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

About this dataset

Context

The dataset tabulates the population of Washington by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Washington across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.39% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Washington is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Washington total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Washington Population by Race & Ethnicity. You can refer the same here

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

District-wide 2020 Redistricting Data (P.L. 94-171).Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: District of ColumbiaCurrent Vintage: 2020P.L. 94-171 Table(s): P1. Race; P2. Hispanic or Latino, and Not Hispanic or Latino by Race; P3. Race for the Population 18 Years and Over; P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over; P5. Group Quarters Population by Major Group Quarters Type; H1. Housing Occupancy StatusData downloaded from: https://res1wwwd-o-tcensusd-o-tgov.vcapture.xyz/programs-surveys/decennial-census/about/rdo/summary-files.htmlNational Figures: data.census.govPublic Law 94-171, enacted in 1975, directs the U.S. Census Bureau to make special preparations to provide redistricting data needed by the 50 states.1 It specifies that within 1 year following Census Day, the Census Bureau must send the governor and legislative leadership in each state the data they need to redraw districts for the U.S. Congress and state legislatures. To meet this legal requirement, the Census Bureau set up a program that affords state officials an opportunity before each decennial census to define the small areas for which they wish to receive census population totals for redistricting purposes. Officials may receive data for voting districts (e.g., election precincts, wards) and state house and senate districts, in addition to standard census geographic areas such as counties, cities, census tracts, and blocks. State participation in defining areas is voluntary and nonpartisan. For further information on Public Law 94-171 and the 2020 Census Redistricting Data Program, see:www.census.gov/programs-surveys/decennial-census/about/rdo/program -management.htmlData processed using R statistical package and ArcGIS Desktop.

About this dataset

Context

The dataset tabulates the District of Columbia population by age. The dataset can be utilized to understand the age distribution and demographics of District of Columbia.

Content

The dataset constitues the following three datasets

• District of Columbia, DC Age Group Population Dataset: A complete breakdown of District of Columbia age demographics from 0 to 85 years, distributed across 18 age groups
• District of Columbia, DC Age Cohorts Dataset: Children, Working Adults, and Seniors in District of Columbia - Population and Percentage Analysis
• District of Columbia, DC Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

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.

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

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: 2022 Wards (State Legislative Districts [Upper Chamber]). Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

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.

About this dataset

Context

The dataset tabulates the data for the Washington, DC population pyramid, which represents the Washington population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Washington, DC, is 22.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Washington, DC, is 17.0.
• Total dependency ratio for Washington, DC is 39.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Washington, DC is 5.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Washington population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Washington for the selected age group is shown in the following column.
• Population (Female): The female population in the Washington for the selected age group is shown in the following column.
• Total Population: The total population of the Washington for the selected age group is shown in the following column.

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 Washington Population by Age. You can refer the same here

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.

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. 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.

About this dataset

Context

The dataset tabulates the Washington population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Washington. The dataset can be utilized to understand the population distribution of Washington by age. For example, using this dataset, we can identify the largest age group in Washington.

Key observations

The largest age group in Washington, DC was for the group of age 30 to 34 years years with a population of 76,902 (11.44%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Washington, DC was the 85 years and over years with a population of 9,859 (1.47%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Washington is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Washington total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Washington Population by Age. You can refer the same here

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 2022, the population of District of Columbia was 671,803, a 0.45% increase year-by-year from 2021. Previously, in 2021, District of Columbia population was 668,791, a decline of 0.31% compared to a population of 670,868 in 2020. Over the last 20 plus years, between 2000 and 2022, population of District of Columbia increased by 100,027. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• 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

A web map used for the Police Service Area Details web application.In addition to Police Districts, every resident lives in a Police Service Area (PSA), and every PSA has a team of police officers and officials assigned to it. Residents should get to know their PSA team members and learn how to work with them to fight crime and disorder in their neighborhoods. Each police district has between seven and nine PSAs. There are a total of 56 PSAs in the District of Columbia.Printable PDF versions of each district map are available on the district pages. Residents and visitors may also access the PSA Finder to easily locate a PSA and other resources within a geographic area. Just enter an address or place name and click the magnifying glass to search, or just click on the map. The results will provide the geopolitical and public safety information for the address; it will also display a map of the nearest police station(s).Each Police Service Area generally holds meetings once a month. To learn more about the meeting time and location in your PSA, please contact your Community Outreach Coordinator. To reach a coordinator, choose your police district from the list below. The coordinators are included as part of each district's Roster.Visit https://mpdc.dc.gov for more information.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

Census Blocks from 2020. Redistricting Data (P.L. 94-171).Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: Census BlocksCurrent Vintage: 2020P.L. 94-171 Table(s): P1. Race; P2. Hispanic or Latino, and Not Hispanic or Latino by Race; P3. Race for the Population 18 Years and Over; P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over; P5. Group Quarters Population by Major Group Quarters Type; H1. Housing Occupancy StatusData downloaded from: https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.htmlNational Figures: data.census.govPublic Law 94-171, enacted in 1975, directs the U.S. Census Bureau to make special preparations to provide redistricting data needed by the 50 states.1 It specifies that within 1 year following Census Day, the Census Bureau must send the governor and legislative leadership in each state the data they need to redraw districts for the U.S. Congress and state legislatures. To meet this legal requirement, the Census Bureau set up a program that affords state officials an opportunity before each decennial census to define the small areas for which they wish to receive census population totals for redistricting purposes. Officials may receive data for voting districts (e.g., election precincts, wards) and state house and senate districts, in addition to standard census geographic areas such as counties, cities, census tracts, and blocks. State participation in defining areas is voluntary and nonpartisan. For further information on Public Law 94-171 and the 2020 Census Redistricting Data Program, see:www.census.gov/programs-surveys/decennial-census/about/rdo/program -management.htmlData processed using R statistical package and ArcGIS Desktop.

Census tracts are designated as urban, rural center, or rural through SB 1000 analysis. These designations are being used for the REV 2.0 and Community Charging in Urban Areas GFOs. Rural centers are contiguous urban census tracts with a population of less than 50,0000. Urban census tracts are tracts where at least 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria. Rural communities are census tracts where less than 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria. Urban communities are contiguous urban census tracts with a population of 50,000 or greater. Urban census tracts are tracts where at least 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Data Dictionary:OBJECTID: Unique IDSTATEFP: State FIPS CodeCOUNTYFP: County FIPS CodeTRACTCE: Census Tract IDGEOID: Geographic IdentifierName: Census Tract ID Name (short)NAMELSAD: Census Tract ID Name (long)ALAND: Land Area (square meters)AWATER: Water Area (square meters)DAC: Whether or not a census tract is a disadvantaged community as defined by SB 535 and designated by CalEPA using CalEnviroScreen 4.0 (May 2022 update)Income_Group: Whether or not a census tract is low-, middle-, or high-income as defined by AB 1550 and designated by CARB and the CEC (June 2023 update)Urban_Rural_RuralCenter: Whether or not a census tract is urban, rural, or rural center as defined and designated by the CEC through the SB 1000 Assessment (2024 update)PerCap_100k_L2DCFC: Number of public Level 2 and DC fast chargers per 100,000 people in a census tractDAC_andor_LIC: Whether or not a census tract is a disadvantaged or low-income community as defined by SB 535 and AB 1550 and designated by CalEPA and CARBUCC_eligible: Whether or not the census tract is an eligible area for the Community Charging in Urban Areas GFO. For a site to be eligible, it must be in a census tract that is either a disadvantaged or low-income community, and urban, and has below the state average for per capita public Level 2 and DC fast chargers as defined by the CEC.REV2_eligible: Whether or not the census tract is an eligible area for the Rural Electric Vehicle Charging 2.0 GFO. For a site to be eligible, it must be in a rural or rural center census tract as defined by the CEC.Shape_Area: Census tract shape area (square meters)Shape_Length: Census tract shape length (square meters)