Context

The San Francisco Bay Area (nine-county) is one of the largest urban areas in the US by population and GDP. It is home to over 7.5 million people and has a GDP of $995 billion (third highest by GDP output and first highest by GDP per capita). Home to Silicon Valley (a global center for high technology and innovation) and San Francisco (the second largest financial center in the US after New York), the Bay Area contains some of the most profitable industries and sophisticated workforces in the world. This dataset describes where these workers live and commute to work in 2018.

Content

This data file includes all needed information as a means to find out more about the different commute patterns, geographical locations, and necessary metrics to make predictions and draw conclusions.

Inspiration

• What can we learn about the different residence and workplace locations? What is the average distance between these locations?
• Which counties contain a high/low concentration of residence and workplace locations? Why?
• What counties do most of the commuters usually commute to work? What counties do most of the commuters call home?
• Are most of these commute patterns county-by-county or within a single county?
• Are there any noticeable outliers (e.g., long commute patterns) in this dataset? What counties contain a high concentration of these outliers?

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

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to 3/2/2020 when testing began.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling basis.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case and death data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

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.

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.

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, and clipped using, California 2020 TIGER/Line shapefiles 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.

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

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

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 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 male population projections, by age, from Projections 2040 for the San Francisco Bay Region. This forecast represents male population projections resulting from Plan Bay Area 2040. Numbers are provided by county. Male 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 male population for the following age ranges: 0-4 (under 5), 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, and 85+ (85 and over).This feature set was assembled using unclipped county features. For those who prefer Projections 2040 data using county features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region Counties (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 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 layer contains census tracts for the San Francisco Bay Region for Census 2000. The features were extracted from a statewide data set downloaded from the United States Census Bureau by Metropolitan Transportation Commission staff.The purpose of this feature layer is for the production of feature sets for public access and download to avoid licensing issues related to the agency's base data.Source data downloaded from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html_The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the United States Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the Census 2000 Participant Statistical Areas Program (PSAP). The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,500 and 8,000 people, with an optimum size of 4,000 people.When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, etc. may require boundary revisions before a census. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries are always census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a particular day with the prior six days. Percent of total new cases shows the percent of all cases on each day that were among a particular vaccination status.

Here is more information on how each case rate is calculated:

1. The case rate for all cases is equal to the number of new cases among all residents divided by the estimated total resident population.

2. Unvaccinated case rates are equal to the number of new cases among unvaccinated residents divided by the estimated number of unvaccinated residents. The estimated number of unvaccinated residents is calculated by subtracting the number of residents that have received at least one dose of a vaccine from the total estimated resident population.

3. Completed primary series case rates are equal to the number of new cases among completed primary series residents divided by the estimated number of completed primary series residents. The estimated number of completed primary series residents is calculated by taking the number of residents who have completed their primary series over time and adding a 14-day delay to the “date_administered” column, to align with the definition of “Completed primary series cases” above.

E. CHANGE LOG

• 6/28/2023 - data on cases by vaccination status are no longer being updated. This data is currently through 6/20/2023 (as of 6/28/2023) and will not include any new data after this date.
  
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “sf_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 7/15/2022 - references to “fully vaccinated” replaced with “completed primary series” in column “vaccination_status".
• 7/15/2022 - rows with “partially vaccinated” in column “vaccination_status” removed from dataset.

This dataset contains data collected at 5 monitoring stations in Golden Gate National Recreation Area and Point Reyes National Seashore as part the San Francisco Bay Area Network Rocky Intertidal Monitoring Program. Monitoring involves annual summer-fall sampling of permanent photoquadrats, ochre star (Pisaster ochraceus) transects, surfgrass (Phyllospadix spp.) transects, mussel beds, motile invertebrates, and owl limpet (Lottia gigantea) plots. This dataset also includes legacy photoplot and seastar data collected at Golden Gate NRA from 1989-2005. Permanent photoplots (50 x 75 cm) are established in target assemblages (i.e. barnacles, mussels, turfweed, etc.) and percent cover of the major taxa within these photoplots is assessed using photographic analysis or field scoring. Owl limpet plots were established and sampled once in 2016 at Pt. Bonita, while motile invertebrate surveys were discontinued in 2014 at all sites. Table 'checklist_sampled_PORE_GOGA.csv' defines the monitoring components by site, survey date, survey type, target assemblage and quatraplot by survey event that were sampled. This table should be referenced to understand sampling effort and completeness of sampling by events. The purpose of this dataset is to provide monitoring information on the temporal dynamics of target invertebrate, algal species and surfgrass across accessible, representative, and historically sampled rocky intertidal sites at Point Reyes National Seashore and Golden Gate National Recreation Area to help assess level of impacts and changes outside normal limits of variation due to oil spills, non-point source pollution, or other anthropogenic stressors that may come from within and outside the parks; to help determine status, trends, and effect sizes through time for morphology, color ratios, and other key parameters describing population status (e.g., size, structure) of the selected intertidal organisms; and to document invasions of non-native species, changes in species ranges, disease spread, and rates and scales of processes affecting the structure and function of rocky intertidal populations and communities to better understand normal limits of variation. All tables were exported from the Multi-Agency Rocky Intertidal Network database managed by the University of California Santa Cruz.

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)

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

The data in this data release are comprised of one geospatial vector dataset and three tabular datasets related to the HayWired earthquake scenario, a magnitude 7.0 earthquake hypothesized to occur on April 18, 2018, with an epicenter in the city of Oakland, CA. The geospatial vector data are a representation of identified economic subareas for use in selected analyses related to selected counties in and around the San Francisco Bay region in California. Census tracts in seven economic subareas were identified, as was whether a tract potentially has a high concentration of building stock extensively or completely damaged by (1) earthquake hazards (ground shaking, landslide, liquefaction) and (2) all hazards (ground shaking, landslide, liquefaction, and fire following earthquake) resulting from the HayWired earthquake scenario mainshock. The tabular data are (1) counts of employed residents (total and by industry grouping) in identified economic subareas within four counties (Alameda, Contra Costa, Marin, Solano) or (2) employed resident/worker commute flow counts (total and by general industry sector) for employees who work or reside in areas of concentrated damage in economic subareas within four counties (Alameda, Contra Costa, Marin, Solano) in the San Francisco Bay region in California. Basic employed resident counts are presented at the census tract level with the associated economic subarea and area of concentrated damage designation included as ancillary information. Employed resident/worker commute flows are presented as aggregations based on: seven economic subareas in Alameda, Contra Costa, Marin, and Solano Counties (distinguished by whether or not an area is considered an area of concentrated damage as a result of damage from ground shaking, landslide, liquefaction, and fire); the remaining counties touching San Francisco Bay; and three regions from beyond the nine-county San Francisco Bay region. These summary data are intended for use in selected analyses related to the regional impact resulting from the HayWired earthquake scenario mainshock. The vector .SHP dataset was 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 GIS applications (such as ESRI's ArcGIS software suite) and (or) standalone spreadsheet or database applications (such as Microsoft Excel or Access). These data support the following publication: Wein, A.M., Belzer, D., Kroll, C., Au, C., Jones, J.L., Johnson, L.A., Olsen, A., and Peters, J., 2020, Spatial analysis of industries, employment, and commute flows in areas of concentrated damage from the HayWired earthquake scenario, chap. V5 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.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update. A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases. All cases: Includes cases among all San Francisco residents regardless of vaccination status. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.” On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available. B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address. We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included. C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day. D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”. Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time. The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category. New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days. New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a part

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)

Please refer to the downloadable XLSX attachment for the complete dataset, metadata, and instructions for use. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) conducted the 2021 Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties (Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano). This study has been conducted periodically by CC&G since 2013. Primary goals of this study include: • Estimate mode share for trips into, within, and out of San Francisco, focusing on the percentage of trips made by walking, biking, and public transit compared to other modes (e.g. driving, TNC, etc.). This survey is the primary instrument for tracking the SFMTA Strategic Plan metric “transportation mode share” and provides information used by the agency across divisions. • Evaluation of the above statement based on the number of trips to, from, and within San Francisco by Bay Area residents. Note that trips by visitors from outside the Bay Area, and for commercial purposes whether originating in the Bay Area or not, are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. The survey was conducted as a telephone study among 756 Bay Area residents aged 18 and older. Telephone surveying was conducted during May – September 2021. Interviewing was conducted in English, Spanish, and Chinese. Surveying was conducted via random digit dial (RDD) and cell phone sample. To enhance statistical accuracy, data was weighted by age and location. Not all totals will add to 100% due to rounding. In portions of this report, a dash (-) is used to indicate no respondents answered with the response code listed. The margin of error at the 95% confidence level is +/-3.56% for the total sample (n = 756). For other sample sizes, the margin of error is as follows: • San Francisco residents; n = 470. Margin of error = +/- 4.52% • Bay Area residents (outside San Francisco) n = 286. Margin of error = +/- 5.79%

Please refer to the downloadable XLSX attachment (http://bit.ly/SFMTATravelSurvey2019) for the complete dataset, metadata, and instructions for use. This workbook provides data and data dictionaries for the SFMTA 2019 Travel Decision Survey. On behalf of San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis (CC&G) undertook a Mode Share Survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. The primary goals of this study were to: • Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.2: Mode Share target of 80% sustainable travel by 2030. • Evaluate the above statement based on the following parameters: number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. • Provide additional trip details, including trip purpose for each trip in the mode share question series. • Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. • Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs. The survey was conducted as a telephone study among 801 Bay Area residents aged 18 and older. Interviewing was conducted in English, Spanish, Mandarin, Cantonese, and Tagalog. Surveying was conducted via random digit dial (RDD) and cell phone sample. All survey datasets incorporate respondent weighting based on age and home location; utilize the “weight” field when appropriate in your analysis. The survey period for this survey is as follows: 2019: May - August 2019 The margin of error is related to sample size (n). For the total sample, the margin of error is 3.3% for a confidence level of 95%. When looking at subsets of the data, such as just the SF population, just the female population, or just the population of people who bicycle, the sample size decreases and the margin of error increases. Below is a guide of the margin of error for different samples sizes. Be cautious in making conclusions based off of small sample sizes. At the 95% confidence level is: • n = 801(Total Sample). Margin of error = +/- 3.3% • n = 400. Margin of error = +/- 4.85% • n = 100. Margin of error = +/- 9.80%