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

Curious about age demographics of your clientele in The Netherlands? 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 The Netherlands. 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.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Curious about age demographics of your clientele in Germany? 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 Germany. 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.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Curious about age demographics of your clientele in France? 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 France. 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.

Analysis of ‘Youth and old age quotient by district-free cities and districts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/a4c1412b-14b3-5319-80d8-c14d462414b6 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Definition: In order to illustrate the long-term demographic change and the associated changes in the age structure of the population, the ratio of certain age groups is shown. The youth ratio compares the generation of children and adolescents, which are mostly in the education and training phase, with the middle generation, which is predominantly in employment. For the generation of children and adolescents the age limit “under 20 years” is chosen and for the middle generation the age limit “20 to under 65 years” is chosen. The old-age dependency ratio compares the older generation, who have left the labour force, to the middle generation. For the older generation, the age limit “from 65 years” is chosen.

Data source:
It.NRW, update of population

--- Original source retains full ownership of the source dataset ---

On behalf of the Press and Information Office of the Federal Government, the opinion research institute Kantar conducted a target group survey of the ´Generation Z´. For this purpose, 1,022 people between the ages of 14 and 24 were surveyed online between 05 and 18 July 2021. The focus of the survey was on the values and orientation of the generation, their situation in the pandemic, political interest and information behaviour as well as political and social attitudes. In order to map the influence of the corona pandemic on the attitudes and social image of Generation Z, the results of this survey were compared with a survey from 2019. Current life circumstances: life satisfaction; highest school-leaving qualification of father and mother; material situation: frequency of renunciation for financial reasons; source of money (from own work, from parents, from state support, from elsewhere); primary source of money; negative effects of the Corona crisis on personal income; organisation of distance learning (communication via a digital learning platform, via video conference, via e-mail, via messenger/chats such as e.g. WhatsApp, via a cloud, by telephone, by post or by other means); agreement with statements on the situation in schools/colleges (I was able to concentrate well on my tasks at home, I missed direct contact with my classmates/ fellow students, my grades deteriorated during the pandemic, distance learning at my school/college worked well, I had insufficient equipment to follow lessons, the accessibility of teachers was very good even in times of distance learning, learning became more strenuous for me during the pandemic); opinion on the future recognition of school, university or professional degrees made during the Corona pandemic; leisure activities during the pandemic (less sport since the beginning of the pandemic than before, relationships with friends have deteriorated during the pandemic, significantly more time on the internet since the beginning of the pandemic than before, started a new hobby during the pandemic); vaccination status; likelihood of Corona vaccination. 2. Values and attitudes: personally most important life goals (e.g. self-discovery, independence, enjoying life, career, etc.); importance of various aspects for pursuing a profession (secure job, adequate income, interesting work that is fun, compatibility of private life and profession (work-life balance), career opportunities, responsibility, opportunities for further training and development); comparison of values : comparison of values Corona: extensive collection of data for infection protection vs. data protection, especially young vs. especially old people have suffered from the pandemic, pandemic as a chance for change vs. after the pandemic back to the usual normality, comparison of values State: debts in favour of education and infrastructure not a problem vs. always a burden for future generations, active role of the state for important future tasks such as climate protection and educational justice vs. leaving a passive role and shaping of the future to society and the economy, orienting politics towards future generations vs. protecting the interests of those who have already made a contribution to society, comparison of lifestyle values: conscious renunciation in favour of sustainability vs. doing what I feel like doing, doing without in favour of health vs. having fun in the foreground, self-realisation vs. putting aside one´s own needs in favour of one´s personal environment, today´s generation has completely different values than the generation before it vs. in principle very similar values as the generation before it). 3. Media and information: interest in politics; points of contact with politics in everyday life (e.g. media consumption, when using social networks, in personal conversations with friends and family, at work, at school or university, in public spaces, in leisure time/hobbies); being informed about politics; most frequently used sources of political information (media) (e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, etc.). e.g. news programmes on TV, talk shows on TV, websites of public institutions and authorities, satire programmes on TV, etc.); change in political information behaviour in the Corona pandemic. 4. Politics and society: satisfaction with democracy; opinion on democracy as an idea; need for reform of politics in Germany; most important political problems in Germany (open); satisfaction with the work of the federal government; trust in institutions (judiciary, environmental and aid organisations such as Greenpeace or Amnesty International, public health authorities such as the Robert Koch Institute, federal government, Bundestag, police, churches, school/university); perception of social lines of conflict (between rich and poor, employers and employees, young and old, foreigners and Germans, East Germans and West Germans, women and men, people in the city and people in the countryside); attitudes towards Corona (politicians take young people´s concerns seriously, young people received sufficient financial support from the state during the pandemic, young people´s needs were not taken into account enough by politicians during the Corona pandemic, the Corona pandemic will affect my generation´s future opportunities in the long term, my generation will benefit significantly from the awakening after the Corona pandemic, the Corona crisis has changed my perspective on many things in life, young people´s career opportunities have deteriorated as a result of the pandemic); agreement with various statements on Corona vaccination (children and young people aged 12 and over should also be vaccinated against Corona, young people currently have to wait too long for a vaccination appointment, vaccination prioritisation should have been lifted earlier, vaccination of young people against Corona is not necessary, there should be compulsory vaccination for schoolchildren, I personally feel that Corona vaccinations in Germany are treated fairly); currently appropriate measures to support children and young people (open). 5. Future perspectives: assessment of personal future opportunities; assessment of the future opportunities of one´s own generation in Germany; future vision of politics: agreement with various statements (a council of randomly selected citizens should be created to draw up political recommendations for the federal government, voting in elections should be possible via app, the voting age in federal elections should be lowered to 16, the population should be represented in the Bundestag by means of quotas, the population should vote directly on important political issues by referendum). Demography: age; sex; federal state; current attendance at school, college or university; type of educational institution currently attended; highest level of education attained to date; employment; subjective class classification; housing situation; household size; party sympathies; migration background. Additionally coded was: serial number; city size; weighting factor. Im Auftrag des Presse- und Informationsamt der Bundesregierung hat das Meinungsforschungsinstitut Kantar eine Zielgruppenbefragung der „Generation Z“ durchgeführt. Dazu wurden im Zeitraum vom 05. – 18. Juli 2021 1.022 Personen zwischen 14 und 24 Jahren online befragt. Die Schwerpunkte der Befragung lagen auf den Werten und Orientierung der Generation, ihrer Situation in der Pandemie, dem politischen Interesse und Informationsverhalten sowie auf den politischen und gesellschaftlichen Einstellungen. Um den Einfluss der Coronapandemie auf die Einstellungen und das Gesellschaftsbild der Generation Z abzubilden, wurden die Ergebnisse dieser Befragung mit einer Befragung aus dem Jahr 2019 verglichen. Aktuelle Lebensumstände: Lebenszufriedenheit; höchster Schulabschluss von Vater und Mutter; materielle Situation: Häufigkeit des Verzichts aus finanziellen Gründen; Geldquelle (aus eigener Arbeit, von den Eltern, aus staatlicher Unterstützung, von woanders her); primäre Geldquelle; negative Auswirkungen der Corona-Krise auf das persönliche Einkommen; Organisation des Fernunterrichts (Kommunikation über eine digitale Lernplattform, per Videokonferenz, per E-Mail, per Messenger/Chats wie z.B. WhatsApp, über eine Cloud, per Telefon, per Post oder auf sonstige Weise); Zustimmung zu Aussagen zur Situation in Schulen/ an Hochschulen (ich konnte mich zu Hause gut auf meine Aufgaben konzentrieren, der direkte Kontakt zu meinen Mitschüler/innen/ Kommilitonen/innen hat mir gefehlt, meine Noten sind während der Pandemie schlechter geworden, der Fernunterricht an meiner Schule/ Hochschule hat gut funktioniert, ich hatte nur ungenügende Ausstattung zur Verfügung, um dem Unterricht folgen zu können, die Erreichbarkeit der Lehrkräfte war auch in Zeiten des Fernunterrichts sehr gut, das Lernen ist für mich während der Pandemie anstrengender geworden); Meinung zur künftigen Anerkennung von Schul-, Universitäts- oder Berufsabschlüssen, die während der Corona-Pandemie gemacht wurden; Freizeitgestaltung während der Pandemie (seit Beginn der Pandemie weniger Sport als davor, Beziehungen zu Freunden haben sich in der Pandemie verschlechtert, seit Beginn der Pandemie deutlich mehr Zeit im Internet als davor, in der Pandemie ein neues Hobby begonnen); Impfstatus; Wahrscheinlichkeit einer Corona-Impfung. 2. Werte und Einstellungen: persönlich wichtigste Lebensziele (z.B. Selbstfindung, Unabhängigkeit, Leben genießen, Karriere, etc.); Wichtigkeit verschiedener Aspekte für die Ausübung eines Berufs (sicherer Arbeitsplatz, angemessenes Einkommen, interessante Arbeit, die Spaß macht, Vereinbarkeit von Privatleben und Beruf (Work-Life-Balance), Karrieremöglichkeiten, Verantwortung, Weiterbildungs- und Entwicklungsmöglichkeiten); Gegenüberstellung von Werten :

This table provides statistical information about people in Canada by their demographic, social and economic characteristics as well as provide information about the housing units in which they live.

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.
All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors:
Sensors:
This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer.

Statistics:
These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces.

Voice:
This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding.


A summary of the details of the collected database.
- Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

Curious about age demographics of your clientele in Austria? 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 Austria. 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.

Excel file of Superior National Forest female wolf capture data (Sheet 1) and summary of data from hunter-killed female wolves from throughout Minnesota wolf range (Sheet 2). (XLSX)

Credit: Rajan Ghimire for its implementation.

Description:

We present the Nepali Handwriting Dataset (NHD), which is a collection of camera-captured images of Nepali handwritten text from various regions in Nepal. The dataset aims to provide a benchmark for researchers to explore new techniques in handwriting detection and recognition. We also present benchmark results for text localization and recognition using well-established deep-learning frameworks. The dataset and benchmark results are available here.

Key Features:

The role of data collection and preprocessing in the research on handwritten text detection cannot be overstated. It is a crucial aspect that plays a significant role in obtaining a comprehensive and diverse dataset. To this end, the researchers personally collected 1,000 mobile phone-captured data samples from various sources, including schools, government offices, universities, and student councils.

The dataset was carefully curated to encompass three distinct categories based on age groups, namely kids, youth, and adults, with 599, 152, and 249 samples, respectively. Each of the 1,000 pages was meticulously annotated by the researchers to ensure accurate labeling and create a reliable dataset. The data collection process focused on capturing a wide range of handwriting styles and variations prevalent among different age groups and settings.

The collected dataset served as a valuable resource for training and evaluating the handwritten text detection models in the research. It provided a rich and diverse set of data that enabled the researchers to develop robust models capable of accurately detecting handwritten text across different age groups and settings.

Use Cases:

1. Real-Time Text Detection
2. Text Recognition

Results:

You can find its implementation here: https://github.com/R4j4n/Nepali-Text-Detection-DBnet

Recall: 0.9069154470416869

Precision: 0.9178659178659179

HMean: 0.9123578206927347

Test Image:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4786384%2Ff8d9aa282a42848b359aeeb021b97937%2Foutput.png?generation=1695433752833462&alt=media" alt="">

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂

Thank you

The Montgomery County Government has a diverse workforce of employees that cross five generations and multiple age, race, gender and ethnic groups. The dataset is a summary of the County's size and composition by generational category, age, race, ethnicity, gender, years of service and job class. Update Frequency : Annually

This table provides statistical information about people in Canada by their demographic, social and economic characteristics as well as provide information about the housing units in which they live.

The Generations and Gender Survey (GGS) is an individual-level survey which aims to improve understanding of family life, such as partnership formation and dissolution, fertility and intergenerational solidarity. The GGS provides detailed information on: household composition, biological and non-biological children, current and previous partnerships, household organisation and task division, parents and parental home, pregnancy and fecundity, health and well-being, individual's and partner's activity status and income, earnings assets and transfers, and value orientations and attitudes. The GGS has as a panel design, collecting information on the same persons at three-year intervals. Data processing included central harmonization of the national datasets. Data was collected among men and women between the ages of 18 and 80. GGS Wave 2 was fielded in 13 countries (Australia, Austria, Bulgaria, Czech Republic, France, Georgia, Germany, Hungary, Italy, Lithuania, Netherlands, Poland, Russian Federation) in the period from 2006 to 2014. Wave 2 has an average of 6,700 respondents per country. Individual-level data access is provided via www.ggp-i.org.

The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

EN:

Preface

I would like to begin this work by offering a few introductory words. This is the first time I am writing this type of work, and I want to emphasize that I am open to any comments and suggestions regarding my work. I know that there is always room for improvement, and I would gladly take advantage of your advice to become better at what I do.

github with Dashboard and python file: https://github.com/Dzynekz/Poland-s-population-by-voivodeship-2002-2021-

Thank you in advance for your time and I wish you a pleasant reading.

Introduction

The aim of the study is to approximate the trends and changes in selected demographic data describing the population of Poland from 2002 to 2021. The collected data allows for analysis, taking into account the administrative division into voivodeships, age groups and gender. The study focuses on answering the following research questions: 1. How has the population of Poland changed? 2. Does the introduction of the "500+" program in 2016 have a positive impact on increasing the number of births? 3. How have economic age groups changed over the years?

Data acquisition

One of the key tools used during the acquisition of reliable data was the API of the Central Statistical Office, which allowed me to access a huge database containing, among other things, information about the population in Poland from 2002 to 2021. Through analysis of the open API documentation of the CSO and the use of provided methods, I selected the most interesting ranges of information about the population, divided by voivodeships, age groups, and gender. I downloaded the complete set of statistical data using self-developed Python code, which, based on defined parameters, automated the necessary API method calls, conversion, and saving of the received data in CSV format. Having the data in the selected format, I was able to easily and efficiently import, process, and analyze the collected information using chosen tools. Without access to the open API of the CSO and the ability to use it, collecting data on population changes over the years would have been much more difficult and time-consuming. Thanks to widely used API interfaces in today's times, we can effectively acquire, gather, and process valuable data that can be used for analysis, forecasting trends, creating long-term strategies, or making daily decisions in many aspects of our daily lives (economy, finance, economics, etc.).

Visualization

Below I present a visualization that illustrates changes in the population of Poland over the years: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2Fb7b5b7b2d92cfc75b225df87a9fd004f%2FDashboard.png?generation=1681134675762237&alt=media" alt="">

How has the population changed over the years?

Overall population of the country

Analyzing the data on the population of Poland from 2002 to 2021, we can see that it underwent interesting changes. From 2002 to 2006, the population slightly decreased and amounted to: 38.21 million, 38.18 million, 38.17 million, 38.15 million, and 38.13 million, respectively. Then, from 2007 to 2011, the population strongly increased, reaching a peak of 38.53 million in 2011. In the following years, the population began to slightly decrease until 2019, to the level of 38.38 million. The largest decrease in population was recorded in 2020-2021, reaching a level of 37.9 million people, most likely due to the COVID-19 pandemic. Overall, over the entire period under investigation, the population in Poland decreased by about 1.3%.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2F1a9ca78c8df280505efccfddb4d73cb5%2Fobraz_2023-04-10_155200671.png?generation=1681134723226009&alt=media" alt="">

Population by voivodeships

The changes in the population of residents in individual voivodeships are very interesting. The largest increase in population was recorded in the Mazowieckie voivodeship and amounted to 380 thousand. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2F6de93256154094f2462b9f3c27bcba06%2Fobraz_2023-04-10_155258708.png?generation=1681134780752830&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14257214%2Fad4b988567ac7a915f2ed4461c5b9c82%2Fobraz_2023-04-10_155318168.png?generation=1681134799959003&alt=media" alt="">

The largest population growth was recorded in the Mazowieckie, Małopolskie, Wielkopolskie and Pomorskie voivodeships. At the same time, the trend in the Śląskie and Lubelskie voivodeships was the opposite, with the population decreasing.

Furthermore, the data shows that in the remaining voivodeships of Poland, the number of inhabitants decreased. The largest decrease was recorded in the Śląskie voivodeship, which amounted to 350,000, and the...