Historical dataset of population level and growth rate for the Washington DC metro area from 1950 to 2025.

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 detailed records of reported crimes in the Washington, D.C. metropolitan area from 2015 to 2023. It includes various types of offenses, from minor incidents to serious felonies, providing insights into crime patterns and trends over the years.

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.

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.

This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 2975 block groups in Washington, DC Metro region. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Washington, DC Metro region. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company 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 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

Airbnb Listings and Reviews in Washington, DC

Exploring Room Availability, Host Profiles, and Pricing Data

By Code for DC [source]

About this dataset

Welcome to the Airbnb Listings and Reviews dataset of Washington, DC. This valuable dataset offers insight into the vast rental market in the USA's capital city, providing information on hosts, room type and availability for hundreds of listings. With detailed listings about price, neighbourhood group, latitude and longitude coordinates and more - you can quickly find out what each listing is able to offer.

The Airbnb Listings dataset contains over 400 columns that provide vital parameter measurements for each location listed in Washington - including date when the listing was created or reviewed; name; host name; neighbourhood group; latitude and longitude coordinates furnished by satellite positioning system (GPS); room type; price per night paid by customers/renters; minimum number of nights required to stay at a particular place/lodging facility; calculated host listings count based on how many other accommodations they have opened within a given area or city of operation, reviews_per_month factors determined by calculating customer feedback received over an interval ranging from weeks to months as given in the reviews data-set & last but not least 'availability_365' column name –no amount signifies neither maximum limit nor no availability throughout year around hence availability 365 columns provides a guide on whether a listing is usually available at least 300 days in a calendar year period (calendar meaning 365 days).
We encourage you to explore this complex data set further – it can be used for analyses regarding market trends & characterizing shifts related pricing strategies channelled through hotel owners/operators. Let’ insight be your guide as you uncover new information about this amazing city!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the Airbnb Listings and Reviews dataset in DC. This dataset contains reviews of Airbnb listings as well as information about each listing, such as the host, location, room type, price, and availability. It is a great resource for researchers or anyone looking to learn more about hotel booking trends in the Washington DC area.

This guide will help you explore and make use of the data contained in this dataset. We’ll cover how to download it and how to access both types of data (listings information and reviews). Finally, we’ll go over some tips for using this dataset as effectively as possible.

Research Ideas

• Using the Airbnb listings and reviews in DC, create a machine learning application that can facilitate more efficient price recommendations for hosts.
• Use this dataset to develop a personalised recommendation system for visitors to Washington DC, by suggesting Airbnb listings based on their past preferences and location preferences.
• Leverage this dataset to build an interactive visualization tool mapping out neighborhoods in Washington D.C., with filters mapping out different Airbnb listings -- room type, price, availability etc. This could help potential visitors to make informed decisions about where they might want stay when visiting D.C

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Airbnb Reviews.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | date | The date at which the listing or review was made. (Date) |

File: Airbnb Listings.csv | Column name | Description | |:-----------------------------------|:-----------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group ...

The United States is a country located in North America. It is made up of 50 states and the capital district of Washington. The US federal republic has its capital in Washington D.C., which means this dataset can be used to study demographics, geography, and population density for different cities across the United States. This information can help researchers, policymakers and businesses understand how people live and work within different geographical areas in the USA

This dataset comes from simplemaps.com, check out the dataset at https://simplemaps.com/data/us-cities

Thumbnail from https://www.vecteezy.com/vector-art/3798082-red-square-map-of-united-states-of-america-with-long-shadow

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.

A dataset listing Washington cities by population for 2024.

This map shows areas where population and jobs growth will be concentrated in the District through the year 2045.

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).

These datasets contain comprehensive information on current real estate listings in Washington, D.C., obtained from Zillow, and offer a detailed overview of the Washington, D.C. housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The full dataset includes all details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With over 5,000 current listings, this dataset is perfect for in-depth analysis of the Washington, D.C. housing market and the Washington, D.C. real estate scene. Potential applications include:

• Comparing listing prices and price per square foot across various neighborhoods and property types
• Mapping listings to visualize the spatial distribution of available inventory
• Analyzing the age of available housing stock using year-of-construction data
• Assessing typical HOA fees and property taxes for listings
• Identifying listings with desirable amenities
• Evaluating school quality near listings using GreatSchools ratings
• Contacting listing agents programmatically using the provided agent information

Whether you're a real estate professional, market analyst, data scientist, or simply interested in the Washington, D.C., housing market, this dataset offers a wealth of information to explore. You can begin investigating and discovering insights into Washington, D.C. real estate today.

The dataset contains locations and attributes of organizations who deliver programs and services specifically targeted toward those experiencing homelessness. Although some organization listed in the dataset also provide housing resources, the inclusion of programs in this dataset indicates that the organization provides a non-housing resource or service which is available (by drop-in, referral, or appointment) during the daytime hours for people experiencing homelessness. The dataset was created by the Department of Human Services as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO).

Dataset contains n=115 facilities in the DC metropolitan area.

Data Dictionary:

OBJECTID ( type: esriFieldTypeOID, alias: OBJECTID ) PROGRAM_NAME ( type: esriFieldTypeString, alias: PROGRAM_NAME, length: 255 ) DESCRIPTION ( type: esriFieldTypeString, alias: DESCRIPTION, length: 1073741822 ) WEBSITE_URL ( type: esriFieldTypeString, alias: WEBSITE_URL, length: 255 ) CITY ( type: esriFieldTypeString, alias: CITY, length: 255 ) STATE ( type: esriFieldTypeString, alias: STATE, length: 255 ) ACCESSIBILITY_SERVICES ( type: esriFieldTypeString, alias: ACCESSIBILITY SERVICES, length: 255 ) ADULT_LITERACY ( type: esriFieldTypeString, alias: ADULT_LITERACY, length: 255 ) ART_THERAPY ( type: esriFieldTypeString, alias: ART_THERAPY, length: 255 ) ASSESSMENT ( type: esriFieldTypeString, alias: ASSESSMENT, length: 255 ) BORROW_MATERIALS ( type: esriFieldTypeString, alias: BORROW_MATERIALS, length: 255 ) CASE_MANAGEMENT ( type: esriFieldTypeString, alias: CASE_MANAGEMENT, length: 255 ) CHILD_CARE ( type: esriFieldTypeString, alias: CHILD_CARE, length: 255 ) CLOTHING ( type: esriFieldTypeString, alias: CLOTHING, length: 255 ) COMPUTERS ( type: esriFieldTypeString, alias: COMPUTERS, length: 255 ) DENTAL_SERVICES ( type: esriFieldTypeString, alias: DENTAL_SERVICES, length: 255 ) DOCUMENTATION_ASSISTANCE ( type: esriFieldTypeString, alias: DOCUMENTATION_ASSISTANCE, length: 255 ) DOMESTIC_VIOLENCE_SERVICES ( type: esriFieldTypeString, alias: DOMESTIC_VIOLENCE_SERVICES, length: 255 ) FOOD_GROCERIES ( type: esriFieldTypeString, alias: FOOD_GROCERIES, length: 255 ) GROUPS ( type: esriFieldTypeString, alias: GROUPS, length: 255 ) HAIRCUTS ( type: esriFieldTypeString, alias: HAIRCUTS, length: 255 ) HARM_REDUCTION ( type: esriFieldTypeString, alias: HARM_REDUCTION, length: 255 ) HIV_TESTING ( type: esriFieldTypeString, alias: HIV_TESTING, length: 255 ) HOUSING ( type: esriFieldTypeString, alias: HOUSING, length: 255 ) HOUSING_NAVIGATION ( type: esriFieldTypeString, alias: HOUSING_NAVIGATION, length: 255 ) INCOME_TAX_HELP ( type: esriFieldTypeString, alias: INCOME_TAX_HELP, length: 255 ) LAUNDRY ( type: esriFieldTypeString, alias: LAUNDRY, length: 255 ) LIBRARY_CARD ( type: esriFieldTypeString, alias: LIBRARY_CARD, length: 255 ) LEGAL_SERVICES ( type: esriFieldTypeString, alias: LEGAL_SERVICES, length: 255 ) MEDICAL_BENEFITS ( type: esriFieldTypeString, alias: MEDICAL_BENEFITS, length: 255 ) MEDICAL_SERVICES ( type: esriFieldTypeString, alias: MEDICAL_SERVICES, length: 255 ) MAIL ( type: esriFieldTypeString, alias: MAIL, length: 255 ) MEALS ( type: esriFieldTypeString, alias: MEALS, length: 255 ) MENTAL_HEALTH ( type: esriFieldTypeString, alias: MENTAL_HEALTH, length: 255 ) MINISTRY ( type: esriFieldTypeString, alias: MINISTRY, length: 255 ) PHONE ( type: esriFieldTypeString, alias: PHONE, length: 255 ) PUBLIC_RESTROOMS ( type: esriFieldTypeString, alias: PUBLIC_RESTROOMS, length: 255 ) REFRESHMENTS ( type: esriFieldTypeString, alias: REFRESHMENTS, length: 255 ) SHOWERS ( type: esriFieldTypeString, alias: SHOWERS, length: 255 ) SNAP_FOOD_STAMPS ( type: esriFieldTypeString, alias: SNAP_FOOD_STAMPS, length: 255 ) STORAGE ( type: esriFieldTypeString, alias: STORAGE, length: 255 ) SUBSTANCE_ABUSE_TREATMENT ( type: esriFieldTypeString, alias: SUBSTANCE_ABUSE_TREATMENT, length: 255 ) SUPPORTED_EMPLOYMENT ( type: esriFieldTypeString, alias: SUPPORTED_EMPLOYMENT, length: 255 ) TANF_FINANCIAL_ASSISTANCE ( type: esriFieldTypeString, alias: TANF_FINANCIAL_ASSISTANCE, length: 255 ) TRANSPORTATION ( type: esriFieldTypeString, alias: TRANSPORTATION, length: 255 ) VOCATIONAL_TRAINING ( type: esriFieldTypeString, alias: VOCATIONAL_TRAINING, length: 255 ) CLIENTS_SERVED_PER_DAY ( type: esriFieldTypeDouble, alias: CLIENTS_SERVED_PER_DAY ) TARGET ( type: esriFieldTypeString, alias: TARGET, length: 255 ) OPEN_TO_PUBLIC ( type: esriFieldTypeString, alias: OPEN_TO_PUBLIC, length: 255 ) HOURS_OF_OPERATION ( type: esriFieldTypeString, alias: HOURS_OF_OPERATION, length: 255 ) RECORD_LAST_UPDATED ( type: esriFieldTypeDate, alias: RECORD_LAST_UPDATED, length: 8 ) SHAPE ( type: esriFieldTypeGeometry, alias: SHAPE ) LATITUDE ( type: esriFieldTypeDouble, alias: LATITUDE ) LONGITUDE ( type: esriFieldTypeDouble, al...

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: 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.

Context

OpenAddresses's goal is to connect the digital and physical worlds by sharing geographic coordinates, street names, house numbers and postal codes.

Content

This dataset contains one datafile for each state in the U.S. South region (although some are arguably not in the South).

States included in this dataset:

• Alabama - al.csv
• Arkansas - ar.csv
• Washington D.C. - dc.csv
• Delaware - de.csv
• Florida - fl.csv
• Georgia - ga.csv
• Kentucky - ky.csv
• Louisiana - la.csv
• Maryland - md.csv
• Mississippi - ms.csv
• North Carolina - nc.csv
• Oklahoma - ok.csv
• South carolina - sc.csv
• Tennessee - tn.csv
• Texas - tx.csv
• Virginia - va.csv
• West Virginia - wv.csv

Field descriptions:

• LON - Longitude
• LAT - Latitude
• NUMBER - Street number
• STREET - Street name
• UNIT - Unit or apartment number
• CITY - City name
• DISTRICT - ?
• REGION - ?
• POSTCODE - Postcode or zipcode
• ID - ?
• HASH - ?

Acknowledgements

Data collected around 2017-07-25 by OpenAddresses (http://openaddresses.io).

Address data is essential infrastructure. Street names, house numbers and postal codes, when combined with geographic coordinates, are the hub that connects digital to physical places.

Data licenses can be found in LICENSE.txt.

Data source information can be found at https://github.com/openaddresses/openaddresses/tree/9ea72b079aaff7d322349e4b812eb43eb94d6d93/sources

Inspiration

Use this dataset to create maps in conjunction with other datasets for crime or weather