The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1900. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

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.

United States Population: All Ages data was reported at 325,719.000 Person th in 2017. This records an increase from the previous number of 323,406.000 Person th for 2016. United States Population: All Ages data is updated yearly, averaging 176,356.000 Person th from Jun 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 325,719.000 Person th in 2017 and a record low of 76,094.000 Person th in 1900. United States Population: All Ages data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.G002: Population by Age. Series Remarks Population data for the years 1900 to 1949 exclude the population residing in Alaska and Hawaii. Population data for the years 1940 to 1979 cover the resident population plus Armed Forces overseas. Population data for all other years cover only the resident population.

The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1700. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Brazil Population Census: Total data was reported at 203,080,756.000 Person in 2022. This records an increase from the previous number of 190,755,799.000 Person for 2010. Brazil Population Census: Total data is updated yearly, averaging 121,150,573.000 Person from Jul 1900 (Median) to 2022, with 13 observations. The data reached an all-time high of 203,080,756.000 Person in 2022 and a record low of 17,438,434.000 Person in 1900. Brazil Population Census: Total data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAC001: Population Census.

Labour Relations in Italy: 1900, 2000

An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

Historical chart and dataset showing total population for Japan by year from 1950 to 2025.

The Anthropogenic Biomes of the World, Version 2: 1900 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1900. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Labour Relations in Argentina: 1900, 2000An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population. (2020-11-09)

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

We exploit regional variation in suitability for cultivating potatoes, together with time variation arising from their introduction to the Old World from the Americas, to estimate the impact of potatoes on Old World population and urbanization. Our results show that the introduction of the potato was responsible for a significant portion of the increase in population and urbaniza- tion observed during the eighteenth and nineteenth centuries. According to our most conservative estimates, the introduction of the potato accounts for approximately one-quarter of the growth in Old World population and urbanization between 1700 and 1900. Additional evidence from within-country comparisons of city populations and adult heights also confirms the cross-country findings.

The RCS-Dem dataset reports estimates of religious demographics, both country by country and region by region. RCS was created to fulfill the unmet need for a dataset on the religious dimensions of countries of the world, with the state-year as the unit of observation. It covers 220 independent states, 26 selected substate entities, and 41 geographically separated dependencies, for every year from 2015 back to 1900 and often 1800 (more than 42,000 state-years). It estimates populations and percentages of adherents of 100 religious denominations including second level subdivisions within Christianity and Islam, along with several complex categories such as 'Western Christianity.' RCS is designed for easy merger with datasets of the Correlates of War and Polity projects, datasets by the United Nations, the Religion And State datasets by Jonathan Fox, and the ARDA national profiles.

Labour Relations in South Africa: 1900, 2000An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

Data published by Our World in Data based on EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir)

Variable time span 1900 – 2010

This dataset has been calculated and compiled by Our World in Data based on raw disaster data published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir). EM-DAT publishes comprehensive, global data on each individual disaster event – estimating the number of deaths; people affected; and economic damages, from UN reports; government records; expert opinion; and additional sources. Our World in Data has calculated annual aggregates, and decadal averages, for each country based on this raw event-by-event dataset. Decadal figures are measured as the annual average over the subsequent ten-year period. This means figures for ‘1900’ represent the average from 1900 to 1909; ‘1910’ is the average from 1910 to 1919 etc. We have calculated per capita rates using population figures from Gapminder (gapminder.org) and the UN World Population Prospects (https://population.un.org/wpp/). Economic damages data is provided by EM-DAT in concurrent US$. We have calculated this as a share of gross domestic product (GDP) using the World Bank’s GDP figures (also in current US$) (https://data.worldbank.org/indicator). Definitions of specific metrics are as follows: – ‘All disasters’ includes all geophysical, meteorological, and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. – People affected are those requiring immediate assistance during an emergency situation. – The total number of people affected is the sum of injured, affected, and homeless.Link www.emdat.be

Historical chart and dataset showing Japan population growth rate by year from 1961 to 2023.

The Anthropogenic Biomes of the World, Version 2: 1700 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1700. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Historical chart and dataset showing total population for Kenya by year from 1950 to 2025.

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The data of cultivated land in 1800 comes from tiehu Inventory, in which the data of Lazi county and Xietongmen County in the modern administrative unit are not recorded. Therefore, the missing data of these two counties should be interpolated.The farmland data in 1900 came from the annals of Lhasa and other county Chronicles.The land area recorded in the data is converted into modern mu units, and the missing counties are calculated using the area's per capita cultivated land and population.Tibetan Plateau with high altitude,cold climate,poor natural conditions and fragile ecological environment become the sensitive and promoter region of global climate change.Studying for Land reclamation of historical period in Qinghai-Tibet Plateau is not only the specific way to participate in the global environmental change, but also can provide the comprehensive research of land use change with abundant regional information,there is important significance for studying history in our country even the whole world of land use/cover change research.The region of Brahmaputra River and its two tributaries in Tibetan Plateau pastoral transitional zone is one of the important typical agricultural area, and is the area with the most intense land reclamation activities and the fastest population growing.Proceeding deep historical data mining in the study area to reconstruct the cropland spatial patterns over the past 300 years has important significance to study the human land use activities under the background of global climate change.