This ethnicity dataset (GREG) is a digital version of the paper Soviet Narodov Mira atlas created in 1964. In 2010 the GREG (Geo-referencing of ethnic groups) project, used maps and data drawn from the Narodov Mira atlas to create a GIS (Geographic Information Systems) version of the atlas (2010). ETH ZurichFirst developed by G.P. Murdock in the 1940s, is an ethnographic classification system on human behavior, social life and customs, material culture, and human-ecological environments (2003). University of California

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.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

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.

Community Specific Profiles are grouped by race and ethnicity. We measure by race, ethnicity, and other demographics to understand the specific needs of different communities and evaluate effective service delivery and accountability. This dataset is the groupings used to combine projects with multiple levels and types of data standards. These include the minimum and comprehensive race and ethnicity categories from the City of Portland Rescue Plan Data Standards. They also include race and ethnicity categories in the HUD HMIS data standards.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60968

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined differences in youth's mental health and substance abuse needs in seven different racial/ethnic groups of justice-involved youth. Using de-identified data from the Survey of Youth in Residential Placement (SYRP), it was assessed whether differences in mental health and substance abuse needs and services existed in a racially/ethnically diverse sample of youth in custody. Data came from a nationally representative sample of 7,073 youth in residential placements across 36 states, representing five program types. An examination of the extent to which there were racial/ethnic disparities in the delivery of services in relation to need was also conducted. This examination included assessing the differences in substance-related problems, availability of substance services, and receipt of substance-specific counseling. One SAS data file (syrp2017.sas7bdat) is included as part of this collection and has 138 variables for 7073 cases, with demographic variables on youth age, sex, race and ethnicity. Also included as part of the data collection are two SAS Program (syntax) files for use in secondary analysis of youth mental health and substance use.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf

In the academic year of 2020/21 in the United States, about 9,545 Bachelor's degrees were earned by American Indian or Alaskan Native students. In that same year, white students earned about 1.17 million Bachelor's degrees.

The statistic reflects the origin of ethnic groups in Canada in 2021. That year, approximately 14.7 percent of the total population in Canada have English roots.

The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

Census 2021 data: 19 tick-box ethnic groups, by age, sex, and age and sex.

From 2013 to 2021, it was estimated that among different racial/ethnic groups of adults in the United States, Samoans presented the highest prevalence of diabetes, with 20.3 percent diagnosed with diabetes. This statistic depicts the prevalence of diabetes among adults in the United States from 2013 to 2021, by detailed race and ethnicity.

The most common blood type among the population in the United States is O-positive. Around 53 percent of the Latino-American population in the U.S. has blood type O-positive, while only around 37 percent of the Caucasian population has this blood type. The second most common blood type in the United States is A-positive. Around 33 percent of the Caucasian population in the United States has A-positive blood type. Blood type O-negative Those with blood type O-negative are universal donors as this type of blood can be used in transfusions for any blood type. O-negative blood type is most common in the U.S. among Caucasian adults. Around eight percent of the Caucasian population has type O-negative blood, while only around one percent of the Asian population has this blood type. Only around seven percent of all adults in the United States have O-negative blood type. Blood Donations The American Red Cross estimates that someone in the United States needs blood every two seconds. However, only around three percent of age-eligible people donate blood yearly. The percentage of adults who donated blood in the United States has not fluctuated much for the past two decades. In 2021, around 15 percent of U.S. adults donated blood, the same share reported in the year 2003.

Asian cuisine seems to have conquered the palate of Brazilian consumers. According to a big data study, Japanese food was by far the most popular type of ethnic food in Brazil in 2019, as more than ** percent of the consumers surveyed quoted it as their favorite. The second most preferred type of ethnic cuisine was Indian, with *** percent. When it comes to fast-food, McDonald's was the favorite fast-food restaurant among Brazilians.

*** 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 COVID-19 cases 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 Thursday.

Why does ethnic violence occur in some places but not others? This paper argues that the local ethnic configuration below the national level is an important determinant of how likely conflict is in any particular place. Existing studies of ethnicity and conflict focus on national-level fractionalization or dominance, but much of the politics surrounding ethnic groups’ grievances and disputes takes place at a more local level. We argue that the existence of multiple ethnic groups competing for resources and power at the level of sub-national administrative regions creates a significant constraint on the ability of states to mitigate ethnic groups’ grievances. This in turn increases the likelihood of conflict between ethnic groups and the state. In particular, we argue that diverse administrative regions dominated by one group should be most prone for conflict. Using new data on conflict and ethnic group composition at the region level, we test the theory and find that units with one demographically dominant ethnic group among multiple groups are most prone to conflict.

Time-series dataset of the demographic characteristics of the UK ethnic minority populations and religious groups up to 2006, to study ethnic and religious demographic diversity and its impact upon future population size, age-structure and the ethnic and religious composition of the UK population. This dataset is compiled from various existing data sources: 2001 Census, Labour Force Survey (LFS) and International Passenger Survey (IPS) data. In the absence of vital statistics by ethnic groups, indirect methods were used to estimate vital rates, including the ‘Own Child’ method applied to LFS household data to derive fertility estimates of ethnic and religious groups. Building on previous work, fertility rates of ethnic groups were produced up to 2006, distinguishing between UK-born and foreign-born populations. Migration rates were based on ONS International Migration Statistics (using IPS data), LFS and census data and projected on various assumptions. The results served population projections to mid-century and beyond of the main ethnic minority populations, including mixed populations, and using cohort-component methods. Furthermore, estimates of fertility rates for the major religious (and non-religious) groups were produced. Datasets include: (1) Calculated fertility estimates for all women aged 15 to 49 in the UK, by 5 years age group, by ethnic group, religion and place of birth (UK/non-UK), based on LFS data; (2) Data on mixed children by ethnic group of the mother; (3) Data on country of birth by ethnic group (all populations); (4) Data on immigration flow by country of origin. This project aims to analyse ethnic and religious demographic diversity, to investigate the potential for convergence of trends over time and its impact upon future population size, age-structure and the ethnic and religious composition of the UK population. Existing statistical sources (especially the 2001 Census, the Labour Force Survey (LFS) and the Office for National Statistics (ONS) Longitudinal Survey) will be used to produce time-series of the demographic characteristics of the ethnic minority populations and religious groups up to 2006. In the absence of vital statistics by ethnic groups, the Own Child method applied to LFS and census data will be used to derive fertility estimates of ethnic and religious groups. The results will serve population projections to mid-century and beyond of the main ethnic minority populations, including mixed populations, and using cohort-component methods. Migration rates will be based on ONS International Migration Statistics, LFS and census data and projected on various assumptions. Furthermore, estimates of fertility rates and other demographic information for the major religious (and non-religious) groups will be produced with a view to making preliminary projections of their future size. The potential convergence of the demographic characteristics of ethnic and religious groups will be analysed, including mixed unions as an indicator for integration. Derivation from existing data sources: Labour Force Survey data (output from analysis); ONS commissioned tables (census and IPS data).

Little research has been conducted on how internal migration of different ethnic groups, in and out of areas, contributes to population patterns. Research that has explored migration by ethnicity has compared 'white' with 'non-white' people to discuss patterns of segregation, ethnic concentration and majority population 'ghettos' or 'enclaves'. However, it is likely that there will be variations within the minority ethnic group that will offer important insights to these debates. This research will explore patterns of residential migration within different ethnic groups within England and Wales. Specifically, whether there is a tendency for people from different ethnic groups to move into or out of areas where their own ethnic group has a high or low density. These research questions will be explored through secondary analysis of the 2001 census using a specially commissioned table containing information about migration and ethnicity. Inflows and outflows of individual ethnic groups (as defined by the 2001 census) will be separately measured at the ward level (to assess local level migration). Patterns will be mapped using GIS software. This detailed analysis will help to establish if patterns of internal migration for different ethnic groups are related to the densities of their own and other groups. N/A - secondary data analysis of the census 2001

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

ABSTRACTIn March 2024, the Office of Management and Budget updated guidelines for measuring race/ethnicity on federal forms in the United States (US). By March 2029, Middle Eastern and North African (MENA) Americans will have a new category. This population was previously included in the definition for the White race. It is unknown how this change will alter health estimates for other racial/ethnic groups, particularly among the aging population that has become increasingly diverse. Using cognitive difficulty as the health outcome of interest, our objectives were to 1) compare the prevalence of cognitive difficulty using 2020 and 2030 US Census racial/ethnic categories and 2) determine whether the odds of cognitive difficulty differs with and without a MENA checkbox. We used 2018-2022 American Community Survey data (ages >=65 years; n=3,351,611). We categorized race/ethnicity based on 2020 US Census categories (White, Black, AI/AN, Asian, NH/OPI, Some Other Race, Two or More Races, Hispanic/Latino) then created a separate category for older adults of MENA descent using questions on ancestry and place of birth to align with 2030 categories. Bivariate statistics and multivariable logistic regression models were calculated. Using 2020 categories, the odds of cognitive difficulty were higher among all racial/ethnic groups compared to Whites. Using 2030 categories, the odds of cognitive difficulty were 1.53 times greater (95%CI=1.43-1.62) among MENA compared to Whites. The odds of cognitive difficulty using 2020 and 2030 US Census racial/ethnic categories for other groups were not significantly different. Our results highlight the disparity in cognitive health among MENA and White older adults. Including a separate MENA checkbox on the ACS starting in 2027 is critical to provide baseline data and move forward discussions on health disparities among older adults.