Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA (S1701ACS006075) from 2012 to 2023 about San Francisco County/City, CA; San Francisco; poverty; percent; CA; 5-year; population; and USA.

Graph and download economic data for Estimate of People of All Ages in Poverty in San Francisco County/City, CA (PEAACA06075A647NCEN) from 1989 to 2023 about San Francisco County/City, CA; San Francisco; child; poverty; CA; persons; and USA.

San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA was 10.60% in January of 2023, according to the United States Federal Reserve. Historically, San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA reached a record high of 13.50 in January of 2013 and a record low of 10.10 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Estimate of Related Children Age 5-17 in Families in Poverty for San Francisco County/City, CA (PE5T17CA06075A647NCEN) from 1989 to 2023 about San Francisco County/City, CA; San Francisco; 5 to 17 years; family; child; poverty; CA; persons; and USA.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in San Mateo County, CA (S1701ACS006081) from 2012 to 2023 about San Mateo County, CA; San Francisco; poverty; percent; CA; 5-year; population; and USA.

San Francisco Flood Health Vulnerability

Socioeconomic, Demographic, Health, and Housing Indicators

By City of San Francisco [source]

About this dataset

This dataset provides a comprehensive composite index that captures the relative vulnerability of San Francisco communities to the health impacts of flooding and extreme storms. Predominantly sourced from local governmental health, housing, and public data sources, this index is constructed from an array of socio-economic factors, exposure indices,Health indicators and housing attributes. Used as a valuable planning tool for both health and climate adaptation initiatives throughout San Francisco, this dataset helps to identify vulnerable populations within the city such as areas with high concentrations of children or elderly individuals. Data points included in this index include: census blockgroup numbers; the percentage of population under 18 years old; percentage of population above 65; percentage non-white; poverty levels; education level; yearly precipitation estimates; diabetes prevalence rate; mental health issues reported in the area; asthma cases by geographic location;; disability rates within each block group measure as well as housing quality metrics. All these components provide a broader understanding on how best to tackle issues faced within SF arising from any form of climate change related weather event such as floods or extreme storms

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How to use the dataset

This dataset can be used to analyze the vulnerability of the population in San Francisco to the health impacts of floods and storms. This dataset includes a number of important indicators such as poverty, education, demographic, exposure and health-related information. These indicators can be useful for developing effective strategies for health and climate adaptation in an urban area.

To get started with this dataset: First, review the data dictionary provided in the attachments section of this metadata to understand each variable that you plan on using in your analysis. Second, see if there are any null or missing values in your columns by checking out ‘Null Value’ column provided in this metadata sheet and look at how they will affect your analysis - use appropriate methods to handle those values based on your goals and objectives. Thirdly begin exploring relationships between different variables using visualizations like pandas scatter_matrix() & pandas .corr() . These tools can help you identify potential strong correlations between certain variables that you may have not seen otherwise through simple inspection of the data.

Lastly if needed use modelling techniques like regression analysis or other quantitative methods like ANOVA’s etc., for further elaboration on understanding relationships between different parameters involved as per need basis

Research Ideas

• Developing targeted public health interventions focused on high-risk areas/populations as identified in the vulnerability index.
• Establishing criteria for insurance premiums and policies within high-risk areas/populations to incentivize adaption to climate change.
• Visual mapping of individual indicators in order to identify trends and correlations between flood risk and socioeconomic indicators, resource availability, and/or healthcare provision levels at a granular level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: san-francisco-flood-health-vulnerability-1.csv | Column name | Description | |:---------------------------|:----------------------------------------------------------------------------------------| | Census Blockgroup | Unique numerical identifier for each block in the city. (Integer) | | Children | Percentage of population under 18 years of age. (Float) | | Children_wNULLvalues | Percentage of population under 18 years of age with null values. (Float) | | Elderly | Percentage of population over 65 years of age. (Float) | | Elderly_wNULLvalues | Percentage of population over 65 years of age with null values. (Float) | | NonWhite | Percentage of non-white population. (Float) ...

Place types were defined by grouping United States Census Designated Places as Central Cities, Inner Suburbs, Outer Suburbs, and Balance of Counties for the San Francisco Bay Region, based on their population, employment and travel characteristics.Viewed regionally, the percentage increase in poverty was highest in the Outer Suburbs (62% regional average), as compared to the Central Cities (22% regional average) and Inner Suburbs (24% regional average) showing a pattern of larger increases in poverty in the Region's periphery. The percentage increases in poverty in the Outer Suburbs by County range from 38% to 89%, as compared to the Inner Suburbs which range from 17% to 30% and Central Cities from 3% to 40%.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Contra Costa County, CA (S1701ACS006013) from 2012 to 2023 about Contra Costa County, CA; San Francisco; poverty; percent; CA; 5-year; population; and USA.

Average adjusted predicted probability of high SSB consumptiona in San Francisco and San Jose before, one, and two years after San Francisco’s sugar sweetened beverages tax implementation, stratified by federal poverty level (FPL) (n = 1,443).

The federal tax bill, passed in December 2017, allows investors to defer or eliminate capital gains on investments made in “Opportunity Zones”. These zones must be designated by the governor in each state from a set of eligible Census tracts. Governors must select no more than 25 percent of eligible tracts statewide.Federal criteria for determining eligible areas states that tracts must either have poverty rates above 20 percent or median family income below 80 percent of either the statewide or metropolitan area income. 3,516 Census tracts in California qualify under this criteria, spread across 54 counties. Of these, the governor must select tracts as Opportunity Zones in California.The state’s final recommendation is provided on the map. Within the San Francisco Bay Region, 530 tracts were eligible under the federal criteria, of which 107 were designated by the governor. Of the 107 designated tracts, 94 tracts were Metropolitan Transportation Commission Communities of Concern (now Equity Priority Communities).

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

Characteristics of sample at baseline, overall and by city (2017–2018).

The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)

Field Name Data Type Definition Notes (optional)

Census Blockgroup Text San Francisco Census Block Groups

Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.

Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.

Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.

NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.

Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.

Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.

English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.

SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.

Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.

Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.

MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.

Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.

Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.

Disability_wNULLvalues

Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.

Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.

LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.

LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.

FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Alameda County, CA (S1701ACS006001) from 2012 to 2023 about Alameda County, CA; San Francisco; poverty; percent; CA; 5-year; population; and USA.

Difference-in-differences of likelihood of high sugar-sweetened beverage (SSB) consumptiona pre- and post-tax implementation between San Francisco and San José (n = 1,443).

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

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

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

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

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

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

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

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

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Marin County, CA (S1701ACS006041) from 2012 to 2023 about Marin County, CA; San Francisco; poverty; percent; CA; 5-year; population; and USA.

This data set represents all urbanized tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities (EPC) tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern (CoC).MTC 2018 Equity Priority Communities (tract geography) is based on eight ACS 2012-2016 tract-level variables: Persons of Color (70% threshold) Low-Income (less than 200% of Fed. poverty level, 30% threshold) Level of English Proficiency (12% threshold) Elderly (10% threshold) Zero-Vehicle Households (10% threshold) Single Parent Households (20% threshold)Disabled (12% threshold) Rent-Burdened Households (15% threshold) If a tract exceeds both threshold values for Low-Income and Person of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

This data set represents American Community Survey (ACS) 2018-2022 tract information related to Equity Priority Communities (EPCs) for Plan Bay Area 2050+.

The Plan Bay Area 2050+ Equity Priority Communities incorporate EPCs identified with 2014-2018 ACS data, as well as EPCs identified with 2018-2022 ACS data into a single consolidated map of Plan Bay Area 2050+ Equity Priority Communities.

This data set was developed using American Community Survey 2018-2022 data for eight variables considered.

This data set represents all tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Community tract-level variables for exploratory purposes. Equity Priority Communities are defined by MTC Resolution No. 4217-Equity Framework for Plan Bay Area 2040.

As part of the development of the [DRAFT] Equity Priority Communities - Plan Bay Area 2050+ features, the source Census tracts had portions that overlapped either the Pacific Ocean or San Francisco Bay removed. The result is this feature set has fewer Census tracts than the unclipped tract source data.

Analysis producing the Plan Bay Area 2050+ Equity Priority Communities features (tract geography) are based on eight American Community Survey 2018-2022 tract-level variables:

People of Color (72% threshold) Low-Income (less than 200% of Federal poverty level, 24% threshold) Limited English Proficiency (11% threshold) Seniors 75 Years and Over (10% threshold) Zero-Vehicle Households (16% threshold) Single-Parent Family (16% threshold) People with a Disability (12% threshold) Severely Rent-Burdened Households (14% threshold)

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

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