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.

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

This dataset contains aggregate data concerning the number of children who entered DCF placement during a given SFY (July 1 – June 30). These figures are broken out by the DCF Region and Office responsible for the child's care, the child's Age Group (based on age at date of entry), and the Placement Type category into which the child was initially placed.

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/

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.

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

The dataset shows, for 2014, the division of permanent staff of the Municipality by category (A, B1, B3, C, D1, D3) and by age group. The following age groups are proposed: * 20-24 years old * 25-29 years * 30-34 years old * 35-39 years old * 40-44 years old * 45-49 years old * 50-54 years * 55-59 years old * 60-64 years old * 65 and above This dataset has been 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 Malaysia, 2016

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

Per a 2023 survey by Rakuten Insight on user-generated content (UGC), Indian consumers between the ages of ** and ** primarily consumed UGC related to the beauty, health, and wellness category, reflected in more than ** percent of the responses. It was also noteworthy that ** percent of consumers aged over 55 years were interested in electronics and home goods-related UGC.

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

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.

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.

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.

This dataset provides information about different age categories along with population and percentage in the town of Dumfries in the year 2020

Vintage 2024 Population projections by race and age group for North Carolina counties. Includes population by race (American Indian/Alaska Native), Asian and 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.

This provides a count of Veterans on the rolls for Pension benefits in FY 2024 with expenditures for claims by state and the age groups of Veterans in each category. See VBA's Annual Benefits Report for additional information: www.benefits.va.gov/REPORTS/abr/ To protect Veteran privacy any counts consisting of fewer than ten Veterans are not included.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

This provides a count of Veterans on the rolls for Compensation Service in FY 2023 with expenditures for compensation claims by state and the age groups of Veterans in each category. See VBA's Annual Benefits Report for additional information: www.benefits.va.gov/REPORTS/abr/ To protect Veteran privacy any categories containing less than ten Veterans are not included.