The statistic provides a breakdown of the size of the world's population by age and world region in 2020. In 2020, there are projected to be 718.65 million people aged 20-29 living in Asia.

Until 2100, the world's population is expected to be ageing. Whereas people over 60 years made up less than 13 percent of the world's population in 2024, this share is estimated to reach 28.8 percent in 2100. On the other hand, the share of people between zero and 14 years was expected to decrease by almost ten percentage points over the same period.

The statistic shows the age group distribution of the populations of the various world regions in 2020. In 2020, it is projected that people aged 0 to 14 years will account for 40.3 percent of the population of Africa.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

This is a hybrid gridded dataset of demographic data for China from 1979 to 2100, given as 21 five-year age groups of population divided by gender every year at a 0.5-degree grid resolution.

The historical period (1979-2020) part of this dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4, UN WPP-Adjusted Population Count) with gridded population from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, Histsoc gridded population data).

The projection (2010-2100) part of this dataset is resampled directly from Chen et al.’s data published in Scientific Data.

This dataset includes 31 provincial administrative districts of China, including 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government (Taiwan, Hong Kong, and Macao were excluded due to missing data).

Method - demographic fractions by age and gender in 1979-2020

Age- and gender-specific demographic data by grid cell for each province in China are derived by combining historical demographic data in 1979-2020 with the national population census data provided by the National Statistics Bureau of China.

To combine the national population census data with the historical demographics, we constructed the provincial fractions of demographic in each age groups and each gender according to the fourth, fifth and sixth national population census, which cover the year of 1979-1990, 1991-2000 and 2001-2020, respectively. The provincial fractions can be computed as:

\(\begin{align*} \begin{split} f_{year,province,age,gender}= \left \{ \begin{array}{lr} POP_{1990,province,age,gender}^{4^{th}census}/POP_{1990,province}^{4^{th}census} & 1979\le\mathrm{year}\le1990\\ POP_{2000,province,age,gender}^{5^{th}census}/POP_{2000,province}^{5^{th}census} & 1991\le\mathrm{year}\le2000\\ POP_{2010,province,age,gender}^{6^{th}census}/POP_{2010,province}^{6^{th}census}, & 2001\le\mathrm{year}\le2020 \end{array} \right. \end{split} \end{align*}\)

Where:

- \( f_{\mathrm{year,province,age,gender}}\)is the fraction of population for a given age, a given gender in each province from the national census from 1979-2020.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province,age,gender}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for a given age, a given gender in each province from the X^th national census.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for all ages and both genders in each province from the X^th national census.

Method - demographic totals by age and gender in 1979-2020

The yearly grid population for 1979-1999 are from ISIMIP Histsoc gridded population data, and for 2000-2020 are from the GPWv4 demographic data adjusted by the UN WPP (UN WPP-Adjusted Population Count, v4.11, https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11), which combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP to improve accuracy. These two gridded time series are simply joined at the cut-over date to give a single dataset - historical demographic data covering 1979-2020.

Next, historical demographic data are mapped onto the grid scale to obtain provincial data by using gridded provincial code lookup data and name lookup table. The age- and gender-specific fraction were multiplied by the historical demographic data at the provincial level to obtain the total population by age and gender for per grid cell for china in 1979-2020.

Method - demographic totals and fractions by age and gender in 2010-2100

The grid population count data in 2010-2100 under different shared socioeconomic pathway (SSP) scenarios are drawn from Chen et al. published in Scientific Data with a resolution of 1km (~ 0.008333 degree). We resampled the data to 0.5 degree by aggregating the population count together to obtain the future population data per cell.

This previously published dataset also provided age- and gender-specific population of each provinces, so we calculated the fraction of each age and gender group at provincial level. Then, we multiply the fractions with grid population count to get the total population per age group per cell for each gender.

Note that the projected population data from Chen’s dataset covers 2010-2020, while the historical population in our dataset also covers 2010-2020. The two datasets of that same period may vary because the original population data come from different sources and are calculated based on different methods.

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. Spatial or temporal consistency across dataset boundaries cannot be guaranteed.

Population, female (% of total population) in World was reported at 49.72 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The statistic shows the global population as of mid-2022, sorted by age. In mid-2022, approximately two thirds of the global population were aged between 15 and 64 years.

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

30-lt_input.csv

Life table input data.

id: unique row identifier

region_iso: iso3166-2 region codes

sex: Male, Female, Total

year: iso year

age_start: start of age group

age_width: width of age group, Inf for age_start 100, otherwise 1

nweeks_year: number of weeks in that year, 52 or 53

death_total: number of deaths by any cause

population_py: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)

death_total_nweeksmiss: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)

death_total_minnageraw: the minimum number of age-groups in the raw input data within this region-sex-year stratum

death_total_maxnageraw: the maximum number of age-groups in the raw input data within this region-sex-year stratum

death_total_minopenageraw: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_maxopenageraw: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_source: source of the all-cause death data

death_total_prop_q1: observed proportion of deaths in first quarter of year

death_total_prop_q2: observed proportion of deaths in second quarter of year

death_total_prop_q3: observed proportion of deaths in third quarter of year

death_total_prop_q4: observed proportion of deaths in fourth quarter of year

death_expected_prop_q1: expected proportion of deaths in first quarter of year

death_expected_prop_q2: expected proportion of deaths in second quarter of year

death_expected_prop_q3: expected proportion of deaths in third quarter of year

death_expected_prop_q4: expected proportion of deaths in fourth quarter of year

population_midyear: midyear population (July 1st)

population_source: source of the population count/exposure data

death_covid: number of deaths due to covid

death_covid_date: number of deaths due to covid as of

death_covid_nageraw: the number of age groups in the covid input data

ex_wpp_estimate: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year

ex_hmd_estimate: life expectancy estimates from the Human Mortality Database

nmx_hmd_estimate: death rate estimates from the Human Mortality Database

nmx_cntfc: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

source:

STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)

ONS for GB-EAW pre 2020

CDC for US pre 2020

STMF:

harmonized to single ages via pclm

pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110

smoothing parameters estimated via BIC grid search seperately for every pclm iteration

last age group set to [110,111)

ages 100:110+ are then summed into 100+ to be consistent with mid-year population information

deaths in unknown weeks are considered; deaths in unknown ages are not considered

ONS:

data already in single ages

ages 100:105+ are summed into 100+ to be consistent with mid-year population information

PCLM smoothing applied to for consistency reasons

CDC:

The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

source:

for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019

for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100

mid-year population

mid-year population translated into exposures:

if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates

if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

source: COVerAGE-DB (https://osf.io/mpwjq/)

the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

source:

World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019

Human Mortality Database (https://mortality.org/), single year and age tables

According to a Capgemini survey conducted from December 2019 to February 2020, young people from ages 18 to 21 make up the smallest share of the offline population worldwide, with just 8 percent of 18 to 21 year olds not having internet access. In comparison, adults from ages 22 to 36 were most likely to be offline, as 35 percent of survey respondents from this age group were not online.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office (INV-009579, formerly OPP1182425), and GRID3 COVID-19 Support Scale-up (INV-018067). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network in the Columbia Climate School at Columbia University, and the Flowminder Foundation. The new age-structured population estimates are based on the existing Census-based gridded population estimates for Burkina Faso (2019), version 1.0 (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020). Duygu Cihan, Heather Chamberlain and Thomas Abbott led the data processing, with advice from Édith Darin.RELEASE CONTENT Aggregated_BFA_under18_population_100m.tif Aggregated_BFA_18_45_population_100m.tif Aggregated_BFA_over45_population_100m.tifFILE DESCRIPTIONS The coordinate system for all GIS files is the geographic coordinate system WGS84 (World Geodetic System 1984, EPSG: 4326). Aggregated_BFA_ under18_population _100m.tifThis geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged under 18 (0-17) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.Aggregated_BFA_18_45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged 18 to 45 (18-45) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas. Aggregated_BFA_over45_population_100m.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population of persons aged over 45 (46+) per grid cell across Burkina Faso. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.METHODS OVERVIEW Processing: The existing 2019 gridded population estimates (WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso, 2020) include age- and sex- structured population estimates for 5 year age classes, based on the age and sex breakdown of population totals at the national level, from the preliminary census results. A Sprague multiplier approach was used to further disaggregate the 5-year age classes at the national level, to create three custom age-classes (under 18, 18-45 and over 45). The population for each of these custom age classes, was calculated as the proportion of the total population at the national level. This proportion was applied to the count of total population at the grid cell level.ASSUMPTIONS AND LIMITATIONS The custom age classes are estimated using a Sprague multiplier approach to interpolate the 5-year age classes and provide the population for a single year age class, which is then summed to provide the custom age classes. Interpolation introduces uncertainty in the estimates.The population estimates for the custom age classes were calculated from national level totals for 5-year age classes. A constant age-structure across all grid cells was assumed in applying the national proportions for the custom age classes to the grid cell level.RELEASE HISTORYVersion 1.0 (25/05/2022) - Original release of this data set.WORKS CITEDDooley, C. A. and Tatem, A.J. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.0. University of Southampton: Southampton, UK. Source of building Footprints “Ecopia Vector Maps Powered by Maxar Satellite Imagery”© 2020. https://dx.doi.org/10.5258/SOTON/WP00666.WorldPop and Institut National de la Statistique et de la Demographie du Burkina Faso. 2020. Census-based gridded population estimates for Burkina Faso (2019), version 1.0. WorldPop, University of Southampton. https://dx.doi.org/10.5258/SOTON/WP00687

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by the Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

The zip files contain the following files:

MOZ_population_v1_1_gridded.tifThis GeoTIFF raster (.tif), at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimated population counts per grid cell across Mozambique. The projection is the geographic coordinate system WGS84 (World Geodetic System 1984). ‘NoData’ values represent areas that were mapped as unsettled based on building footprints from “Digitize Africa data © 2020 Maxar Technologies, Ecopia.AI”. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated populations for larger areas.MOZ_population_v1_1_agesex.zipThis zip file contains 40 rasters in GeoTIFF format. Each raster provides gridded population estimates for an age-sex group. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. The files are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1 year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”.We provide an additional four rasters that represent aggregated demographic groups of special interest for development programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced using the peanutButter R package (Leasure et al. 2020b) which multiplied the gridded population estimates (MOZ_population_v1_1_gridded.tif) by gridded age-sex proportions that differ by region (WorldPop et al. 2018, Pezzulo et al. 2017).While each data file represents population counts, individual values contain decimals, i.e. fractions of people. This is because we do not estimate which grid cell each individual in a given age group occupies. For example, if four grid cells next to each other have values of 0.25 this indicates that there is one person of that age group somewhere in those four grid cellsThis work provides an estimate of the geographic distribution of the population of Mozambique in 2017. The outputs are intended as an interim population product to support ongoing development and operations work until such time as the official 2017 Population and Housing Census results are available in a spatial gridded format. At that time, this interim gridded population layer will be superseded and users will be advised to use the official gridded population release from INE.Data Citation: Bondarenko M, Jones P, Leasure D, Lazar AN, Tatem AJ. 2020. Census disaggregated gridded population estimates for Mozambique (2017), version 1.1. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00672 CREDITS: Patricia Jones and Donna Clarke (WorldPop) supported the generation of inputs for the application of the random forest-based dasymetric mapping approach developed by Stevens et al. (2015). The population disaggregation was done by Maksym Bondarenko (WorldPop), using the random forest population modelling R scripts (Bondarenko et al., 2020). The age-sex rasters, SQL database, and map tileswere created by Doug Leasure (WorldPop). The administrative boundaries and population totals were provided by Arlindo Charles, at INE (National Institute of Statistics) in Mozambique. Claire Dooley and Chris Jochem reviewed the population raster and its documentation, and provided comments and suggestions. Attila N. Lazar (WorldPop) coordinated the work with substantial engagement and translation support from Sandra Baptista (CIESIN). The whole WorldPop group and GRID3 partners are acknowledged for overall project support.These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation. The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go here.

Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020.

The fertility rates are consistent with the United Nation's World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract:

Consistent and reliable time series of education- and age-specific fertility rates for the past are difficult to obtain in developing countries, although they are needed to evaluate the impact of women’s education on fertility along periods and cohorts. In this paper, we propose a Bayesian framework to reconstruct age-specific fertility rates by level of education using prior information from the birth history module of the Demographic and Health Surveys (DHS) and the UN World Population Prospects. In our case study regions, we reconstruct age- and education-specific fertility rates which are consistent with the UN age specific fertility rates by four levels of education for 50 African and Latin American countries from 1970 to 2020 in five-year steps. Our results show that the Bayesian approach allows for estimating reliable education- and age-specific fertility rates using multiple rounds of the DHS surveys. The time series obtained confirm the main findings of the literature on fertility trends, and age and education specific differentials.

Funding:

These data sets are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

Variables:

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Age group: Five-year age groups between 15-19 and 45-49.

Year: Five-year periods between 1970 and 2020.

Median: Median education and age-specific fertility rate estimate

Upper_CI: 95% Upper Credible Interval

Lower_CI: 95% Lower Credible Interval

List of countries:

Angola

Benin

Brazil

Burkina Faso

Burundi

Cameroon

Central African Republic

Chad

Colombia

Comoros

Congo

Côte D'Ivoire

DR Congo

Ecuador

Egypt

Eswatini

Ethiopia

Gabon

Gambia

Ghana

Guatemala

Guinea

Honduras

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mexico

Morocco

Mozambique

Namibia

Nicaragua

Niger

Nigeria

Paraguay

Peru

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Tunisia

Uganda

Zambia

Zimbabwe

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.

These data feature gridded population estimates (~100 m grid cells) and population estimates for specific age groups, with national coverage for Burkina Faso. The zip files contain the following files:BFA_population_v1_0_gridded.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of total population size per grid cell across Burkina Faso. The projection is the geographic coordinate system WGS84 (World Geodetic System 1984). NA values represent areas that were mapped as unsettled based on building footprints from “Digitize Africa data © 2020 Maxar Technologies, Ecopia.AI [2]. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated populations for larger areas.BFA_population_v1_0_agesex.zipThis zip file contains 39 rasters in geotiff format. Each raster provides gridded population estimates for an age-sex group. Files are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age within the age group represented by the data. The age groups are in five-year bins labelled with a “5”, “10”, etc. For instance, “f0” and “m0” are population counts of under 5 year olds for females and males, respectively. Eighty-five year olds and over are represented in the groups “f85” and “m85”. We provide three additional rasters that represent demographic groups often targeted by programmes and interventions. These are “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced using age-sex national proportions from the 2019 census. The age-sex proportions were applied to the gridded population estimates (BFA_population_v1_0_gridded.tif) to allocate the population to the different age-sex classes. While this data represents estimated population counts, values contain decimals, i.e. fractions of people. This is because we do not estimate which grid cell each individual in a given age group occupies. For this reason, it is advised to aggregate the rasters at a coarser scale. For example, if four grid cells next to each other have values of 0.25 this indicates that there is 1 person of that age group somewhere in those four grid cells. Note: f0 and m0 in this application represent under 5 population, contrary to other datasets on the WorldPop Open Population Repository where f0 and m0 represent population under the age of 1.These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation. The Burkina Faso Institut National de la Statistique et de la Démographie supported, facilitated this work, reviewed the results and provided the census database. The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go here.

Graph and download economic data for Population ages 65 and above for the United States (SPPOP65UPTOZSUSA) from 1960 to 2024 about 65-years +, population, and USA.

These data include gridded estimates of population at approximately 1km for 2021 and 2022. These datasets results were produced based on using the spatial distribution of unconstrained and constrained population datasets for individual countries for 2020 datasets with country totals were adjusted to match the corresponding official United Nations population estimates, that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (World Population Prospects 2022) for the relevant years, and broken down by gender and age groups.

"GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2020 layer represents persons per square kilometer for year 2020.

Data Summary GPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).

The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2020. This data is being released now to allow users access to the population grids.

Recommended Citation Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR"

In 2024, just under 41 percent of Sub-Saharan Africa's population was below the age of 15; in contrast, this figure was just 17 percent in Europe & Central Asia and in North America. Across these regions, the share of the population aged 65 and over inversely correlated with the younger population, in that the regions with the largest share aged under 15 had the smallest share aged over 64, and vice versa. For most regions, the share of the population aged between 15 and 64 years ranged between 64 and 65 percent, except for Sub-Saharan Africa where it was below 56 percent. These trends can largely be explained by looking at global demographic development.

About this dataset

Context

The dataset tabulates the Blue Earth household income by age. The dataset can be utilized to understand the age-based income distribution of Blue Earth income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Blue Earth, MN annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Blue Earth, MN household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Blue Earth income distribution by age. You can refer the same here