About this dataset

Context

The dataset tabulates the Country Life Acres 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 Country Life Acres. The dataset can be utilized to understand the population distribution of Country Life Acres by age. For example, using this dataset, we can identify the largest age group in Country Life Acres.

Key observations

The largest age group in Country Life Acres, MO was for the group of age 60 to 64 years years with a population of 13 (16.25%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Country Life Acres, MO was the 40 to 44 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Country Life Acres is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Country Life Acres 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 Country Life Acres Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Brazos Country 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 Brazos Country. The dataset can be utilized to understand the population distribution of Brazos Country by age. For example, using this dataset, we can identify the largest age group in Brazos Country.

Key observations

The largest age group in Brazos Country, TX was for the group of age 20 to 24 years years with a population of 92 (15.46%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Brazos Country, TX was the Under 5 years years with a population of 2 (0.34%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Brazos Country is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Brazos Country 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 Brazos Country Population by Age. You can refer the same here

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

Global Share of Population Having at Least Completed Lower Secondary Education by Country, 2023 Discover more data with ReportLinker!

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

As of February 2025, 87.5 percent of the population in Burundi did not have internet access. Chad followed, with around 86.8 percent reportedly being offline. North Korea ranked first with an internet penetration of nearly zero percent, as the internet remains blocked for its citizens. Global internet freedom and access The degree of internet freedom varies drastically across countries, with some providing open access and others imposing significant restrictions. In 2023, Iceland was leading worldwide in terms of internet freedom, while China ranked the lowest in the world, with strict government surveillance and censorship. The struggle with limited internet access is particularly evident in Africa, which had the lowest internet penetration rates as of 2023. Global privacy concerns The limitations and restrictions to internet access around the world prove that internet users' privacy is extremely vulnerable. And those who have access to the web are somewhat aware of that. By the first quarter of 2024, around 30.7 percent of internet users expressed concerns about companies misusing their data. This growing awareness of privacy risks is reflected in users’ behaviour. By June 2023, three in ten internet users worldwide had already taken measures to protect their online privacy, with 22.7 percent opting for tools like VPNs. Additionally, many internet users reported taking further steps, such as enabling multi-factor authentication, to for stronger privacy protections.

About this dataset

Context

The dataset tabulates the Country Club Hills 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 Country Club Hills. The dataset can be utilized to understand the population distribution of Country Club Hills by age. For example, using this dataset, we can identify the largest age group in Country Club Hills.

Key observations

The largest age group in Country Club Hills, IL was for the group of age 15 to 19 years years with a population of 1,704 (10.38%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Country Club Hills, IL was the 80 to 84 years years with a population of 345 (2.10%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Country Club Hills is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Country Club Hills 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 Country Club Hills Population by Age. You can refer the same here

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

Series Name: Proportion of countries that have conducted at least one population and housing census in the last 10 years (percent)Series Code: SG_REG_CENSUSRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.19.2: Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 per cent birth registration and 80 per cent death registrationTarget 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countriesGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

The average for 2023 based on 196 countries was 10.17 percent. The highest value was in Monaco: 36.36 percent and the lowest value was in Qatar: 1.57 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

In 2022, the total population of all ASEAN states amounted to an estimated 673.02 million inhabitants. The ASEAN (Association of Southeast Asian Nations) member countries are Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam.

ASEAN opportunity

The Association of Southeast Asian Nations was founded by five states (Thailand, Indonesia, the Philippines, Malaysia, and Singapore) in 1967 to improve economic and political stability and social progress among the member states. It was originally modelled after the European Union. Nowadays, after accepting more members, their agenda also includes an improvement of cultural and environmental conditions. ASEAN is now an important player on the global stage with numerous alliances and business partners, as well as more contenders wanting to join.

The major player in the South

Indonesia is not only a founding member of ASEAN, it is also its biggest contributor in terms of gross domestic product and is also one of the member states with a positive trade balance. In addition, it has the highest number of inhabitants by far. About a third of all people in the ASEAN live in Indonesia – and it is also one of the most populous countries worldwide. Among the ASEAN members, it is certainly the most powerful one, not just in numbers, but mostly due to its stable and thriving economy.

Series Name: Proportion of countries with birth registration data that are at least 90 percent complete (percent)Series Code: SG_REG_BRTH90Release Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.19.2: Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 per cent birth registration and 80 per cent death registrationTarget 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countriesGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

Population and housing census dataset as of Census 2022 by Bangladesh Bureau of Statistics (BBS). The population and Housing census has two parts that include- (1) The Population Census, which provides socio-economic and demographic information on every person living in a country at a point in time, down to the smallest geographical unit. (2) The Housing Census, which provides data on all dwelling units prevailing in a country, their conditions, and facilities available, down to the smallest geographical unit. Although it covered smallest geographical unit of Bangladesh; till date it's available up to District (Admin 02) level.
All information contains this dataset is collated from Final Population and Housing census report published by BBS to ensure meaningful access. For more detail information and further query/questions it’s recommended to check the full report.

Please click in Below link to access the full report-
https://bbs.portal.gov.bd/sites/default/files/files/bbs.portal.gov.bd/page/b343a8b4_956b_45ca_872f_4cf9b2f1a6e0/2024-01-31-15-51-b53c55dd692233ae401ba013060b9cbb.pdf

The datasets were produced in the following article. When using the data, please use the following citation:

Niva V, Kallio M, Muttarak R, Taka M, Varis O, Kummu M. 2021. Global migration is driven by the complex interplay between environmental and social factors. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac2e86

The data include the following files:

AC.tif

    Composite index computed based on the four AC variables by taking a mean over the respective variables:


    economy.tif


          Downscaled and min-max normalized income data.


    education.tif


          Min-max normalized education data.


    governance.tif


          Min-max normalized governance data.


    health.tif


          Min-max normalized health data.

For all of the above data, 0 and 1 represent the lowest and highest capacity, respectively.

ES.tif

    Composite index computed based on the four ES variables by taking a mean over the respective variables:


    foodProdScarcityScaled.tif


          Food production scarcity data based on food production data.


    droughtRiskScaled.tif


          Computed and scaled drought risk based on SPEI index.


    waterRiskScaled.tif


          Computed and scaled water risk data based on three water stress indices.

For all of the above data 0 and 1 represent the lowest and highest stress, respectively. Kindly note that data for natural hazards is available at its source (please see the list below).

class_raster.tif

    Spatial representation of the classification matrix.

cntryID.gpkg

    Country polygons with country IDs.

cntry_raster_masked.tif

    Country raster with country IDs.

countriesRegionsZones.csv

    Country groups and countries.

Dataset specifications:

spatial extent: -180, 180, -90, 90

spatial resolution: 5 arc-min (0.083333333 degrees)

projection: long/lat WGS84

no data value: NA

Original data to produce the above indicators and to replicate the full analysis is available at the following sources:

Net-migration data (30 arc-sec resolution): https://doi.org/10.7927/H4319SVC

Natural hazards: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398

Governance effectiveness: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398

Human Development Indicators (income, education, health): https://doi.org/10.1038/sdata.2019.38

Water risk indicators: https://doi.org/10.46830/writn.18.00146

Drought (SPEI index): https://doi.org/10.1175/2009JCLI2909.1

Food production: https://doi.org/10.1038/nature11420

Population data: https://doi.org/10.1177/0959683609356587

About this dataset

Context

The dataset tabulates the Country Club Heights 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 Country Club Heights. The dataset can be utilized to understand the population distribution of Country Club Heights by age. For example, using this dataset, we can identify the largest age group in Country Club Heights.

Key observations

The largest age group in Country Club Heights, IN was for the group of age 40 to 44 years years with a population of 27 (12.98%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Country Club Heights, IN was the 35 to 39 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Country Club Heights is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Country Club Heights 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 Country Club Heights Population by Age. You can refer the same here

China is a vast and diverse country and population density in different regions varies greatly. In 2023, the estimated population density of the administrative area of Shanghai municipality reached about 3,922 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

The Holocaust was the systematic extermination of Europe's Jewish population in the Second World War, during which time, up to six million Jews were murdered as part of Nazi Germany's "Final Solution to the Jewish Question". In the context of the Second World War, the term "Holocaust" is traditionally used to reference the genocide of Europe's Jews, although this coincided with the Nazi regime's genocide and ethnic cleansing of an additional eleven million people deemed "undesirable" due to their ethnicity, beliefs, disability or sexuality (among others). During the Holocaust, Poland's Jewish population suffered the largest number of fatalities, with approximately three million deaths. Additionally, at least one million Jews were murdered in the Soviet Union, while Hungary, Latvia, Lithuania, the Netherlands and Yugoslavia also lost the majority of their respective pre-war Jewish populations. The Holocaust in Poland In the interwar period, Europe's Jewish population was concentrated in the east, with roughly one third living in Poland; this can be traced back to the Middle Ages, when thousands of Jews flocked to Eastern Europe to escape persecution. At the outbreak of the Second World War, it is estimated that there were 3.4 million Jews living in Poland, which was approximately ten percent of the total population. Following the German invasion of Poland, Nazi authorities then segregated Jews in ghettos across most large towns and cities, and expanded their network of concentration camps throughout the country. In the ghettos, civilians were deprived of food, and hundreds of thousands died due to disease and starvation; while prison labor was implemented under extreme conditions in concentration camps to fuel the German war effort. In Poland, six extermination camps were also operational between December 1941 and January 1945, which saw the mass extermination of approximately 2.7 million people over the next three years (including many non-Poles, imported from other regions of Europe). While concentration camps housed prisoners of all backgrounds, extermination camps were purpose-built for the elimination of the Jewish race, and over 90% of their victims were Jewish. The majority of the victims in these extermination camps were executed by poison gas, although disease, starvation and overworking were also common causes of death. In addition to the camps and ghettos, SS death squads (Einsatzgruppen) and local collaborators also committed widespread atrocities across Eastern Europe. While the majority of these atrocities took place in the Balkan, Baltic and Soviet regions, they were still prevalent in Poland (particularly during the liquidation of the ghettos), and the Einsatzgruppen alone are estimated to have killed up to 1.3 million Jews throughout the Holocaust. By early 1945, Soviet forces had largely expelled the German armies from Poland and liberated the concentration and extermination camps; by this time, Poland had lost roughly ninety percent of its pre-war Jewish population, and suffered approximately three million further civilian and military deaths. By 1991, Poland's Jewish population was estimated to be just 15 thousand people, while there were fewer than two thousand Jews recorded as living in Poland in 2018.