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Graph and download economic data for Net Change in Total Assets and Liabilities by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUCHGASLILB0902M) from 1990 to 2023 about change, asian, liabilities, white, Net, assets, and USA.
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.
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Graph and download economic data for Expenditures: Miscellaneous Household Equipment by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUMISCHHEQLB0902M) from 1984 to 2023 about miscellaneous, asian, equipment, white, expenditures, households, and USA.
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License information was derived automatically
Context
The dataset tabulates the population of Banks by race. It includes the population of Banks across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Banks across relevant racial categories.
Key observations
The percent distribution of Banks population by race (across all racial categories recognized by the U.S. Census Bureau): 75.81% are white, 0.72% are Black or African American, 1.43% are Asian, 0.72% are Native Hawaiian and other Pacific Islander, 8.27% are some other race and 13.05% are multiracial.
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:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Banks Population by Race & Ethnicity. You can refer the same here
In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.
Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.
The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.
The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.
The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.
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License information was derived automatically
Context
The dataset tabulates the population of Oak Ridge by race. It includes the population of Oak Ridge across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Oak Ridge across relevant racial categories.
Key observations
The percent distribution of Oak Ridge population by race (across all racial categories recognized by the U.S. Census Bureau): 81.17% are white, 6.24% are Black or African American, 0.60% are American Indian and Alaska Native, 2.71% are Asian, 0.21% are Native Hawaiian and other Pacific Islander, 1.94% are some other race and 7.12% are multiracial.
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:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Oak Ridge Population by Race & Ethnicity. You can refer the same here
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?
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Graph and download economic data for Consumer Unit Characteristics: Percent Women Reference Persons by Race: White, Asian, and All Other Races, Not Including Black or African American (CXU980220LB0902M) from 1984 to 2023 about consumer unit, females, asian, white, percent, personal, and USA.
Section 95 of the Criminal Justice Act 1991 requires the Government to publish statistical data to assess whether any discrimination exists in how the CJS treats individuals based on their ethnicity.
These statistics are used by policy makers, the agencies who comprise the CJS and others (e.g. academics, interested bodies) to monitor differences between ethnic groups, and to highlight areas where practitioners and others may wish to undertake more in-depth analysis. The identification of differences should not be equated with discrimination as there are many reasons why apparent disparities may exist. The main findings are:
The 2012/13 Crime Survey for England and Wales shows that adults from self-identified Mixed, Black and Asian ethnic groups were more at risk of being a victim of personal crime than adults from the White ethnic group. This has been consistent since 2008/09 for adults from a Mixed or Black ethnic group; and since 2010/11 for adults from an Asian ethnic group. Adults from a Mixed ethnic group had the highest risk of being a victim of personal crime in each year between 2008/09 and 2012/13.
Homicide is a rare event, therefore, homicide victims data are presented aggregated in three-year periods in order to be able to analyse the data by ethnic appearance. The most recent period for which data are available is 2009/10 to 2011/12.
The overall number of homicides has decreased over the past three three-year periods. The number of homicide victims of White and Other ethnic appearance decreased during each of these three-year periods. However the number of victims of Black ethnic appearance increased in 2006/07 to 2008/09 before falling again in 2009/10 to 2011/12.
For those homicides where there is a known suspect, the majority of victims were of the same ethnic group as the principal suspect. However, the relationship between victim and principal suspect varied across ethnic groups. In the three-year period from 2009/10 to 2011/12, for victims of White ethnic appearance the largest proportion of principal suspects were from the victim’s own family; for victims of Black ethnic appearance, the largest proportion of principal suspects were a friend or acquaintance of the victim; while for victims of Asian ethnic appearance, the largest proportion of principal suspects were strangers.
Homicide by sharp instrument was the most common method of killing for victims of White, Black and Asian ethnic appearance in the three most recent three-year periods. However, for homicide victims of White ethnic appearance hitting and kicking represented the second most common method of killing compared with shooting for victims of Black ethnic appearance, and other methods of killing for victims of Asian ethnic appearance.
In 2011/12, a person aged ten or older (the age of criminal responsibility), who self-identified as belonging to the Black ethnic group was six times more likely than a White person to be stopped and searched under section 1 (s1) of the Police and Criminal Evidence Act 1984 and other legislation in England and Wales; persons from the Asian or Mixed ethnic group were just over two times more likely to be stopped and searched than a White person.
Despite an increase across all ethnic groups in the number of stops and searches conducted under s1 powers between 2007/08 and 2011/12, the number of resultant arrests decreased across most ethnic groups. Just under one in ten stop and searches in 2011/12 under s1 powers resulted in an arrest in the White and Black self-identified ethnic groups, compared with 12% in 2007/08. The proportion of resultant arrests has been consistently lower for the Asian self-identified ethnic group.
In 2011/12, for those aged 10 or older, a Black person was nearly three times more likely to be arrested per 1,000 population than a White person, while a person from the Mixed ethnic group was twice as likely. There was no difference in the rate of arrests between Asian and White persons.
The number of arrests decreased in each year between 2008/09 and 2011/12, consistent with a downward trend in police recorded crime since 2004/05. Overall, the number of arrests decreased for all ethnic groups between 2008/09 and 2011/12, however arrests of suspects from the Black, Asian and Mixed ethnic groups peaked in 2010/11.
Arrests for drug offences and sexual offences increased for suspects in all ethnic groups except the Chinese or Other ethnic group between 2008/09 and 2011/12. In addition, there were increases in arrests for burglary, robbery and the other offences category for suspects from the Black and Asian ethnic groups.
The use of out of court disposals (Penalty Notices for Disorder and caution
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of London by race. It includes the population of London across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of London across relevant racial categories.
Key observations
The percent distribution of London population by race (across all racial categories recognized by the U.S. Census Bureau): 89.24% are white, 1.70% are Black or African American, 0.23% are American Indian and Alaska Native, 1.86% are Asian, 0.02% are Native Hawaiian and other Pacific Islander, 0.15% are some other race and 6.79% are multiracial.
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:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for London Population by Race & Ethnicity. You can refer the same here
In 2023, TV consumption in the United States was highest among African Americans, who watched an average of **** hours of TV each day. While the viewing time dropped among white, Black, and Hispanic consumers, Asian Americans’ time spent grew from 2021 to 2022 to around *** hours per day. TV and streaming engagement among racial groups As of mid 2022, The U.S. population still spent most of their time watching TV and streaming content on traditional media, such as cable and broadcast. Roughly ** percent of their viewing time was allocated to streaming. It was also observed that Hispanics were more likely to use online video platforms than any other ethnic group, with a viewing time share of over ** percent. By contrast, using streaming services accounted for around ****third of the total viewing time among white and Asian consumers. Traditional TV vs. digital video The way consumers watch TV shows and movies has changed significantly. A forecast showed that traditional TV consumption among Americans will continue to steadily decline in the upcoming years. Meanwhile, with an increasing number of consumers adopting streaming and social videos, digital video soars, outpacing TV viewing time by the end of 2023.
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License information was derived automatically
Context
The dataset tabulates the population of Milan town by race. It includes the population of Milan town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Milan town across relevant racial categories.
Key observations
The percent distribution of Milan town population by race (across all racial categories recognized by the U.S. Census Bureau): 84.63% are white, 2.60% are Black or African American, 1.90% are Asian, 2.39% are some other race and 8.49% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Milan town Population by Race & Ethnicity. You can refer the same here
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License information was derived automatically
Context
The dataset tabulates the population of Rosemead by race. It includes the population of Rosemead across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Rosemead across relevant racial categories.
Key observations
The percent distribution of Rosemead population by race (across all racial categories recognized by the U.S. Census Bureau): 10.17% are white, 0.46% are Black or African American, 0.93% are American Indian and Alaska Native, 64.45% are Asian, 0.01% are Native Hawaiian and other Pacific Islander, 20.13% are some other race and 3.85% are multiracial.
https://i.neilsberg.com/ch/rosemead-ca-population-by-race.jpeg" alt="Rosemead population by race">
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:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Rosemead Population by Race & Ethnicity. You can refer the same here
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Graph and download economic data for Expenditures: Pork by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUPORKLB0902M) from 1984 to 2023 about meat, asian, white, expenditures, and USA.
FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
Race categories for White, Black, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, other race, and two or more races are non-Hispanic. Due to rounding, race and ethnicity categories may not sum to 100%. Estimates are based on provisional data and subject to change.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.
In the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.
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 i
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of New Germany by race. It includes the population of New Germany across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of New Germany across relevant racial categories.
Key observations
The percent distribution of New Germany population by race (across all racial categories recognized by the U.S. Census Bureau): 96.24% are white, 0.17% are Black or African American, 0.34% are Asian, 0.85% are some other race and 2.39% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for New Germany Population by Race & Ethnicity. You can refer the same here
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Graph and download economic data for Expenditures: Other Food at Home by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUOTHRFOODLB0902M) from 1984 to 2023 about asian, white, expenditures, food, and USA.
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Graph and download economic data for Net Change in Total Assets and Liabilities by Race: White, Asian, and All Other Races, Not Including Black or African American (CXUCHGASLILB0902M) from 1990 to 2023 about change, asian, liabilities, white, Net, assets, and USA.