Knowing the racial and ethnic composition of a community is often one of the first steps in understanding, serving, and advocating for various groups. This information can help enforce laws, policies, and regulations against discrimination based on race and ethnicity. These statistics can also help tailor services to accommodate cultural differences.This multi-scale map shows the most common race/ethnicity living within an area. Map opens at tract-level in Los Angeles, CA but has national coverage. Zoom out to see counties and states.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.

The National Survey on Drug Use and Health (NSDUH) series (formerly titled National Household Survey on Drug Abuse) primarily measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions included age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including pain relievers, tranquilizers, stimulants, and sedatives. The survey covered substance abuse treatment history and perceived need for treatment, and included questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders that allow diagnostic criteria to be applied. The survey included questions concerning treatment for both substance abuse and mental health-related disorders. Respondents were also asked about personal and family income sources and amounts, health care access and coverage, illegal activities and arrest record, problems resulting from the use of drugs, and needle-sharing. Questions introduced in previous administrations were retained in the 2014 survey, including questions asked only of respondents aged 12 to 17. These "youth experiences" items covered a variety of topics, such as neighborhood environment, illegal activities, drug use by friends, social support, extracurricular activities, exposure to substance abuse prevention and education programs, and perceived adult attitudes toward drug use and activities such as school work. Several measures focused on prevention-related themes in this section. Also retained were questions on mental health and access to care, perceived risk of using drugs, perceived availability of drugs, driving and personal behavior, and cigar smoking. Questions on the tobacco brand used most often were introduced with the 1999 survey. For the 2008 survey, adult mental health questions were added to measure symptoms of psychological distress in the worst period of distress that a person experienced in the past 30 days and suicidal ideation. In 2008, a split-sample design also was included to administer separate sets of questions (WHODAS vs. SDS) to assess impairment due to mental health problems. Beginning with the 2009 NSDUH, however, all of the adults in the sample received only the WHODAS questions. Background information includes gender, race, age, ethnicity, marital status, educational level, job status, veteran status, and current household composition.

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 is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity 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).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

The dataset provides information about the demographics and characteristics of deaths with COVID-19 by racial/ethnic groups among Santa Clara County residents. Source: California Reportable Disease Information Exchange. Data notes: The Other category for the race/ethnicity graph includes American Indian/Alaska Native and people who identify as multi-racial.

This table is updated every Friday.

PRIME Survey Dataset of Minoritised Ethnic People’s Engagement with Online Services

Our dataset is now publicly available via the university's open access repository:

DOI: 10.17861/db813826-e45d-4274-b4c3-7ecdbf2336a5 License: This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0)license.

Note: Please use the DOI link above to access and download the data. This directory is designated for dataset documentation, metadata, and any… See the full description on the dataset page: https://huggingface.co/datasets/JunhaoSong/prime-survey-question-answering.

Annual Resident Population Estimates by Age, Sex, Race, and Hispanic Origin; for the United States, States, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

Dataset population: Persons

Ethnic group (UK harmonised)

Ethnic group classifies people according to their own perceived ethnic group and cultural background.

Due to question and response category differences in the country-specific ethnic group question asked in the 2011 Censuses of the UK, some responses are not directly comparable. The UK output on ethnic group is therefore presented using a high-level classification as recommended by the ONS: Primary Standards for Harmonised Concepts and Questions for Social Data sources.

US Census data describing national gender and race demographics from 2000 to 2020.

The 2000 and 2010 data is fairly straight-forward. The US census website only had the caveat that the 2010 category for "Some other race-only" may have been between (19.1-20.1 million / 6.2-6.5%) and the category for "2 or more races" may have been a range (8.0-9.0 million / 2.6-2.9%). The numbers used in the dataset were the final numbers that the US census gives as their final numbers.

The official 2020 Census data will not be released until May 2023, so the numbers given are not official yet.

2020 Gender: The gender numbers are an estimate (163.8-164.8 million female / 166.9-167.8 million male). I used numbers that kept the ratio and summed to the total population. 2020 Race: The categories "Some other race-only" and "2 or more races" increased significantly for 2020. These changes are mainly due to a difference in how the race and ethnicity questions were asked. (It wasn't only because the demographics themselves changed, but mainly in how people answer the question.) The "Some other race-only" includes mostly Latino and Hispanic people (94%). The "2 or more races" category includes mostly people who are both White and another race(s) (86%). You should take this change into account when comparing an earlier census to the 2020 census. Race "Minority": Lastly, the minority category is calculated by subtracting the population of White-only, Non-Hispanic people from the total US population. Anyone who is any other race besides white AND anyone who is Latino/Hispanic would fall into the minority category.

Sources: 2000 Gender (1st paragraph), 2000 Race (page 3) 2010 Gender (2nd paragraph), 2010 Race (page 4) 2020 Gender Estimates (Estimates by Age and Sex table), 2020 Race (1) (throughout article), 2020 Race (2) ("What are facts for my country" section), 2020 Race (3) (Extra, similar)

The Los Angeles County Social Survey (LACSS) continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Log Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. The 1992 principal investigator was Dr. Lawrence Bobo, who was an Associate Professor of Sociology at UCLA. The LACSS 1992 was conducted between February and July 1992. Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Questionnaires were provided in both English and Spanish languages. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

This research examines the question of parental choice of secondary schools in Manchester and Stockport. In particular, it will explore the extent to which parents are seeking schools with specific ethnic and class populations ('people like us'). The project investigates the way in which schools are intimately connected to local spaces and the ways in which parents perceive and participate in their local communities. In-depth qualitative interviews with parents, as well as observations in schools, will be analysed to illuminate how people often view local areas as marked by specific race and class characteristics and how this influences their everyday interactions. It will also enable an analysis of how this affects the way they engage with local institutions, such as schools. The research will analyse how and why parents do or do not consider questions of race, ethnicity and class in their evaluations of schools. This links to questions of the extent to which they are engaged producing a sense of ethnic, race or class identity for their children. The research will also throw light on parents' perceptions of local areas and community and how this is influenced by their understanding of its class and ethnic make-up.This research examines the question of parental choice of secondary schools in Manchester and Stockport. In particular, it will explore the extent to which parents are seeking schools with specific ethnic and class populations ('people like us'). The project investigates the way in which schools are intimately connected to local spaces and the ways in which parents perceive and participate in their local communities. In-depth qualitative interviews with parents, as well as observations in schools, will be analysed to illuminate how people often view local areas as marked by specific race and class characteristics and how this influences their everyday interactions. It will also enable an analysis of how this affects the way they engage with local institutions, such as schools. The research will analyse how and why parents do or do not consider questions of race, ethnicity and class in their evaluations of schools. This links to questions of the extent to which they are engaged producing a sense of ethnic, race or class identity for their children. The research will also throw light on parents' perceptions of local areas and community and how this is influenced by their understanding of its class and ethnic make-up.

Dataset Card for "race"

  Dataset Summary

RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/ehovy/race.

City of LA job applicants by the job they applied for and demographic information. We are currently undergoing a data inventory to improve usability on the site. We're aware that this dataset is out of date but wanted to err on the side of making incomplete data available. Thank you for your patience, please contact the dataset owner or mayor.opendata@lacity.org with questions or ideas.

Relative concentration of the Sierra Nevada region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 775 block groups in the Sierra Nevada RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Sierra Nevada RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

This data set is no longer being updated and is historical, last update 10/10/2022.Provides the percentage of COVID-19 cases by race/ethnicity in Jefferson County, KY. In addition, percentage of Jefferson county vaccine recipients broken out by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. Fieldname Definition race description of race/ethnicity CensusCountPCT percentage of population make-up of Jefferson county ConfirmedCaseCountPCT percentage of confirmed cases by race/ethnicity (rounded to the whole percent) DeceasedCountPCT percentage of deceased cases by race/ethnicity (rounded to the whole percent) RecoveredCountPCT percentage of recovered cases by race/ethnicity (rounded to the whole percent) VaccinatedCountPCT percentage of Jefferson county vaccine recipients by race/ethnicity, excluding doses administered by Walgreens and CVS clinics. (rounded to the whole percent) Loaded Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to change For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

Abstract copyright UK Data Service and data collection copyright owner.Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. This is a teaching resource for those who are new to data analysis. It is a step-by-step guide starting from exploring a survey, understanding the structure of the survey data and then using the data to do some simple exercises to measure differences in health and wellbeing across ethnic groups. The survey used here is Understanding Society: the UK Household Longitudinal Study which interviews individuals in the sampled households every year. To make it easier to use the teaching dataset accompanying this teaching resource only includes responses given by adults (16+ year olds) during the first interview to questions about ethnicity, health and wellbeing and some key socio-demographic characteristics such as age, sex, education, income, labour market status etc. The statistical software used to construct the dataset is Stata, but it is also available to download in SPSS and tab-delimited text formats.Nandi, Alita and Wiltshire, Deborah. (2019). "Teaching Resource: Analysing ethnic differences in health using data from Understanding Society".For information on the main Understanding Society study, see SN 6614, Understanding Society and Harmonised BHPS.Latest edition informationFor the second edition (August 2020), updated data and documentation files were deposited. Main Topics: Social behaviour and attitudesMinoritiesGeneral health and well-being Multi-stage stratified random sample

This collection contains a cumulative datafile for The Los Angeles County Social Survey (LACSS) comprised of participants from years 1992 and 1994-1998. The LACSS continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Los Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. Data for this collection represents the LACSS conducted between February 1992 and June 1998. No data was included for the year 1993. Each year, Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

Between 2016 and 2019, nearly ** percent of white, non-Hispanic children and adolescents aged 3 to 17 years in the United States reported that they had current anxiety problems, while only *** percent of Asian, non-Hispanic children and adolescents reported the same. This statistic displays the percentage of children and adolescents in the U.S. who had or have ever had anxiety problems from 2016 to 2019, by race/ethnicity.

Relative concentration of the Central California region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) American population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 4,961 block groups in the Central California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Central California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEthnicityThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.Definition: The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.Respondents could choose one out of 19 tick-box response categories, including write-in response options.This dataset includes data relating to Leicester City and England overall.

This survey of minority groups was part of a larger project to investigate the patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. Conducted in Chicago and New York City, the survey was designed to assess the well-being of middle-aged, urban, ethnic minority adults living in both hyper-segregated neighborhoods and in areas with lower concentrations of minorities. Respondents' views were sought on issues relevant to quality of life, including health, childhood and family background, religion, race and ethnicity, personal beliefs, work experiences, marital and close relationships, financial situation, children, community involvement, and neighborhood characteristics. Questions on health explored the respondents' physical and emotional well-being, past and future attitudes toward health, physical limitations, energy level and appetite, amount of time spent worrying about health, and physical reactions to those worries. Questions about childhood and family background elicited information on family structure, the role of the parents with regard to child rearing, parental education, employment status, and supervisory responsibilities at work, the family financial situation including experiences with the welfare system, relationships with siblings, and whether as a child the respondent slept in the same bed as a parent or adult relative. Questions on religion covered religious preference, whether it is good to explore different religious teachings, and the role of religion in daily decision-making. Questions about race and ethnicity investigated respondents' backgrounds and experiences as minorities, including whether respondents preferred to be with people of the same racial group, how important they thought it was to marry within one's racial or ethnic group, citizenship, reasons for moving to the United States and the challenges faced since their arrival, their native language, how they would rate the work ethic of certain ethnic groups, their views on race relations, and their experiences with discrimination. Questions on personal beliefs probed for respondents' satisfaction with life and confidence in their opinions. Respondents were asked whether they had control over changing their life or their personality, and what age they viewed as the ideal age. They also rated people in their late 20s in the areas of physical health, contribution to the welfare and well-being of others, marriage and close relationships, relationships with their children, work situation, and financial situation. Questions on work experiences covered respondents' employment status, employment history, future employment goals, number of hours worked weekly, number of nights away from home due to work, exposure to the risk of accident or injury, relationships with coworkers and supervisors, work-related stress, and experience with discrimination in the workplace. A series of questions was posed on marriage and close relationships, including marital status, quality and length of relationships, whether the respondent had control over his or her relationships, and spouse/partner's education, physical and mental health, employment status, and work schedule. Questions on finance explored respondents' financial situation, financial planning, household income, retirement plans, insurance coverage, and whether the household had enough money. Questions on children included the number of children in the household, quality of respondents' relationships with their children, prospects for their children's future, child care coverage, and whether respondents had changed their work schedules to accommodate a child's illness. Additional topics focused on children's identification with their culture, their relationships with friends of different backgrounds, and their experiences with racism. Community involvement was another area of investigation, with items on respondents' role in child-rearing, participation on a jury, voting behavior, involvement in charitable organizations, volunteer experiences, whether they made monetary or clothing donations, and experiences living in an institutional setting or being homeless. Respondents were also queried about their neighborhoods, with items on neighborhood problems including racism, vandalism, crime, drugs, poor schools, teenag