About this dataset

Context

The dataset presents the median household income across different racial categories in San Francisco. 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 population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 40.49% of the total residents in San Francisco. Notably, the median household income for White households is $177,030. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $177,030.

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

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 San Francisco Community Alternative vs. California and San Francisco Unified School District

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value i

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

Graph and download economic data for Resident Population in San Francisco County/city, CA from 1970 to 2024 about San Francisco County/City, CA; San Francisco; residents; CA; population; and USA.

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.

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 1991 to 2023 for South San Francisco High School vs. California and South San Francisco Unified School District

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

According to figures recently released by the United States Census, America’s largest metro areas are currently gaining population at impressive rates. The growth in these areas is in fact driving much of the population growth across the nation. Upon closer examination of the data, this growth is the result of two very different migrations – one coming from the location choices of Americans themselves, the other shaped by where new immigrants from outside the United States are heading.While many metro areas are attracting a net-inflow of migrants from other parts of the country, in several of the largest metros – New York, Los Angeles., and Miami, especially – there is actually a net outflow of Americans to the rest of the country. Immigration is driving population growth in these places. Sunbelt metros like Houston, Dallas, and Phoenix, and knowledge hubs like Austin, Seattle, San Francisco, and the District of Columbia are gaining much more from domestic migration.This map charts overall or net migration – a combination of domestic and international migration. Most large metros, those with at least a million residents, had more people coming in than leaving. The metros with the highest levels of population growth due to migration are a mix of knowledge-based economies and Sunbelt metros, including Houston, Dallas, Miami, District of Columbia, San Francisco, Seattle, and Austin. Eleven large metros, nearly all in or near the Rustbelt, had a net outflow of migrants, including Chicago, Detroit, Memphis, Philadelphia, and Saint Louis.Source: Atlantic Cities

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in San Francisco County. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In San Francisco County, the median household income for the households where the householder is White increased by $57,156(47.68%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $119,874 in 2013 and $177,030 in 2023.
• Black or African American: In San Francisco County, the median household income for the households where the householder is Black or African American increased by $10,818(26.52%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $40,792 in 2013 and $51,610 in 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

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 County.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are 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 County median household income by race. You can refer the same here

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 ...

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.

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.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward. A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable. 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 characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more. Data notes on each population characteristic type is 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 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). This dataset includes many different types of characteristics. 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 deaths on each date. New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed. This data may not be immediately available for more recent deaths. Data updates as more information becomes available. To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset. E. CHANGE LOG 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

Hydrodynamic and sediment transport time-series data, including water depth, velocity, turbidity, suspended particle size, conductivity, and temperature, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at two locations in south San Francisco Bay. Data were collected in the channel (one platform) and in the shallows (three co-located platforms) for 2 weeks in July 2020. Data files are grouped by site (channel or shallows). Each site contained instrumentation to collect the data listed, with slight instrument and setup variations between the two sites due to logistics. Users are advised to assess data quality carefully, and to check metadata for instrument information, as platform deployment times and data-processing methods varied.

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

Note: As of March 2022, the race/ethnicity label changed from Native American to American Indian or Alaska Native to align with the Census.

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

Note: As of February 2022, the way race/ethnicity is categorized has been changed. See Section B for additional information.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. Each positive test result is investigated by the health department. While the city tries to only report on tests for San Francisco residents (or tests in San Francisco for those with no locating address listed), some test results purported to be for San Francisco residents are actually for people living outside the city. This can be discovered during a case investigation or data quality assurance. In such an instance, the test would be counted as a positive test in the SF data but would not be counted as a COVID-19 case in San Francisco. If a person tests positive for COVID-19 on different dates, they would be included each of those times in the testing data but only one case. To track the number of cases by race/ethnicity, see this dashboard: https://sf.gov/data/covid-19-population-characteristics#race-or-ethnicity-

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

On February 10, 2022, the method for which race/ethnicity is categorized was updated for the sake of data accuracy, clarity, and stability. The new categorization increases data clarity by emulating the methodology used by the U.S. Census in the