs in the Bay Area was gleaned from the CTPP2000 data and used to assess the proximity of low-income jobs to low-income communities. One key component in interpreting the results for access and travel time to jobs was the use of population density categories derived from Census 2000 population and land area data. The Bay Area was divided into two population density categories: urban and suburban. These density categories were important when interpreting the transit and auto access results since the majority of communities of concern are located in, or within close proximity to, urban areas while many non-minority and non-low-income communities are scattered throughout more suburban parts of the region. Rachel Gossen Page 2 of

9 KEY POINTS Summary File data used to target areas with high concentrations of minority and lowincome populations, or communities of concern. CTPP2000 Part 2 data used to locate jobs held by low-income workers. Travel measures compared between communities of concern and the remainder of Bay Area communities to make sure minority and low-income communities shared equitably in the planned transportation investment packages without bearing a disproportionate share of the burdens. CONCLUSION Census data was essential in conducting the equity analysis for the Bay Area s 2005 Regional Transportation Plan. Using race/ethnicity and income data, communities of concern were identified and key travel and accessibility measures for communities of concern were compared to results for the remainder of Bay Area communities. In this way, MTC was able to measure the distributional effects of the planned transportation investment packages included in the Transportation 200 Plan and determine whether communities of concern shared equitably in the benefits of the planned transportation investments without bearing a disproportionate share of the burdens. Rachel Gossen Page of