Skip to main content

Native Community Data Profiles: About the Data

General

  1. Data recency
  2. Except as otherwise noted below, all information shown in the visualization reflects data available as of October 2023. The Center for Indian Country Development (CICD) plans to update this tool annually.

  3. Programming acknowledgments

    All compilation, processing, and calculations are performed in R. CICD is particularly grateful to the authors of the tigris, tidycensus, and tidygeocoder packages and their dependencies, which we use for all interactions with U.S. Census Bureau (Census) data for the purposes of this project.

  4. About the American Community Survey (ACS) data

    Information drawn from the American Community Survey (ACS) uses Census’ 2017–2021 five-year data products, reported for American Indian/Alaska Native/Native Hawaiian (AIANNH) Area geographies. Throughout, we refer to AIANNH Areas as Native geographies. CICD acknowledges that Native geographies tabulated by Census and displayed in this tool do not encompass all geographies having current or historical associations with Native nations.

    Information about race in the ACS is self-reported by survey respondents. Individuals who report a single race and identify as American Indian or Alaska Native (AIAN) alone are included here in the category denoted AIANa. Individuals who report a single race and identify as Native Hawaiian and Other Pacific Islander (NHOPI) alone are included in the category denoted NHOPIa. (As is evident in the name, this broad category represents populations other than Native Hawaiians, including both Indigenous and non-Indigenous groups.) Respondents who report multiple races and identify as AIAN or NHOPI alone or in combination with one or more other races are included here in the racial categories denoted AIANac and NHOPIac, respectively. Wherever possible, we use the more inclusive NHOPIac and AIANac definitions—especially important given that well over half of Native census respondents self-identify as more than one race. However, for most ACS variables, AIANa and NHOPIa are the only categorizations publicly available.

    AIANNH Areas are encoded by type, which we use to determine the most geographically relevant Native ACS data to display in the visualization. When a user selects a geography categorized as a Hawaiian Home Land (specifically, geographies having Census-issued AIANNHCE codes from 5000 to 5499), NHOPI-related measures are shown. Otherwise, AIAN-related measures are shown.

    The ACS variables used in specific calculations are listed below by topic. For further detail, consult Census’ extensive ACS documentation (2022’s here). In addition, when hovering over the link icon below the map, geography-specific links appear to Census’ My Tribal Area and Narrative Profiles ACS tools. The links are pre-populated using Census’ standardized identifier for each geography (that is, its AIANNHCE code), which appears as geoID when hovering over the geography on the map. Users are encouraged to refer to Census’ ACS tools for in-depth ACS data, including margins of error.

  5. Translating non-Native geographies to Indian Country (geographic apportionment)

    In non-ACS data, it is common that a measure is reported on the basis of a geographic unit that does not map in a straightforward way to Native geographies. Native geographies often overlap several partial counties or census tracts and can cross state boundaries. Neither counties nor census tracts are regularly or comparably sized. Further, the boundaries of census tracts are created based on population estimates and so change markedly with each decennial census. To translate county- and tract-level measures to estimates that are specific to each Native geography, we use apportionment.

    To apportion tract-level data for a Native geography, for example, we sum up the total overlap of that Native geography with a given tract and divide by the total area of the Native geography, which gives the proportion of the Native geography made up by that tract. We then use these proportions as weights in calculating a weighted summary of the measure of interest. Notably, estimating in this way relies on an assumption that the measure is evenly distributed in each of the component tracts. Cases are further detailed below.

    To learn more about various geographic definitions of Indian Country, see CICD’s What Is Indian Country?

  6. Adjustments to geospatial data

    When performing area calculations, overlaying Native geographies and states, and locating Native entity headquarters within Native geographies, we make no modifications to the shapefiles provided by Census.

    When locating other types of points (e.g., schools) within Native geographies, we use a 250-meter (0.155-mile) external buffer around shapes to account for minor real-world geographic imprecisions.

    A selection of aggregated geographies is presented as options in the tool. Conceptually, these geographies represent the measures that would be calculated if the constituent geographies were combined into one unit. For example, when a user selects “All federal reservations, off-reservation trust lands, and joint-use areas” in the tool, the measures shown reflect all Native geographies with Census geographic codes from 0001 to 4989, combined into one geographic unit.

    The shapes of Native geographies displayed on the map have been modestly simplified for faster web performance, and do not represent official legal or political boundaries.

  7. Additions and corrections

    While we have made strong efforts to ensure the accuracy of manually compiled information, CICD welcomes additions and corrections from representatives of tribes, federal government agencies, and other stakeholder bodies. Please email cicd.data@mpls.frb.org to share input.

About the mapped data

  1. Geographic units

    We use Census’ TIGER/Line shapefiles to geospatially define all geographic units. 

    Specifically, we use Census’ shapefiles to delineate states, counties, ZIP code tabulation areas (ZCTAs), census tracts, and AIANNH Areas (Native geographies).

    We rely on the most recent available (2022) shapefiles by default, with use of 2010 shapefiles in certain circumstances detailed below.

    Shapefiles are used to visualize the boundaries of Native geographies, to locate key points of interest within Native geographies, and to calculate area overlaps between Native geographies and other geographic units. When we perform area overlap calculations, we transform Census’ geospatial data from its original, unprojected North American Datum 1983 (ESPG 4269) coordinate reference system (CRS) to the projected USA Contiguous Albers Equal Area Conic (ESPG 5070/102003) CRS. Transformation to a projected CRS for area calculations is particularly important given the locations of many Native geographies at far northern latitudes in Alaska.

    For some measures, a complete list of geographic units at a given time is needed to characterize the extent of missing data across geographies. In these circumstances, we use Census’ gazetteer files (2010’s here).

  2. Native entities
    1. Federally recognized tribes

      Names, locations, and websites of federally recognized tribes are drawn from the U.S. Bureau of Indian Affairs (BIA) Tribal Leaders Directory, which itself reflects the most recent annual Federal Register Notice “Indian Entities Recognized by and Eligible To Receive Services From the United States Bureau of Indian Affairs.”

      Tribal governance structures are diverse. In some cases, a tribe may be listed as a single entry on the Federal Register but as multiple entries on the Tribal Leaders Directory (e.g., to reflect constituent bands), or vice versa.

      If a tribe did not have a website listed in the Tribal Leaders Directory but belongs to a multi-tribe self-governance association according to BIA’s Office of Self-Governance, we display the association’s website.

      Tribes without a physical address listed in the Tribal Leaders Directory were geocoded using their listed mailing address. Four Tribal Leaders Directory entries had notes referring users to affiliates for contact information and had no location information themselves. These entries were dropped.

    2. State-recognized entities

      State-recognized entities refers to those Native political entities recognized by individual states, as documented on state government websites.

      The most recent comprehensive compilation of nonfederally recognized tribes was published by the U.S. Government Accountability Office (GAO) in 2012. There are no known plans to update it.

      In the absence of an up-to-date, centralized source, we manually compiled a list of state-recognized entities from individual state websites, using the 2012 GAO report as a cross-reference. As of September 2023, we document 71 state-recognized tribes; four state-recognized Groups (South Carolina only); 1 state-recognized Special Interest Organization (South Carolina only); and 1 Indian Community (North Dakota only). Names shown reflect state government sources.

      Locations of state-recognized entities reflect the headquarters addresses given on entities’ websites (wherever available) and were compiled manually.

      Some state-recognized tribes are also federally recognized. In these cases, the tribe is displayed here as federally recognized.

  3. Geocoding

    Except as otherwise noted below, all location data in this project reflect latitude and longitude obtained through Google Maps’ Geocoding API via tidygeocoder.

  4. Links between Native entities and geographic units

    To pair Native entities with their related geographies, we first geocode each entity’s headquarters location, then overlay Census’ AIANNH Areas geographies. If an entity’s headquarters is physically located within a Native geography, it is shown with that geography.

    In those cases in which an entity’s headquarters is located outside a Native geography but an association by name is readily apparent, we create associations manually.

    Some geographies are neither associated with an entity by physical location nor obviously associated by name. In these cases, we create these associations manually using supporting evidence from Native entity, federal government, and/or state government sources online.

    Associations between geographies and Native entities are not always one-to-one. A single geography may be associated with multiple Native entities (e.g., joint-use areas), and a single Native entity may be associated with multiple geographies. Of 704 Native geographies, there were a total of five that we were unable to definitively match to at least one Native entity.

    Because the measures displayed in this tool are geographically based, Native entities not associated with at least one Native geography are not shown. We mark this as a meaningful data limitation.

  5. Bureau of Indian Education (BIE) schools

    Housed within the U.S. Department of the Interior, the mission of the BIE is “to provide quality education opportunities from early childhood through life in accordance with a tribe’s needs for cultural and economic well-being, in keeping with the wide diversity of Indian tribes and Alaska Native villages as distinct cultural and governmental entities.”

    The locations of BIE schools are drawn from the BIE School Directory, which delineates between BIE-operated schools, Navajo schools, and tribally controlled schools. Most BIE schools (68 percent) are tribally controlled.

    To associate BIE schools with Native geographies, we first geocode each school’s location, then overlay Census’ AIANNH Areas geographies. If a school is physically located within a buffered Native geography, it is shown with that geography.

    BIE schools serving post-secondary students only are mapped as Tribal Colleges and Universities (TCUs).

  6. Tribal Colleges and Universities (TCUs)

    The American Indian Higher Education Consortium describes TCUs as post-secondary education institutions chartered by their respective tribal governments. The Consortium notes, “Tribal identity is the core of every TCU, and they all share the mission of tribal self-determination and service to their respective communities.”

    For an up-to-date, comprehensive list of TCUs that included satellite sites, we compiled entries manually by cross-referencing directory information from the American Indian Higher Education Consortium; the American Indian College Fund; the National Center for Education Statistics’ Integrated Postsecondary Education Data System; the U.S. Department of Education’s White House Initiative on Advancing Educational Equity, Excellence, and Economic Opportunity for Native Americans and Strengthening Tribal Colleges and Universities; and institutions’ individual websites.

    To associate TCU sites with Native geographies, we first geocode each site’s location, adding latitude and longitude coordinates manually for locations that could not be readily geocoded, determined using supporting evidence from the institution’s website. We then overlay Census’ AIANNH Areas geographies. If a TCU site is physically located within a buffered Native geography, it is shown with that geography.

    For the small number of TCU sites not physically located in Native geographies, we make associations manually for those having clear documentation on their websites of affiliation with one tribe. TCU sites not physically located in Native geographies and with associations to multiple tribes are not shown.

  7. Native American Financial Institutions (NAFIs)

    NAFIs include banks, credit unions, and loan funds that serve Native communities across the United States and foster financial inclusion in Indian Country. They are distinguished by their public commitments to providing affordable and culturally informed financial services, credit, and capital to Native communities.

    We associate NAFIs with Native geographies as described above for BIE schools.

    Data about NAFIs is curated by CICD and is the focus of CICD’s Mapping Native American Financial Institutions visualization. Visit the tool to explore the data and learn more about NAFIs and CICD’s methodology.

  8. Indian Health Service (IHS) facilities

    The IHS, an agency within the Department of Health and Human Services, is responsible for providing federal health services to American Indians and Alaska Natives.

    IHS facilities operate under a variety of models, ranging from IHS operation to full tribal control. Further, a geography may contain non-IHS health facilities. Thus, the distribution of IHS facilities may not fully characterize the availability of health services in a Native geography.

    IHS facilities data are drawn from the IHS Office of Public Health Support’s Geographic Information System (GIS) Service, one of IHS' Mapping and Visualization resources. Data include facilities’ latitude and longitude coordinates and the availability of various health services by type. Available information about each facility’s operating model is also included; to learn more about these models, see explanations from the IHS Office of Direct Service and Contracting Tribes.

    We associate IHS facilities with Native geographies as described above for BIE schools.

About the Place data

  1. Geography type

    This field reflects the AIANNHCE field in Census’ TIGER/Line shapefiles.

  2. Size

    These fields reflect sums of the ALAND and AWATER fields on Census TIGER/Line shapefiles. Reservations and their associated off-reservation trust lands—which share an AIANNHCE code—are combined. The Fallon Paiute-Shoshone’s Colony and Reservation geographies are reported separately, reflecting Census’ convention in the My Tribal Area tool.

  3. Self-governance

    The self-governance status of federally recognized tribes is drawn from the BIA’s Office of Self-Governance published list. When self-governance is enacted through an association of tribal representatives—as is the case for a number of Alaska Native tribes—individual tribes’ self-governance status is documented using information available on each association’s website.

  4. State overlaps

    To identify these, we overlay the shapefiles of states and Native geographies.

  5. Climate

    Climate zone data come from the Commission for Environmental Cooperation (CEC), a collaboration between the governments of Canada, Mexico, and the United States.

    CEC’s North American Climate Zones shapefile shows the spatial distribution of climate types based on the Köppen-Geiger climate classification. We assign each Native geography’s climate zone by determining the zone with which the Native geography has the greatest area overlap, making manual assignments using CEC’s North American Environmental Atlas when necessary.

  6. Related Native entities

    Native entities were paired with geographies as described above.

    At times, there will be more than one Native entity headquarters associated with a Native geography. Reasons for this include:

    • A tribal government and an associated band may be headquartered within the same geography (see Minnesota Chippewa Tribe and its Leech Lake band).
    • More than one Native entity may be associated with one Native geography, such as with joint-use areas—an area that is administered jointly or claimed by two or more tribes (see Creek/Seminole joint-use OTSA). Or, two Native entities may reside within one geography, but only one of the entities is legally associated with that geography (see Navajo Nation and the San Juan Southern Paiute Tribe).

  7. No- or low-population notes

    Some Native geographies—such as seasonal villages—have an ACS-tabulated population of 0. A note appears for these geographies flagging limited data availability.

    Users are cautioned to use care in interpreting data for low-population geographies. All ACS estimates have margins of error, which for low-population areas can result in considerable uncertainty. A note to this effect appears for geographies with populations below 500. To see ACS margin of error details for a geography of interest, users can click the link icon below the map to visit Census’ My Tribal Area tool.

About the People data

  1. Total population: ACS estimate B01003_001 (Total population)
  2. Native population: Total population denominator above, along with ACS estimates B02001_004 and B02001_006 (AIANa and NHOPIa; Race); estimate B02010_001 (AIANac); and estimate B02012_001 (NHOPIac). Estimates B01003_001 and B02001_004 are identical.
  3. Population density: For each geography, we first convert land and water areas (see “About the Place data” above) from square meters to square miles. We then divide total population (above) by this quantity.
  4. Median age: ACS groups B01002_001 (Median age by sex), B01002C_001 (AIANa), and B01002E_001 (NHOPIa). For aggregated geographies (e.g., all federal reservations), we report the population-weighted averages using the populations described above.
  5. Age groups: Age category sums using ACS groups B01001 (Sex by age), B01001C (AIANa), and B01001E (NHOPIa)

About the Housing data

  1. Total housing units: ACS estimate B25001_001 (Housing units)
  2. Share owner-occupied: ACS groups B25003 (Tenure), B25003C (AIANa), and B25003E (NHOPIa). Note that this housing unit-level measure reflects the share of units that are owner-occupied, not the share of people or households residing in an owner-occupied unit.
  3. Share crowded: Sums from ACS groups B25014 (Tenure by occupants per room), B25014C (AIANa), B25014E (NHOPIa). We follow the Census convention of defining “crowded” as more than one occupant per room, though we note the role of preference in housing arrangements and use the term neutrally.
  4. Share cost-burdened: Sums from ACS group B25106 (Tenure by housing costs as a percentage of household income in the past 12 months). We follow the Census and U.S. Department of Housing and Urban Development convention of defining “cost-burdened” as housing cost representing 30 percent or more of household income. Households reporting no income or no cash rent are excluded from the denominator.
  5. Housing stock: Sums from ACS groups B25032 (Tenure by units in structure), B25032C (AIANa), and B25032E (NHOPIa). Note that the housing type category displayed here as “manufactured home” corresponds to the term “mobile home” on the ACS questionnaire. Single-family housing includes both detached and attached (e.g., townhome) units. To facilitate comparison across household types, we include occupied units only.

About the Education data

  1. Total Kindergarten through 12th-grade enrollment: Sums from ACS group B14007 (School enrollment by detailed level of school for the population 3 years and over)
  2. Native-focused learning institutions: See “About the mapped data” above.
  3. Educational attainment: Sums from ACS groups B15003 (Educational attainment for the population 25 years and over), C15002C (AIANa), and C15002E (NHOPIa)

About the Healthcare data

  1. Life expectancy

    Life expectancy data are drawn from the U.S. Small-area Life Expectancy Estimates Project (USALEEP), a joint project of the National Center for Health Statistics and partners. Currently, the most recently data available span 2010–2015 and are reported at the census tract level using 2010 tract geographies. 

    To estimate life expectancy for Native geographies using tract-level data, we use apportionment as described above, using 2022 Native geographies and 2010 tracts. We report a geographically weighted average life expectancy.

    Life expectancy data are not available for all tracts. Data are missing for a total of 6,854 of 74,002 2010 census tracts, which represents approximately 9.5 percent of the area of the United States and 7.6 percent of the total area of Native geographies. We report life expectancy only for those Native geographies for which data are available for at least 90 percent of the total area.

  2. Medical professionals, hospital facilities, and birth and death events

    Medical professionals, hospital facilities, and birth and death events data are drawn from the Health Resources and Services Administration’s Area Health Resources Files (AHRF).

    The most recently available AHRF data are for the period 2021–2022 and are reported at the county, state, and national levels, using time-current geographies.

    Count data for medical professionals and hospital facilities included in the 2021–2022 AHRF data period reflect the year 2020. Birth and death events data in the AHRF reflect three-year annual averages. We use the 2020 county-level population estimates included in the AHRF to scale measures for comparability (e.g., dentists per 1,000 people).

    To create estimates for Native geographies using county-level data, we use apportionment as described above, using current (2022) Native and county geographies. We report geographically weighted averages.

    Per AHRF documentation, National Center for Health Statistics restrictions prohibit release of any subnational data with fewer than 10 occurrences (including data averaged across years) to protect privacy. Estimates shown here reflect apportionment of available data, though we report only for those Native geographies for which data are available for at least 90 percent of the total area.

About the Connectivity data

  1. Degree of geographic remoteness

    Degree of geographic remoteness reflects frontier and remote area (FAR) codes, produced by the U.S. Department of Agriculture’s (USDA’s) Economic Research Service.

    FAR codes are ZIP code-level codes assigned using data and geographies from the most recent decennial census. They are used “to describe territory characterized by some combination of low population size and high geographic remoteness. FAR areas are defined in relation to the time it takes to travel by car to the edges of nearby Urban Areas.”

    The most recently available data are from 2015 (based on the 2010 census) and provide four FAR definition levels:

    • FAR Level 1 characterizes ZIP codes with majority populations living 60 minutes or more from urban areas of 50,000 or more. We display these geographies as “Somewhat remote.”
    • FAR Level 2 characterizes ZIP codes with majority populations living 60 minutes or more from urban areas of 50,000 or more people and 45 minutes or more from urban areas of 25,000–49,999 people. We display these geographies as “Remote.”
    • FAR Level 3 characterizes ZIP codes with majority populations living 60 minutes or more from urban areas of 50,000 or more people; 45 minutes or more from urban areas of 25,000–49,999 people; and 30 minutes or more from urban areas of 10,000–24,999 people. We display these geographies as “Very remote.”
    • FAR Level 4 characterizes ZIP codes with majority populations living 60 minutes or more from urban areas of 50,000 or more people; 45 minutes or more from urban areas of 25,000–49,999 people; 30 minutes or more from urban areas of 10,000–24,999 people; and 15 minutes or more from urban areas of 2,500–9,999 people. We display these geographies as "Most remote.”

    Metro areas are displayed as “Not remote.”

    To categorize Native geographies using ZIP code-level data, we use a modified version of the apportionment approach described above, using 2022 Native geographies and 2010 ZIP code tabulation areas (ZCTAs). Notably, the ZIP codes (a U.S. Postal Service geography) at which FAR data are provided are not always interchangeable with ZCTAs (a Census geography), requiring the use of a 2010 ZIP-to-ZCTA crosswalk produced by the U.S. Health Resources and Services Administration and its partners. We match FAR data on ZCTA where possible, using ZIP secondarily. For Native geographies having a range of FAR codes among their constituent geographies, we show the range.

  2. Vehicles per adult: Sums from ACS group B25046 (Aggregate number of vehicles available by tenure), with estimate B15001_001 (Sex by age by educational attainment for the population 18 years and over) as the denominator. We define adults as individuals 18 and older.

  3. Share of households with a computer: ACS estimate B28008_002 (Presence of a computer and type of Internet subscriptions in household)

  4. Share of households with an Internet subscription: ACS estimate B28002_002 (Presence and types of Internet subscriptions in household)

  5. Share of households with a high-speed broadband subscription: ACS estimate B28002_007 (Presence and types of Internet subscriptions in household). By “high-speed,” we refer to a response of “broadband such as cable, fiber optic, or DSL” (as distinct from “broadband of any type,” which Census defines to include cellular data plans and satellite Internet service).

  6. Food access

    Food access data are drawn from the Food Access Research Atlas, produced by the USDA Economic Research Service. Currently, the most recent data available are 2019’s, reported at the census tract level using 2010 tract geographies.

    A “low food access” tract is defined by USDA as one in which “at least 500 people or 33% of the population lives farther than [X] mile (urban) or [Y] miles (rural) from the nearest supermarket,” with X and Y being pairs of distance thresholds. We report food access using the 1-mile and 10-mile threshold pair.

    To estimate food access for Native geographies using tract-level data, we use apportionment as described above, using 2022 Native geographies and 2010 tracts. We report the share of the Native geography with low food access.

    Food access data are not available for all tracts. Data are missing for total of 1,496 of 74,002 2010 census tracts, which represents approximately 3.5 percent of the area of the United States and 1.7 percent of the total area of Native geographies. We report only for those Native geographies for which data are available for at least 90 percent of the total area.

About the Jobs data

The measures below follow typical labor force statistics conventions. For example, they include people 16 years of age or older and exclude institutionalized people and people on active duty in the U.S. Armed Forces. A notable modification in the calculations here is that, because ACS AIANa and NHOPIa employment variable groups do not disaggregate civilians and military personnel for workers 65 years of age and over, we rely on the assumption that the number of 65+ military labor force participants is small.

For detailed explanation of how labor force concepts are defined in ACS data, see Census’ documentation.

To explore national-level trends in Native labor market experiences, see CICD’s Native American Labor Market Dashboard.

  1. Labor force participation

    Labor force participation (LFP) is the proportion of the population that is in the labor force (that is, the labor force divided by the population). The labor force, in turn, is the sum of employed and unemployed workers.

    For the All category, we calculate LFP using ACS estimate B23025_003 (Employment status for the population 16 years and over: Civilian labor force). For AIANa and NHOPIa LFP, we use analogous sums from groups C23002C and C23002E, respectively (Sex by age by employment status for the population 16 years and over).

  2. Employment-to-population ratio (EPOP; employment rate)

    The employment-to-population ratio is the number of employed workers divided by the population.

    For the All category, we calculate EPOP using ACS estimate B23025_004 (Employment status for the population 16 years and over: Civilian labor force: Employed). For AIANa and NHOPIa EPOP, we use analogous sums from groups C23002C and C23002E, respectively (Sex by age by employment status for the population 16 years and over).

  3. Unemployment

    Unemployment is the number of non-employed people who are available and looking for work—or who expect recall to a previous job—divided by the number of people in the labor force.

    For the All category, we calculate unemployment using ACS estimate B23025_005 (Employment status for the population 16 years and over: Civilian labor force: Unemployed) and estimate B23025_003 (Employment status for the population 16 years and over: Civilian labor force). For AIANa and NHOPIa EPOP, we use analogous sums from groups C23002C and C23002E, respectively (Sex by age by employment status for the population 16 years and over).

  4. Total workers

    This is the civilian labor force, given for the All category by ACS estimate B23025_003 (Employment status for the population 16 years and over: Civilian labor force) and calculated for the AIANa and NHOPIa categories with sums from C23002C and C23002E, respectively (Sex by age by employment status for the population 16 years and over).

  5. Top three industries of employment

    Using sums from ACS group C24030 (Sex by Industry for the Civilian Employed Population 16 Years and Over), we first count within each unit of geography how many people are employed overall and in each industry, summing at the most disaggregated industry category available. We then calculate shares by category and report the top three for each geography.

About the Money data

  1. Median household income: ACS estimates B19013_001 (All), B19013C_001 (AIANa), and B19013E_001 (NHOPIa), Median household income in the past 12 months (in 2021 inflation-adjusted dollars). For aggregated geographies (e.g., all federal reservations), we report the household count-weighted averages using estimates B25003_001 (All), B25003C_001 (AIANa), and B25003E_001 (NHOPIa), Tenure, as weights.

  2. Per person income: ACS estimates B19301_001 (All), B19301C_001 (AIANa), and B19301E_001 (NHOPIa), Per capita income in the past 12 months (in 2021 inflation-adjusted dollars). For aggregated geographies (e.g., all federal reservations), we report the population-weighted averages using as weights the total population, AIANa population, and NHOPIa population estimates described above in “About the People data.”

  3. Share experiencing poverty and share of youth experiencing poverty: Sums from ACS group B17001 (Poverty status in the past 12 months by sex by age), group B17001C (AIANa), and group B17001E (NHOPIa). We define youth as people under the age of 18. For detailed explanation of how poverty concepts are defined in ACS data, see Census’ documentation.