Society data

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Abstract: The Global Gridded Relative Deprivation Index (GRDI), Version 1 (GRDIv1) data set characterizes the relative levels of multidimensional deprivation and poverty in each 30 arc-second (~1 km) pixel, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDIv1 is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final index raster. Inputs were selected from the best-available data that either continuously vary across space or have at least administrative level 1 (provincial/state) resolution, and which have global spatial coverage. Purpose: To provide a global gridded relative deprivation index characterizing the levels of multidimensional deprivation in each pixel at ~1 km resolution. Citation: Global Gridded Relative Deprivation Index (GRDI), Version 1. Center for International Earth Science Information Network - CIESIN - Columbia University. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/3xxe-ap97 Publication References: Documentation for the Global Gridded Relative Deprivation Index (GRDI), Version 1. Center for International Earth Science Information Network - CIESIN - Columbia University. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/xwf1-k532

The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.   Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred). Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained. Data may also be viewed off-site in map form for just the last 6 months on communitycrimemap.com Data Dictionary: Field Name Field Description Incident Number the number associated with either the incident or used as reference to store the items in our evidence rooms Date Reported the date the incident was reported to LMPD Date Occurred the date the incident actually occurred Badge ID Badge ID of responding Officer Offense Classification NIBRS Reporting category for the criminal act committed Offense Code Name NIBRS Reporting code for the criminal act committed NIBRS_CODE the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/view NIBRS Group hierarchy that follows the guidelines of the FBI National Incident Based Reporting System Was Offense Completed Status indicating whether the incident was an attempted crime or a completed crime. LMPD Division the LMPD division in which the incident actually occurred LMPD Beat the LMPD beat in which the incident actually occurred Location Category the type of location in which the incident occurred (e.g. Restaurant) Block Address the location the incident occurred City the city associated to the incident block location Zip Code the zip code associated to the incident block location Contact: LMPD Open Records    lmpdopenrecords@louisvilleky.gov

This dataset only includes incident reports for fires responded to by the Charlotte Fire Department (CFD). For all CFD incidents including EMS, HazMat, Rescue, and others, refer to the “CFD Public Incident Reports (All)” dataset. Included in this data are public fields pertaining to CFD fire-related incidents (IncidentType field in the 100s range), including fire-specific fields that are not relevant to other incident types. The data covers all locked incident reports for fire incidents worked within the past two years up to the last data update. This does not include false alarms, sprinkler activations, smoke scares, or other fire-related incidents where there was no fire to extinguish. CFD reports its incidents in a manner consistent with National Fire Incident Reporting System (NFIRS) guidelines. END OF USE CONSTRAINT: (3)...accuracy or completeness of any information archived and distributed.

The dataset was created to make social value catering options available to municipal staff across every district who are seeking catering services. Metadata

A list of all reported interactions between a Metro Government department or office and federal immigration authorities, as required by Mayor O’Connell’s Executive Order #30. Source Link: https://www.nashville.gov/departments/metro-clerk/legal-resources/executive-orders/mayor-freddie-oconnell/fo030 Metadata Document: Metro Immigration Interactions Metadata.pdf Contact Data Owner: opendata@nashville.gov

To provide transparent, self‑serve access to arena scheduling for planning, reporting, and public insight, while giving partners and vendors consistent, automated data and reducing reliance on manual report requests. Not all HRM arenas are yet included. Data reflects the booking system at the time of extract and may change. Times use Atlantic Time (Halifax, NS; DST observed). Individual client names are masked as “Private Booking”. Metadata

Household type, Education, Disability, Language, Computer/Internet Use, and more. 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: District-wide. Current Vintage: 2020-2024. ACS Table(s): DP02. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: March 11, 2026. 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.

Household type, Education, Disability, Language, Computer/Internet Use, and more. 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: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP02. 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.

Vector features representing tribal block groups in the United States, published by the US Census Bureau. Each feature includes a GEOID and base name. Sample rows include block groups E, F, and A.

Records of historic burials, with each row listing a person's last name, first name, and age, death date and miscellaneous notes. Published by SCGISAdmin with the Sacramento County Cemetery Advisory Commission.

This dataset provides the percentage of cases cleared in a 4-year period out of the total number of offenses reported for the City of Tempe and cohort cities. Clearance rates are developed as part of the Department’s report for the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) under the Uniform Crime Report (UCR) Program. NIBRS reporting includes Group A and Group B offenses. Group A offenses are categorized as Crimes Against Persons, Crime Against Property, or Crimes Against Society and are used for this dataset. The statistics in the UCR Program are based on reports the Tempe Police Department officially submits to the Arizona Department of Public Safety (DPS).  In the UCR Program, there are two ways that a law enforcement agency can report that an offense is cleared:  (1) cleared by arrest or solved for crime reporting purposes or  (2) cleared by exceptional means.   An offense is cleared by arrest, or solved for crime reporting purposes, when three specific conditions have been met. The three conditions are that at least one person has been: (1) arrested; (2) charged with the commission of the offense; and (3) turned over to the court for prosecution.   In some situations, an agency may be prevented from arresting and formally charging an offender due to factors outside of the agency’s control. In these cases, an offense can be cleared by exceptional means, if the following four conditions are met: (1) identified the offender; (2) gathered enough evidence to support an arrest, make a charge, and turn over the offender to the court for prosecution; (3) identified offender’s exact location so that suspect can immediately be taken into custody; and (4) encountered a circumstance outside law enforcement’s control that prohibits arresting, charging and prosecuting the offender.  The UCR clearance rate is one tool for helping the police to understand and assess success at investigating crimes. However, these rates should be interpreted with an understanding of the unique challenges faced with reporting and investigating crimes. Clearance rates for a given year may be greater than 100% because a clearance is reported for the year the clearance occurs, which may not be the same year that the crime occurred. Often, investigations may take months or years, resulting in cases being cleared years after the actual offense. Additionally, there may be delays in the reporting of crimes, which would push the clearance of the case out beyond the year it happened.  The performance measure dashboard is available at 1.12 Violent Cases Clearance Rate   Data Dictionary - (pending)   Additional Information  Source: Tempe Police Department (TPD) Versadex Records Management System (RMS) submitted to Arizona Department of Public Safety (AZ DPS) who submits data to the Federal Bureau of Investigations (FBI) Contact:  Miranda O'Boyle Contact E-Mail: miranda_oboyle@tempe.gov Data Source Type:  Excel Preparation Method:  Drawn from the Monthly AZ DPS reports/online publications Publish Frequency:  Annually Publish Method: Manual

An Officer Involved Shooting (OIS) is the discharge of a firearm, whether accidental or intentional, by a police officer, whether on or off duty. For the purposes of this posting, an OIS will only refer to the instances in which an officer discharged a firearm at a person. CMPD posts Officer Involved Shootings in an effort to create greater transparency of the actions of our employees. It is important to us that members of the community are informed whenever an officer discharges his/her firearm at a person and whether the shooting follows department policies and procedures. We believe that your trust and confidence in the Charlotte-Mecklenburg Police Department will increase as you understand what our officers encounter and how we hold them accountable for their actions. The CMPD is continuously reviewing and improving our practices to reduce the likelihood of deadly force incidents.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder. On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

This data details the Ring Buoy Locations within Co. Monaghan If you are aware of a missing ring buoy or would like to suggest a new location please contact our Water Safety Officer. Monaghan County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Monaghan County Council accepts no liability for any loss or damage suffered by those using this data for any purpose. The Projection of this dataset is WGS84.

Polygon features representing tribal block groups in the United States, published by the US Census Bureau. Each feature includes attributes such as land and water area, geographic identifiers, and coordinates. Sample rows show block groups E, F, and A with area measurements.

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized

This dataset represents arrests made by the Baltimore Police Department. Data are updated weekly. To leave feedback or ask a question about this dataset, please fill out the following form: BPD Arrests feedback form.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows education level for adults 25+. Counts broken down by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released 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 by the count of total adults (25+) and the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B15002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This 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. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). 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 file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Visão geral Este serviço apresenta informações socioeconômicas e urbanas por bairro do município do Rio de Janeiro, com foco no Índice de Desenvolvimento Social e em seus componentes associados. A camada reúne indicadores territoriais que retratam condições de renda, escolaridade, abastecimento de água, esgotamento sanitário, coleta de lixo, banheiro e outros atributos de caracterização dos bairros. Conteúdo da camada Polígonos de bairros com identificação territorial e administrativa. Indicadores sociais e domiciliares relacionados a renda, alfabetização, saneamento básico e condições de moradia. Atributos de referência para associação com regiões administrativas, áreas de planejamento e outras divisões territoriais do município. Aplicações Análise espacial de desigualdades socioeconômicas. Mapeamento de condições de infraestrutura urbana e saneamento. Comparação territorial entre bairros e regiões da cidade. Suporte a estudos de planejamento urbano, políticas públicas e monitoramento social.

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage

Alaska Native Regional Corporations (ANRC), Tribal Subdivisions, Federal American Indian Reservations, Off-Reservation Trust Lands (ORTL), State American Indian Reservations, Hawaiian Home Lands (HHL), Alaska Native Village Statistical Areas (ANVSA), Oklahoma Tribal Statistical Areas (OTSA), State Designated Tribal Statistical Areas (SDTSA), Tribal Designated Statistical Areas (TDSA), American Indian Joint-Use Areas (AIJUA); January 1, 2020 vintage