Dataset Description: Worldometer Data Introduction This dataset contains detailed information on the population statistics of various countries, compiled from Worldometer. It includes demographic data such as yearly population changes, migration numbers, fertility rates, and urbanization metrics over multiple years.

Dataset Overview Total Entries: 4,104 Total Columns: 14 Columns Description country (object):

The name of the country. Example: 'India', 'China'. year (float64):

The year for which the data is recorded. Example: 2024, 2023. population (object):

The total population for the given year. Example: '1,441,719,852', '1,428,627,663'. yearly_change_pct (object):

The percentage change in population from the previous year. Example: '0.92%', '0.81%'. yearly_change (object):

The absolute change in population from the previous year. Example: '13,092,189', '11,454,490'. migrants (object):

The net number of migrants for the given year. Example: '-486,784', '-486,136'. median_age (object):

The median age of the population. Example: '28.6', '28.2'. fertility_rate (object):

The fertility rate for the given year. Example: '1.98', '2.00'. density_p_km2 (object):

The population density per square kilometer. Example: '485', '481'. urban_pop_pct (object):

The percentage of the population living in urban areas. Example: '36.8%', '36.3%'. urban_pop (object):

The total urban population for the given year. Example: '530,387,142', '518,239,122'. share_of_world_pop_pct (object):

The country's share of the world's population as a percentage. Example: '17.76%', '17.77%'. world_pop (object):

The total world population for the given year. Example: '8,118,835,999', '8,045,311,447'. global_rank (float64):

The global population rank of the country for the given year. Example: '1.0', '2.0'. Data Quality Missing Values:

Some columns have missing values which need to be handled before analysis. Columns with significant missing data: year, population, yearly_change_pct, yearly_change, migrants, median_age, fertility_rate, density_p_km2, urban_pop_pct, urban_pop, share_of_world_pop_pct, world_pop, global_rank. Data Types:

Most columns are of type object due to the presence of commas and percentage signs. Conversion to appropriate numeric types (e.g., integers, floats) is required for analysis. Potential Uses Demographic Analysis: Study population growth trends, migration patterns, and changes in fertility rates. Urbanization Studies: Analyze urban population growth and density changes over time. Global Ranking: Evaluate and compare the population statistics of different countries. Conclusion This dataset provides a comprehensive view of the world population trends over the years. Cleaning and preprocessing steps, including handling missing values and converting data types, will be necessary to prepare the data for analysis. This dataset can be valuable for researchers, demographers, and data scientists interested in population studies and demographic trends.

File Details Filename: worldometer_data.csv Size: 4104 rows x 14 columns Format: CSV Source Website: Worldometer Scraped Using: Scrapy

• The dataset is part of Eurostat's collection of population and employment statistics. The code "NAMQ_10_PE" specifically refers to data related to employment and population trends in European countries and likely spans a range of years from 1980 to 2024.

Eurostat provides statistical data on various aspects of the labor market across Europe, including:

• Total Population – The total number of people residing in a particular country or region.
• Labor Force – The portion of the population that is either employed or actively looking for work.
• Employment Rate – The percentage of the working-age population that is employed.
• Unemployment Rate – The percentage of the labor force that is unemployed.
• Youth Employment Rate – The employment rate among young people (typically aged 15-24).

• Sectoral Employment – Employment distribution across various sectors like agriculture, industry, and services.

• **Details of the Dataset **

This dataset would typically cover European Union countries and potentially other European countries (depending on the specific version). The data likely spans multiple years (1980-2024) and provides insights into the demographic and economic changes in these countries over time.

-**Some example insights you might explore:**

Trends in Employment: Analyzing the employment and unemployment rates over time to see how they correlate with major economic events, such as the global financial crisis. Sectoral Shifts: Investigating how the structure of employment has shifted from agriculture and industry to services over the decades. Impact of Population Growth: Exploring how changes in population size relate to changes in employment, labor force participation, and unemployment.

• Link to Eurostat’s Dataset

You can access the Eurostat dataset directly using the following link:

• Eurostat – NAMQ_10_PE Dataset

This link takes you to Eurostat's Labor Force Survey (LFS) data, which includes datasets related to employment, unemployment, and other labor force indicators across EU countries. You can navigate and search for NAMQ_10_PE by using Eurostat’s filtering and search tools. Here, you can download data in various formats such as CSV, Excel, or TSV.

About this dataset

Context

The dataset tabulates the Town And Country population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Town And Country across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Town And Country was 11,553, a 0.28% decrease year-by-year from 2022. Previously, in 2022, Town And Country population was 11,585, a decline of 0.46% compared to a population of 11,638 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Town And Country increased by 600. In this period, the peak population was 11,644 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Town And Country is shown in this column.
• Year on Year Change: This column displays the change in Town And Country population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Town And Country Population by Year. You can refer the same here

These summary metadata refer to the first results on the main demographic developments in the year of reference.

Member States send to Eurostat the first results on the main demographic developments in the year of reference (T), containing the total population figure on 31 December of year T (further published by Eurostat as Population on 1 January of year T+1), total births and total deaths during year T. This data collection is defined under http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32013R1260&from=EN" target="_blank">Regulation 1260/2013 on European demographic statistics. Countries may also transmit to Eurostat, on voluntary basis, provisional data on total immigration, emigration and net migration during the year (T).

Eurostat's data collection on the above figures is called DEMOBAL and it is carried out in June of each year. Eurostat publishes these first demographic estimates in July of each year in the online database, in the table Population change - Demographic balance and crude rates (demo_gind).

These first demographic estimates may either be confirmed or updated by Eurostat's demographic data collection taking place in December each year (called Unidemo), whereby countries submit detailed breakdowns (e.g. by age and sex) of their yearly population data, including data on migration, both at national and at regional level. The online table Population change - Demographic balance and crude rates (demo-gind) will be accordingly updated. This table includes the latest updates on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Eurostat database (Demography domain and Migration, for example the Population by citizenship and by country of birth table) may be transmitted to Eurostat at a subsequent date.

The online table Population change - Demographic balance and crude rates (demo-gind) contains time series going back to 1960; data before 2013 were collected by Eurostat from the national statistical offices on voluntary basis.

The individual metadata files reported by the countries are attached to this metadata file.

About this dataset

Context

The dataset tabulates the Hill Country Village population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hill Country Village across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hill Country Village was 947, a 0.21% increase year-by-year from 2022. Previously, in 2022, Hill Country Village population was 945, an increase of 0.21% compared to a population of 943 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hill Country Village decreased by 77. In this period, the peak population was 1,130 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hill Country Village is shown in this column.
• Year on Year Change: This column displays the change in Hill Country Village population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Hill Country Village Population by Year. You can refer the same here

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time. Data and Resources Country of Birth - Borough Shows estimates of the population by their country/region of birth by Borough

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

About this dataset

Context

The dataset tabulates the Country Club Hills population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Country Club Hills across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Country Club Hills was 994, a 0.40% decrease year-by-year from 2022. Previously, in 2022, Country Club Hills population was 998, a decline of 1.09% compared to a population of 1,009 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Country Club Hills decreased by 367. In this period, the peak population was 1,361 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Country Club Hills is shown in this column.
• Year on Year Change: This column displays the change in Country Club Hills population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Country Club Hills Population by Year. You can refer the same here

This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Two files are available to download: Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole. A Tableau visualisation tool is also available. Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

US Naturalizations Statistics

Trends and statistics on US naturalizations from 1999 to 2017

By Throwback Thursday [source]

About this dataset

The dataset US Naturalizations 1999-2017 provides information on the naturalization process of immigrants in the United States during the period from 1999 to 2017. The dataset includes various features or columns, capturing valuable insights into trends and statistics related to immigrants becoming US citizens.

Firstly, there is a column that specifies the year in which each naturalization case occurred, allowing for analysis and comparison over time. Additionally, there is a column indicating the country of birth of each individual who went through the naturalization process. This information allows for an exploration of patterns and trends based on country of origin.

The dataset also includes columns providing details about gender and age groups. By examining the distribution of naturalized individuals across different genders and age ranges, one can gain insights into demographic patterns and changes in immigration over time.

Furthermore, this dataset features columns related to occupation and educational attainment. These variables contribute to understanding the socio-economic characteristics of immigrants who became US citizens. By analyzing occupational trends or educational levels among naturalized individuals, researchers can gain valuable knowledge regarding immigrant integration within various industries or sectors.

Moreover, this dataset contains data on whether an applicant had previous experience as a lawful permanent resident (LPR) before being granted US citizenship. This variable sheds light on pathways to citizenship among those who have already obtained legal status in the United States.

Finally, there are columns providing information about processing times for naturalized cases as well as any special exemptions granted under certain circumstances. These details offer insights into administrative aspects related to applicants' journeys towards acquiring US citizenship.

In summary, this comprehensive dataset offers a wide range of variables that capture important characteristics related to immigrants becoming US citizens between 1999 and 2017. Researchers can use this data to analyze trends based on year, country of origin, gender/age groups, occupation/education levels,and pathways to citizenship such as previous LPR status or special circumstances exemptions

How to use the dataset

• Understand the columns: Familiarize yourself with the different columns available in this dataset to comprehend the information it offers. The columns included are:

  • Year: The year of naturalization.
  • United States: The number of individuals naturalized within the United States.
  • Continents:
    • Africa: Number of individuals born in African countries who were naturalized.
    • Asia: Number of individuals born in Asian countries who were naturalized.
    • Europe: Number of individuals born in European countries who were naturalized.
    • North America (excluding Caribbean): Number of individuals born in North American countries (excluding Caribbean nations) who were naturalized.
    • Oceania: Number of individuals born in Oceanian countries who were naturalized, including Australia and New Zealand.
    • South America: Number of individuals born in South American countries who were naturalized.

• Overview by year: Analyze the total number of people being granted US citizenship over time by examining the United States column. Use statistical methods like mean, median, or mode to understand trends or identify any outliers or significant changes across specific years.

• Continent-specific analysis:

  a) Identify patterns among continents over time by examining each continent's respective column (Africa, Asia, Europe, etc.). Compare growth rates and determine any regions experiencing higher or lower rates compared to others.

  b) Determine which continent contributes most significantly to overall US immigration by calculating continent-wise percentages based on total immigrants for each year.

• Identify region-specific trends:

  a) Analyze immigration patterns within individual continents by dividing them further into specific regions or countries. For example, within Asia, you can examine trends for East Asia (China, Japan, South Korea), Southeast Asia (Vietnam, Philippines), or South Asia (India, Bangladesh).

  b) Perform comparative analysis between regions/countries to identify variations in immigration rates or any interesting factors influencing these variances. ...

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

for formulas and more details check: https://github.com/Michael-Matta1/datasets-collection/tree/main/Global%20Development

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000...

In the early-mid 1990s, Albania entered a new phase of major changes, moving from a totalitarian to a democratic system and shifting gradually to the free market economy. This process led, naturally, to changes in various demographic and health characteristics of the Albanian society. The 2008-09 Albania Demographic and Health Survey (ADHS) is a nationally representative study aimed at collecting and providing information on population, demographic, and health characteristics of the country. Population-based studies of this magnitude are a major undertaking that provide information on important indicators which measure the progress of a country. The ADHS results help provide the necessary information to assess, measure, and evaluate the existing programs in the country. They also provide crucial information to policy-makers when drafting new policies and strategies related to the health sector and health services in Albania. The information collected in the 2008-09 Albania Demographic and Health Survey will be used not only by local decision-makers and programme managers, but also by partners and foreign donors involved in various development areas in Albania, as well as by academic institutions to do further analysis with the collected data. The 2008-09 Albania Demographic and Health Survey (ADHS) was implemented by the Institute of Statistics (INSTAT) and the Institute of Public Health (IPH), of the Ministry of Health. ICF Macro provided technical assistance to the ADHS through funding from the United Nations Children’s Fund (UNICEF) and the United State Agency for International Development (USAID)-funded MEASURE DHS programme. Local costs of the survey were supported by USAID, the Swiss Cooperation Office in Albania (SCO-A), UNICEF, the United Nations Population Fund (UNFPA), and the World Health Organization (WHO). Data collection was conducted from 28 October, 2008 to 26 April, 2009 using a nationally representative sample of almost 9,000 households. All women age 15-49 in these households and all men age 15-49 in half of the households were eligible to be individually interviewed. In addition to the data collected through interviews with these women and men, capillary blood samples were collected from all children age 6-59 months and all eligible women and men age 15-49 for anaemia testing. All children under five years of age and eligible women and men age 15-49 were weighed and measured to assess their nutritional status. Finally, blood pressure (BP) was measured for eligible women and men in the households selected for the men’s interview to estimate the prevalence of hypertension in the adult population. The 2008-09 ADHS is designed to provide data to monitor the population and health situation in Albania. Specifically, the 2008-09 ADHS collected information on fertility levels, marriage, sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted infections. Additional features of the 2008-09 ADHS include the collection of information on migration (out-migration, returning migrants and internal migration), haemoglobin testing to detect the presence of anaemia, blood pressure (BP) measurements among the adult population, and questions related to accessibility and affordability of health services. The information collected in the 2008-09 ADHS provides updated estimates of an array of demographic and health indicators that will assist in the development of appropriate policies and programmes to address the most important health issues in Albania.

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

About

The 2024 Revision of World Population Prospects is the twenty-eighth edition of official United Nations population estimates and projections that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. It presents population estimates from 1950 to the present for 237 countries or areas, underpinned by analyses of historical demographic trends. This latest assessment considers the results of 1,910 national population censuses conducted between 1950 and 2023, as well as information from vital registration systems and from 3,189 nationally representative sample surveys. The 2024 revision also presents population projections to the year 2100 that reflect a range of plausible outcomes at the global, regional and national levels.

Column Description

• Total Population, as of 1 January (thousands)
• Total Population, as of 1 July (thousands)
• Male Population, as of 1 July (thousands)
• Female Population, as of 1 July (thousands)
• Population Density, as of 1 July (persons per square km) (UPDATED on 14 July 2022)
• Population Sex Ratio, as of 1 July (males per 100 females)
• Median Age, as of 1 July (years)
• Natural Change, Births minus Deaths (thousands)
• Rate of Natural Change (per 1,000 population)
• Population Change (thousands)
• Population Growth Rate (percentage)
• Population Annual Doubling Time (years)
• Births (thousands)
• Births by women aged 15 to 19 (thousands)
• Crude Birth Rate (births per 1,000 population)
• Total Fertility Rate (live births per woman)
• Net Reproduction Rate (surviving daughters per woman)
• Mean Age Childbearing (years)
• Sex Ratio at Birth (males per 100 female births)
• Total Deaths (thousands)
• Male Deaths (thousands)
• Female Deaths (thousands)
• Crude Death Rate (deaths per 1,000 population)
• Life Expectancy at Birth, both sexes (years)
• Male Life Expectancy at Birth (years)
• Female Life Expectancy at Birth (years)
• Life Expectancy at Age 15, both sexes (years)
• Male Life Expectancy at Age 15 (years)
• Female Life Expectancy at Age 15 (years)
• Life Expectancy at Age 65, both sexes (years)
• Male Life Expectancy at Age 65 (years)
• Female Life Expectancy at Age 65 (years)
• Life Expectancy at Age 80, both sexes (years)
• Male Life Expectancy at Age 80 (years)
• Female Life Expectancy at Age 80 (years)
• Infant Deaths, under age 1 (thousands)
• Infant Mortality Rate (infant deaths per 1,000 live births)
• Live Births Surviving to Age 1 (thousands)
• Under-Five Deaths, under age 5 (thousands)
• Under-Five Mortality (deaths under age 5 per 1,000 live births)
• Mortality before Age 40, both sexes (deaths under age 40 per 1,000 live births)
• Male Mortality before Age 40 (deaths under age 40 per 1,000 male live births)
• Female Mortality before Age 40 (deaths under age 40 per 1,000 female live births)
• Mortality before Age 60, both sexes (deaths under age 60 per 1,000 live births)
• Male Mortality before Age 60 (deaths under age 60 per 1,000 male live births)
• Female Mortality before Age 60 (deaths under age 60 per 1,000 female live births)
• Mortality between Age 15 and 50, both sexes (deaths under age 50 per 1,000 alive at age 15)
• Male Mortality between Age 15 and 50 (deaths under age 50 per 1,000 males alive at age 15)
• Female Mortality between Age 15 and 50 (deaths under age 50 per 1,000 females alive at age 15)
• Mortality between Age 15 and 60, both sexes (deaths under age 60 per 1,000 alive at age 15)
• Male Mortality between Age 15 and 60 (deaths under age 60 per 1,000 males alive at age 15)
• Female Mortality between Age 15 and 60 (deaths under age 60 per 1,000 females alive at age 15)
• Net Number of Migrants (thousands)
• Net Migration Rate (per 1,000 population)

Copyright © 2024 by United Nations, made available under a Creative Commons license CC BY 3.0 IGO: http://creativecommons.org/licenses/by/3.0/igo/ Suggested citation: United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.

Foto von kazi arifuzzaman auf Unsplash

About this dataset

Context

The dataset tabulates the Country Club population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Country Club across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Country Club was 2,497, a 0.64% increase year-by-year from 2022. Previously, in 2022, Country Club population was 2,481, a decline of 0.16% compared to a population of 2,485 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Country Club increased by 101. In this period, the peak population was 2,499 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Country Club is shown in this column.
• Year on Year Change: This column displays the change in Country Club population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Country Club Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Country Life Acres population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Country Life Acres across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Country Life Acres was 71, a 1.39% decrease year-by-year from 2022. Previously, in 2022, Country Life Acres population was 72, a decline of 0% compared to a population of 72 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Country Life Acres decreased by 10. In this period, the peak population was 84 in the year 2001. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Country Life Acres is shown in this column.
• Year on Year Change: This column displays the change in Country Life Acres population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Country Life Acres Population by Year. You can refer the same here

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Abstract

SYPE 2014 IS A HARMONIZED-PANEL DATA SET WITH SYPE 2009

The five years that have passed since the Population Council's Survey of Young People in Egypt of 2009 (SYPE 2009) have proved to be a tumultuous period for the country. The year 2011 marked a historic year for Egyptian youth, as young people from around the country took an active role in the January 25 revolution. Through their activism in early 2011, Egypt's young revolutionaries gained a platform to denounce their social and political marginalization, and demand their rights to freedom, justice, equality, and opportunity.

This unprecedented voice for Egypt's youth pointed a national spotlight on many of the challenges that were found in the 2009 SYPE, including an educational system unresponsive to youth needs, difficult employment conditions, low civic and political engagement, and a social environment that denies youth access to essential information about their transition to adulthood.

Since 2011, Egypt has undergone several political fluctuations and changes of power, with civil unrest and continued protests marking many events during the transition. Furthermore, the past four years have proven costly to Egypt's economic well-being and the labor market. Post-revolutionary political instability has resulted in the widespread divestment of foreign-owned firms, the declining value of the Egyptian pound, and a looming debt crisis the Egyptian state is still struggling to avoid. The tumultuous climate has resulted in an enormous drop in revenues for particular economic sectors, such as tourism. Moreover, the return of large numbers of migrants from Libya and other countries in the region affected by the “Arab Spring” has also negatively affected the Egyptian labor market.

This post-revolutionary economic stagnation is expected to have resulted in a steady deterioration of job quality and increasing employment informality, in the context of labor market conditions that were already difficult for young entrants. Such economic challenges could not come at a worse time for Egypt's youth.

Like other countries in the region, Egypt is currently experiencing a demographic “youth bulge,” meaning that the population of young people is significantly larger than other age groups. Although more highly educated than previous generations, this population of young people has struggled to achieve economic stability. Even prior to the 2011 uprisings, Egypt's youth constituted an estimated 90% of the country's unemployed.

It is therefore vital to question how Egypt's youth are now faring in a significantly more unfavorable economic climate, and whether they are able to access the professional opportunities needed to work toward economic independence and complete key life transitions such as getting married and starting a family. At the same time, the transitional period may have opened up new opportunities to youth in other areas of life, most notably deeper engagement with media, politics, and civic life. Such questions regarding youth employment and civic participation in the current tumultuous era, along with potential changes in the institutions and decisions that shape the transition to adulthood, such as health and access to health care, quality of education, migration, marriage, and youth attitudes and life outlooks, are what this report seeks to better understand.

The 2009 Survey of Young People in Egypt (SYPE) was fielded in May 2009 and collected data on several key areas of interest to youth, including education, employment, migration, health, family formation, social issues, and civic and political participation. In order to observe how young people have been faring during the transition period in Egypt in comparison to 2009, the Population Council designed the second wave of SYPE in 2014, which re-interviewed the same sample of young people who were interviewed in 2009. This yields a panel data set that spans the periods before and after the January 25, 2011 revolution, and that is nationally representative for both time periods.

The data collected for SYPE 2009 was harmonized, by the Economic Research Forum (ERF) Data Team, with SYPE 2014 to produce a comparable and harmonized version of the data set to facilitate cross-temporal research.

Geographic coverage

The SYPE sample is nationally representative, covering all governorates in Egypt, including the five Frontier governorates. The SYPE sample is considered to be an innovative design, because it allows for a priori inclusion of slum areas within the urban sample.

Analysis unit

1- Households. 2- Youth aged (13-35) years.

Universe

The survey covered a national sample of households and selected youth aged 13-35.

Kind of data

Sample survey data [ssd]

Sampling procedure

SYPE 2014 IS A HARMONIZED-PANEL DATA SET WITH SYPE 2009

----> Survey design and implementation SYPE 2009 targeted young people aged 10-29, thus encompassing both "youth" and "adolescents. The SYPE team chose this age range in order to track young people throughout the complete duration of their transition to adulthood, allowing for an extended period to account for the phenomenon of delayed marriage and, in some cases, delayed transitions to productive work. The SYPE 2014 survey built a panel dataset by going back to re-interview the same sample of young people (now aged 13-35) interviewed in SYPE 2009 in all governorates of Egypt.

----> Survey sample A brief explanation of the sampling design for the previous wave of SYPE is essential for understanding the 2014 SYPE sampling. SYPE 2009 is a uniquely comprehensive survey in that it is nationally representative, covering all the governorates in Egypt including the five frontier governorates, and was specifically designed for a priori inclusion of informal urban areas, also known as slums (or ashwaiyyat in Arabic). The Frontier Governorates and informal areas are often not covered in largescale surveys. The sample is designed so that the data are not only nationally representative, but also representative of Egypt's six major administrative regions: the Urban Governorates, rural Upper Egypt, urban Upper Egypt, rural Lower Egypt, urban Lower Egypt, and the Frontier Governorates.

The 2009 SYPE sample is a stratified, multi-stage cluster sample. Sampling was determined using primary sampling units (PSUs) drawn from the master sample provided by the Central Agency for Public Mobilization and Statistics (CAPMAS), which was based on the 2006 national census. SYPE 2009 consisted of 455 PSUs, with 239 PSUs in rural areas and 216 PSUs in urban areas. Rural PSUs were divided equally between large and small villages, in order to accurately represent the diversity of rural demographics and account for peri-urbanization.

Informal settlements were selected from a list developed by the Information and Decision Support Center of the Egyptian Cabinet of Ministers (IDSC). The 2009 SYPE data collection and processing were conducted in collaboration with the IDSC.

Out of the 11,372 households selected from the CAPMAS master sample for the 2009 SYPE sample, 20,200 young people were eligible to participate, and the Kish grid technique was used to draw a sample of 16,061 subjects from this pool of potential participants.

In total, 15,029 of the sampled 16,061 young people were interviewed, with attrition primarily being due to the individual's refusal to participate or unavailability during data collection periods.

SYPE 2014 sampled the same young people who were part of the original sample of 15,029 individuals surveyed in 2009. Of the 15,029 young people interviewed in 2009, data collectors managed to completely interview 10,916 (72.6%) aged 13-35 for the SYPE 2014 study (A few respondents reported being below age 14 at the time of the 2014 SYPE interview. These cases were left as is and included in the analysis, after carefully checking their exact age.) Every effort was made to track down the current contact information of households and/or eligible young people who had changed their location since the 2009 interview. During the SYPE 2014 data collection phase, a household was not interviewed (i.e., the household questionnaire was not filled out) if the eligible young person could not be located either in the original or in a split household.

Weights based on the probability of non-response were constructed to adjust the sample of the 2014 SYPE for attrition (Very few cases were reported as missing due to migration or death of an eligible young person. These cases were assigned to the "household not found" or "individual not found" categories. However, it is suspected that some of the households that were unable to be tracked in 2014 may also have been missing due to the migration or death of household members).

The harmonized sample includes the 10,916 individuals re-interviewed in 2014.

** For information on the 2009 SYPE sample, See the English report of SYPE 2009 available among the external resources in the Survey of Young People in Egypt 2009 study on the ERF data portal.

Sampling deviation

Attrition was mainly due to family refusal to participate (9%) as well as the relocation of respondents (14%) who could not be tracked in 2014, 60% of the interviewed individuals were still in their original 2009 households, while 12.6% were found in split households (A split household is defined in this 2014 SYPE panel as a household that was formed due to the move of at least one eligible young person out of his/her original 2009 household to form a new household after the 2009 interview).

Mode of data collection

Face-to-face

About this dataset

Context

The dataset tabulates the Brazos Country population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Brazos Country across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Brazos Country was 511, a 0.78% decrease year-by-year from 2022. Previously, in 2022, Brazos Country population was 515, an increase of 1.58% compared to a population of 507 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Brazos Country increased by 174. In this period, the peak population was 515 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Brazos Country is shown in this column.
• Year on Year Change: This column displays the change in Brazos Country population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Brazos Country Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Hill Country Village population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hill Country Village across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Hill Country Village was 946, a 0.21% increase year-by-year from 2021. Previously, in 2021, Hill Country Village population was 944, a decline of 0.11% compared to a population of 945 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Hill Country Village decreased by 78. In this period, the peak population was 1,130 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Hill Country Village is shown in this column.
• Year on Year Change: This column displays the change in Hill Country Village population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Hill Country Village Population by Year. You can refer the same here