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 earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

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.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Bermuda population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Wallis and Futuna Islands population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand (LINZ Data Service) NZ Building Outlines and OpenStreetMap data.

Gobal version of population dataset: Kontur Population: Global Population Density for 400m H3 Hexagons

Armenia population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Yemen population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Western Sahara population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand (LINZ Data Service) NZ Building Outlines and OpenStreetMap data.

Gobal version of population dataset: Kontur Population: Global Population Density for 400m H3 Hexagons

Kosovo population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Mayotte population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

Democratic Republic of the Congo population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Somalia population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

As of 2024, the estimated number of internet users worldwide was 5.5 billion, up from 5.3 billion in the previous year. This share represents 68 percent of the global population. Internet access around the world Easier access to computers, the modernization of countries worldwide, and increased utilization of smartphones have allowed people to use the internet more frequently and conveniently. However, internet penetration often pertains to the current state of development regarding communications networks. As of January 2023, there were approximately 1.05 billion total internet users in China and 692 million total internet users in the United States. Online activities Social networking is one of the most popular online activities worldwide, and Facebook is the most popular online network based on active usage. As of the fourth quarter of 2023, there were over 3.07 billion monthly active Facebook users, accounting for well more than half of the internet users worldwide. Connecting with family and friends, expressing opinions, entertainment, and online shopping are amongst the most popular reasons for internet usage.

At the beginning of the 19th century, the area of modern-day Italy, at the time a collection of various states and kingdoms, was estimated to have a population of nineteen million, a figure which would grow steadily throughout the century, and by the establishment of the Kingdom of Italy in 1861, the population would rise to just over 26 million.

Italy’s population would see its first major disruption during the First World War, as Italy would join the Allied Forces in their fight against Austria-Hungary and Germany. In the First World War, Italy’s population would largely stagnate at 36 million, only climbing again following the end of the war in 1920. While Italy would also play a prominent role in the Second World War, as the National Fascist Party-led country would fight alongside Germany against the Allies, Italian fatalities from the war would not represent a significant percentage of Italy’s population compared to other European countries in the conflict. As a result, Italy would exit the Second World War with a population of just over 45 million.

From this point onwards the Italian economy started to recover from the war, and eventually boomed, leading to increased employment and standards of living, which facilitated steady population growth until the mid-1980s, when falling fertility and birth rates would cause growth to largely cease. From this point onward, the Italian population would remain at just over 57 million, until the 2000s when it began growing again due to an influx of migrants, peaking in 2017 at just over 60 million people. In the late 2010s, however, the Italian population began declining again, as immigration slowed and the economy weakened. As a result, in 2020, Italy is estimated to have fallen to a population of 59 million.

Greenland population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Detailed methods can be found in the publication, and highlights are provided below. The following original data sources were aggregated/disaggregated to a common hexagonal grid (cell size 290 km2, mean internode spacing 18.3 km): Gridded Livestock of the World (GLW 2), doi:10.1371/journal.pone.0096084, reporting year 2006, resolution 3 arcminutes (~5 km2 at equator); Gridded Population of the World (GPWv4), doi:10.7927/H4HX19NJ, reporting year 2010, resolution 30 arcseconds (~1 km at equator); GlobCover 2009, doi:10.1594/PANGAEA.787668, reporting year 2009, resolution 300m; FAOSTAT Fertilizers by Nutrient dataset (downloaded on 26 Feb 2018), http://www.fao.org/faostat/en/#data/RFN/metadata, reporting years 2002-2014, resolution national. ---Subnational methods and calculations Livestock densities, human population density, and cropland extent were summarized for each grid cell in a global hexagonal grid. This grid had consistent grid cell areas across latitudes, and was generated using the dggrid package (Barnes, 2016; Sahr, 2011) in the platform R (R Core Team, 2016). In the finer hexagonal grid, each grid cell had a mean area of 290 km2 and a mean internode spacing of 18.3 km. In the coarser grid, each grid cell had a mean side length of 95 km (mean hexagon area of 23,300 km2, mean internode spacing of 165 km), which was large enough to encompass megacities such as London and Paris along with peri-urban areas, but small enough to maintain subnational resolution in relatively small nations. For a minority of hexagonal grid cells, slight deviations in the dimensions were mathematically necessary to avoid overlapping cells and gaps over the world's surface (Barnes, 2016). Total manure P production in each grid cell was calculated by summing the contributions from each animal type, using animal-specific and nation-specific P excretion factors from Bouwman et al. (2017). For cattle we used 16.6 kg P per head yr-1 in Canada, USA, and Japan, 13.1 kg P per head yr-1 in the other OECD countries, and 8.75 kg P per head yr-1 in the remaining countries (Bouwman et al. 2017). For other animals we used 1.8 kg P per head yr-1 for pigs, 0.1 kg P per head yr-1 for chickens, 1.5 kg P per head yr-1 for sheep and goats for all countries (Bouwman et al. 2017). Cells with zero cropland extent were excluded from the analysis (and thus also gridcelldata.csv). --National methods and calculations We used nation-level P fertilizer data from FAOSTAT including import, export, agricultural use, and production for the most recent available years (2002-2014). FAOSTAT data were downloaded on 26 Feb 2018. Fertilizer data are reported annually, and we took the nation-specific means for each budgetary term over two different five year intervals (2010-2014, 2002-2006); these years deliberately exclude the global food crisis of 2007/2008 when the global phosphate rock price spiked by 400% (Chowdhury et al., 2017). A small number of countries had data gap years, requiring that the mean be calculated over fewer years. Import ratios, an indicator of fertilizer P import dependency, were calculated as net import : consumption, where net import = import - export. Recent fertilizer P consumption trends were summarized by calculating a consumption ratio of the 2010s to 2000s (2010-2014:2002-2006). Calculations involving P import ratios and consumption trends were conducted directly on FAO data, prior to disaggregation within the global grid. In cases where grid cells overlapped multiple countries, the nation representing the largest share of the grid cell was assigned to the whole cell using administrative data from Natural Earth. A minority of nations lacked P import or P consumption data and were excluded from P import ratio calculations. Nations that lacked P export data were assumed to have zero gross P export in these calculations. Attributes of the two compiled subnational datasets: "gridcelldata_fine.csv" and "gridcelldata_coarse.csv" (each row represents one hexagonal grid cell) nation: Name of the nation that possessed the largest share of the grid cell. lat: Decimal latitude of the grid cell centroid. lon: Decimal longitude of the grid cell centroid. crop_pct: Mean percent of land as cropland (i.e., cropland extent) within the grid cell. For coastal grid cells, only the land portion of the cell was used in this calculation. popd_indperkm2: Mean population density of the grid cell. manurep_kgperkm2: Calculated manure P production of the grid cell. This is the sum across multiple animal types using animal-specific, nation-specific P excretion factors from Bouwman et al. 2017. cattle_indperkm2: Mean cattle density of the grid cell. pigs_indperkm2: Mean pig density of the grid cell. chickens_indperkm2: Mean chicken density of the grid cell. sheep_indperkm2: Mean sheep density of the grid cell. goats_indperkm2: Mean goat density of the grid cell. pfertnatcons10s_metrictons: Mean nation-level P fertilizer consumption (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatimp_metrictons: Mean nation-level P fertilizer import (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT.pfertnatout_metrictons: Mean nation-level P fertilizer export (P2O5 total nutrients) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatnetimpratio_unitless: Nation-level net fertilizer P import ratios ([import-export]/consumption) for years 2010-2014, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatcons00s_metrictons: Mean nation-level P fertilizer consumption (P2O5 total nutrients) for years 2002-2006, for the nation that possessed the largest share of the grid cell. Calculated from FAOSTAT. pfertnatconstr_unitless: Nation-level P fertilizer consumption trend, for the nation that possessed the largest share of the grid cell. This is the ratio of 2010s:2000s (that is, mean of 2010-2014 divided by mean of 2002-2006). Calculated from FAOSTAT.