VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Graph and download economic data for Resident Population in San Francisco-Oakland-Hayward, CA (MSA) (SFCPOP) from 2000 to 2022 about San Francisco, residents, CA, population, and USA.

In 2023, the population of the San Francisco-Oakland-Berkeley metropolitan area in the United States was about 4.57 million people. This is a slight decrease from the previous year, when the population was about 4.58 million people.

This raster dataset depicts the population denisty of the nine county San Francisco Bay Area Region, California produced with a Dasymetric Mapping Technique, which is used to depict quantitative areal data using boundaries that divide an area into zones of relative homogeneity with the purpose of better portraying the population distribution. The source data was then adjusted in order to get convert the units to persons per acre. This dataset is an accurate representation of population distribution within census boundaries and can be used in a number of ways, including as the Conservation Suitability layer for the Marxan inputs and the watershed integrity analysis.

Table of population and demographic forecast numbers from Plan Bay Area 2040 for the San Francisco Bay Region. Population and demographic numbers are included for 2005, 2010, 2015, 2020, 2030, 2035, and 2040. There are no forecast numbers for 2025.The Plan Bay Area forecast numbers were generated by Transportation Analysis Zone (TAZ). The Population and Demographics forecast table will need to be joined to TAZ features in order to spatially visualize the data. The TAZ features are available for download here.2005-2040 data in this table:Total PopulationHousehold PopulationGroup Quarters Population0 - 4 Age Group5 - 19 Age Group20 - 44 Age Group44 - 64 Age Group65+ Age GroupShare of Total Population that is 62 and OverHigh School EnrollmentCollege Enrollment (full-time)College Enrollment (part-time)Other Plan Bay Area 2040 forecast tables:Employment (total employment, TAZ resident employment, retail employment, financial and professional services employment, health, educational, and recreational employment, manufacturing, wholesale, and transportation employment, agricultural and natural resources employment, and other employment)Households (number of households and household income quartile)Land Use and Transportation (area type, commercial or industrial acres, residential acres, number of single-family and multi-family dwelling units, time to get from automobile storage location to origin/destination, and hourly parking rates)

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

According to population estimates recently released by the California Department of Housing and Community Development, the San Francisco Bay Region is the fastest growing region in the state.San Jose, followed by San Francisco and Oakland have the highest populations in the region, and three bay area cities made the top 10 ranking. In addition, our region also has 4 counties; Santa Clara (1), Alameda (2), San Francisco (5) and San Mateo (9), in the top 10 fastest growing counties. Dublin (3), Campbell (7) and Rio Vista (8) each had a significant percentage change in their population growth. The state data reports population and housing trends for 482 California cities. Last year, all but 43 cities saw an increase in residents, with the declines typically experienced in the state's rural areas.

Table of household forecast numbers from Plan Bay Area 2040 for the San Francisco Bay Region. Household numbers are included for 2005, 2010, 2015, 2020, 2030, 2035, and 2040. There are no forecast numbers for 2025.The Plan Bay Area forecast numbers were generated by Transportation Analysis Zone (TAZ). The Household forecast table will need to be joined to TAZ features in order to spatially visualize the data. The TAZ features are available for download here.2005-2040 data in this table:Total HouseholdsNumber of Households in Lowest Income QuartileNumber of Households in Second Lowest Income QuartileNumber of Households in Second highest Income QuartileNumber of Households in Highest Income QuartileOther Plan Bay Area 2040 forecast tables:Employment (total employment, TAZ resident employment, retail employment, financial and professional services employment, health, educational, and recreational employment, manufacturing, wholesale, and transportation employment, agricultural and natural resources employment, and other employment)Land Use and Transportation (area type, commercial or industrial acres, residential acres, number of single-family and multi-family dwelling units, time to get from automobile storage location to origin/destination, and hourly parking rates)Population and Demographics (total population, household and group quarter populations, population by age group, share of population that is 62+, high school enrollment, and college enrollment)

This polygon shapefile contains superdistrict boundaries and population statistics created by the Metropolitan Transportation Comission (MTC) using Census 2000 data. Superdistricts are used in the analysis of sub-county level demographic and travel forecasts. MTC maintains a set of regional travel analysis zones for use in MTC planning studies. These travel analysis zones (TAZs) are typically small area neighborhoods or communities that serve as the smallest geographic basis for travel demand model forecasting systems. From time to time, MTC updates the regional travel analysis zone system to reflect changes in decennial census geography, changes in computing capability, and forecasting needs for particular corridor studies. In addition to regional TAZs and counties, MTC supports an intermediate geographic scale, "superdistricts," for analysis and reporting purposes. There are 34 superdistricts in the nine-county Bay Area. This layer is part of the Bay Area Metropolitan Transportation Commission (MTC) GIS Maps and Data collection.

Incorporated Places (cities and towns) are those reported to the Census Bureau as legally in existence as of May 28, 2021, under the laws of their respective states. Features were extracted from California 2020 TIGER/Line shapefile by the Metropolitan Transportation Commission. An incorporated place provides governmental functions for a concentration of people, as opposed to a minor civil division, which generally provides services or administers an area without regard, necessarily, to population. Places may extend across county and county subdivision boundaries, but never across state boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions.

This polygon shapefile contains superdistrict boundaries and population statistics created by the Metropolitan Transportation Comission (MTC) using Census 1990 data. Superdistricts are used in the analysis of sub-county level demographic and travel forecasts. MTC maintains a set of regional travel analysis zones for use in MTC planning studies. These travel analysis zones (TAZs) are typically small area neighborhoods or communities that serve as the smallest geographic basis for travel demand model forecasting systems. From time to time, MTC updates the regional travel analysis zone system to reflect changes in decennial census geography, changes in computing capability, and forecasting needs for particular corridor studies. In addition to regional TAZs and counties, MTC supports an intermediate geographic scale, "superdistricts," for analysis and reporting purposes. There are 34 superdistricts in the nine-county Bay Area. This layer is part of the Bay Area Metropolitan Transportation Commission (MTC) GIS Maps and Data collection.

This feature set contains household and population projections from Projections 2040 for the San Francisco Bay Region. This forecast represents household and population projections resulting from Plan Bay Area 2040. Numbers are provided by 2010 Census Tract. Household and population numbers are included for 2010 (two versions), 2015, 2020, 2025, 2030, 2035, and 2040. For 2010, two data points are provided:A tabulation (base year A) from the 2010 model simulation (base year A); and(Preferred) A tabulation (base year B) from the 2010 pre-run microdata, designed to approximate (but may still differ from) Census 2010 counts.Projection data is included for total households, total population, household population, and group quarters population.This feature set was assembled using unclipped Census Tract features. For those who prefer Projections 2040 data using jurisdiction features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region 2010 Census Tracts (clipped). Clipping the Census Tract features does result in the removal of some water tracts, which are usually empty, so there is a difference in the number of features between the two services.Other Projections 2040 feature sets:Households and population per countyHouseholds and population per jurisdiction (incorporated place and unincorporated county)Jobs and employment per countyJobs and employment per jurisdiction (incorporated place and unincorporated county)Jobs per Census TractFemale population, by age range, per countyFemale population, by age range, per jurisdiction (incorporated place and unincorporated county)Male population, by age range, per countyMale population, by age range, per jurisdiction (incorporated place and unincorporated county)Total population, by age range, per countyTotal population, by age range, per jurisdiction (incorporated place and unincorporated county)

This feature set contains total population (male and female), by age, projections from Projections 2040 for the San Francisco Bay Region. This forecast represents total population projections resulting from Plan Bay Area 2040. Numbers are provided by jurisdiction (incorporated places (cities and towns) and unincorporated county lands). Total population numbers are included for 2010 (two versions), 2015, 2020, 2025, 2030, 2035, and 2040. For 2010, two data points are provided:A tabulation (base year A) from the 2010 model simulation (base year A); and(Preferred) A tabulation (base year B) from the 2010 pre-run microdata, designed to approximate (but may still differ from) Census 2010 counts.Projection data is included for total population for the following age ranges: 0-4 (under 5), 5-19, 20-44, 45-64, and 65+ (65 and over).This feature set was assembled using unclipped jurisdiction features. For those who prefer Projections 2040 data using jurisdiction features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region Jurisdictions (Incorporated Places and Unincorporated County Lands) (clipped).Other Projections 2040 feature sets:Households and population per countyHouseholds and population per jurisdiction (incorporated place and unincorporated county)Households and population per Census TractJobs and employment per countyJobs and employment per jurisdiction (incorporated place and unincorporated county)Jobs per Census TractFemale population, by age range, per countyFemale population, by age range, per jurisdiction (incorporated place and unincorporated county)Male population, by age range, per countyMale population, by age range, per jurisdiction (incorporated place and unincorporated county)Total population, by age range, per county

Comprehensive demographic dataset for Mission Bay, San Francisco, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

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.

Table of employment forecast numbers from Plan Bay Area 2040 for the San Francisco Bay Region. Employment numbers are included for 2005, 2010, 2015, 2020, 2030, 2035, and 2040. There are no forecast numbers for 2025.The Plan Bay Area forecast numbers were generated by Transportation Analysis Zone (TAZ). The Employment forecast table will need to be joined to TAZ features in order to spatially visualize the data. The TAZ features are available for download here.2005-2040 data in this table:Total EmploymentNumber of Employed ResidentsNumber of Persons Employed in RetailNumber of Persons Employed in Financial and Professional ServicesNumber of Persons Employed in Health, Education, and Recreational ServicesNumber of Persons Employed in Manufacturing, Wholesale Trade, and TransportationNumber of Persons Employed in Other CategoriesOther Plan Bay Area 2040 forecast tables:Households (number of households and household income quartile)Land Use and Transportation (area type, commercial or industrial acres, residential acres, number of single-family and multi-family dwelling units, time to get from automobile storage location to origin/destination, and hourly parking rates)Population and Demographics (total population, household and group quarter populations, population by age group, share of population that is 62+, high school enrollment, and college enrollment)

U.S. Census Bureau QuickFacts statistics for Half Moon Bay city, California. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

Daily Miles Traveled (T14)

FULL MEASURE NAME

Total vehicle miles traveled

LAST UPDATED

August 2022

DESCRIPTION

Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE

California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php

2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm

2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/

2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html

2020

CONTACT INFORMATION

vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This data release is comprised of geospatial and tabular data developed for the HayWired communities at risk analysis. The HayWired earthquake scenario is a magnitude 7.0 earthquake hypothesized to occur on the Hayward Fault on April 18, 2018, with an epicenter in the city of Oakland, CA. The following 17 counties are included in this analysis unless otherwise specified: Alameda, Contra Costa, Marin, Merced, Monterey, Napa, Sacramento, San Benito, San Francisco, San Joaquin, San Mateo, Santa Clara, Santa Cruz, Solano, Sonoma, Stanislaus, and Yolo. The vector data are a geospatial representation of building damage based on square footage damage estimates by Hazus occupancy class for developed areas covering all census tracts in 17 counties in and around the San Francisco Bay region in California, for (1) earthquake hazards (ground shaking, landslide, and liquefaction) and (2) all hazards (ground shaking, landslide, liquefaction, and fire) resulting from the HayWired earthquake scenario mainshock. The tabular data cover: (1) damage estimates, by Hazus occupancy class, of square footage, building counts, and households affected by the HayWired earthquake scenario mainshock for all census tracts in 17 counties in and around the San Francisco Bay region in California; (2) potential total population residing in block groups in nine counties in the San Francisco Bay region in California (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma); (3) a subset of select tables for 17 counties in and around the San Francisco Bay region in California from the U.S. Census Bureau American Community Survey 5-year (2012-2016) estimates at the block group level selected to represent potentially vulnerable populations that may, in the event of a major disaster, leave an area rather than stay; and (4) building and contents damage estimates (in thousands of dollars, 2005 vintage), by Hazus occupancy class, for the HayWired earthquake scenario mainshock for 17 counties in and around the San Francisco Bay region in California. The vector .SHP datasets were developed and intended for use in GIS applications such as ESRI's ArcGIS software suite. The tab-delimited .TXT datasets were developed and intended for use in standalone spreadsheet or database applications (such as Microsoft Excel or Access). Please note that some of these data are not optimized for use in GIS applications (such as ESRI's ArcGIS software suite) as-is--census tracts or counties are repeated (the data are not "one-to-one"), so not all information belonging to a tract or county would necessarily be associated with a single record. Separate preparation is needed in a standalone spreadsheet or database application like Microsoft Excel or Microsoft Access before using these data in a GIS. These data support the following publications: Johnson, L.A., Jones, J.L., Wein, A.M., and Peters, J., 2020, Communities at risk analysis of the HayWired scenario, chaps. U1-U5 of Detweiler, S.T., and Wein, A.M., eds., The HayWired earthquake scenario--Societal consequences: U.S. Geological Survey Scientific Investigations Report 2017-5013, https://doi.org/10.3133/sir20175013.