This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

BackgroundA significantly higher proportion of UK Black ethnic adults live with overweight or obesity, compared to their White British counterparts. The role of obesity in excess infection rates and mortality from COVID-19 has increased the need to understand if weight management interventions are appropriate and effective for Black ethnic groups. There is a paucity of existing research on weight management services in Black populations, and whether anticipated or experienced institutional and interpersonal racism in the healthcare and more widely affects engagement in these services. Understanding the lived experience of target populations and views of service providers delivering programmes is essential for timely service improvement.MethodsA qualitative study using semi-structured interviews was conducted in June–October 2021 among 18 Black African and Black Caribbean men and women interested in losing weight and 10 weight management service providers.ResultsThe results highlighted a positive view of life in the United Kingdom (UK), whether born in the UK or born abroad, but one which was marred by racism. Weight gain was attributed by participants to unhealthy behaviours and the environment, with improving appearance and preventing ill health key motivators for weight loss. Participants relied on self-help to address their overweight, with the role of primary care in weight management contested as a source of support. Anticipated or previously experienced racism in the health care system and more widely, accounted for some of the lack of engagement with services. Participants and service providers agreed on the lack of relevance of existing services to Black populations, including limited culturally tailored resources. Community based, ethnically matched, and flexibly delivered weight management services were suggested as ideal, and could form the basis of a set of recommendations for research and practice.ConclusionCultural tailoring of existing services and new programmes, and cultural competency training are needed. These actions are required within systemic changes, such as interventions to address discrimination. Our qualitative insights form the basis for advancing further work and research to improve existing services to address the weight-related inequality faced by UK Black ethnic groups.

This statistic shows the number of Army National Guard members in the United States from 1995 to 2010 by ethnic group. The Army National Guard had 291,000 white and 48,000 black members in 2010.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

This dataset represents ethnic group (19 tick-box level) by economic activity status and by occupation, for England and Wales combined. The census data are also broken down by age and by sex for each subtopic.

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.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

This dataset shows population counts for usual residents aged between 16 to 64 years old only. This is to focus on ethnic groups differences among the working age. Population counts in these tables may be different from other publications which use different age breakdowns.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This 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.

For quality information in general, please read more from here.

For specific quality information about labour market, please read more from here

Ocupation counts classifiy people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation. (Occupation is classified using the Standard Occupation Classification 2020 version). Details of SOC code can be found here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

Table showing ethnic group statistics by aggregated groupings.

Categories covered:

• White - includes White British, Irish, Gypsy or Irish Traveller, and Other
• Black - includes Black African, Caribbean, and Other
• Asian - includes Indian, Pakistani, Bangladeshi, Chinese and Other
• Mixed/Other - includes White and Black Caribbean, White and Asian, White and Black African, Other Mixed, Arab and any Other ethnicity not covered above.

Figures may not add exactly due to rounding. Numbers rounded to the nearest thousand.

Data is from the Annual Population Survey.

About this dataset

Context

The dataset tabulates the Black population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Black.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Black, AL Population Breakdown by Race
• Black, AL Non-Hispanic Population Breakdown by Race
• Black, AL Hispanic or Latino Population Distribution by Their Ancestries

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/.

Report from the North Yorkshire Equality and Diversity Strategic Partnership - September 2016

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

About this dataset

Context

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

Key observations

The percent distribution of Washington population by race (across all racial categories recognized by the U.S. Census Bureau): 40.46% are white, 44.66% are Black or African American, 0.29% are American Indian and Alaska Native, 4.10% are Asian, 0.05% are Native Hawaiian and other Pacific Islander, 4.76% are some other race and 5.69% are multiracial.

https://i.neilsberg.com/ch/washington-dc-population-by-race.jpeg" alt="Washington population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Washington
• Population: The population of the racial category (excluding ethnicity) in the Washington is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Washington 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 Washington Population by Race & Ethnicity. You can refer the same here

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Dataset population: Persons

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Ethnic group

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

This topic contains ethnic group write-in responses without reference to the five broad ethnic group categories, e.g. all Irish people, irrespective of whether they are White, Mixed/multiple ethnic groups, Asian/Asian British, Black/African/Caribbean/Black British or Other ethnic group, are in the 'Irish' response category. This topic was created as part of the commissioned table processing.

General health

General health is a self-assessment of a person's general state of health. People were asked to assess whether their health was very good, good, fair, bad or very bad.

For England and Wales, this assessment is not based on a person's health over any specified period of time.

For Northern Ireland, 'General health' refers to a person's health over the 12 months prior to Census day (27 March 2011).

Sex

The classification of a person as either male or female.

Biometric Attack Dataset, Black People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of black people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/black-people-liveness-detection-video-dataset.

On 31 March 2024, 91.6% of police officers were White, and 8.4% were from Asian, Black, Mixed, and Other ethnic backgrounds.

Students from the Chinese ethnic group had the highest entry rate into higher education in every year from 2006 to 2024.

This measures percentage point differences in the proportions of each Black Minority and Ethnic (BME) group of young people on youth justice disposals against the proportions of each BME group in the equivalent local population. BME main groups are classified as those main groups in the 2001 census. The ONS latest mid-year estimated figures used for the local youth population by BME group. It is important to do the analysis by each of the ethnic groups and not the overall BME group, otherwise the under-represented groups counter-balance the over-represented groups – giving the false impression there is no over-representation of particular BME groups. Areas would only need to submit values for the number of offenders in each ethnic group, as the local population data would be sourced from ONS to do the required calculations. Young people are those aged 10-17 years.

Households from the Black Caribbean, Mixed White and Black Caribbean, and Bangladeshi ethnic groups were most likely to rent social housing in the 2 years from April 2021 to March 2023.

Key Stage 4 is the stage of the National Curriculum between ages 14 and 16 years. This indicator relates to examinations taken at the end of the Key Stage. Minority ethnic groups measured for the purposes of this indicator include any ethnic group where there are over 30 pupils from that group in any particular cohort. Good performance is typified by higher percentages of pupils attaining 5 A*-C, including English and mathematics, accompanied by a narrowing of the attainment gap between minority ethnic pupils and all pupils.