In 2023, the population of the Washington-Arlington-Alexandria metropolitan area was about 6.3 million people. This was a slight increase from the previous year, when the population was about 6.26 million people.

Historical dataset of population level and growth rate for the Washington DC metro area from 1950 to 2026.

Graph and download economic data for Resident Population in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA) (WSHPOP) from 2000 to 2025 about DC, Washington, WV, MD, VA, residents, population, and USA.

The DC Metropolitan Area Drug Study (DCMADS) was conducted in 1991, and included special analyses of homeless and transient populations and of women delivering live births in the DC hospitals. DCMADS was undertaken to assess the full extent of the drug problem in one metropolitan area. The study was comprised of 16 separate studies that focused on different sub-groups, many of which are typically not included or are under-represented in household surveys.The DCMADS: Household and Non-household Populations examines the prevalence of tobacco, alcohol, and drug use among members of household and non-household populations aged 12 and older in the District of Columbia Metropolitan Statistical Area (DC MSA). The study also examines the characteristics of three drug-abusing sub-groups: crack-cocaine, heroin, and needle users. The household sample was drawn from the 1991 National Household Survey on Drug Abuse (NHSDA). The non-household sample was drawn from the DCMADS Institutionalized and Homeless and Transient Population Studies. Data include demographics, needle use, needle-sharing, and use of tobacco, alcohol, cocaine, crack, inhalants, marijuana, hallucinogens, heroin, sedatives, stimulants, psychotherapeutics (non-medical use), tranquilizers, and analgesics.This study has 1 Data Set.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

The DC Metropolitan Area Drug Study (DCMADS) was
conducted in 1991, and included special analyses of homeless and
transient populations and of women delivering live births in the DC
hospitals. DCMADS was undertaken to assess the full extent of the
drug problem in one metropolitan area. The study was comprised of 16
separate studies that focused on different sub-groups, many of which
are typically not included or are underrepresented in household
surveys. The Homeless and Transient Population
study examines the prevalence of illicit drug, alcohol, and tobacco
use among members of the homeless and transient population aged 12 and
older in the Washington, DC, Metropolitan Statistical Area (DC
MSA). The sample frame included respondents from shelters, soup
kitchens and food banks, major cluster encampments, and literally
homeless people. Data from the questionnaires include history of
homelessness, living arrangements and population movement, tobacco,
drug, and alcohol use, consequences of use, treatment history, illegal
behavior and arrest, emergency room treatment and hospital stays,
physical and mental health, pregnancy, insurance, employment and
finances, and demographics. Drug specific data include age at first
use, route of administration, needle use, withdrawal symptoms,
polysubstance use, and perceived risk.This study has 1 Data Set.

About this dataset

Context

The dataset tabulates the District of Columbia population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of District of Columbia across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of District of Columbia was 678,972, a 1.20% increase year-by-year from 2022. Previously, in 2022, District of Columbia population was 670,949, an increase of 0.29% compared to a population of 669,037 in 2021. Over the last 20 plus years, between 2000 and 2023, population of District of Columbia increased by 107,196. In this period, the peak population was 708,253 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the District of Columbia is shown in this column.
• Year on Year Change: This column displays the change in District of Columbia population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 District of Columbia Population by Year. You can refer the same here

Graph and download economic data for Employed Persons in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA) (LAUMT114790000000005) from Jan 1990 to Dec 2025 about DC, Washington, WV, MD, VA, household survey, persons, employment, and USA.

Census Tracts from 2020. The TIGER/Line shapefiles 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 (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2010 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area.

The TIGER/Line shapefiles and related database files (.dbf) 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 (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in District of Columbia (S1701ACS011001) from 2012 to 2024 about DC, Washington, percent, poverty, 5-year, population, and USA.

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 1-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 3, 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.

This Dataset shows the location of the Bank of America branches and ATMs in the Washington DC area. I was able to geocode these locations based on street addresses provided by this website: http://www.insiderpages.com/s/DC/Washington/Banks_page277?sort=alpha&radius=50

This layer shows population in households data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Combined Statistical Area, Core Based Statistical Area, Metropolitan Division boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description. Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P15, P17, PCT8, PCT9, PCT9B, PCT9C, PCT9D, PCT9E, PCT9F, PCT9G, PCT9H, PCT9I, PCT11, PCT13B, PCT13C, PCT13D, PCT13E, PCT13F, PCT13G, PCT13H, PCT13I, PCT17B, PCT17C, PCT17D, PCT17E, PCT17F, PCT17G, PCT17H, PCT17IData downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Combined Statistical Area, Core Based Statistical Area, Metropolitan DivisionNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all U.S. states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Polygons in this layer represent Census Tracts in the DMV (DC, Maryland, and Virginia). Data are included for each tract which estimate hunger and food insecurity. Data were compiled by the CAFB through internal tracking, and the layer was shared with the DC government as a courtesy. Fields include (all available for 2015 and 2014): 15_FI_Rate: The estimated portion of the population in the census tract experiencing food insecurity (by CAFB standards). 15/14 indicates year measured. 15_FI_Pop: The estimated number of people in the census tract experiencing food insecurity (by CAFB standards). 15/14 indicates year measured. 15_LB_Need: The estimated pounds of food needed by the food insecure population in the census tract. 15/14 indicates year measured. 15_Distrib: The number of pounds of food distributed by CAFB and partners in the census tract. 15/14 indicates year in which the distribution took place. 15_LB_Unme: The difference between the estimated pounds of food needed and the real pounds of food distributed by CAFB and partners, representing the unmet need for food assistance in the census tract. 15/14 indicates year. The layer was shared with the DC government in May 2016 and is based on 2015 and 2014 data.

This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Combined Statistical Area, Core Based Statistical Area, Metropolitan Division boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Combined Statistical Area, Core Based Statistical Area, Metropolitan DivisionNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Overview

Gender distribution from ACS. This dataset is produced by the Social Data Commons at the University of Virginia as part of the Gender Demographics data pipeline.

Coverage

• Temporal coverage: 2009–2024 (ACS 5-year estimates)
• Geographic levels: Block Group, County, Tract
• Coverage areas: National Capital Region (DC metro), Virginia (statewide)

Methodology

The Male population percent. 5-year ACS estimates in provided census geolevels. For non-census geolevels (such as zip codes), spatial overlaps are calculated between census block groups and parcels, then the value of each block group is divided across all living units within each intersecting parcel. Once parcel-level values are assigned, spatial overlaps are calculated between parcels and target regions such that parcel values can be aggregated to each intersecting target region based on proportion of overlap. Redistribution to non-census geolevels was performed using the redistribute R package.

The Male population percent. 5-year ACS estimates in provided census geolevels. For non-census geolevels (such as zip codes), spatial overlaps are calculated between census block groups and target regions, then the value of each block group is aggregated/disaggregated to any intersecting target region based on proportion of overlap. Redistribution to non-census geolevels was performed using the redistribute R package.

Source Tables

• 5-Year estimates, table S0101, via the API
• Boundaries > Zip Codes
• Boundaries > Supervisor Districts
• Health & Human Service > Human Services Regions
• MHUD Polygons
• Civic Association Polygons

Census Variables

• B01001_001: Total Pop
• B01001_002: Male
• B01001_026: Female

Measures (10)

• gender_male_count_parcels: The count of males in population. (arithmetic mean)
• gender_male_count_direct: The count of males in population. (arithmetic mean)
• gender_female_count_parcels: The count of females in population. (arithmetic mean)
• gender_female_count_direct: The count of females in population. (arithmetic mean)
• gender_male_percent_parcels: The percent of males in the total population. (percent)
• gender_male_percent_direct: The percent of males in the total population. (percent)
• gender_female_percent_parcels: The percent of females in the total population. (percent)
• gender_female_percent_direct: The percent of females in the total population. (percent)
• gender_total_count_parcels: Total count of the population. (arithmetic mean)
• gender_total_count_direct: Total count of the population. (arithmetic mean)

Data Sources

• American Community Survey (accessed 2023)
• Fairfax County Geospatial Data (accessed 2022)
• Arlington County GIS Open Data (accessed 2023)

File Format

Data files are provided as xz-compressed CSV (.csv.xz) with the following columns: geoid, region_type, region_name, year, measure, value, moe (margin of error, where available). A measure_info.json file provides per-measure metadata.

Graph and download economic data for Unemployed Persons in Washington-Arlington-Alexandria, DC-VA-MD-WV (MSA) (LAUMT114790000000004) from Jan 1990 to Dec 2025 about DC, Washington, WV, MD, VA, household survey, unemployment, persons, and USA.

As included in this EnviroAtlas dataset, the community level domestic water use is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). Three reports were used with city- or water supply authority- level domestic water demand data, in addition to county level data. The 2011 Northern Virginia Regional Water Supply Plan provides detailed publicly, privately, and self supplied water use and population served for 2007 and covers most of the Virginia side of the EnviroAtlas study area. The 2011 Fauquier County Regional Water Supply Plan provides detailed publicly, privately, and self supplied water use and population served for 2007 and covers Fauquier County, Virginia. The 2010 Washington Metropolitan Area Water Supply Reliability Study, Part 1 from the Interstate Commission on the Potomac River Basin provides detailed publicly, privately, and self supplied water use and population served for 2008 by water supplier for suppliers drawing from the Potomac River. Data from these reports were weighted across publicly, privately, and self-supplied sources by population served, resulting in a single water use estimate between 25 and 204 GPD for each of the subregions in the study area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The TIGER/Line shapefiles and related database files (.dbf) 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 (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Public Use Microdata Areas (PUMAs) are decennial census areas that permit the tabulation and dissemination of Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) data, and data from other census and surveys. For the 2020 Census, the State Data Centers (SDCs) in each state, the District of Columbia, and the Commonwealth of Puerto Rico had the opportunity to delineate PUMAS within their state or statistically equivalent entity. All PUMAs must nest within states and have a minimum population threshold of 100,000 persons. 2020 PUMAs consist of census tracts and cover the entirety of the United States, Puerto Rico and Guam. American Samoa, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands do not contain any 2020 PUMAs because the population is less than the minimum population requirement. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. The 2020 PUMAs will appear in the 2022 TIGER/Line Shapefiles.