About this dataset

Context

The dataset presents the median household income across different racial categories in San Francisco township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of San Francisco township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 89.67% of the total residents in San Francisco township. Notably, the median household income for White households is $129,375. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $129,375.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in San Francisco township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 township median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within San Francisco County. The dataset can be utilized to gain insights into gender-based income distribution within the San Francisco County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within San Francisco County, among individuals aged 15 years and older with income, there were 350.46 thousand men and 319.41 thousand women in the workforce. Among them, 200,656 men were engaged in full-time, year-round employment, while 158,204 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.61% fell within the income range of under $24,999, while 4.78% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 61.29% of men in full-time roles earned incomes exceeding $100,000, while 52.53% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2011 to 2023 for Academy - Sf mcateer vs. California and San Francisco Unified School District

This dataset tracks annual diversity score from 2019 to 2023 for The New School Of San Francisco School District vs. California

We used genome-wide single nucleotide polymorphism (SNP) data and capture-mark-recapture methods to evaluate the genetic diversity and demography within seven focal sites of the endangered San Francisco gartersnake (Thamnophis sirtalis tetrataenia). As Thamnophis sirtalis tetrataenia is listed as endangered by the U.S. Fish and Wildlife Service (USFWS), sensitive location information can be made available upon request by contacting Brian J. Halstead and/or Amy G. Vandergast.

Racial diversity is measured by a diversity index that is calculated using United States Census racial and ethnic population characteristics from the PL-94 data file. The diversity index is a quantitative measure of the distribution of the proportion of five major ethnic populations (non-Hispanic White, non-Hispanic Black, Asian and Pacific Islander, Hispanic, and Two or more races). The index ranges from 0 (low diversity meaning only one group is present) to 1 (meaning an equal proportion of all five groups is present). The diversity score for the United States in 2010 is 0.60. The diversity score for the San Francisco Bay Region is 0.84. Within the region, Solano (0.89) and Alameda (0.90) Counties are the most diverse and the remaining North Bay (0.55 - 0.64) Counties are the least diverse.

This dataset tracks annual diversity score from 1991 to 2023 for South San Francisco High School vs. California and South San Francisco Unified School District

Fragmentation and loss of natural habitat have important consequences for wild populations and can negatively affect long-term viability and resilience to environmental change. Salt marsh obligate species, such as those that occupy the San Francisco Bay Estuary in western North America, occupy already impaired habitats as result of human development and modifications and are highly susceptible to increased habitat loss and fragmentation due to global climate change. We examined the genetic variation of the California Ridgway’s rail ( Rallus obsoletus obsoletus), a state and federally endangered species that occurs within the fragmented salt marsh of the San Francisco Bay Estuary. We genotyped 107 rails across 11 microsatellite loci and a single mitochondrial gene to estimate genetic diversity and population structure among seven salt marsh fragments and assessed demographic connectivity by inferring patterns of gene flow and migration rates. We found pronounced genetic structuring ...

This dataset tracks annual diversity score from 1991 to 2023 for San Francisco Community Alternative vs. California and San Francisco Unified School District

This dataset tracks annual diversity score from 2013 to 2023 for San Francisco Public Montessori vs. California and San Francisco Unified School District

These datasets provide information on plant alpha, beta, and gamma diversity, and plant species abundance at several spatial scales for tidal wetlands along a salinity gradient in the San Francisco Bay-Delta and an impounded brackish wetland complex in Suisun Marsh, California. Files include diversity metrics calculated at the patch, site, and region scales, average percent cover of wetland dominant plants at the patch scale, and average percent cover of all wetland plants at the site scale.

Fragmentation and loss of natural habitat have important consequences for wild populations and can negatively affect long-term viability and resilience to environmental change. Salt marsh obligate species, such as those that occupy the San Francisco Bay Estuary in western North America, occupy already impaired habitats as result of human development and modifications and are highly susceptible to increased habitat loss and fragmentation due to global climate change. We examined the genetic variation of the California Ridgway s rail ( Rallus obsoletus obsoletus), a state and federally endangered species that occurs within the fragmented salt marsh of the San Francisco Bay Estuary. We genotyped 107 rails across 11 microsatellite loci and a single mitochondrial gene to estimate genetic diversity and population structure among seven salt marsh fragments and assessed demographic connectivity by inferring patterns of gene flow and migration rates. We found pronounced genetic structuring among four geographically separate genetic clusters across the San Francisco Bay. Gene flow analyses supported a stepping stone model of gene flow from south-to-north. However, contemporary gene flow among the regional embayments was low. Genetic diversity among occupied salt marshes and genetic clusters were not significantly different. However, we detected low effective population sizes and significantly high relatedness among individuals within salt marshes. Preserving genetic diversity and connectivity throughout the San Francisco Bay may require attention to salt marsh restoration in the Central Bay where habitat is both most limited and most fragmented. Incorporating periodic genetic sampling in to the management regime may help evaluate population trends and guide long-term management priorities.

These data support the following in-press publication: Wood, D.A., Bui, T.D., Overton, C.T., Vandergast, A.G., Casazza, M.L., Hull, J.M., and Takekawa, J.Y. Conservation Genetics (2016). doi:10.1007/s10592-016-0888-4.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within San Francisco township. The dataset can be utilized to gain insights into gender-based income distribution within the San Francisco township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within San Francisco township, among individuals aged 15 years and older with income, there were 389 men and 310 women in the workforce. Among them, 243 men were engaged in full-time, year-round employment, while 160 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.65% fell within the income range of under $24,999, while 7.50% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 43.21% of men in full-time roles earned incomes exceeding $100,000, while 26.25% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 township median household income by race. You can refer the same here

This dataset tracks annual diversity score from 2019 to 2023 for KIPP San Francisco Bay Academy School District vs. California

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 2023, the population of San Francisco was 808,988, a 0.15% increase year-by-year from 2022. Previously, in 2022, San Francisco 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 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 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

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within San Francisco. The dataset can be utilized to gain insights into gender-based income distribution within the San Francisco population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within San Francisco, among individuals aged 15 years and older with income, there were 339.20 thousand men and 309.39 thousand women in the workforce. Among them, 199,516 men were engaged in full-time, year-round employment, while 150,495 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.51% fell within the income range of under $24,999, while 4.86% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 60.45% of men in full-time roles earned incomes exceeding $100,000, while 51.09% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/san-francisco-ca-income-distribution-by-gender-and-employment-type.jpeg" alt="San Francisco, CA gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 median household income by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of San Francisco County by race. It includes the distribution of the Non-Hispanic population of San Francisco County across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of San Francisco County across relevant racial categories.

Key observations

Of the Non-Hispanic population in San Francisco County, the largest racial group is White alone with a population of 313,559 (44.60% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the San Francisco County
• Population: The population of the racial category (for Non-Hispanic) in the San Francisco County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of San Francisco County total Non-Hispanic population. 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 Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of San Francisco by race. It includes the population of San Francisco across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of San Francisco across relevant racial categories.

Key observations

The percent distribution of San Francisco population by race (across all racial categories recognized by the U.S. Census Bureau): 40.49% are white, 5.09% are Black or African American, 0.66% are American Indian and Alaska Native, 34.97% are Asian, 0.38% are Native Hawaiian and other Pacific Islander, 7.75% are some other race and 10.66% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the San Francisco
• Population: The population of the racial category (excluding ethnicity) in the San Francisco is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of San Francisco total population. 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 Race & Ethnicity. You can refer the same here

A. SUMMARY It is the policy of the San Francisco Department of Public Health to comply with patient/client/resident rights regarding Protected Health Information (PHI) as set forth in the Health Insurance Portability and Accountability Act of 1996 (HIPAA). These guidelines exists to provide guidance only as it relates to the public release of COVID-19 data through the tracker webpages, so that public reporting of de-identified information of residents’ health status, demographic and other characteristics, and geographical information reflect consistent reporting practices and meaningful differences in health outcomes, conditions that impact health, and delivery of services while safeguarding patient/client/resident rights regarding PHI.

COVID-19 related data will be released routinely in a variety of data products related to the tracker, including datasets through SF OpenData. Some data products may include data by county or smaller analysis unit such as ZIP code, neighborhood, or census tract.

Download the attached PDF for the policy.

Data and R code for the manuscript "Predator response diversity to warming enables ecosystem resilience in the Galápagos".Nicole Chico-Ortiz1, Esteban Agudo-Adriani1,2, Haley E. Capone2, Isabel Silva-Romero1,2, Margarita Brandt1,3, and John F. Bruno1,2,*1Galapagos Science Center GSC, Universidad San Francisco de Quito USFQ & University of North Carolina at Chapel Hill UNC, Puerto Baquerizo Moreno, Galápagos, Ecuador2Department of Biology, University of North Carolina at Chapel Hill; Chapel Hill, North Carolina, USA 275993Colegio de Ciencias Biológicas y Ambientales COCIBA, Universidad San Francisco de Quito USFQ, Diego de Robles s/n, 170901, Quito, EcuadorAbstractAn important impact of global warming in nature is the decline of ecological functions such as primary production, habitat provision, and carbon sequestration. These functions can be disrupted when the species that perform them are impaired by anthropogenic warming or other stressors. Where there is a diversity of responses to warming among the species filling these roles, the function is more likely to be maintained despite the loss of the least tolerant species. However, the response diversity to warming of key functions is generally unknown, particularly for the roles played by predatory and marine species. Here we show that the thermal sensitivity of predation to acute warming varies substantially among four marine invertebrate carnivores: three whelks and a sea star that inhabit rocky reefs around the Galápagos islands. Two of the four predators were clearly adapted to cooler temperatures and their functional performance declined dramatically with experimental warming. In contrast, predation by two whelks, and one in particular, improved with warming, including beyond temperatures expected in 2100 under the most pessimistic emissions scenario. These results suggest that a high level of temperature response diversity of predation could help maintain this critical function in a variable and changing environment.