The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020). More detailed can be found on the page of the original version (https://doi.org/10.5281/zenodo.3768003).

This release increases the resolution to 0.25˚ and is explicitly designed to match with the grid definition of the ERA5 climate reanalysis dataset. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution.

Between 1800 and 2021, the total population of each continent experienced consistent growth, however as growth rates varied by region, population distribution has fluctuated. In the early 19th century, almost 70 percent of the world's population lived in Asia, while fewer than 10 percent lived in Africa. By the end of this century, it is believed that Asia's share will fall to roughly 45 percent, while Africa's will be on course to reach 40 percent. 19th and 20th centuries Fewer than 2.5 percent of the world's population lived in the Americas in 1800, however the demographic transition, along with waves of migration, would see this share rise to almost 10 percent a century later, peaking at almost 14 percent in the 1960s. Europe's share of the global population also grew in the 19th century, to roughly a quarter in 1900, but fell thereafter and saw the largest relative decline during the 20th century. Asia, which has consistently been the world's most populous continent, saw its population share drop by the mid-1900s, but it has been around 60 percent since the 1970s. It is important to note that the world population has grown from approximately one to eight billion people between 1800 and the 2020s, and that declines in population distribution before 2020 have resulted from different growth rates across the continents. 21st century Africa's population share remained fairly constant throughout this time, fluctuating between 7.5 and 10 percent until the late-1900s, but it is set to see the largest change over the 21st century. As Europe's total population is now falling, and it is estimated that the total populations of Asia and the Americas will fall by the 2050s and 2070s respectively, rapid population growth in Africa will see a significant shift in population distribution. Africa's population is predicted to grow from 1.3 to 3.9 billion people over the next eight decades, and its share of the total population will rise to almost 40 percent. The only other continent whose population will still be growing at this time will be Oceania, although its share of the total population has never been more than 0.7 percent.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

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.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

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.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

NOTE: As of 2/16/2023 this table is no longer being updated. For information on COVID-19 Updated (Bivalent) Booster Coverage, go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Race/8267-bg4w.

Important change as of June 1, 2022

As of June 1, 2022, we will be using 2020 DPH provisional census estimates* to calculate vaccine coverage percentages by age at the state level. 2020 estimates will replace the 2019 estimates that have been used. Caution should be taken when making comparisons of percentages calculated using the 2019 and 2020 census estimates since observed difference may result from the shift in the denominator. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

• DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020 State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

This table shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 by race / ethnicity and age group.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

Population size estimates are based on 2019 DPH census estimates until 5/26/2022. From 6/1/2022, 2020 DPH provisional census estimates are used.

In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose.

A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations.

The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported.

Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple races.

A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities suggest that in at least some cases NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the classification of records as NH Unknown Race or NH Other Race.

Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. This includes doses given to residents of CT and to residents of other states vaccinated in CT. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ. Data reported here reflect the vaccination records reported to CT WiZ. However, once CT residents who have received doses in each jurisdiction are added to CT WiZ, the records for residents of that jurisdiction vaccinated in CT are removed. For example, when CT residents vaccinated in NYC were added, NYC residents vaccinated in CT were removed.

Note: This dataset takes the place of the original "COVID-19 Vaccinations by Race/Ethnicity" dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/xkga-ifz3 ), which will not be updated after 5/20/2021 and “COVID-19 Vaccinations by Race / Ethnicity” dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/ybkg-w5x2), which will not be updated after 10/20/2021.

CN: Population Census: Population: Ethnic Minorities Population Percentage to Total Population data was reported at 8.900 % in 12-01-2020. This records an increase from the previous number of 8.500 % for 12-01-2010. CN: Population Census: Population: Ethnic Minorities Population Percentage to Total Population data is updated decadal, averaging 8.000 % from Dec 1953 (Median) to 12-01-2020, with 7 observations. The data reached an all-time high of 8.900 % in 12-01-2020 and a record low of 5.800 % in 12-01-1964. CN: Population Census: Population: Ethnic Minorities Population Percentage to Total Population data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: National Population Census.

According to the 2010 UN Global Demographic Outlook, the world’s population reached 7.1 billion as of 1 July 2012. Asia accounted for the majority of the world’s population (just over 60 % in 2012), with 4.25 billion inhabitants, while Africa was the second most populous continent, with 1.07 billion inhabitants, or 15.2 % of the world’s total. By comparison, the EU had 504 million inhabitants in 2012, just over 7 % of the world’s population. In 2012, the world’s most populous countries were China (19.2 % of the world’s population) and India (17.8 %), followed by the United States (4.5 %), Indonesia (3.5 %) and Brazil (2.8 %).

The table contains the demographic projections of the main European countries from 2020 to 2080. The Eurostat table has been modified by economy-policy in order to obtain better visibility. Indeed, the number of inhabitants is expressed in “million inhabitants” and not in unit data.
In addition, demographic projections for China, India, Brazil and Russia were added for the years 2030, 2040 and 2050.

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.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

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.

CN: Population Census: Population: Male data was reported at 723.340 Person mn in 12-01-2020. This records an increase from the previous number of 686.850 Person mn for 12-01-2010. CN: Population Census: Population: Male data is updated decadal, averaging 584.950 Person mn from Dec 1953 (Median) to 12-01-2020, with 7 observations. The data reached an all-time high of 723.340 Person mn in 12-01-2020 and a record low of 301.900 Person mn in 12-01-1953. CN: Population Census: Population: Male data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: National Population Census.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: 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/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

About this dataset

Context

The dataset tabulates the International Falls median household income by race. The dataset can be utilized to understand the racial distribution of International Falls 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).

• International Falls, MN median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in International Falls, MN (2021, in 2022 inflation-adjusted dollars)

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 International Falls median household income by race. You can refer the same here

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.

A population is a subgroup of individuals within the same species that are living and breeding within a geographic area. The number of individuals living within that specific location determines the population density, or the number of individuals divided by the size of the area.Population density can be used to describe the location, growth, and migration of many organisms. In the case of humans, population density is often discussed in relation to urbanization, immigration, and population demographics.Globally, statistics related to population density are tracked by the United Nations Statistics Division, and the United States Constitution requires population data to be collected every 10 years, an operation carried out by the U.S. Census Bureau. However, data on human population density at the country level, and even at regional levels, may not be very informative; society tends to form clusters that can be surrounded by sparsely inhabited areas. Therefore, the most useful data describes smaller, more discrete population centers.Dense population clusters generally coincide with geographical locations often referred to as city, or as an urban or metropolitan area; sparsely populated areas are often referred to as rural. These terms do not have globally agreed upon definitions, but they are useful in general discussions about population density and geographic location.Population density data can be important for many related studies, including studies of ecosystems and improvements to human health and infrastructure. For example, the World Health Organization, the U.S. Energy Information Administration, the U.S. Global Change Research Program, and the U.S. Departments of Energy and Agriculture all use population data from the U.S. Census or UN statistics to understand and better predict resource use and health trends.Key areas of study include the following:Ecology: how increasing population density in certain areas impacts biodiversity and use of natural resources.Epidemiology: how densely populated areas differ with respect to incidence, prevalence, and transmission of infectious disease.Infrastructure: how population density drives specific requirements for energy use and the transport of goods.This list is not inclusive—the way society structures its living spaces affects many other fields of study as well. Scientists have even studied how happiness correlates with population density. A substantial area of study, however, focuses on demographics of populations as they relate to density. Areas of demographic breakdown and study include, but are not limited to:age (including tracking of elderly population centers);sex (biological classification as male or female); andrace and ethnic group, or cultural characteristics (ethnic origin and language use).