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.

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.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. 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.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.

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.

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.

This dataset explores the farm population of Canada by province by comparing the populations from 1996 to 2001. 1. Refers to all persons who are members of a farm operator's household, living on a farm in a rural or urban area. 2. Refers to sparsely populated lands lying outside urban areas. 3. Refers to areas with minimum population concentrations of 1,000 and a population density of at least 400 per square kilometre, based on the previous census population counts. All territory outside urban areas is considered rural. Taken together, urban and rural areas cover all of Canada. Source: Statistics Canada, Censuses of Agriculture and Population. Last modified: 2004-09-29.

Data on a) Average share of urban areas allocated to streets and open public spaces, and b) Share of urban population with convenient access to an open public space (defined as share of urban population within 400 meters walking distance along the street network to an open public space).
Data on a) Average share of cities/urban areas in green areas (%), and b) Green area per capita (m2/person) for the periods 1990, 2000, 2010 and 2020.

Population of the official entrance age to secondary general education, both sexes (number) in Liechtenstein was reported at 400 Persons in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Liechtenstein - Population of the official entrance age to secondary general education, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

In 2024, the total population in the Arab World increased by ***** million inhabitants (+**** percent) compared to 2023. Therefore, the total population in the Arab World reached a peak in 2024 with ****** million inhabitants. Notably, the total population continuously increased over the last years.The total population of a country refers to the de facto number of people residing in a country, regardless of citizenship or legal status.

Retirement Communities Market Outlook

The global retirement communities market size was valued at approximately USD 250 billion in 2023 and is projected to reach around USD 400 billion by 2032, growing at a CAGR of about 5%. This growth is primarily driven by the aging global population, an increase in life expectancy, and changing lifestyle preferences among seniors. The shift towards comprehensive care and the integration of health and wellness services within retirement communities have further fueled this market's expansion. As societies worldwide continue to experience demographic shifts, the demand for retirement communities that offer a blend of healthcare, hospitality, and recreational amenities is expected to surge, underpinning the robust growth trajectory of the sector.

The burgeoning aging population is one of the primary growth factors for the retirement communities market. As advances in healthcare continue to improve life expectancy, a significant proportion of the global population is projected to fall within the senior age bracket, necessitating adequate living solutions for them. This demographic shift is particularly pronounced in developed regions such as North America and Europe, where a considerable percentage of the population is transitioning into retirement age. Additionally, emerging economies in Asia Pacific are also witnessing an increase in the elderly population, driven by improved healthcare infrastructure and living standards. This demographic evolution necessitates the development of retirement communities equipped with facilities that cater to both the healthcare and lifestyle needs of seniors.

Another significant growth factor is the increased financial independence and spending power among seniors. With many from the baby boomer generation having accrued substantial savings and investments, there is a growing willingness to spend on quality living environments that provide comfort, security, and access to healthcare and recreational activities. This financial capability, coupled with the desire for a community living environment that offers social interaction and reduces isolation, is a key driver for the retirement communities market. Furthermore, these communities are increasingly incorporating technology to enhance the quality of life for residents, with features such as telemedicine, smart home technologies, and digital health monitoring, which are appealing to the tech-savvy senior demographic.

Moreover, the changing societal norms and lifestyle preferences among the elderly are also contributing to the market's growth. TodayÂ’s seniors are more active and health-conscious than ever before, seeking retirement communities that offer wellness programs, fitness centers, and social activities that align with their lifestyle choices. The emphasis on holistic well-being has led to a rise in integrated community models that provide a continuum of care, from independent living to assisted living and nursing care, allowing seniors to age in place with dignity and peace of mind. This trend is expected to intensify in the coming years, further propelling the growth of the retirement communities market globally.

In recent years, the concept of Smart Communities has emerged as a transformative force within the retirement sector. These communities leverage advanced technologies to create interconnected environments that enhance the quality of life for residents. By integrating smart home devices, IoT solutions, and data-driven services, Smart Communities offer personalized and efficient living experiences. This technological integration not only improves safety and convenience for seniors but also promotes sustainable living practices. As the demand for tech-savvy solutions grows, retirement communities are increasingly adopting smart technologies to meet the evolving expectations of their residents, positioning themselves at the forefront of innovation in senior living.

Regionally, North America currently holds the largest share of the retirement communities market, driven by a well-established infrastructure, high disposable incomes, and a significant aging population. Europe follows closely, benefiting from similar demographic trends and a strong emphasis on social welfare programs for the elderly. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, fueled by rapid urbanization, economic growth, and increasing healthcare investments. Countries such as China, Japan, and India are at the forefront of this expansion, as they adapt to th

A dataset listing Florida cities by population for 2024.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Germany.

Analysis unit

• Household
• Individual

Universe

The WVS for Germany covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

Random sample of the overall population in Germany aged 18 and older, sufficiently able to speak German

Procedure: 400 sampling points; random-route; Kish-Selection Grid Separate sampling for East and West Germany. After the selection of municipalities, the sampling procedure consists of three stages. The probability of being selected is proportional to the overall population with principle residence:

(1) Selection of constituencies: Strictly random selection of stratified constituencies (2) Selection of household: Random-Route (3) Selection of respondent: Kish-Selection-Grid Separate sampling for East and West Germany: 200 sample points each, randomly selected from stratified constituencies (according to federal state, population size)

Remarks about sampling: - a slight overrepresentation of women (4 percent points) - a slight underrepresentation of those aged 25-39, a slight overrepresentation of those aged 65+ (4 to 6 percent points)

The sample size for Germany is N=2064 and includes the national population aged 18 years and over for both sexes.

Mode of data collection

Face-to-face [f2f]

Research instrument

Country-specific questions were added.

Response rate

Response rate:

4454 Total number of starting names/addresses 97 - addresses established as empty, demolished or containing no private dwellings 651 - no contact at selected address 26 - no contact with selected person 842 - refusal at selected address 770 - personal refusal by selected respondent 4 - other type of unproductive 2064 - full productive interview

Remarks about non-response: 2.064 (total): 988 in West Germany, 1.076 in East Germany

Sampling error estimates

+/- 2,2%

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.

The North American Breeding Bird Survey (BBS), which is coordinated by the Biological Resources Division and Canadian Wildlife Service, is a primary source of population trend and distribution information for most species of North American birds. The BBS was initiated during 1966 by Chan Robbins and his associates at the Patuxent Wildlife Research Center to monitor the populations of all breeding bird species across the continental U.S., Canada, and Alaska. Approximately 2200 skilled observers participate in the survey each year. The BBS has accumulated 30 years of data on the abundance, distribution, and trends for more than 400 species of birds. These data are widely used by researchers, various federal and state agencies, non-governmental organizations, and the general public. Analyses of BBS data by PWRC statisticians have been instrumental in the development of innovative approaches for analyzing trends of wildlife populations.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

SPLASH (Structure of Populations, Levels of Abundance and Status of Humpbacks) represents one of the largest international collaborative studies of any whale population ever conducted. It was designed to determine the abundance, trends, movements, and population structure of humpback whales throughout the North Pacific and to examine human impacts on this population. This study involved over 50 research groups and more than 400 researchers in 10 countries. It was supported by a number of agencies and organizations including the National Marine Fisheries Service, the National Marine Sanctuary Program, National Fish and Wildlife Foundation, Pacific Life Foundation, Department of Fisheries and Oceans Canada, and Commission for Environmental Cooperation with additional support from a number of other organizations and governments for effort in specific regions. Results presented here include a comprehensive analysis of individual identification photographs. Additional analysis of human impacts, ecosystem markers (e.g., stable isotopes) and the genetic structure of populations are underway or planned pending further funding.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

A description of the modelling methods used for age and gender structures can be found in "https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-11-11" target="_blank"> Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/gender structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646

Freshwater environments vary widely in ion availability, owing to both natural and anthropogenic drivers. Field and laboratory work point to the importance of overall salinity as well as cation depletion, in shaping the physiology, behavior, and ecology of freshwater taxa. Yet, we currently have a poor understanding of the degree to which populations may vary in response to ion availability. Using Daphnia collected from three lakes that differ greatly in salinity and calcium availability, we conducted a laboratory reciprocal transplant experiment to assess how animals representing these populations vary in fecundity, body size, and survival when reared in lake water from each environment. The lake water environment and population of origin strongly interacted to shape Daphnia growth and reproduction. Surprisingly, we found only modest evidence that lake water with abundant calcium (5.5 mg/L vs. 1.2-2.3 mg/L) increased Daphnia growth or reproduction. In contrast, water from a relatively ion-rich lake (400 µS/cm specific conductance) strongly boosted Daphnia fecundity over lower-ion lake water (20-50 µS/cm), especially for the population originating from the high-ion environment. Our results suggest that ion-poor conditions common in regions around the world may exert stress on freshwater organisms, even for populations inhabiting these environments. Meanwhile, moderate salt enrichment may not prove harmful but could even benefit freshwater taxa in these ion-poor regions. The context dependence of how and when lake water chemistry affects Daphnia and other freshwater taxa deserves greater attention, in both ion-depleted and ion-rich conditions. Daphnia are key members of lake food webs and serve as an important model for ecology, evolution, and toxicology research. Consideration of how lake water chemistry may influence how Daphnia populations respond to abiotic and biotic stress may improve the ability to evaluate and predict ecological and evolutionary dynamics in lakes of varying chemical composition. Methods We reared Daphnia from three lakes in lake water from each of these lakes. We included 4 clonal lineages per lake and 4-13 replicates per clonal lineage/ lake/ treatment. We measured body size at 7 days and noted whether Daphnia reproduced by that time. We noted survival to maturity. We counted offspring over 21 days and measured Daphnia again at 21 days. If Daphnia did not survive to 7 or 21 Days, 7-day and/or 21-day length is NA. If Daphnia did not survive to reproduce, totaloffspring = NA and rep_by_day7 = NA Column headers are:

Lake: Lake of origin (from Egypt, Hall, or Sewell Pond) Trt: Lake water rearing conditions (E: Egypt, S: Sewell, H: Hall) Clone: clonal lineage (one of four from each lake) Replicate: experimental replicate for a given lake x treatment x clone Died_before_mature: did the animal die before reproducing? 0: no, 1: yes rep_by_day7: reproduced by day 7 (0: no, 1: yes) totaloffspring: total offspring produced over the 21 day trial Day7_length_mm: body length measured on day 7 in mm Day21_length_mm: body length measured on day 21 in mm (NA if died between days 7 and 21)