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 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.

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.

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.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.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

In 2021, the distribution by race and ethnicity reveals how diverse family caregivers are in the United States. That year, nearly ********** of family caregivers in the United States were white. However, with a ** percent share in 2021, the second-most common race and ethnicity of family caregivers was Hispanic, followed by Black/African American.

How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.

We investigate the empirical relationship between ethnicity and culture, defined as a vector of traits reflecting norms, values, and attitudes. Using survey data for 76 countries, we find that ethnic identity is a significant predictor of cultural values, yet that within-group variation in culture trumps between-group variation. Thus, in contrast to a commonly held view, ethnic and cultural diversity are unrelated. Although only a small portion of a country's overall cultural heterogeneity occurs between groups, we find that various political economy outcomes (such as civil conflict and public goods provision) worsen when there is greater overlap between ethnicity and culture.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97. COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Twin Groves by race. It includes the distribution of the Non-Hispanic population of Twin Groves across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Twin Groves across relevant racial categories.

Key observations

Of the Non-Hispanic population in Twin Groves, the largest racial group is Black or African American alone with a population of 159 (58.24% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Twin Groves
• Population: The population of the racial category (for Non-Hispanic) in the Twin Groves is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Twin Groves total Non-Hispanic 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 Twin Groves Population by Race & Ethnicity. You can refer the same here

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

This data package consists of 26 datasets all containing statistical data relating to the population and particular groups within it belonging to different countries, mostly the United States.

This statistic shows the percentage of adults in the U.S. who used select remedies to treat common colds as of February 2017, by ethnicity. It was found that 51 percent of African Americans drink lots of tea to fight the common cold, compared to 35 percent of White respondents.

About this dataset

Context

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

Key observations

The percent distribution of Brooklyn borough population by race (across all racial categories recognized by the U.S. Census Bureau): 39.26% are white, 28.99% are Black or African American, 0.59% are American Indian and Alaska Native, 12.04% are Asian, 0.05% are Native Hawaiian and other Pacific Islander, 10.23% are some other race and 8.84% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Brooklyn borough
• Population: The population of the racial category (excluding ethnicity) in the Brooklyn borough is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Brooklyn borough 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 Brooklyn borough Population by Race & Ethnicity. You can refer the same here

This dataset contains data files for a research paper, "Ethnicity-Based Name Partitioning for Author Name Disambiguation Using Supervised Machine Learning," published in the Journal of the Association for Information Science and Technology.Four zipped files are uploaded.Each zipped file contains five data files: signatures_train.txt, signatures_test.txt, records.txt, clusters_train.txt, and clusters_test.txt.1. 'Signatures' files contain lists of name instances. Each name instance (a row) is associated with information as follows. - 1st column: instance id (numeric): unique id assigned to a name instance - 2nd column: paper id (numeric): unique id assigned to a paper in which the name instance appears as an author name - 3rd column: byline position (numeric): integer indicating the position of the name instance in the authorship byline of the paper - 4th column: author name (string): name string formatted as surname, comma, and forename(s) - 5th column: ethnic name group (string): name ethnicity assigned by Ethnea to the name instance - 6th column: affiliation (string): affiliation associated with the name instance, if available in the original data - 7th column: block (string): simplified name string of the name instance to indicate its block membership (surname and first forename initial) - 8th column: author id (string): unique author id (i.e., author label) assigned by the creators of the original data2. 'Records' files contain lists of papers. Each paper is associated with information as follows. -1st column: paper id (numeric): unique paper id; this is the unique paper id (2nd column) in Signatures files -2nd column: year (numeric): year of publication * Some papers may have wrong publication years due to incorrect indexing or delayed updates in original data -3rd column: venue (string): name of journal or conference in which the paper is published * Venue names can be in full string or in a shortened format according to the formats in original data -4th column: authors (string; separated by vertical bar): list of author names that appear in the paper's byline * Author names are formatted into surname, comma, and forename(s) -5th column: title words (string; separated by space): words in a title of the paper. * Note that common words are stop-listed and each remaining word is stemmed using Porter's stemmer.3. 'Clusters' files contain lists of clusters. Each cluster is associated with information as follows. -1st column: cluster id (numeric): unique id of a cluster -2nd column: list of name instance ids (Signatures - 1st column) that belong to the same unique author id (Signatures - 8th column). Signatures and Clusters files consist of two subsets - train and test files - of original labeled data which are randomly split into 50%-50% by the authors of this study.Original labeled data for AMiner.zip, KISTI.zip, and GESIS.zip came from the studies cited below.If you use one of the uploaded data files, please cite them accordingly.[AMiner.zip]Tang, J., Fong, A. C. M., Wang, B., & Zhang, J. (2012). A Unified Probabilistic Framework for Name Disambiguation in Digital Library. IEEE Transactions on Knowledge and Data Engineering, 24(6), 975-987. doi:10.1109/Tkde.2011.13Wang, X., Tang, J., Cheng, H., & Yu, P. S. (2011). ADANA: Active Name Disambiguation. Paper presented at the 2011 IEEE 11th International Conference on Data Mining.[KISTI.zip]Kang, I. S., Kim, P., Lee, S., Jung, H., & You, B. J. (2011). Construction of a Large-Scale Test Set for Author Disambiguation. Information Processing & Management, 47(3), 452-465. doi:10.1016/j.ipm.2010.10.001Note that the original KISTI data contain errors and duplicates. This study reuses the revised version of KISTI reported in a study below.Kim, J. (2018). Evaluating author name disambiguation for digital libraries: A case of DBLP. Scientometrics, 116(3), 1867-1886. doi:10.1007/s11192-018-2824-5[GESIS.zip]Momeni, F., & Mayr, P. (2016). Evaluating Co-authorship Networks in Author Name Disambiguation for Common Names. Paper presented at the 20th international Conference on Theory and Practice of Digital Libraries (TPDL 2016), Hannover, Germany.Note that this study reuses the 'Evaluation Set' among the original GESIS data which was added titles by a study below.Kim, J., & Kim, J. (2020). Effect of forename string on author name disambiguation. Journal of the Association for Information Science and Technology, 71(7), 839-855. doi:10.1002/asi.24298[UM-IRIS.zip]This labeled dataset was created for this study. For description about the labeling method, please see 'Method' in the paper below.Kim, J., Kim, J., & Owen-Smith, J. (In print). Ethnicity-based name partitioning for author name disambiguation using supervised machine learning. Journal of the Association for Information Science and Technology. doi:10.1002/asi.24459.For details on the labeling method and limitations, see the paper below.Kim, J., & Owen-Smith, J. (2021). ORCID-linked labeled data for evaluating author name disambiguation at scale. Scientometrics. doi:10.1007/s11192-020-03826-6

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.

About this dataset

Context

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

Key observations

The percent distribution of Twin Groves population by race (across all racial categories recognized by the U.S. Census Bureau): 38.27% are white, 57.40% are Black or African American and 4.33% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Twin Groves
• Population: The population of the racial category (excluding ethnicity) in the Twin Groves is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Twin Groves 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 Twin Groves Population by Race & Ethnicity. You can refer the same here

In 2022, the child abuse rate for children of Hispanic origin was at 7, indicating 7 out of every 1,000 Hispanic children in the United States suffered from some sort of abuse. This rate was highest among American Indian or Alaska Native children, with 14.3 children out of every 1,000 experiencing some form of abuse. Child abuse in the U.S. The child abuse rate in the United States is highest among American Indian or Alaska Native victims, followed by African-American victims. It is most common among children between two to five years of age. While child abuse cases are fairly evenly distributed between girls and boys, more boys than girls are victims of abuse resulting in death. The most common type of maltreatment is neglect, followed by physical abuse. Risk factors Child abuse is often reported by teachers, law enforcement officers, or social service providers. In the large majority of cases, the perpetrators of abuse were a parent of the victim. Risk factors, such as teen pregnancy, violent crime, and poverty that are associated with abuse and neglect have been found to be quite high in the United States in comparison to other countries.

Estimating differences between racial/ethnic groups often requires merging demographic variables from one dataset to variables of interest in another. A common method merges Home Mortgage Disclosure Act data to property databases. One alternative is to acquire this information from voter registration files; another is to predict race with a name-based algorithm. Compared to Census data, which method is more representative varies by location and group. We explore the practical implications of each method by using the matched samples in two empirical applications. Researchers can arrive at different conclusions about racial/ethnic disparities depending on the method selected.