Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the United States 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 United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.
Key observations
The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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 is a part of the main dataset for United States Population by Age. You can refer the same here
This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).
A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836
Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This folder contains data behind the story Most Police Don’t Live In The Cities They Serve.
Includes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.
The Census Bureau numbers are potentially going to differ from other counts for three reasons:
How to read police-locals.csv
Header | Definition |
---|---|
city | U.S. city |
police_force_size | Number of police officers serving that city |
all | Percentage of the total police force that lives in the city |
white | Percentage of white (non-Hispanic) police officers who live in the city |
non-white | Percentage of non-white police officers who live in the city |
black | Percentage of black police officers who live in the city |
hispanic | Percentage of Hispanic police officers who live in the city |
asian | Percentage of Asian police officers who live in the city |
Note: When a cell contains **
it means that there are fewer than 100 police officers of that race serving that city.
This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!
This dataset is maintained using GitHub's API and Kaggle's API.
This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.
THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30
2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.
In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.
A total of 229 people died in mass killings in 2019.
The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.
One-third of the offenders died at the scene of the killing or soon after, half from suicides.
The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.
The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.
This data will be updated periodically and can be used as an ongoing resource to help cover these events.
To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:
To get these counts just for your state:
Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.
This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”
Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.
Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.
Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.
In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.
Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.
Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.
This project started at USA TODAY in 2012.
Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.
The Find Ryan White HIV/AIDS Medical Care Providers tool is a locator that helps people living with HIV/AIDS access medical care and related services. Users can search for Ryan White-funded medical care providers near a specific complete address, city and state, state and county, or ZIP code.
Search results are sorted by distance away and include the Ryan White HIV/AIDS facility name, address, approximate distance from the search point, telephone number, website address, and a link for driving directions.
HRSA's Ryan White program funds an array of grants at the state and local levels in areas where most needed. These grants provide medical and support services to more than a half million people who otherwise would be unable to afford care.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This folder contains data behind the story Higher Rates Of Hate Crimes Are Tied To Income Inequality.
Header | Definition |
---|---|
state | State name |
median_household_income | Median household income, 2016 |
share_unemployed_seasonal | Share of the population that is unemployed (seasonally adjusted), Sept. 2016 |
share_population_in_metro_areas | Share of the population that lives in metropolitan areas, 2015 |
share_population_with_high_school_degree | Share of adults 25 and older with a high-school degree, 2009 |
share_non_citizen | Share of the population that are not U.S. citizens, 2015 |
share_white_poverty | Share of white residents who are living in poverty, 2015 |
gini_index | Gini Index, 2015 |
share_non_white | Share of the population that is not white, 2015 |
share_voters_voted_trump | Share of 2016 U.S. presidential voters who voted for Donald Trump |
hate_crimes_per_100k_splc | Hate crimes per 100,000 population, Southern Poverty Law Center, Nov. 9-18, 2016 |
avg_hatecrimes_per_100k_fbi | Average annual hate crimes per 100,000 population, FBI, 2010-2015 |
Sources: Kaiser Family Foundation Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation Kaiser Family Foundation Census Bureau Kaiser Family Foundation United States Elections Project Southern Poverty Law Center FBI
Please see the following commit: https://github.com/fivethirtyeight/data/commit/fbc884a5c8d45a0636e1d6b000021632a0861986
This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!
This dataset is maintained using GitHub's API and Kaggle's API.
This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
The White Sea branch of the Zoological Museum of Moscow State University is based on the Nikolai Pertsov White Sea Biological Station. The collection consists mainly of fixed sea invertebrates from the White Sea, as well as from other seas and oceans. The majority of the material is available for molecular genetic research: all material fixed with ethanol is stored at -20°C or -80°С. The collection has over 15 thousand units, over 70 thousand specimens, and over 800 species, including type materials for over 12 species. The collection accepts material for storage and provides material upon request, and is integrated into the Noah’s Arc depository—an automatically updating catalogue of the collection is available online on the sites of the Noah’s Arc project and of the Global Biodiversity Information Facility (GBIF). The White Sea branch of the collection is maintained thanks to the efforts of the curator, employees and friends of the biological station, and museum volunteers. All collection units receive a unique number, with the international index ZMMU WS (Zoological Museum of Moscow University; White Sea branch). Collection homepage: (eng) https://wsbs-msu.ru/wsbs-msu-white-sea-biological-station/white-sea-branch-of-the-zoological-museum-of-moscow-state-university/ (rus) https://wsbs-msu.ru/scientific-collection/
Acknowledgements:
We are grateful to people, who participate in collecting, taxonomical identification and catalogisation of the material. Also we thank people, who help us with database service. Also we are grateful to organizers and teachers of GBIF seminar in Russia (2018, May 15-17, IO RAS): N. Ivanova, M. Shashkov, N. Budaeva, D. Schigel, A. Gebruk, A. Rogacheva, S. Pamerlon, L. Smirnova, S. Matland, N. Filippova
Material was cataloged by: E.A. Alieva, M. Chromova, E. Degtyareva, I.A. Ekimova, V. Emelianenko, M. Everett, N. Filippova, A.N. Isaychev, Yu. Khramova, A.I. Kokorin, G.D. Kolbasova, N. Kulikov, A. Martinez Garcia, A.L. Mikhlina, N.Y. Neretin, E Nikitenko, B.V. Osadchenko, M. Pronina, N.N. Rimskaya-Korsakova, T.D. Shcherbakova, A.N. Stupnikova, A.K. Tabachnick, D.D. Vinogradov, E.V. Vortcepneva, A. S. Yakusheff, D. Yurikova, A.E. Zhadan.
Material was identified by: T. Alvestad, T.I. Antokhina, E.V. Bogomolova, M. Capa, M. Cherneva, E. Degtyareva, P.Y. Dgebuadze, I.A. Ekimova, N. Ershova, M. Everett, N. Filippova, D. Gaeva, E. Gerasimova, S.A. Gorin, A.N. Isaychev, O. Katanova, A.I. Kokorin, G.D. Kolbasova, J. Kongsrud, K.N. Kosobokova, N. Kulikov, P. Kuznetsov, N. Lavesque, A. Lavrov, M.V. Mardashova, A. Martinez Garcia, A.L. Mikhlina, V.O. Mokievsky, E. Naduvaeva, I. Nekhaev, N.Y. Neretin, D. Nikishina, E.D. Nikitenko, B.V. Osadchenko, E. Oug, M. Petrova, F. Plandin, M.V. Plyuscheva, E.V. Popova, K. Pronkina, A.A. Prudkovsky, V.I. Radashevsky, N.N. Rimskaya-Korsakova, D.M. Saidov, T.D. Shcherbakova, A.K. Shmyglya, N. Shunatova, A.V. Sikorski, M. Sinkova, students, A.N. Stupnikova, A.K. Tabachnick, A.B. Tzetlin, E.V. Vortcepneva, T. Worsfold, A. S. Yakusheff, D. Yurikova, A.E. Zhadan.
IT support and help: A. Kotenev (MSU), D.N. Vershinin (MSU), Specify Software support team (T. Miller, G. Garett), IT-specialists of Living systems depository "Noah's Ark" (O. Platko, A. Klyukvin), S.K. Garushyants (IITP RAS, Moscow), E. Zotova (MSU), A. Demkiv (MSU), V. Moskalenko (MSU), D. Ilyushin (MSU), Data processing was supported by a GBIF - FinBIF data mobilization grant for European Russia (Russia-02) https://www.gbif.org/project/1r7RSp5iLqqKYssg88WIW2/gbifru-data-mobilization-activities#about
Other participants (as students or volunteers): Students of the Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University; Students of the Invertebrate Zoology Department of Biological Faculty, M.V. Lomonosov Moscow State University; A.A. Babitskaya, M.Y Neretin, O.S. Kozlovskaya, N.S. Kozlovskaya
White Sea Branch of ZMMU was supported by the Russian Scientifc Foundation, grant 14-50-00029, Russian Foundation of Basic Research, grants № 18-05-60158, 19-04-00501, 15-29-02447
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.
The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.
The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.
Data Source: CA Department of Finance, Demographic Research Unit
Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.
This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.
Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj
How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration
Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.
Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.
Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.
Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.
Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.
Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.
Where was the data collected? Data is collected throughout California. This subset of data includes Napa County.
When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.
These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.
Why was the data collected? The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).
Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf
Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the United States 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 United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.
Key observations
The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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 is a part of the main dataset for United States Population by Age. You can refer the same here