The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1-degree resolutions to produce density rasters at these resolutions.

Historical dataset showing North America population density by year from 1961 to 2022.

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.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows the distribution of population in what is now Canada circa 1851, 1871, 1901, 1921 and 1941. The five maps display the boundaries of the various colonies, provinces and territories for each date. Also shown on these five maps are the locations of principal cities and settlements. These places are shown on all of the maps for reference purposes even though they may not have been in existence in the earlier years. Each map is accompanied by a pie chart providing the percentage distribution of Canadian population by province and territory corresponding to the date the map is based on. It should be noted that the pie chart entitled Percentage Distribution of Total Population, 1851, refers to the whole of what was then British North America. The name Canada in this chart refers to the province of Canada which entered confederation in 1867 as Ontario and Quebec. The other pie charts, however, show only percentage distribution of population in what was Canada at the date indicated. Three additional graphs are included on this plate and show changes in the distribution of the population of Canada from 1867 to 1951, changes in the percentage distribution of the population of Canada by provinces and territories from 1867 to 1951 and elements in the growth of the population of Canada for each ten-year period from 1891 to 1951.

This dataset is about land area of counties.

The 'LND110210D' column is the land area measured in 2011.

It can be used for calculating population density.

https://www.census.gov/library/publications/2011/compendia/usa-counties-2011.html

Conservation of grizzly bears (Ursus arctos) is often controversial and the disagreement often is focused on the estimates of density used to calculate allowable kill. Many recent estimates of grizzly bear density are now available but field-based estimates will never be available for more than a small portion of hunted populations. Current methods of predicting density in areas of management interest are subjective and untested. Objective methods have been proposed, but these statistical models are so dependent on results from individual study areas that the models do not generalize well. We built regression models to relate grizzly bear density to ultimate measures of ecosystem productivity and mortality for interior and coastal ecosystems in North America. We used 90 measures of grizzly bear density in interior ecosystems, of which 14 were currently known to be unoccupied by grizzly bears. In coastal areas, we used 17 measures of density including 2 unoccupied areas. Our best model for coastal areas included a negative relationship with tree cover and positive relationships with the proportion of salmon in the diet and topographic ruggedness, which was correlated with precipitation. Our best interior model included 3 variables that indexed terrestrial productivity, 1 describing vegetation cover, 2 indices of human use of the landscape and, an index of topographic ruggedness. We used our models to predict current population sizes across Canada and present these as alternatives to current population estimates. Our models predict fewer grizzly bears in British Columbia but more bears in Canada than in the latest status review. These predictions can be used to assess population status, set limits for total human-caused mortality, and for conservation planning, but because our predictions are static, they cannot be used to assess population trend.

The United States is a country located in North America. It is made up of 50 states and the capital district of Washington. The US federal republic has its capital in Washington D.C., which means this dataset can be used to study demographics, geography, and population density for different cities across the United States. This information can help researchers, policymakers and businesses understand how people live and work within different geographical areas in the USA

This dataset comes from simplemaps.com, check out the dataset at https://simplemaps.com/data/us-cities

Thumbnail from https://www.vecteezy.com/vector-art/3798082-red-square-map-of-united-states-of-america-with-long-shadow

Population Density Estimation via Satellite Market Outlook

According to our latest research, the global Population Density Estimation via Satellite market size reached USD 2.14 billion in 2024, with a robust CAGR of 11.8% projected through 2033. By the end of the forecast period, the market is expected to achieve a value of USD 6.15 billion. This sustained growth is primarily driven by the rising demand for high-precision geospatial data to support urbanization, disaster management, and environmental monitoring initiatives across both developed and emerging economies.

One of the primary growth factors for the Population Density Estimation via Satellite market is the increasing urbanization and rapid expansion of metropolitan areas worldwide. As cities become more densely populated, urban planners and policymakers require accurate, up-to-date population distribution data to optimize infrastructure, transportation networks, and public services. Satellite-based population density estimation offers a scalable, cost-effective solution that provides comprehensive spatial coverage, overcoming the limitations of traditional census methods which are often time-consuming, expensive, and infrequent. The integration of satellite imagery with advanced analytics and artificial intelligence has further enhanced the precision and timeliness of population density assessments, making them indispensable for modern urban development strategies.

Another significant driver is the growing frequency and severity of natural disasters, such as floods, earthquakes, and wildfires, which necessitate real-time population mapping for effective disaster response and resource allocation. Governments and humanitarian organizations increasingly rely on satellite-derived population density data to identify vulnerable communities, plan evacuation routes, and deploy emergency aid efficiently. The ability to monitor population movements in near real-time has proven critical during crises, enabling authorities to make informed decisions that can save lives and minimize damage. Furthermore, advancements in satellite sensor technologies, such as high-resolution optical and radar imaging, have improved the accuracy and reliability of population estimates, fostering greater adoption across disaster management agencies globally.

The market is also propelled by the expanding applications of population density estimation in sectors such as agriculture, environmental monitoring, and defense. In agriculture, understanding population distribution helps optimize land use planning and resource allocation, particularly in regions facing food security challenges. Environmental monitoring agencies utilize population data to assess human impact on ecosystems, track urban sprawl, and design conservation strategies. Meanwhile, defense and intelligence organizations leverage satellite-based population analytics for border surveillance, threat assessment, and mission planning. This broadening spectrum of use cases is encouraging both public and private sector investments in satellite-based population density estimation solutions, further fueling market growth.

From a regional perspective, North America and Europe currently dominate the Population Density Estimation via Satellite market, owing to their advanced satellite infrastructure, robust research ecosystems, and high levels of government funding for geospatial intelligence. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, increasing investments in space technology, and rising demand for smart city solutions. Countries such as China, India, and Japan are at the forefront of leveraging satellite data for urban planning and disaster management. In contrast, regions like Latin America and the Middle East & Africa are gradually adopting satellite-based population estimation technologies, supported by international collaborations and growing awareness of the benefits of geospatial intelligence.

Technology Analysis

The technology segment of the Population Density Estimation via Satellite m

Surveys of wildlife host-pathogen systems often document clear seasonal variation in transmission; conclusions concerning the relationship between host population density and transmission vary. In the field, effects of seasonality and population density on natural disease cycles are challenging to measure independently, but laboratory experiments may poorly reflect what happens in nature. Outdoor manipulative experiments are an alternative that controls for some variables in a relatively natural environment. Using outdoor enclosures, we tested effects of North American deermouse (Peromyscus maniculatus) population density and season on transmission dynamics of Sin Nombre hantavirus. In early summer, mid-summer, late summer, and fall 2007–2008, predetermined numbers of infected and uninfected adult wild deermice were released into enclosures and trapped weekly or bi-weekly. We documented 18 transmission events and observed significant seasonal effects on transmission, wounding frequency, and host breeding condition. Apparent differences in transmission incidence or wounding frequency between high- and low-density treatments were not statistically significant. However, high host density was associated with a lower proportion of males with scrotal testes. Seasonality may have a stronger influence on disease transmission dynamics than host population density, and density effects cannot be considered independent of seasonality.

Range-wide salamander densities reveal a key component of terrestrial vertebrate biomass in eastern North American forests

https://doi.org/10.5061/dryad.h44j0zpvf

Capture data from standardized arrays.

Description of the data and file structure

One file with all data:

• site: name of sampling area
• year: year sampled
• season: season of sampling
• plot: identity of plot within site
• trap: identity of coverboard within plot
• mark: individual id
• occasion: occasion of sampling
• session: sampling session

Code/Software

Data are formatted for analysis in the R package: oSCR

Data are derived from generalized linear models and model selection techniques using 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents. Models were used to determine the strength of association among a diverse set of biotic and abiotic factors associated with wild pig population dynamics. The models and associated factors were used to predict the potential population density of wild pigs at the 1 km resolution. Predictions were then compared with available population estimates for wild pigs on their native range in North America indicating the predicted densities are within observed values. See Lewis et al (2017) and Lewis et al (2019) for more information.Lewis, Jesse S., Matthew L. Farnsworth, Chris L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller. "Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal." Scientific reports7 (2017): 44152.Lewis, Jesse S., Joseph L. Corn, John J. Mayer, Thomas R. Jordan, Matthew L. Farnsworth, Christopher L. Burdett, Kurt C. VerCauteren, Steven J. Sweeney, and Ryan S. Miller. "Historical, current, and potential population size estimates of invasive wild pigs (Sus scrofa) in the United States." Biological Invasions21, no. 7 (2019): 2373-2384.

Human land transformation is one of the leading causes of vertebrate population declines. These declines are thought to be partly due to decreased connectivity and habitat loss reducing animal population sizes in disturbed habitats. With time, this can lead to declines in effective population size and genetic diversity which restricts the ability of wildlife to efficiently cope with environmental change through genetic adaptation. However, it is not well understood whether these effects generally hold across taxa. We address this question by repurposing and synthesizing raw microsatellite data from online repositories for 19 amphibian species sampled at 554 georeferenced sites in North America. For each site, we estimated gene diversity, allelic richness, effective population size, and population differentiation. Using binary urban-rural census designations, and continuous measures of human population density, the Human Footprint Index, and impervious surface cover, we tested for generalizable effects of human land use on amphibian genetic diversity. We found minimal evidence, either positive or negative, for relationships between genetic metrics and urbanization in our repurposed data. Together with previous work on focal species that also found varying effects of urbanization on genetic composition, it seems likely that the consequences of urbanization are not easily generalizable within or across amphibian species. Questions about the genetic consequences of urbanization for amphibians should be addressed on a case-by-case basis. This contrasts with general negative effects of urbanization in mammals and consistent, but species-specific, positive and negative effects in birds.

Population Density Estimation via Satellite Market Outlook

As per our latest research, the global Population Density Estimation via Satellite market size reached USD 1.24 billion in 2024, reflecting the rapidly growing adoption of satellite-based geospatial analytics across diverse sectors. The market is set to expand at a robust CAGR of 13.8% from 2025 to 2033, with the total value forecasted to reach USD 3.76 billion by 2033. This impressive growth is fueled by the increasing need for accurate, real-time population data to support urban planning, disaster response, and environmental monitoring initiatives worldwide.

One of the primary growth drivers for the Population Density Estimation via Satellite market is the accelerating pace of urbanization, particularly in emerging economies. Governments and urban planners are under mounting pressure to make informed decisions regarding infrastructure development, resource allocation, and public service delivery. Satellite-based population density estimation provides a cost-effective and scalable solution for monitoring demographic shifts, identifying high-density clusters, and projecting future urban expansion. The integration of advanced imaging technologies such as optical, radar, and multispectral sensors further enhances the granularity and accuracy of population assessments, empowering stakeholders to implement data-driven policies for sustainable urban growth.

Another significant factor propelling market growth is the increasing frequency and severity of natural disasters, which underscores the critical role of timely population data in disaster management and humanitarian response. Satellite imagery enables authorities to rapidly assess population distribution in affected areas, optimizing evacuation plans, resource deployment, and post-disaster recovery efforts. The ability to estimate population density in near real-time has proven invaluable during crises such as earthquakes, floods, and pandemics, where ground-based data collection is often impractical or delayed. Moreover, the proliferation of commercial satellites and advancements in cloud-based geospatial analytics are making these capabilities more accessible to a wider range of end-users, from government agencies to non-governmental organizations.

Technological advancements in satellite imaging and data analytics are also catalyzing the expansion of the Population Density Estimation via Satellite market. Innovations in LiDAR, multispectral imaging, and artificial intelligence-driven image processing are enabling the extraction of richer, more nuanced population insights from high-resolution satellite data. These technologies facilitate the detection of subtle changes in urban landscapes, informal settlements, and rural areas that might otherwise go unnoticed. As a result, end-users across sectors such as agriculture, defense, and environmental monitoring are increasingly leveraging satellite-derived population density estimates to enhance operational efficiency, mitigate risks, and support evidence-based decision-making.

From a regional perspective, North America and Europe continue to lead the market in terms of adoption and technological innovation, owing to their well-established satellite infrastructure and strong presence of geospatial analytics firms. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, government investments in space technology, and a burgeoning demand for smart city solutions. Latin America and the Middle East & Africa are also witnessing increased uptake, particularly in the context of disaster management and environmental monitoring. As satellite data becomes more accessible and affordable, the global market is expected to witness widespread adoption across both developed and developing regions.

Technology Analysis

The technology landscape of the Population Density Estimation via Satellite market is characterized by a diverse array of imaging modalities, each offering unique advantages and applications. Optical imaging remains the most widely used technology, owing to its ability to capture high-resolution, true-color images that facilitate the identification of buildings, roads, and other man-made structures. These images are invaluable for mapping urban and rural settlements, detecting population clusters, and monitoring changes in land use over time. However, optical imaging is limited by weat

Effective population density and intergenerational dispersal distance are key aspects of population biology, but obtaining empirical estimates of these parameters can be difficult. This is especially true for my study taxa, wild bees. In this paper, I apply and evaluate an existing but underutilized method to estimate the effective density and dispersal distance of bumble bees (Bombus, Apidae). Specifically, using 10 datasets of bumble bees in North America, I use the relationship between genetic isolation-by-distance and Wright’s neighborhood size to define a density-dispersal isocline—that is, a curve describing pairs of density and dispersal values consistent with observed rates of isolation-by-distance. These parameters are inversely related; as one increases, the other decreases. I then use outside estimates of bumblebee dispersal distances to make more specific estimates of effective colony density. Compared to some prior estimates of census density (100s to 1000s colonies/km..., The data on *Bombus impatiens *workers from New Jersey, USA, were collected by me in the summers of 2020 and 2021. I extracted DNA using a bead-based extraction and amplified 11 microsatellite loci. These PCR products were analyzed using fragment analysis by the Rutgers Medical School Genomics Center. I assigned genotypes by first manually defining allele bins, then having software automatically assign allele IDs, and then manually checking allele IDs. Approximately 100 specimens were re-analyzed to assess error rates, which were all < 1%. Using these genotypes, I used the program Colony to determine siblishingship among workers. This dataset includes the full table of all genotypes and another that is filtered to only one worker from each colony. The family assignment output file from Colony is also included. There is also a table that includes geographic location and date of collection for each specimen. The other nine datasets were collected and processed by other author..., , # Using isolation by distance to estimate effective population density and dispersal distance of North American bumble bees

https://doi.org/10.5061/dryad.fj6q5744r

Description of the data and file structure

Data and code to recreate analysis in “Using isolation-by-distance to jointly estimate effective population density and dispersal distance: a practical evaluation using bumble bees†by DT Simpson, published in Oecologia, 2025: https://doi.org/10.1007/s00442-025-05721-4.

A note on data, copyright, and attribution: The analyses in this paper use data that I (DTS) collected as well as data made available by other authors. Those authors' data have been previously published on Dryad, and links to those repositories can be found at the end of this readme. Given that all this data (mine and theirs) is published under the CC0 license, no attribution is strictly necessary. I would, however, encoura...,

Population density and size structure data for macroecology analysis on Littorina littorea from different locations along the Atlantic North American coast

The dataset regards measurements of density, body size, and size structure of L. littorea across 10 locations across Atlantic North America.

Data include Population density and size measurements of Littorina littorea from 10 Locations along the North American Atlantic coast. Environmental variables extrapolated from satellite data for the 10 locations are included, as well as rugosity and biomass data measured in situ.

The R codes included were used for figure creation and statistical analysis. They can be used to reproduce the results of the paper using the raw datasets included.

Data from this study can be used for other purposes, such as invasive species monitoring or species distribution modelling based on size and density.

Description of the data and file structure

This dataset is composed on 5 excel files and 2 c...

Natural selection favors individuals that respond with effective and appropriate immune responses to macro or microparasites. Animals living in populations close to ecological carrying capacity experience increased intraspecific competition, and as a result are often in poor nutritional condition. Nutritional condition, in turn, affects the amount of endogenous resources that are available for investment in immune function. Our objective was to understand the relationship between immune function and density dependence mediated by trade-offs between immune function, nutritional condition, and reproduction. To determine how immune function relates to density-dependent processes, we quantified bacteria killing ability, hemolytic-complement activity, and nutritional condition of North American elk (Cervus elaphus) from populations maintained at experimentally high- and low-population densities. When compared with elk from the low-density population, those from the high-density population had higher bacteria killing ability and hemolytic-complement activity despite their lower nutritional condition. Similarly, when compared with adults, yearlings had higher bacteria killing ability, higher hemolytic-complement activity, and lower nutritional condition. Pregnancy status and lactational status did not change either measure of constitutive immunity. Density-dependent processes affected both nutritional condition and investment in constitutive immune function. Although the mechanism for how density affects immunity is ambiguous, we hypothesize two possibilities: (i) individuals in higher population densities and in poorer nutritional condition invested more into constitutive immune defenses, or (ii) had higher parasite loads causing higher induced immune responses. Those explanations are not mutually exclusive, and might be synergistic, but overall our results provide stronger support for the hypothesis that animals in poorer nutritional condition invest more in constitutive immune defenses then animals in better nutritional condition. This intriguing hypothesis should be investigated further within the larger framework of the cost and benefit structure of immune responses.

As of 2023, the top five most densely populated cities in Latin America and the Caribbean were in Colombia. The capital, Bogotá, ranked first with over ****** inhabitants per square kilometer.

Roads play a key role in movements of goods and people but require large amounts of materials emitting greenhouse gases to be produced. This study assesses the global road material stock and the emissions associated with materials’ production. Our bottom-up approach combines georeferenced paved road segments with road length statistics and archetypical geometric characteristics of roads. We estimate road material stock to be of 254 Gt. If we were to build these roads anew, raw material production would emit 8.4 GtCO2-eq. Per capita stocks range from 0.2 t/cap in Chad to 283 t/cap in Iceland, with a median of 20.6 t/cap. If the average per capita stock in Africa was to reach the current European level, 166 Gt of road materials, equivalent to the road material stock in North America and in East and South Asia, would be consumed. At the urban scale, road material stock increases with the urban area, population density, and GDP per capita, emphasizing the need for containing urban expansion. Our study highlights the challenges in estimating road material stock and serves as a basis for further research into infrastructure resource management.

This dataset is an extension of the original Next Day Wildfire Spread (NDWS) dataset with coverage of North America from 2012 to 2023, extracted using the same data sources. Dataset Information:

• Date Range: 2012-01-01 to 2023-08-31
• Train / Eval Split: 80% / 20%
• Geographic Region: North America
• Coordinates: -140, 24, -52, 84
• Population Density (modified): WorldPop/GP/100m/pop
• Resolution: 1000m

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10055750%2Faeaf4372f3c93d9bb158408926c22f2f%2Fdata_plot3.png?generation=1707178827286732&alt=media" alt="">