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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 60-64 years with a population of 15 (15.96%), according to the 2021 American Community Survey. At the same time, the smallest age group in Country Life Acres, MO was the 40-44 years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the 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 Town And 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 Town And Country. The dataset can be utilized to understand the population distribution of Town And Country by age. For example, using this dataset, we can identify the largest age group in Town And Country.

Key observations

The largest age group in Town And Country, MO was for the group of age 15 to 19 years years with a population of 981 (8.45%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Town And Country, MO was the 25 to 29 years years with a population of 223 (1.92%). 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 Town And Country is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Town And 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 Town And Country Population by Age. You can refer the same here

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.

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

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

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

Series Name: Countries that have conducted at least one population and housing census in the last 10 years (1 = YES; 0 = NO)Series Code: SG_REG_CENSUSNRelease 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/

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Taiwan data available from WorldPop here.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

This dataset, created by ODM, aggregates data from various sources to build out data relating to SDG indicators of interest for the Lower Mekong Region. It is updated on a regular basis. See also the following resources: - https://data.opendevelopmentmekong.net/dataset/sdg3-c-1-health-worker-density-by-type-of-occupation-per-10-000-population-for-lmcs - https://data.opendevelopmentmekong.net/dataset/sdg-3-8-2-proportion-of-population-with-large-household-expenditures-on-health-at-10-of-household-t - https://data.opendevelopmentmekong.net/dataset/sdg-8-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-12-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-4-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-10-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-5-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-1-available-data - https://data.opendevelopmentmekong.net/dataset/sdg-9-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-2-available-data - https://data.opendevelopmentmekong.net/dataset/population-and-proportion-in-urban-slums-lmcs-sdg11-1-1 - https://data.opendevelopmentmekong.net/dataset/data-on-sdg-7-tier-i-indicators-lmcs-only-all-custodian-agencies - https://data.opendevelopmentmekong.net/dataset/wash-dataset-1990-215 - https://data.opendevelopmentmekong.net/dataset/proportion-of-urban-population-living-in-slums - https://data.opendevelopmentmekong.net/dataset/population-density-from-1995-to-2013-thailand-myanmar-laos-vietnam-and-cambodia - https://data.opendevelopmentmekong.net/dataset/sdg-index-dashboards-report - https://data.opendevelopmentmekong.net/dataset/sdg-indicator-15-1-1-forest-area-as-a-proportion-of-total-land-area?type=dataset

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

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Central Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

This dataset provides values for PERSONAL SAVINGS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Population covered by at least one social protection benefit (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.3.1 Population covered by at least one social protection benefit (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/population-covered-by-at-least-one-social-protection-benefit-for-african-countries.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.