About this dataset

Context

The dataset tabulates the San Francisco County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of San Francisco County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of San Francisco County was 808,988, a 0.15% increase year-by-year from 2022. Previously, in 2022, San Francisco County population was 807,774, a decline of 0.51% compared to a population of 811,935 in 2021. Over the last 20 plus years, between 2000 and 2023, population of San Francisco County increased by 31,648. In this period, the peak population was 879,676 in the year 2018. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the San Francisco County is shown in this column.
• Year on Year Change: This column displays the change in San Francisco County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Francisco County Population by Year. You can refer the same here

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

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

Context

DataSF seeks to transform the way that the City of San Francisco works -- through the use of data.

https://datasf.org/about/

Content

This dataset contains the following tables: ['311_service_requests', 'bikeshare_stations', 'bikeshare_status', 'bikeshare_trips', 'film_locations', 'sffd_service_calls', 'sfpd_incidents', 'street_trees']

• This data includes all San Francisco 311 service requests from July 2008 to the present, and is updated daily. 311 is a non-emergency number that provides access to non-emergency municipal services.
• This data includes fire unit responses to calls from April 2000 to present and is updated daily. Data contains the call number, incident number, address, unit identifier, call type, and disposition. Relevant time intervals are also included. Because this dataset is based on responses, and most calls involved multiple fire units, there are multiple records for each call number. Addresses are associated with a block number, intersection or call box.
• This data includes incidents from the San Francisco Police Department (SFPD) Crime Incident Reporting system, from January 2003 until the present (2 weeks ago from current date). The dataset is updated daily. Please note: the SFPD has implemented a new system for tracking crime. This dataset is still sourced from the old system, which is in the process of being retired (a multi-year process).
• This data includes a list of San Francisco Department of Public Works maintained street trees including: planting date, species, and location. Data includes 1955 to present.

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

http://datasf.org/

• https://cloud.google.com/bigquery/public-data/sfo-311
• https://cloud.google.com/bigquery/public-data/sffd-service-calls
• https://cloud.google.com/bigquery/public-data/sfpd-reports
• https://cloud.google.com/bigquery/public-data/sfo-trees

Dataset Source: SF OpenData. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://sfgov.org/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @meric from Unplash.

Inspiration

Which neighborhoods have the highest proportion of offensive graffiti?

Which complaint is most likely to be made using Twitter and in which neighborhood?

What are the most complained about Muni stops in San Francisco?

What are the top 10 incident types that the San Francisco Fire Department responds to?

How many medical incidents and structure fires are there in each neighborhood?

What’s the average response time for each type of dispatched vehicle?

Which category of police incidents have historically been the most common in San Francisco?

What were the most common police incidents in the category of LARCENY/THEFT in 2016?

Which non-criminal incidents saw the biggest reporting change from 2015 to 2016?

What is the average tree diameter?

What is the highest number of a particular species of tree planted in a single year?

Which San Francisco locations feature the largest number of trees?

About this dataset

Context

The dataset tabulates the San Francisco population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of San Francisco across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of San Francisco was 808,437, a 0.35% decrease year-by-year from 2021. Previously, in 2021, San Francisco population was 811,253, a decline of 6.79% compared to a population of 870,393 in 2020. Over the last 20 plus years, between 2000 and 2022, population of San Francisco increased by 31,097. In this period, the peak population was 879,676 in the year 2018. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the San Francisco is shown in this column.
• Year on Year Change: This column displays the change in San Francisco population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Francisco Population by Year. You can refer the same here

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.

This filtered view contains the population estimates for San Francisco demographic groups from the U.S. Census Bureau’s American Community Survey that are used by Controller's Office - City Performance Unit for reporting on Police Stops

San Francisco Population and Demographic Census data dataset filtered on: "reporting_segment" = 'Police Reporting Demographic Categories'

A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey.

B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.

C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org

D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey. B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived. C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease. Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups. B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health. Data on the population characteristics of COVID-19 deaths are from: Case reports Medical records Electronic lab reports Death certificates Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths. To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more. Data notes on select population characteristic types are listed below. Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. Gender * The City collects information on gender identity using these guidelines. C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week. Dataset will not update on the business day following any federal holiday. D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS). This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed. To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset. E. CHANGE LOG

Comprehensive demographic dataset for Bay Area, Corpus Christi, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This filtered view contains the population estimates for San Francisco demographic groups from the U.S. Census Bureau’s American Community Survey that are used in the Department of Public Health’s public reporting. Details on the underlying demographic data from the American Community Survey are available below. The demographics included are race/ethnicity and age groups. Different age groups are used for reporting on cases reporting versus vaccinations. The specific groups used in each of these reports can be found by using the "reporting_segment" column. We are using 2016-2020 ACS estimates in our public reporting, but additional years are included in this view as well for historical purposes.

The COVID-19 reports which use this data are available on SF.gov by clicking here.

San Francisco Population and Demographic Census data dataset filtered on:

• "reporting_segment" =
  • 'COVID-19 cases/testing reporting - age brackets'
  • OR 'Seasonal vaccine reporting - age groups'
  • OR 'from census table B03002' (race/ethnicity)
• AND "geography_name" = 'San Francisco County, California'

B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.

C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org

D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

The City Survey asks residents to indicate their usage and satisfaction with city services and infrastructure like libraries, Muni, public safety, and street cleanliness. The City Survey was conducted every year from 1996 to 2004, and biennially from 2005 onward. The City Survey was not conducted in 2019 due to the COVID-19 pandemic, and resumed in 2023. Survey methodology was changed in 2015 from a mail to a phone survey, and expanded to include in-person and online options in 2023. Comparisons to previous years should be interpreted with caution. Results should be weighted using the column "weight" in order to adjust for demographic differences between the City Survey sample and San Francisco's population. Please note that survey results were originally reported as unweighted until 1997. From 1997 onward, all City Survey results were reweighted with the exception of data from 2011. For ease of use, the column "weight" has been coded with a value of one for these years. A code book is also attached to this dataset under About > Attachments. Neighborhood and Zip Code data have been hidden from this data set and are only available upon special request to citysurvey@sfgov.org. For more information regarding San Francisco City Survey 1996-2023 Database, please visit the City Survey website at https://sf.gov/citysurvey or contact the San Francisco Controller's Office at citysurvey@sfgov.org.

Draft dataset for Bay Area Census website prototype. Includes census 2010 population breakdown by age, sex and race.

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)

VITAL SIGNS INDICATOR Displacement Risk (EQ3)

FULL MEASURE NAME Share of lower-income households living in tracts at risk of displacement

LAST UPDATED December 2018

DESCRIPTION Displacement risk refers to the share of lower-income households living in neighborhoods that have been losing lower-income residents over time, thus earning the designation “at risk”. While “at risk” households may not necessarily be displaced in the short-term or long-term, neighborhoods identified as being “at risk” signify pressure as reflected by the decline in lower-income households (who are presumed to relocate to other more affordable communities). The dataset includes metropolitan area, regional, county and census tract tables.

DATA SOURCE U.S. Census Bureau: Decennial Census 1980-1990 Form STF3 https://nhgis.org

U.S. Census Bureau: Decennial Census 2000 Form SF3a https://nhgis.org

U.S. Census Bureau: Decennial Census 1980-2010 Longitudinal Tract Database http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 2010-2015 Form S1901 5-year rolling average http://factfinder2.census.gov

U.S. Census Bureau: American Community Survey 2010-2017 Form B19013 5-year rolling average http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Aligning with the approach used for Plan Bay Area 2040, displacement risk is calculated by comparing the analysis year with the most recent year prior to identify census tracts that are losing lower-income households. Historical data is pulled from U.S. Census datasets and aligned with today’s census tract boundaries using crosswalk tables provided by LTDB. Tract data, as well as regional income data, are calculated using 5-year rolling averages for consistency – given that tract data is only available on a 5-year basis. Using household tables by income level, the number of households in each tract falling below the median are summed, which involves summing all brackets below the regional median and then summing a fractional share of the bracket that includes the regional median (assuming a simple linear distribution within that bracket).

Once all tracts in a given county or metro area are synced to today’s boundaries, the analysis identifies census tracts of greater than 500 lower-income people (in the prior year) to filter out low-population areas. For those tracts, any net loss between the prior year and the analysis year results in that tract being flagged as being at risk of displacement, and all lower-income households in that tract are flagged. To calculate the share of households at risk, the number of lower-income households living in flagged tracts are summed and divided by the total number of lower-income households living in the larger geography (county or metro). Minor deviations on a year-to-year basis should be taken in context, given that data on the tract level often fluctuates and has a significant margin of error; changes on the county and regional level are more appropriate to consider on an annual basis instead.

Historical dataset of population level and growth rate for the San Francisco metro area from 1950 to 2025.

A. SUMMARY This dataset represents the COVID-19 vaccinations given to residents of San Francisco over time. All vaccines given to SF residents are included, no matter where the vaccination took place (the vaccine may have been administered in San Francisco or outside of San Francisco). The data are broken down by multiple demographic stratifications. This dataset also includes COVID-19 vaccinations given to SF residents by the San Francisco Department of Public Health (SFDPH) over time.

Data provides counts for residents who have received at least one dose, residents who have completed a primary vaccine series, residents who have received one or two monovalent (not bivalent) booster doses, and residents who have received a bivalent booster dose. A primary vaccine series is complete after an individual has received all intended doses of the initial series. There are one, two, and three dose primary vaccine series.

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR2), run by the California Department of Public Health (CDPH). The information on individuals’ city of residence, age, race, and ethnicity are also recorded in CAIR and are self-reported at the time of vaccine administration.

In order to estimate the percent of San Franciscans vaccinated, we provide the 2016-2020 American Community Survey (ACS) population estimates for each demographic group.

C. UPDATE PROCESS Updated daily via automated process

D. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity and age groups 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).

Before analysis, you must filter the dataset to the desired stratification of data using the "overall_segment" column.

For example, filtering "overall_segment" to "All SF Residents by Age Bracket, Administered by All Providers" will filter the data to residents whose vaccinations were administered by any provider. You can then further segment the data and calculate percentages by Age Brackets.

If you filter "overall_segment" to "All SF Residents by Race/Ethnicity, Administered by DPH Only", you will see the race/ethnicity breakdown for residents who received vaccinations from the San Francisco Department of Public Health (SFDPH).

If you filter "overall_segment" to "All SF Residents by Age Group, Administered by All Providers" you will see vaccination counts of various age eligibility groups that were administered by any provider.

To count the number of individuals vaccinated (with any primary series dose) for the first time on a given day, use the "new_recipients" column. To count the number of individuals who have completed their primary vaccine series on a given day, use the "new_series_completed" column. To count the number of primary series doses administered on a day (1st, 2nd, 3rd, or single doses), use the "new_primary_series_doses" column.

To count the number of individuals who received their first or second monovalent (not bivalent) booster dose on a given day, use the "new_booster_recipients" and "new_2nd_booster_recipients" columns. To count the number of individuals who received their first bivalent booster dose on a given day, use the "new_bivalent_booster_recipients" column. To count the number of monovalent (not including bivalent) or bivalent booster doses administered on a given day, use the "new_booster_doses" or "new_bivalent_booster_doses" columns.

To count the number of individuals who have received a vaccine up to a certain date, use the columns beginning with "cumulative_..."

E. ARCHIVED DATA A previous version of this dataset was archived on 10/27/2022. For historical purposes, you can access the archived dataset at the following link:

ARCHIVED: COVID-19 Vaccine Doses Given to San Franciscans by Demographics Over Time

F. CHANGE LOG

• 11/1/2023 - data on COVID-19 vaccinations given to SF residents by demographics over time are no longer being updated. This data is currently through 10/31/2023 and will not include any new data after this date.
• 1/31/2023 - updated “subgroup_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 10/27/2022 - the columns "age_group" and "age_group_population" were removed. To continue to access data by age eligibility groups, filter the dataset on "demographic_group" = ‘Age Group’. The column "overall_segment" was altered to reflect this change.
• 10/27/2022 - the columns "new_1st_doses", "new_2nd_doses", and "new_single_doses" were removed and consolidated into the column "new_primary_series_doses" (which also includes 3rd doses for the Pfizer Under 5 series). Similarly, the columns "cumulative_1st_doses", "cumulative_2nd_doses", "cumulative_single_doses", were replaced by "cumulative_primary_series_doses".

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 jurisdiction (incorporated places (cities and towns) and unincorporated county lands). 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, group quarter population, household population, persons per household, and total population.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 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 countyTotal population, by age range, per jurisdiction (incorporated place and unincorporated county)

Comprehensive demographic dataset for District 7 - North, San Francisco, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset tabulates the data for the San Francisco, CA population pyramid, which represents the San Francisco population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for San Francisco, CA, is 16.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for San Francisco, CA, is 24.1.
• Total dependency ratio for San Francisco, CA is 40.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for San Francisco, CA is 4.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the San Francisco population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the San Francisco for the selected age group is shown in the following column.
• Population (Female): The female population in the San Francisco for the selected age group is shown in the following column.
• Total Population: The total population of the San Francisco for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Francisco Population by Age. You can refer the same here