In 2023, **** percent of residents of the District of Columbia were white. A further **** percent of the population were Black or African American, and ** percent of D.C. residents were Hispanic or Latino in that same year.

About this dataset

Context

The dataset tabulates the population of Washington by race. It includes the population of Washington across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Washington across relevant racial categories.

Key observations

The percent distribution of Washington population by race (across all racial categories recognized by the U.S. Census Bureau): 40.46% are white, 44.66% are Black or African American, 0.29% are American Indian and Alaska Native, 4.10% are Asian, 0.05% are Native Hawaiian and other Pacific Islander, 4.76% are some other race and 5.69% are multiracial.

https://i.neilsberg.com/ch/washington-dc-population-by-race.jpeg" alt="Washington population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Washington
• Population: The population of the racial category (excluding ethnicity) in the Washington is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race 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

About this dataset

Context

The dataset tabulates the population of Washington by race. It includes the population of Washington across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Washington across relevant racial categories.

Key observations

The percent distribution of Washington population by race (across all racial categories recognized by the U.S. Census Bureau): 39.07% are white, 43.26% are Black or African American, 0.30% are American Indian and Alaska Native, 4.09% are Asian, 0.06% are Native Hawaiian and other Pacific Islander, 4.81% are some other race and 8.41% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Washington
• Population: The population of the racial category (excluding ethnicity) in the Washington is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race 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

About this dataset

Context

This list ranks the 1 cities in the District of Columbia, DC by Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Black Population: This column displays the rank of cities in the District of Columbia, DC by their Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Black Population: The Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total District of Columbia Hispanic Black Population: This tells us how much of the entire District of Columbia, DC Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

This dataset tracks annual black student percentage from 2000 to 2023 for The Seed Pcs Of Washington Dc vs. District Of Columbia and SEED PCS School District

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Washington by race. It includes the distribution of the Non-Hispanic population of Washington across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Washington across relevant racial categories.

Key observations

Of the Non-Hispanic population in Washington, the largest racial group is Black or African American alone with a population of 285,926 (48.11% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Washington
• Population: The population of the racial category (for Non-Hispanic) in the Washington is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Washington total Non-Hispanic 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 tracks annual black student percentage from 1993 to 2023 for District of Columbia

This dataset tracks annual black student percentage from 2005 to 2023 for DC Prep PCS School District vs. District of Columbia

Data Source: Open Data DC and American Community Survey (ACS) 5-Year Estimates

Why This Matters

Urban green spaces provide an array of health benefits, including protection from extreme heat, reducing stress and anxiety, and offering a place to stay physically active.

Parks can serve as a social gathering space in neighborhoods, offering a location for residents to host events, play sports, and connect with their neighbors. This benefit can be particularly beneficial for elderly individuals as they are more likely to suffer from social isolation.

While the District is considered a national leader in park equity today, this has not always been the case. Until 1954, many DC parks and playgrounds were segregated, either prohibiting their use by Black residents or only allowing them to be used during certain hours.

The District Response

The District consistently ranks well nationally for park equity, receiving a higher Trust for Public Land ParkScore®rating than any other city for four consecutive years (2021-2024). Unlike most cities in the US, District residents have access to a similar amount of park space regardless of their neighborhood’s racial demographics.

The District Department of Transportation’s Urban Forestry Division is on track to reach a goal of tree canopy coverage for 40% of the District, promoting better air quality and cooling our neighborhoods. Residents can also request the planting of a new street tree near them.

The Department of Parks and Recreation and the Department of General Services are modernizing and renovating parks across the District to improve park services, safety, and utilization.

This dataset tracks annual black student percentage from 2003 to 2023 for Hospitality High School vs. District Of Columbia and Washington Hospitality Foundation School District

NMCDC Copy of Living Atlas map. Source: https://www.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8fIllustration by Brian BrenemanThis layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 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 still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Data Source: Open Data DC and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Living further from full-service grocery stores can force residents to shop for food at locations that are more expensive or have fewer healthy options, leading to worse health outcomes for conditions such as obesity or diabetes.

Beyond basic nutrition, food is an integral part of culture. Having access to a wide array of culturally relevant foods has been shown to improve well-being among Black, Indigenous, and people of color communities.

Across the United States, predominantly-Black communities have fewer supermarkets than predominantly white and Hispanic communities. A pattern of disinvestment limits the availability of fresh and healthy foods.

The District Response

The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8. The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets.

The SUN Bucks program provides additional grocery-buying benefits to income-eligible families when schools are closed for the summer and children no longer have access to free or reduced-cost meals at school.

The DC Food Policy Council convenes six working groups, including the Food Access & Equity working group that aims to communicate and collaborate with residents to increase awareness of District food benefit programs and healthy food retail.

The 1960 US presidential election was the first to take place in all fifty states (although not Washington DC), and the first time where the 22nd Amendment to the Constitution prevented the incumbent president from running for a third term in office. The race was contested between John F. Kennedy of the Democratic Party, and incumbent Vice President Richard Nixon of the Republican Party. Kennedy defeated future-President Lyndon B. Johnson in the Democratic National Convention and asked Johnson to serve as his running mate, while Nixon won the Republican nomination comfortably, despite an early challenge from Nelson Rockefeller. This campaign is also notable for being the first to use televised debates between the candidates, including one that used split-screen technology, allowing the candidates to speak live from opposite sides of the country.

Campaign

Early in the campaign, both candidates were vibrant and charismatic, and garnered a loyal follower base. Kennedy spent most of his campaign criticizing the previous administration for falling behind the Soviet Union in terms of the military, economy and the space race, while Nixon highlighted the achievements made by Eisenhower's administration, and promised to build on them. Most historians agree that Kennedy's campaign was more structured and used better tactics than Nixon's, by canvassing heavily in swing states and districts instead of giving equal attention to all parts of the country (as Nixon did), with Kennedy focusing on metropolitan areas while Johnson canvassed in the south. Nixon's campaign was also more prone to mistakes, such as not preparing and refusing make-up for televised debates (making him look ill), while his running mate promised to elect African-Americans to the cabinet, however this just alienated black voters who were ambivalent in their reaction. Kennedy's connection with Martin Luther King Jr. also helped him to take a much larger share of the black vote than his opponent.

Results and Controversy

The popular vote was split by fewer than 120,000 out of seventy million votes. Kennedy took 49.7 percent of the popular vote, while Nixon took 49.5 percent. Nixon, however took more states than Kennedy, carrying 26 to Kennedy's 22, but Kennedy's tactical campaigning paid off, as his 22 states returned 303 electoral votes to Nixon's 219. Unpledged Democratic electors in the south gave 15 electoral votes to Harry F. Byrd, as they opposed Kennedy's stance on civil rights. Due to the close nature of the results, many Republicans called for recounts and accused the Kennedy campaign of cheating or committing voter fraud. For example, they highlighted that more votes were cast in certain districts of Texas (Johnson's home state) than the number of registered voters, and when Nixon lost Illinois despite winning 92 out of 101 counties, many suggested a link between the Kennedy campaign and organized crime syndicates in Chicago. These claims have subsequently been proven to be false, and historians generally agree that Kennedy's campaigning methods and Nixon's wastefulness won Kennedy the election. John F. Kennedy was subsequently named the 35th President of the United States, and is remembered favorably as one of the most popular and charismatic leaders in US history. Kennedy was assassinated in November 1963, less than three years into his first term, and was succeeded by his Vice President, Lyndon B. Johnson.

This dataset tracks annual black student percentage from 2003 to 2023 for Booker T Washington Pcs High School vs. District Of Columbia and Booker T Washington PCS School District

About this dataset

Context

The dataset tabulates the Non-Hispanic population of District of Columbia by race. It includes the distribution of the Non-Hispanic population of District of Columbia across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of District of Columbia across relevant racial categories.

Key observations

Of the Non-Hispanic population in District of Columbia, the largest racial group is Black or African American alone with a population of 285,926 (48.11% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the District of Columbia
• Population: The population of the racial category (for Non-Hispanic) in the District of Columbia is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of District of Columbia total Non-Hispanic 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 District of Columbia Population by Race & Ethnicity. You can refer the same here

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

2020 data points are the average of 2019 and 2021 data points and are included solely to maintain chart continuity. The U.S. Census Bureau did not release 2020 ACS 1-year estimates due to COVID-19. These figures should not be interpreted as an actual estimate for 2020. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters Housing is a basic necessity, and affordable housing is essential for individuals and families to live and thrive in DC.The rising cost of housing threatens residents’ access to safe and stable housing as well as their ability to cover other essential expenses like food, transportation, and childcare.Racial segregation, housing discrimination, and racist urban renewal programs, among other policies and practices, have meant that Black residents and residents of color in the District disproportionately experience the effects of rapidly rising housing costs. The District's Response Leading the nation in policies and investments for low-income rental households. Target of 12,000 new affordable housing units by 2025. Steps taken to preserve and expand affordable housing include the Housing Production Trust Fund, the Affordable Housing Preservation Fund, and the Home Purchasing Assistance Program, among others.

Release Date: 2015-12-15.[NOTE: Includes firms with payroll at any time during 2012. Employment reflects the number of paid employees during the March 12 pay period. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for All U.S. Firms With Paid Employees by Race and Number of States in Which They Operate for the U.S.: 2012. ..Release Schedule. . This file was released in December 2015. Included are statistics for:. . Black-Owned Firms (BLK). American Indian- and Alaska Native-Owned Firms (AIAN). Asian-Owned Firms (ASIAN). Native Hawaiian- and Other Pacific Islander-Owned Firms (NHPI). Company Summary (CS)-- Includes estimates for minority- and nonminority-owned firms. . ..Key Table Information. . This data supersedes all preliminary results released on August 18, 2015, and is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. ..Geographic Coverage. . The data are shown for the United States at the national level.. ..Industry Coverage. . The data are shown for the total of all sectors (00) code level.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms With Paid Employees by Race and Number of States in Which They Operate for the U.S.: 2012 contains data on:. . Numbers of firms with paid employees. Number of establishments with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Race. . White. Black or African American. American Indian and Alaska Native. Asian. . Asian Indian. Chinese. Filipino. Japanese. Korean. Vietnamese. Other Asian. . . Native Hawaiian and Other Pacific Islander. . Native Hawaiian. Samoan. Guamanian or Chamorro. Other Pacific Islander. . . Some other race. Minority. Equally minority/nonminority. Nonminority. . . Number of states business operates in. . Total. One state. Two states or more. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Race (RACE_GROUP). Number of states business operates in (NUMSTE). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSA18 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSA18.zip. ..Contact Information. . To contact the Survey of Business Owners staff:. . Visit the website at www.census.gov/programs-surveys/sbo.html.. Email general, nonsecure, and unencrypted messages to ewd.survey.of.business.owners@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Survey of Business Owners. 4600 Silver Hill Road. Washington, DC 20233. . . ...Source: U.S. Census Bureau, 2012 Survey of Business Owners.Note: The data in this file are based on the 2012...

"Note: Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%. Margins of error are estimated at the 90% confidence level.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters

Today, people rely on high-speed internet to search for jobs, work remotely, receive medical care, access education, connect with the community, read the news, and more.

Despite our reliance on broadband, many homes lack access to high-speed internet and the opportunities it makes possible, whether due to affordability or a lack of infrastructure.

Black and Latino/a children are disproportionately likely to live in homes without broadband. This can lead to negative long-term educational and professional outcomes including worse grades and being under-prepared for the growing number of jobs that require digital skills.

The District's Response

The District provides free Wi-Fi at key community anchor locations throughout the city.

The Community Internet Program (CIP) leverages government buildings to provide free or reduced-cost internet to households who qualify for the Affordable Connectivity Program (ACP).


 The DC Community Access Network (DC-CAN) project encourages public-private partnership in delivering affordable broadband services to residents and businesses in underserved areas of the District (in particular Wards 5, 7, and 8).

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20