This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Plan Bay Area 2050 utilized this single data layer to inform the Plan Bay Area 2050 Equity PriorityCommunities (EPC).

This data set was developed using American Community Survey (ACS) 2014-2018 data for eight variables considered.

This data set represents all tracts within the San Francisco Bay Region and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern.

Plan Bay Area 2050 Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:

People of Color (70% threshold) Low-Income (less than 200% of Federal poverty level, 28% threshold) Level of English Proficiency (12% threshold) Seniors 75 Years and Over (8% threshold) Zero-Vehicle Households (15% threshold) Single-Parent Households (18% threshold) People with a Disability (12% threshold) Rent-Burdened Households (14% threshold)

If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds thethreshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.

Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Key findings in the Struggling to Get By report show that one in three California households (31%) do not have sufficient income to meet their basic costs of living. This is nearly three times the number officially considered poor according to the Federal Poverty Level.Families with inadequate incomes are found throughout California, but are most concentrated in the northern coastal region, the Central Valley, and in the southern metropolitan areas.The costs for the same family composition in different geographic regions of California also vary widely. In expensive regions such as the San Francisco Bay Region and the Southern California coastal region, the Real Cost Budget, a monthly budget calculation of what is needed to meet basic needs, can range from 32% to 48% more (depending on family type) than in less expensive counties such as Kern, Tulare, and Kings counties. Nevertheless, incomes in the higher cost regions are also higher, relatively and absolutely, so that the proportions below the Real Cost Measure are generally lower in high-cost than low-cost regions.

Background: Sugar-sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018. Methods & findings: A longitudinal panel of adults (n = 1,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline (11/17–1/18), one (11/18–1/19), and two years (11/19-1/20) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. Weighted generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by 34.1% (-3.68 oz, p = 0.004) from baseline to 2 years post-tax, versus San José which declined 16.5% by 2 years post-tax (-1.29 oz, p = 0.157), a non-significant difference-in-differences (-17.6%, adjusted AMR = 0.79, p = 0.224). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, p = 0.031). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, p = 0.010) among those living below 200% of the federal poverty level 2-years post-tax. Conclusions: Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents.