In 2022, the state with the highest median age of its population was Maine at 45.1 years. Utah had the lowest median age at 32.1 years. View the distribution of the U.S. population by ethnicity here.
Additional information on the aging population in the United States
High birth rates during the so-called baby boom years that followed World War II followed by lower fertility and morality rates have left the United States with a serious challenge in the 21st Century. However, the issue of an aging population is certainly not an issue unique to the United States. The age distribution of the global population shows that other parts of the world face a similar issue.
Within the United States, the uneven distribution of populations aged 65 years and over among states offers both major challenges and potential solutions. On the one hand, federal action over the issue may be contentious as other states are set to harbor the costs of elderly care in states such as California and Florida. That said, domestic migration from comparably younger states may help to fill gaps in the workforce left by retirees in others.
Nonetheless, aging population issues are set to gain further prominence in the political and economic decisions made by policymakers regardless of the eventual distribution of America’s elderly. Analysis of the financial concerns of Americans by age shows many young people still decades from retirement hold strong concern over their eventual financial position.
This statistic shows the leading metropolitan areas with the highest percentage of population aged under 18 years in the United States in 2019. In 2019, Provo-Orem, Utah was ranked first with **** percent of its population being under 18 years old.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 3 cities in the Young County, TX by Multi-Racial Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
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/.
https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 3 cities in the Young County, TX by Multi-Racial 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.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
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/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 3 cities in the Young County, TX by Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
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/.
A downloadable, printable 8.5 x 11 inch PDF map of the City of Norwood Young America and surrounding area.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 3 cities in the Young County, TX by Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
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/.
Unadjusted decennial census data from 1950-2000 and projected figures from 2010-2040: summary table of New York City population numbers and percentage share by Borough, including school-age (5 to 17), 65 and Over, and total population.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name
https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de449683https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de449683
Abstract (en): The research team collected data on homicide, robbery, and assault offending from 1984-2006 for youth 13 to 24 years of age in 91 of the 100 largest cities in the United States (based on the 1980 Census) from various existing data sources. Data on youth homicide perpetration were acquired from the Supplementary Homicide Reports (SHR) and data on nonlethal youth violence (robbery and assault) were obtained from the Uniform Crime Reports (UCR). Annual homicide, robbery, and assault arrest rates per 100,000 age-specific populations (i.e., 13 to 17 and 18 to 24 year olds) were calculated by year for each city in the study. Data on city characteristics were derived from several sources including the County and City Data Books, SHR, and the Vital Statistics Multiple Cause of Death File. The research team constructed a dataset representing lethal and nonlethal offending at the city level for 91 cities over the 23-year period from 1984 to 2006, resulting in 2,093 city year observations. The purpose of this study was to estimate temporal trends in youth violence rates variation across 91 of the 100 largest cities in the United States from 1984-2006, and to model city-specific explanatory predictors influencing these trends. In order to estimate trends in homicide offending for youth 13 to 24 years of age in 91 of the 100 largest cities in the United States from 1984-2006, data for youth homicide were acquired from the Supplementary Homicide Report (SHR), a component of the FBI's Uniform Crime Reporting Program (UCR). Measures of youth arrests for the nonlethal violent crimes of robbery and assault were acquired from UCR city arrest data for the same time period. Annual homicide, robbery, and assault arrest rates per 100,000 age-specific (i.e., 13 to 17 and 18 to 24 year olds) population were calculated by year for each city in the study. Annual homicide rates were calculated through a conventional procedure: annual incidents in a specific city, divided by the age-specific population of that city, multiplied by 100,000. Partial reporting during the time period resulted in dropping 9 cities from the homicide data and 10 cities from the robbery and assault data. Data on city-level characteristics including measures of structural disadvantage, drug market activities, gang presence-activity, and firearm availability were derived from the County and City Data Books, SHR, and the Vital Statistics Multiple Cause of Death File, respectively. Missing data came from two sources; failure to report in homicide and some of the Census collections, and lack of data for specific years, mainly in Census data, between major data collection points like the Decennial Census and the Mid-decade estimates from Census related sources. Missing data in the homicide measures were addressed using an Iterative Chain equation procedure to conduct Multiple Imputation. Variables from the original source used in the multiple imputation procedure included age of victim, race, ethnicity, gender, seven available measures of homicide circumstances, and city population size. Extrapolation methods were used to adjust for missing data in the robberies and assaults by age, and in the census and economic data sources. To estimate a missing year between two reported values, the missing year was estimated to be mid-way between the two observed years on either side of the missing year. Longer gaps involved further averaging and allocating according to the number of years missing; these estimates amount to maximum likelihood estimates of the missing years or in the case of the robberies and assaults, months as well. The study contains a total of 39 variables including city name, year, crime rate variables, and city characteristics variables. Crime rate variables include imputed and non-imputed homicide rate variables for juveniles aged 13 to 17, young adults aged 18 to 24, and adults aged 25 and over. Other crime variables include the number of imputed and non-imputed homicides as well as the robbery rate and assault rate for juveniles and young adults. City characteristics variables include population, poverty rates, percentage of African Americans, percentage of female-headed households, percentage of residents unemployed, percentage of residents receiving public assistance, home-ownership rates, gang presence and activity, and alcohol outlet density. None. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of dis...
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
In 2022, San Jose, CA, was the hottest market for millennial homebuyers in the United States. Millennials in San Jose were responsible for nearly 64 percent of the house purchase requests. Denver, CO, and Boston, MA, completed the top three with over 60 percent of purchase requests. Which are the states with the youngest population in the U.S.? It should come as no surprise that the demographic composition plays a central role in the development of the housing market in different states. In 2020, the median age in the United States was 38.2 years, but some states, such as Alaska, District of Columbia, and Utah had much younger population. In contrast, Maine, Puerto Rico, and Hampshire had the highest median age of population. Millennials’ attitudes towards homeownership While many millennials have given up on homeownership, one in three people share that they are in the process of saving for a home purchase. These results suggest that young Americans have not entirely given up on the American dream of owning a home of their own.
New York City Population By Community Districts
The data was collected from Census Bureaus' Decennial data dissemination (SF1) for the years 1970, 1980, 1990, 2000 and 2010.
Compiled by the Population Division – New York City Department of City Planning
In 2021, the five most popular cities in Texas, U.S. for Gen Z renters had applications from this generation ranging from ** to ** percent. Denton had the highest share of Gen Z renter applicants of 47percent.
Gen Z apartment seekers have shown consistently high interest in all Texas cities, compared to New York or California. This can be explained with the fact that Texas is one of the states with the youngest population in the United States.
https://www.newyork-demographics.com/terms_and_conditionshttps://www.newyork-demographics.com/terms_and_conditions
A dataset listing New York cities by population for 2024.
https://www.iowa-demographics.com/terms_and_conditionshttps://www.iowa-demographics.com/terms_and_conditions
A dataset listing Iowa cities by population for 2024.
Among the 81 largest metropolitan areas (by population) in the United States, Knoxville, Tennessee was ranked first with **** percent of residents reporting as white, non-Hispanic in 2023.
https://www.washington-demographics.com/terms_and_conditionshttps://www.washington-demographics.com/terms_and_conditions
A dataset listing Washington cities by population for 2024.
https://www.newmexico-demographics.com/terms_and_conditionshttps://www.newmexico-demographics.com/terms_and_conditions
A dataset listing New Mexico cities by population for 2024.
In 2022, the state with the highest median age of its population was Maine at 45.1 years. Utah had the lowest median age at 32.1 years. View the distribution of the U.S. population by ethnicity here.
Additional information on the aging population in the United States
High birth rates during the so-called baby boom years that followed World War II followed by lower fertility and morality rates have left the United States with a serious challenge in the 21st Century. However, the issue of an aging population is certainly not an issue unique to the United States. The age distribution of the global population shows that other parts of the world face a similar issue.
Within the United States, the uneven distribution of populations aged 65 years and over among states offers both major challenges and potential solutions. On the one hand, federal action over the issue may be contentious as other states are set to harbor the costs of elderly care in states such as California and Florida. That said, domestic migration from comparably younger states may help to fill gaps in the workforce left by retirees in others.
Nonetheless, aging population issues are set to gain further prominence in the political and economic decisions made by policymakers regardless of the eventual distribution of America’s elderly. Analysis of the financial concerns of Americans by age shows many young people still decades from retirement hold strong concern over their eventual financial position.