Curious about age demographics of your clientele in United Kingdom? Wondering about which generation can be most often seen flocking to your store? Dive deep into customer insights using our population by age group data of United Kingdom. Whether your customers are down your street or across the globe, we empower you to pinpoint the ideal demographic for your marketing campaigns or projects. Our dataset offers intricate details on this country's age distribution.

During the second quarter of 2022, ****** apps hosted and distributed in the Google Play Store registered higher engagement among global users aged between 18 and 24 years. ******* users were the most active demographic across all examined app categories, with approximately **** of all downloads in the music and audio category generated in the Google Play Store coming from users in the 18 to 24 demographic group. Approximately ***** in ** users downloading news and magazines apps were aged between 50 and 64 years, while ***percent of parenting apps were downloaded by users aged 25 and 34 years.

Provides information highlights by topic via key indicators for various levels of geography.

About this dataset

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.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States 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 United States Population by Age. You can refer the same here

The dataset shows, for the year 2014, the subdivision of the Municipality's permanent staff by category (A, B1, B3, C, D1, D3) and by age group. The following age groups are proposed: * 20-24 years * 25-29 years * 30-34 years * 35-39 years * 40-44 years * 45-49 years * 50-54 years * 55-59 years * 60-64 years old * 65 and over This dataset was released by the municipality of Milan through two different csv files, one in tabular format and one in pivot format.

Estimate Population By Category Of Patients & Age Groups By State 2018-2020 Notes: 1. Data for live birth CURRENT year is not yet available from Department of Statistics, Malaysia (DOSM). Therefore, data for PREVIOUS year is provided for reference. 2. P - Preliminary figure 3. The added total differ due to rounding. (1) Current Population Estimates (related year) (2) Primary & Secondary School enrolment (PG 203 & PG204) (3) Data calculated by HIC (3)(b) Input for calculating Estimated number of antenatal mothers is number of live births. Therefore, the estimated number of antenatal mothers is based on previous year live births. (4)(b) Estimated number of antenatal mothers based on new attendances to MCHC. Sources: (a) Department of Statistics, Malaysia (b) Health Informatics Centre, Planning Division, MOH No. of Views : 215

When comparing by age the products which will be purchased on Amazon Prime Day in 2022, one can see that the share of 18 to 34 year olds planning to make purchases is greater than the other two age groups listed for nearly every product category. For the older age groups, products for the home and other items not listed are the two product categories most likely to be purchased on Amazon Prime Day, while the youngest age group is more likely to buy makeup and skincare products.

Image Classification Age Range Labels

This repository uses the following labels to categorize age ranges in image classification tasks:

[!Warning] labels_list = ['0-12', '13-20', '21-44', '45-64', '65+']

the values are age range in iamge classification give the README.MD for Repository

0-12: Images depicting individuals aged 0 to 12 years old. 13-20: Images depicting individuals aged 13 to 20 years old. 21-44: Images depicting individuals aged 21 to 44 years old. 45-64: Images… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Age-Classification-Set.

Table from the American Community Survey (ACS) B01001A-I sex by age by race - data is grouped into three age group categories for each race, under 18, 18-64 and 65 and older. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.Data on total number of people by each race alone and in combination by each census tract has been transposed to support dashboard visualizations.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2010, 2015, 2020, 2021, <a href='https://www.census.gov/programs-surveys/acs/news/data-releases/2022/release.html#5yr' style='font-famil

As of 09/24/24, this dataset is being retired and will no longer be updated.

On 10/1/2021, VDH adjusted the Vaccine Age Group categories to better serve the response's needs. This resulted in a decrease in cases, hospitalizations, and deaths among the 16-17 Year age group and an addition of cases, hospitalizations, and deaths to the 18-24 Years age group.

This dataset includes the cumulative (total) number of COVID-19 cases, hospitalizations, and deaths for each health district in Virginia by report date and by age group. This dataset was first published on March 29, 2020. The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set. The Virginia Department of Health’s Thomas Jefferson Health District (TJHD) will be renamed to Blue Ridge Health District (BRHD), effective January 2021. More information about this change can be found here: https://www.vdh.virginia.gov/blue-ridge/name-change/

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Viola: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 1(0.98%) households where the householder is under 25 years old, 71(69.61%) households with a householder aged between 25 and 44 years, 11(10.78%) households with a householder aged between 45 and 64 years, and 19(18.63%) households where the householder is over 65 years old.
• In Viola, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Viola median household income by age. You can refer the same here

Age Range Classification

## Overview

Age Range Classification is a dataset for classification tasks - it contains Face annotations for 9,672 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

As of January 2025, 24.2 percent of Facebook users in the United States were aged between 25 and 34 years, making up Facebook’s largest audience in the country. Overall, 19 percent of users belonged to the 18 to 24-year age group. Does everyone in the U.S. use Facebook? In 2023, there were approximately 247 million Facebook users in the U.S., a figure which is projected to steadily increase, and reach 262.8 million by 2028. Social media users in the United States have a very high awareness of the social media giant. Expectedly, 94 percent of users had heard of the brand in 2023. Although the vast majority of U.S. social networkers knew of Facebook, the likeability of the platform was not so impressive at 68 percent. Nonetheless, usage, loyalty, and buzz around the brand remained relatively high. Facebook, Meta, and the metaverse A strategic rebranding from Facebook to Meta Platforms in late 2021 boded well for the company in Mark Zuckerberg’s attempt to be strongly linked to the metaverse, and to be considered more than just a social media company. According to a survey conducted in the U.S. in early 2022, Meta Platforms is the brand that Americans most associated with the metaverse.  

Feature set developed to support "Predicting Age Groups of Twitter Users Based on Language and Metadata Features" by Morgan-Lopez et al. (2017). The feature set encompasses candidate variables for each of the four models referenced in the paper ("Tweet Language Only","Twitter Handle Metadata Only","Tweet Language Use and Handle Metadata", and "WWBP Words"). Description of target variables listed below -- for further description of features and methodology please reference manuscript.Target Variablesage_cat: User age category {"1" : 13-17, "2" : 18-24, "3" : 25-50} age_cat_sen: User age category used for sensitivity analysis {"1" : 13-17, "2" : 18-29, "3" : 30-50}user_age: User ageOtherrand_id: Random ID assigned to Twitter user

This layer shows age and sex demographics. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to the percent of the population ages 18 to 24 years old. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the filter settings. Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges)Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Current Vintage: 2018-2022ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyDate of Census update: Dec 15, 2023Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

Estimate Population By Category Of Patients & Age Groups By State Malaysia, 2016

This dataset shows the number of new cases of Persons With Disabilities registered by category of disabilities and age group, Malaysia, 2018

This table provides annual data on the estimated population aged 16 and over in the Canary Islands by activity and age groups.

Vintage 2024 Population projections by race, sex and age group for North Carolina counties. Includes population by race (American Indian/Alaska Native), Asian & Pacific Islander (Asian), Black, White, Other (includes persons identified as two or more races). In some counties not all race groups will be reported separately. For population of less than 250 for any race group, the population by age will be reported within the other category and the "group n" for the other category show a number larger than 1 indicating that the other category includes population from other race groups that are separately reported for other counties. For this reason, users should take care in aggregating race group population across counties.

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.