The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 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 live 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 decade 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.

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 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 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 2021. 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 statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

In the Cook Islands in 2024, the population decreased by about 2.24 percent compared to the previous year, making it the country with the highest population decline rate in 2024. Of the 20 countries with the highest rate of population decline, the majority are island nations, where emigration rates are high (especially to Australia, New Zealand, and the United States), or they are located in Eastern Europe, which suffers from a combination of high emigration rates and low birth rates.

The Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 consists of estimates of human population (number of persons per pixel), consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

These data consist of modelled gridded population estimates produced at a spatial resolution of approximately 100m across Mali. The estimates comprise a combination of total population counts at enumeration area level collected by the census cartography team of the Mali Statistics Office and modelled population counts created using a Bayesian statistical model for areas that could not be covered by the cartography team because of security issues. The main input data for the model are the cartography data collected in the safe part of the country in 2019-2020 (628 out of 714 Communes –administrative level 3–, that is 87% of the country territory). Other essential input data include metrics derived from building footprints, which were automatically delineated by Ecopia.AI in 2021 using satellite imagery collected by Maxar Technologies between 2010 and 2021. The modelled population estimates represent the period of the census cartography, but their consistency may be impacted by the accuracy of the building footprints. These data were produced by the WorldPop Research Group at the University of Southampton as part of the GRID3 Project, 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 (INV 009579, formerly OPP 1182425). The study was approved by the Faculty Ethics Committee of the University of Southampton (ERGO II 64957). The project was led by the Center for International Earth Science Information Network (CIESIN) at Columbia University, in collaboration with the WorldPop Research Group at the University of Southampton, the United Nations Fund for Population (UNFPA) and the Malian Institut National de la Statistique (INSTAT). The production of these data was led by Edith Darin (WorldPop) with support from Matthias Kuépié and Jean Wakam (UNFPA), Abdoul Karim Diawara, Assa Gakou and Siaka Cissé (Institut National de la Statistique), and Attila N Lazar (WorldPop) and Andrew J Tatem (WorldPop). The authors acknowledge the support of their respective institutions in the completion of this work.

When cuckoldry is frequent we can expect fathers to withhold investment in offspring that may not be theirs. Human paternal investment can be substantial and is in line with observations from tens of thousands of conceptions that suggest that cuckoldry is rare in humans. The generality of this claim seems to be in question as the rate of cuckoldry varies across populations and studies have mostly been on Western populations. Two additional factors complicate our conclusions, (1) current estimates of the rate of cuckoldry in humans may not reflect our past behaviour as adultery can be concealed by the use of contraceptives; and (2) it is difficult to obtain samples that are random with respect to their paternity certainty. Studies that combine genealogies with Y-chromosome haplotyping are able to circumvent some of these problems by probing into humans' historical behaviour. Here we use this approach to investigate 1273 conceptions over a period of 330 years in 23 families of the Afrikaner population in South Africa. We use haplotype frequency and diversity and coalescent simulations to show that the male population did not undergo a severe bottleneck and that paternity exclusion rates are high for this population. The rate of cuckoldry in this Western population was 0.9% (95% confidence interval 0.4–1.5%), and we argue that given the current data on historical populations we have to conclude that, at least for Western human populations, cuckoldry rate is probably in the range of 1%.

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.

Data Usage Notice

This version is not recommended for download. Please check the newest version.

We would like to inform you that the updated GlobPOP dataset (2021-2022) have been available in version 2.0. The GlobPOP dataset (2021-2022) in the current version is not recommended for your work. The GlobPOP dataset (1990-2020) in the current version is the same as version 1.0.

Thank you for your continued support of the GlobPOP.

If you encounter any issues, please contact us via email at lulingliu@mail.bnu.edu.cn.

Introduction

Continuously monitoring global population spatial dynamics is essential for implementing effective policies related to sustainable development, such as epidemiology, urban planning, and global inequality.

Here, we present GlobPOP, a new continuous global gridded population product with a high-precision spatial resolution of 30 arcseconds from 1990 to 2020. Our data-fusion framework is based on cluster analysis and statistical learning approaches, which intends to fuse the existing five products(Global Human Settlements Layer Population (GHS-POP), Global Rural Urban Mapping Project (GRUMP), Gridded Population of the World Version 4 (GPWv4), LandScan Population datasets and WorldPop datasets to a new continuous global gridded population (GlobPOP). The spatial validation results demonstrate that the GlobPOP dataset is highly accurate. To validate the temporal accuracy of GlobPOP at the country level, we have developed an interactive web application, accessible at https://globpop.shinyapps.io/GlobPOP/, where data users can explore the country-level population time-series curves of interest and compare them with census data.

With the availability of GlobPOP dataset in both population count and population density formats, researchers and policymakers can leverage our dataset to conduct time-series analysis of population and explore the spatial patterns of population development at various scales, ranging from national to city level.

Data description

The product is produced in 30 arc-seconds resolution(approximately 1km in equator) and is made available in GeoTIFF format. There are two population formats, one is the 'Count'(Population count per grid) and another is the 'Density'(Population count per square kilometer each grid)

Each GeoTIFF filename has 5 fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below with the example filename:

GlobPOP_Count_30arc_1990_I32

Field 1: GlobPOP(Global gridded population)
Field 2: Pixel unit is population "Count" or population "Density"
Field 3: Spatial resolution is 30 arc seconds
Field 4: Year "1990"
Field 5: Data type is I32(Int 32) or F32(Float32)

More information

Please refer to the paper for detailed information:

Liu, L., Cao, X., Li, S. et al. A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning. Sci Data 11, 124 (2024). https://doi.org/10.1038/s41597-024-02913-0.

The fully reproducible codes are publicly available at GitHub: https://github.com/lulingliu/GlobPOP.

The genetic diversity of humans, like many species, has been shaped by a complex pattern of population separations followed by isolation and subsequent admixture. This pattern, reaching at least as far back as the appearance of our species in the paleontological record, has left its traces in our genomes. Reconstructing a population’s history from these traces is a challenging problem. Here we present a novel approach based on the Multiple Sequentially Markovian Coalescent (MSMC) to analyze the separation history between populations. Our approach, called MSMC-IM, uses an improved implementation of the MSMC (MSMC2) to estimate coalescence rates within and across pairs of populations, and then fits a continuous Isolation-Migration model to these rates to obtain a time-dependent estimate of gene flow. We show, using simulations, that our method can identify complex demographic scenarios involving post-split admixture or archaic introgression. We apply MSMC-IM to whole genome sequences from 15 worldwide populations, tracking the process of human genetic diversification. We detect traces of extremely deep ancestry between some African populations, with around 1% of ancestry dating to divergences older than a million years ago.

Information was obtained from the ANARE Health Register. See Metadata record entitled ANARE Health Register.

INDICATOR DEFINITION Human population in stations and ships expressed in person-days.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: PRESSURE

RATIONALE FOR INDICATOR SELECTION It is generally accepted that the potential impact on the natural environment is proportional to the human population. This is the 'human footprint'. Human activities can cause disruption in physical, chemical and biological systems. As stated by the Australian Bureau of Statistics (1996): 'To understand the human impact on the Australian environment, it is necessary to know how many people live here, and how they are distributed across the continent.'

This indicator reveals where the greatest direct pressures related to size of the human population (e.g. fuel usage, sewerage and other waste generation etc) occur.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctic and sub-Antarctic stations and ANARE ships travelling to and from these stations.

Frequency: Monthly figures reported annually.

Measurement technique: The Polar Medicine Branch collects data on all expeditioner movements. These data are entered into the Health Register and updated as personnel arrive on or leave a station.

RESEARCH ISSUES Now that this figure is available, research is required to ascertain the quantitive relationships of station and ship population to other indicators such as fuel usage and waste generation. This measure may be able to deliver a quantitative estimate of human pressure on the Antarctic environment.

LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 49 - Medical consultations per 1000 person years SOE Indicator 50 - Effluent monitoring - Volume of coastal discharge from Australian stations SOE Indicator 51 - Effluent monitoring - Biological oxygen demand SOE Indicator 52 - Effluent monitoring - Suspended solids content SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 54 - Amount of waste incinerated at Australian Stations SOE Indicator 56 - Monthly fuel usage of the generator sets and boilers SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 59 - Monthly electricity usage SOE Indicator 60 - Total helicopter hours SOE Indicator 61 - Total potable water consumption

The fields in this dataset are: Location Date Population (person-days) Illness Rate (per 1000 person years) Injury Rate (per 1000 person years)

Aedes aegypti, the primary vector of several medically significant arboviruses, including dengue fever, yellow fever, chikungunya, and Zika viruses, was successfully eradicated from Egypt in 1963. However, reports of its re-emergence and associated dengue outbreaks in southern Egyptian governorates since 2011 have raised concerns. This study aimed to model the current and future distribution of Ae. aegypti in Egypt. Locally collected occurrence data were combined with bioclimatic, anthropogenic, and biological environmental variables to identify key factors driving the distribution of Ae. aegypti. The modeling of maximum entropy (MaxEnt) showed good performance (AUC mean = 0.975; TSS mean = 0.789) and identified the density of the human population, the annual precipitation and the normalized difference vegetation index (NDVI) as key determinants of the habitat suitability of Ae. aegypti. The present-day predictions highlight the Nile Valley, Nile Delta, Fayoum Basin, Red Sea coast, and ..., The study considers bioclimatic, anthropogenic, and biological environmental variables to understand Aedes aegypti habitat. Bioclimatic variables: Nineteen bioclimatic variables were sourced from the WorldClim database. To avoid the influence of highly correlated variables, the researchers conducted a principal component analysis and selected three key variables (annual precipitation, mean temperature of the warmest quarter, and temperature seasonality). Anthropogenic variable: Human population density data was obtained from the WorldPop dataset through the Google Earth Engine platform, using the median range from 2000 to 2020. Biological environmental variable: Normalized difference vegetation index (NDVI) data, representing vegetation cover, was acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13A2 product on the Google Earth Engine platform, calculating the median range from 2000 to 2015. Future Projections: To predict the impact of future changes, the resea..., , # Re-emergence of Aedes aegypti (Linnaeus) in Egypt under climate changes

General Information

Created: December 2024 Authors:

• Mustafa M. Soliman (msoliman@cu.edu.eg) | ORCID: 0000-0002-0905-4473
• Abdallah M. Samy (samy@sci.asu.edu.eg) | ORCID: 0000-0003-3978-1134
• Magdi S. El-Hawagry (elhawagry@cu.edu.eg) | ORCID: 0000-0001-9162-5265

Dataset Description

This dataset supports research modeling the current and future distribution of Aedes aegypti mosquitoes in Egypt using MaxEnt. The study combines locally collected data with bioclimatic, anthropogenic, and biological variables to identify key environmental factors influencing mosquito distribution.

Data Collection

• 48 Ae. aegypti occurrence records spanning 1925-2023
• Collected from nine Egyptian governorates:
  • Alexandria
  • Assiut
  • Aswan
  • Beni Suef
  • Cairo
  • Fayoum
  • Minya
  • Qena
  • Red Sea

Data Sources...

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.

Distinguishing among policy problems based on whether or not their definition and resolution are independent or interdependent of other policy problems.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. CIESIN used proportional allocation to distribute population data from subnational census data to the settlement extents. The population data have been developed for 33 countries. Read more about the project here.

Chart and table of Philippines population from 1950 to 2025. United Nations projections are also included through the year 2100.

BackgroundWith population aging, this demographic dividend diminishes, which may have implications for innovation in a region. Understanding the relationship between population aging and innovation is crucial for addressing economic challenges associated with an aging population.MethodsThis study utilized panel data on population aging and innovation from 252 cities between 2005 and 2014. Various estimation methods, including the fixed effects model, the generalized method of moments (GMM), and the mediation model, were used to analyze the data. These methods allowed for a comprehensive examination of the impact of population aging on innovation and the role of human capital in mediating this relationship.ResultsThe findings of the study indicate that both the 60-year-old and 65-year-old population significantly hinder innovation. The GMM suggests that innovation is “path dependent,” meaning that past levels of innovation do not alleviate the negative effects of population aging on future innovation. Additionally, the mediation model analysis demonstrates that human capital plays a crucial role in mediating the relationship between population aging and innovation, highlighting the importance of investing in human capital development.ConclusionThe findings of this research highlight the obstacles that population aging presents to fostering innovation. Overcoming these obstacles necessitates strategic investments in human capital and policies that support innovation. It is imperative for policymakers to implement recommendations that address population aging and encourage innovation in order to navigate the challenges posed by an aging population and promote a vibrant and dynamic economy.

This dataset contains 735 radiocarbon date from coastal sites in Arctic Norway. The dates are used for palaeodemographic modeling, based on summed probability distribution methodology. Abstract: Synchronized demographic and behavioral patterns among distinct populations is a well-known, natural phenomenon. Intriguingly, similar patterns of synchrony occur among prehistoric human populations. However, the drivers of synchronous human ecodynamics are not well understood. Addressing this issue, we review the role of environmental variability in causing human demographic and adaptive responses. As a case study, we explore human ecodynamics of coastal hunter-gatherers in Holocene northern Europe, comparing population, economic and environmental dynamics in two separate areas (northern Norway and western Finland). Population trends are reconstructed using temporal frequency distributions of radiocarbon dated and shoreline dated archaeological sites. These are correlated to regional environmental proxies and proxies for maritime resource use. The results demonstrate remarkably synchronous patterns across population trajectories, marine resource exploitation, settlement pattern and technological responses. Crucially, the population dynamics strongly correspond to significant environmental changes. We evaluate competing hypotheses and suggest that the synchrony stems from similar responses to shared environmental variability. We take this to be a prehistoric human example of the “Moran effect”, positing similar responses of geographically distinct populations to shared environmental drivers. The results imply that intensified economies and social interaction networks have limited impact on long-term hunter-gatherer population trajectories beyond what is already proscribed by environmental drivers.

As cities expand worldwide, understanding how species adapt to novel urban habitats will become increasingly important to conservation. The adrenocortical stress response enables vertebrates to cope with novel environmental challenges to homeostasis. We examined baseline and stress-induced corticosterone (CORT) concentrations in three songbird species within and around Phoenix, Arizona. We tested whether baseline and stress-induced CORT patterns differed among species living at varying densities in Phoenix and tested the hypothesis that, for species capable of successfully colonizing cities, individuals living in urban areas have a decreased acute stress response compared to individuals living in native desert. Baseline CORT levels were generally similar in urban and desert birds. Capture and handling stress typically produced greater total CORT responses in urban birds than in desert birds, although these responses differed as a function of sampling date. Urban birds showed less seasonal variability in stress responses than desert birds. We propose that more predictable resources in the city than in rural areas may decrease the need to vary stress responsiveness across life history stages. The results highlight the species-specific effects of urbanization on stress physiology and the difficulty to predict how urbanization impacts organisms.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.