10 datasets found
  1. What is the most common race/ethnicity?

    • hub.arcgis.com
    Updated Apr 14, 2020
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    Urban Observatory by Esri (2020). What is the most common race/ethnicity? [Dataset]. https://hub.arcgis.com/maps/2603a03fc55244c19f7f73d04cd53cea
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    Dataset updated
    Apr 14, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    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.

  2. American Identity and Representation Survey, 2012

    • icpsr.umich.edu
    ascii, delimited, r +3
    Updated Jul 22, 2016
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    Schildkraut, Deborah (2016). American Identity and Representation Survey, 2012 [Dataset]. http://doi.org/10.3886/ICPSR36410.v1
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    delimited, stata, r, ascii, spss, sasAvailable download formats
    Dataset updated
    Jul 22, 2016
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Schildkraut, Deborah
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/36410/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36410/terms

    Time period covered
    2012
    Area covered
    United States
    Description

    This survey was designed to investigate whether having psychological connections to particular groups (ex: racial, ethnic, and national origin groups) and perceptions of discrimination lead to alienation from the structure and operation of representative democracy in the United States. The data allow for comparative ethnic analyses of people's views regarding the representative-constituent relationship and of the conditions under which group identifications and perceptions of discrimination matter. The survey includes oversamples of Black, Latino, and Asian respondents. A Spanish version of the survey was available. Demographic information retrieved about respondents include age, race/ethnicity, gender, education (highest degree received), employment status, marital status, religion, household size and income. In addition, ancestry was assessed with the question, "From what countries or parts of the world did your ancestors come?" Respondents also reported United States citizenship status, primary home language, and nationality. Variables focusing on respondent perceived representation in the United States include political ideology and political party affiliation.

  3. d

    Educational Attainment of Washington Population by Age, Race/Ethnicity/, and...

    • catalog.data.gov
    • data.wa.gov
    • +1more
    Updated Sep 15, 2023
    + more versions
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    data.wa.gov (2023). Educational Attainment of Washington Population by Age, Race/Ethnicity/, and PUMA Region [Dataset]. https://catalog.data.gov/dataset/educational-attainment-of-washington-population-by-age-race-ethnicity-and-puma-region
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    Dataset updated
    Sep 15, 2023
    Dataset provided by
    data.wa.gov
    Area covered
    Washington
    Description

    The American Community Survey (ACS) is designed to estimate the characteristic distribution of populations and estimated counts should only be used to calculate percentages. They do not represent the actual population counts or totals. Beginning in 2019, the Washington Student Achievement Council (WSAC) has measured educational attainment for the Roadmap Progress Report using one-year American Community Survey (ACS) data from the United States Census Bureau. These public microdata represents the most current data, but it is limited to areas with larger populations leading to some multi-county regions*. *The American Community Survey is not the official source of population counts. It is designed to show the characteristics of the nation's population and should not be used as actual population counts or housing totals for the nation, states or counties. The official population count — including population by age, sex, race and Hispanic origin — comes from the once-a-decade census, supplemented by annual population estimates (which do not typically contain educational attainment variables) from the following groups and surveys: -- Washington State Office of Financial Management (OFM): https://www.ofm.wa.gov/washington-data-research/population-demographics -- US Census Decennial Census: https://www.census.gov/programs-surveys/decennial-census.html and Population Estimates Program: https://www.census.gov/programs-surveys/popest.html **In prior years, WSAC used both the five-year and three-year (now discontinued) data. While the 5-year estimates provide a larger sample, they are not recommended for year to year trends and also are released later than the one-year files. Detailed information about the ACS at https://www.census.gov/programs-surveys/acs/guidance.html

  4. Race and Ethnicity 2018-2022 - STATES

    • mce-data-uscensus.hub.arcgis.com
    • covid19-uscensus.hub.arcgis.com
    Updated Feb 5, 2024
    + more versions
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    US Census Bureau (2024). Race and Ethnicity 2018-2022 - STATES [Dataset]. https://mce-data-uscensus.hub.arcgis.com/maps/973245d9cd914f58a8fe87baacea1f4a
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    Dataset updated
    Feb 5, 2024
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    US Census Bureau
    Area covered
    Description

    This layer shows Race and Ethnicity. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of population that are Hispanic or Latino (of any race). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B02001, B03001, DP05Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

  5. u

    American Community Survey

    • gstore.unm.edu
    csv, geojson, gml +5
    Updated Mar 6, 2020
    + more versions
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    Earth Data Analysis Center (2020). American Community Survey [Dataset]. https://gstore.unm.edu/apps/rgis/datasets/5991c4f8-db89-49d1-a501-1f18e7371e21/metadata/FGDC-STD-001-1998.html
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    zip(1), csv(5), xls(5), geojson(5), gml(5), json(5), kml(5), shp(5)Available download formats
    Dataset updated
    Mar 6, 2020
    Dataset provided by
    Earth Data Analysis Center
    Time period covered
    2017
    Area covered
    West Bounding Coordinate -109.05017 East Bounding Coordinate -103.00196 North Bounding Coordinate 37.000293 South Bounding Coordinate 31.33217, New Mexico
    Description

    A broad and generalized selection of 2013-2017 US Census Bureau 2017 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

  6. u

    American Community Survey

    • gstore.unm.edu
    csv, geojson, gml +5
    Updated Mar 6, 2020
    + more versions
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    Earth Data Analysis Center (2020). American Community Survey [Dataset]. https://gstore.unm.edu/apps/rgis/datasets/474fef30-414f-4269-b37a-5103c84b141f/metadata/FGDC-STD-001-1998.html
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    json(5), gml(5), shp(5), kml(5), csv(5), xls(5), zip(1), geojson(5)Available download formats
    Dataset updated
    Mar 6, 2020
    Dataset provided by
    Earth Data Analysis Center
    Time period covered
    2018
    Area covered
    New Mexico, West Bounding Coordinate -109.05017 East Bounding Coordinate -103.00196 North Bounding Coordinate 37.000293 South Bounding Coordinate 31.33217
    Description

    A broad and generalized selection of 2014-2018 US Census Bureau 2018 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

  7. u

    Evidence for Equality National Survey: a Survey of Ethnic Minorities During...

    • beta.ukdataservice.ac.uk
    Updated 2024
    + more versions
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    N. Finney; J. Nazroo; N. Shlomo; D. Kapadia; L. Becares; B. Byrne (2024). Evidence for Equality National Survey: a Survey of Ethnic Minorities During the COVID-19 Pandemic, 2021 [Dataset]. http://doi.org/10.5255/ukda-sn-9116-1
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    Dataset updated
    2024
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    datacite
    Authors
    N. Finney; J. Nazroo; N. Shlomo; D. Kapadia; L. Becares; B. Byrne
    Description
    The Centre on the Dynamics of Ethnicity (CoDE), led by the University of Manchester with the Universities of St Andrews, Sussex, Glasgow, Edinburgh, LSE, Goldsmiths, King's College London and Manchester Metropolitan University, designed and carried out the Evidence for Equality National Survey (EVENS), with Ipsos as the survey partner. EVENS documents the lives of ethnic and religious minorities in Britain during the coronavirus pandemic and is, to date, the largest and most comprehensive survey to do so.

    EVENS used online and telephone survey modes, multiple languages, and a suite of recruitment strategies to reach the target audience. Words of Colour coordinated the recruitment strategies to direct participants to the survey, and partnerships with 13 voluntary, community and social enterprise (VCSE) organisations[1] helped to recruit participants for the survey.

    The ambition of EVENS was to better represent ethnic and religious minorities compared to existing data sources regarding the range and diversity of represented minority population groups and the topic coverage. Thus, the EVENS survey used an 'open' survey approach, which requires participants to opt-in to the survey instead of probability-based approaches that invite individuals to participate following their identification within a pre-defined sampling frame. This 'open' approach sought to overcome some of the limitations of probability-based methods in order to reach a large number and diverse mix of people from religious and ethnic minorities.

    EVENS included a wide range of research and policy questions, including education, employment and economic well-being, housing, social, cultural and political participation, health, and experiences of racism and discrimination, particularly with respect to the impact of the COVID-19 pandemic. Crucially, EVENS covered a full range of racial, ethnic and religious groups, including those often unrepresented in such work (such as Chinese, Jewish and Traveller groups), resulting in the participation of 14,215 participants, including 9,702 ethnic minority participants and a general population sample of 4,513, composed of White people who classified themselves as English, Welsh, Scottish, Northern Irish, and British. Data collection covered the period between 16 February 2021 and 14 August 2021.

    Further information about the study can be found on the EVENS project website.

    A teaching dataset based on the main EVENS study is available from the UKDS under SN 9249.

    [1] The VCSE organisations included Business in the Community, BEMIS (Scotland), Ethnic Minorities and Youth Support Team (Wales), Friends, Families and Travellers, Institute for Jewish Policy Research, Migrants' Rights Networks, Muslim Council Britain, NHS Race and Health Observatory, Operation Black Vote, Race Equality Foundation, Runnymede Trust, Stuart Hall Foundation, and The Ubele Initiative.
  8. d

    ACS 5-Year Demographic Characteristics DC Census Tract

    • catalog.data.gov
    • opdatahub.dc.gov
    • +5more
    Updated Apr 30, 2025
    + more versions
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    City of Washington, DC (2025). ACS 5-Year Demographic Characteristics DC Census Tract [Dataset]. https://catalog.data.gov/dataset/acs-5-year-demographic-characteristics-dc-census-tract
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    Dataset updated
    Apr 30, 2025
    Dataset provided by
    City of Washington, DC
    Area covered
    Washington
    Description

    Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

  9. American Community Survey of the Foreign Born

    • search.datacite.org
    Updated 2009
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    :Unav (2009). American Community Survey of the Foreign Born [Dataset]. http://doi.org/10.7910/dvn/eenujp
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    Dataset updated
    2009
    Dataset provided by
    DataCitehttps://www.datacite.org/
    Harvard Dataverse
    Authors
    :Unav
    Description

    Users can obtain demographic characteristics of the foreign-born population in each state. Topics include: language, education, income, and poverty. Background The American Community Survey and Census Data on the Foreign-Born interactive map was created by the Migration Policy Institute using Census data. This website provides information pertaining to the immigrant population in the United States. Topics include: demographics, language, education, income and poverty of the foreign-born population. User Functionality Users can click on states to generate fact sheets about the demographic, social, language, educ ation, workforce, income, and poverty characteristics of the population in each state. Data can be downloaded into SAS statistical software. Users can view demographic information by race/ethnicity, Hispanic origin, place of origin, citizenship status, sex/gender, and marital status. Data Notes Data are derived from the 1990 and 2000 Decennial Censuses and the 2007 American Community Surveys (ACS). Information is available on national and state levels. The website does not indicate when the data will be updated.

  10. N

    Sault Ste. Marie, MI Population Breakdown By Race (Excluding Ethnicity)...

    • neilsberg.com
    csv, json
    Updated Jul 7, 2024
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    Neilsberg Research (2024). Sault Ste. Marie, MI Population Breakdown By Race (Excluding Ethnicity) Dataset: Population Counts and Percentages for 7 Racial Categories as Identified by the US Census Bureau // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/2e6698a5-230c-11ef-bd92-3860777c1fe6/
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    json, csvAvailable download formats
    Dataset updated
    Jul 7, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sault Ste. Marie
    Variables measured
    Asian Population, Black Population, White Population, Some other race Population, Two or more races Population, American Indian and Alaska Native Population, Asian Population as Percent of Total Population, Black Population as Percent of Total Population, White Population as Percent of Total Population, Native Hawaiian and Other Pacific Islander Population, and 4 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the racial categories idetified by the US Census Bureau. It is ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories, and do not rely on any ethnicity classification. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

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

    Key observations

    The percent distribution of Sault Ste. Marie population by race (across all racial categories recognized by the U.S. Census Bureau): 69.62% are white, 1.12% are Black or African American, 13.85% are American Indian and Alaska Native, 1.28% are Asian, 0.08% are Native Hawaiian and other Pacific Islander, 1.03% are some other race and 13.02% are multiracial.

    Content

    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:

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

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Urban Observatory by Esri (2020). What is the most common race/ethnicity? [Dataset]. https://hub.arcgis.com/maps/2603a03fc55244c19f7f73d04cd53cea
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What is the most common race/ethnicity?

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Dataset updated
Apr 14, 2020
Dataset provided by
Esrihttp://esri.com/
Authors
Urban Observatory by Esri
Area covered
Description

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.

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