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.

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.

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

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.

About this dataset

Context

The dataset tabulates the White Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of White Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of White Earth was 93, a 0% decrease year-by-year from 2022. Previously, in 2022, White Earth population was 93, a decline of 4.12% compared to a population of 97 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Earth increased by 28. In this period, the peak population was 99 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the White Earth is shown in this column.
• Year on Year Change: This column displays the change in White Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Year. You can refer the same here

Published in The Anthropocene Review. Abstract: Enormous growth of the world population during the last two centuries and its present slowing down pose questions about precedents in history and broader forces shaping the population size. Population estimates collected in an extensive survey of literature (873 estimates from 25 studies covering 1,000,000 BCE to 2100 CE) show that world population growth has proceeded in two distinct phases of acceleration followed by stoppage—from at least 25,000 BCE to 100 BCE, and from 400 CE to the present, interrupted by centuries of standstill and 10% decrease. Both phases can be fitted with a mathematical function that projects to a peak at 11.2 ± 1.5 billion around 2100 CE. An interaction model can account for this acceleration-stoppage pattern in quantitative detail: Technology grows exponentially, with rate boosted by population. Population grows exponentially, capped by Earth’s carrying capacity. Technology raises this cap, but only until it approaches Earth’s ultimate carrying capacity.

The numerical data for Fig 10 is stored in a shapefile, which can be accessed through this link: https://www.kaggle.com/datasets/keminzhu/basemap-shenzhen-subzones. (XLSX)

Analysis of ‘World Population Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kuntalmaity/world-population-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Population in the world is currently (2020) growing at a rate of around 1.05% per year (down from 1.08% in 2019, 1.10% in 2018, and 1.12% in 2017). The current average population increase is estimated at 81 million people per year. Annual growth rate reached its peak in the late 1960s, when it was at around 2%.

--- Original source retains full ownership of the source dataset ---

Data for the paper "Synchronized peak years of global resources use" by Ralf Seppelt, Ameur M. Manceur, Jianguo Liu, Eli P. Fenichel, Stefan Klotz in Ecology & Society: ES-2014-7039

The Columbia University RABiT (Rapid Automated Biodosimetry Tool) quantifies DNA damage using fingerstick volumes of blood. One RABiT protocol quantifies the total γ-H2AX fluorescence per nucleus, a measure of DNA double strand breaks (DSB) by an immunofluorescent assay at a single time point. Using the recently extended RABiT system, that assays the γ-H2AX repair kinetics at multiple time points, the present small scale study followed its kinetics post irradiation at 0.5 h, 2 h, 4 h, 7 h and 24 h in lymphocytes from 94 healthy adults. The lymphocytes were irradiated ex vivo with 4 Gy γ rays using an external Cs-137 source. The effect of age, gender, race, ethnicity, alcohol use on the endogenous and post irradiation total γ-H2AX protein yields at various time points were statistically analyzed. The endogenous γ-H2AX levels were influenced by age, race and alcohol use within Hispanics. In response to radiation, induction of γ-H2AX yields at 0.5 h and peak formation at 2 h were independent of age, gender, ethnicity except for race and alcohol use that delayed the peak to 4 h time point. Despite the shift in the peak observed, the γ-H2AX yields reached close to baseline at 24 h for all groups. Age and race affected the rate of progression of the DSB repair soon after the yields reached maximum. Finally we show a positive correlation between endogenous γ-H2AX levels with radiation induced γ-H2AX yields (RIY) (r=0.257, P=0.02) and a negative correlation with residuals (r=-0.521, P=

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated) decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

This supporting document should allow one to recreate the analysis performed as part of “The optimal species richness environments for human populations, ” By Freeman et al. 2018 Submitted to PNAS July 2018. The annotated scripts in this directory (RichnessSIScripts.pdf) contain code to replicate the analysis, as well as code for additional analyses not included in the main paper or supplemental information. To replicate the analysis, one can either analyze the data files provided or build their own data set. As discussed in the main body of the text, we built three data sets following the procedures outlined by Tallavaara et al. (2017) for linking species richness values, net primary productivity and pathogen stress to each ethnographic case. We do not replicate the scripts provided by Tallavaara et al.(2017) as these are available, clear and should be cited when used.To replicate our analysis, one needs to set their working directory in R to the file location that contains the data files. There are 11 files that follow the naming convention “name.csv.” The 11 files are “MainFinal.csv”. “AGPOP3Eco.csv”, “HGFEM4R.csv”, “AGPOPClass.csv”, “CountryMeansEco2.csv”, “AGPOP3EcoH.csv”, “AGPOP3EcoL.csv”, “HiHG.csv”, “LowHG,csv”, “CountryMeansEco2H.csv”, and “CountryMeansEco2L.csv”. The first five files are the main files, the second six files are divided into high and low species richness environments by economy type for convenience. In each file, the variables are defined as follows:

1. Group/Country–name of the ethnographic society of country

2. Latitude–the latitude at the geographic center of a group’s territory or a country’s territory.

3. Longitude–the longitude at the geographic center of a group’s territory or a country’s territory.

4. Class–an ordinal ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details)

5. Class2–an binary ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details).

6. ECI–The average economic complexity index since 1973 as measured among modern countries.

7. DENSITY–Population density in people per square kilometer. This is a point in time estimatefor hunter-gatherer and agricultural groups and an average density since 1973 among nation states.

8. LnDENSITY–The natural log of population density

9. npp–net primary productivity estimated at the center of each group’s territory

10. npp2-Net primary productivity squared

11. biodiv–Standardized estimate of species richness at the center of each group’s range.

12. biodiv2–Species richness *100 ad squared.

13. pathos–Index of pathogen stress at the center of a group’s territory.

14. DivDiff–The absolute value of species richness-the species richness value of peak population density (values identified in Fig. 2 of the main manuscript).1

15. ID–A nominal variable that denotes economy type. HG=hunter-gatherer, AG=subsistence agriculturalist, IND=modern nation state

Tallavaara, M., J. T. Eronen, and M. Luoto2017. Supporting data and script for ”productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” (Tallavaara et al. pnas 2018).https://doi.org/10.5281/zenodo.1167852

Animals utilize their environment across a range of scales, which is bounded by their extent, the broadest spatial area which organisms respond to their environment within their lifetime, and the spatial grain, the smallest area they respond to their environment (Kotlier and Wiens 1990). Within this range, organisms likely respond to their environment at a hierarchy of levels. Johnson (1980) recognizes four distinct levels of hierarchical habitat selection. At the very largest scale, first order selection, includes the entire area that an organism utilizes within its lifetime, and is also known as an organisms global home range or extent. In contrast, second order selection is an organisms local home range, or the area that it occupies within a unique ecosystem. This distinction is most apparent with migratory animals who utilize more than one distinct landscape for their survival (i.e. summer vs. winter feeding grounds), and much less so for organisms resident of one specific landscape for their entire life span. Third order selection is the selection of specific habitat patches within an ecosystem. For example, a Monarch butterfly would tend to select patches of milkweed within a prairie. And the lowest level, fourth order selection, involves the physical procurement of food within a selected patch, in our example, specific flowers within a milkweed patch, and is also known as grain. Realizing the importance of hierarchical habitat selection, it has become apparent that single-scale studies of animals responses to their environment may fail to adequately represent how that specific animal is responding to ecological parameter of interest, especially if they are not responding to the landscape at that scale (Holling 1992). The range of scales which an animal of interest is utilizing a landscape is important to determine prior to any further ecological investigation, as inappropriate scalar mismatch between organism and environment can lead to ambiguous or even deceptive conclusions. To do this, we compared the correlation coefficients of bird abundances for different functional groups (e.g. foraging guilds, natives vs. exotics) with vegetation cover, as a proxy for habitat, across a range of scales (from 100m to 10km). Theoretically, a unimodal (hump-shaped) relationship should exist for the correlation coefficients across a range of scales, under the assumption that vegetation cover is an adequate estimate of bird abundance. The peak of that relationship, if statistically significant, would represent the strongest correlation between habitat and bird abundance, and thus signifies the average third order selection unit for that group. A strong peak is expected for species directly dependent on vegetation for food (herbivores), a weaker peak for omnivores, and the weakest relationship for those species indirectly dependent on vegetation (insectivores). The regional distributional patterns of the varying bird functional groups was also estimated by utilizing interpolation techniques designed for avian censuses in urban systems. Exotic species were expected to be spatially aligned to the urban ecosystem, and native species tied to the desert ecosystem. Herbivores were expected to exist in higher densities were vegetation is greatest, which typically exists within the city and agricultural fields in arid ecosystems. The ongoing project (since October 2000) is documenting the abundance and distribution of birds in four habitats (51 sites): Urban (18) Desert (15) Riparian (11) and agricultural (7). The 40 non-riparian sites are a subset of the 200 CAP- LTER points. We are using point counts to survey birds four times a year (January, April, July and October). During each session each point is visited by three birders who count all birds seen or heard for 15 minutes. Our goal is to study how different land-use forms affect bird abundance, distribution and diversity in the grea... Visit https://dataone.org/datasets/knb-lter-cap.394.7 for complete metadata about this dataset.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method’s efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

This dataset comprises crowdsourced data using digitized herbarium specimen images from two comprehensively digitized regional floras; the Consortium of Northeastern Herbaria (CNH; http://portal.neherbaria.org/portal/) and Southeast Regional Network of Expertise and Collections (SERNEC; http://sernecportal.org/portal/index.php) for 200 plant species in the eastern United States, and four reproductive phenophases (i.e., flowering, peak flowering, fruiting, and peak fruiting) extracted from the herbarium specimens with associated climate data from PRISM and human population density from US Census Bureau.

In 2024, the total population of Singapore is estimated to be approximately 6.04 million peopl. Population growth in the country is slow and numbers have still not recovered to pre-pandemic levels, where the pandemic's economic impact on migration saw the population fall by a quarter of a million people between 2019 and 2021. The youth is fading Singapore’s population is getting older, with the age bracket of those aged 65 and older increasing with every year. The median age of Singaporeans is increasing rapidly, from 34.1 years in the year 2000 to an estimated 42.4 by 2020, and it is estimated to peak at around 55 years in the middle of the century. The old are here to stay The majority of Singaporeans are between 25 and 60 years old. In the years to come, improving healthcare and one of the highest life expectancies at birth will see this majority shift to the elderly. Additionally, Singapore’s fertility rate is among the lowest in the world and is well below the replacement rate, which means that Singapore’s population is not only getting older but its rate of natural increase (i.e. population growth not including migration) is now negative. This trend could have economic consequences, such as lower GDP growth and increasing old-age dependency.

The statistic shows the total population in Canada from 2020 to 2024, with projections up until 2030. In 2024, the total population in Canada amounted to about 41.14 million inhabitants. Population of Canada Canada ranks second among the largest countries in the world in terms of area size, right behind Russia, despite having a relatively low total population. The reason for this is that most of Canada remains uninhabited due to inhospitable conditions. Approximately 90 percent of all Canadians live within about 160 km of the U.S. border because of better living conditions and larger cities. On a year to year basis, Canada’s total population has continued to increase, although not dramatically. Population growth as of 2012 has amounted to its highest values in the past decade, reaching a peak in 2009, but was unstable and constantly fluctuating. Simultaneously, Canada’s fertility rate dropped slightly between 2009 and 2011, after experiencing a decade high birth rate in 2008. Standard of living in Canada has remained stable and has kept the country as one of the top 20 countries with the highest Human Development Index rating. The Human Development Index (HDI) measures quality of life based on several indicators, such as life expectancy at birth, literacy rate, education levels and gross national income per capita. Canada has a relatively high life expectancy compared to many other international countries, earning a spot in the top 20 countries and beating out countries such as the United States and the UK. From an economic standpoint, Canada has been slowly recovering from the 2008 financial crisis. Unemployment has gradually decreased, after reaching a decade high in 2009. Additionally, GDP has dramatically increased since 2009 and is expected to continue to increase for the next several years.