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

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.

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

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.

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 95 percent of the consumers surveyed quoted it as their favorite. The second most preferred type of ethnic cuisine was Indian, with 1.5 percent. When it comes to fast-food, McDonald's was the favorite fast-food restaurant among Brazilians.

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.

Information about the ethnic affiliation(s) and characteristics of a human population. Includes, for example, information about: the ethnic groups located within a geographic region, their community social structures, their mutual associations and conflicts with other groups, their historic roles and influence, and the physical distribution of their members. Ethnic groups are human populations whose members identify with each other, usually on the basis of having a common cultural traditions and heritage (for example: as distinguished by customs, language, religious practices, or common history) or a presumed common genealogy or ancestry.

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.

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

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.

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.

This web map shows the distribution of racial and ethnic groups in the City of Redlands. Groups include non-Hispanic White, Hispanic, non-Hispanic Asian, non-Hispanic American Indian, non-Hispanic Black, non-Hispanic other race, and non-Hispanic Pacific Islander.Feature layers include Census tract and block groups and Redlands boundary.

As of 2023, more than one fifth of Black women in the United States reported to have been treated unfairly or with disrespect in the last three years by a health care provider or staff because of their racial background. Subsequently, Black men were the second most discriminated against group in the healthcare sector, with 13 percent experiencing unfair or disrespectful treatment in the last three years.

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.

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.

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.

Ethnic diversity is generally associated with less social capital and lower levels of trust. However, most empirical evidence for this relationship is focused on generalized trust, rather than more theoretically appropriate measures of group-based trust. This paper evaluates the relationship between ethnic diversity – at national, regional, and local levels – and the degree to which coethnics are trusted more than non-coethnics, a value I call the “coethnic trust premium.” Using public opinion data from sixteen African countries, I find that citizens of ethnically diverse states express, on average, more ethnocentric trust. However, within countries, regional ethnic diversity is actually associated with less ethnocentric trust. This same negative pattern between diversity and ethnocentric trust appears across districts and enumeration areas within Malawi. I then show, consistent with these patterns, that diversity is only detrimental to intergroup trust at the national level in the presence of ethnic group segregation. These results highlight the importance of the spatial distribution of ethnic groups on intergroup relations, and question the utility of micro-level studies of interethnic interactions for understanding macro-level group dynamics.

This method returns Census 2021 estimates that classify households by the diversity in ethnic group of household members in different relationships.

This dataset classifies households by whether members identify as having the same or different ethnic groups. If multiple ethnic groups are present, this identifies whether they differ between generations or partnerships within the household.

Multiple ethnic groups in household are split into 6 categories including total.

The estimates are as at Census Day, 21 March 2021.

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 Documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

In 2019, the Indian and White ethnic groups had the lowest percentage of recent internet users (90.4% and 90.5%). The Chinese group had the highest (98.6%).