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.Source: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). Available at https://doi.org/10.7927/H49C6VHW. (October 2022)Files Download

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.

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

Contained within the Atlas of Canada Poster Map Series, is a poster showing population density across Canada. There is a relief base to the map on top of which is shown all populated areas of Canada where the population density is great than 0.4 persons per square kilometer. This area is then divided into five colour classes of population density based on Statistics Canada's census divisions.

Prior to the arrival of European explorers in the Americas in 1492, it is estimated that the population of the continent was around sixty million people. Over the next two centuries, most scholars agree that the indigenous population fell to just ten percent of its pre-colonization level, primarily due to the Old World diseases (namely smallpox) brought to the New World by Europeans and African slaves, as well as through violence and famine.

Distribution

It is thought that the most densely populated region of the Americas was in the fertile Mexican valley, home to over one third of the entire continent, including several Mesoamerican civilizations such as the Aztec empire. While the mid-estimate shows a population of over 21 million before European arrival, one estimate suggests that there were just 730,000 people of indigenous descent in Mexico in 1620, just one hundred years after Cortes' arrival. Estimates also suggest that the Andes, home to the Incas, was the second most-populous region in the Americas, while North America (in this case, the region north of the Rio Grande river) may have been the most sparsely populated region. There is some contention as to the size of the pre-Columbian populations in the Caribbean, as the mass genocides, forced relocation, and pandemics that followed in the early stages of Spanish colonization make it difficult to predict these numbers.

Varying estimates Estimating the indigenous populations of the Americas has proven to be a challenge and point of contention for modern historians. Totals from reputable sources range from 8.4 million people to 112.55 million, and while both of these totals were published in the 1930s and 1960s respectively, their continued citation proves the ambiguity surrounding this topic. European settlers' records from the 15th to 17th centuries have also created challenges, due to their unrealistic population predictions and inaccurate methodologies (for example, many early settlers only counted the number of warriors in each civilization). Nonetheless, most modern historians use figures close to those given in the "Middle estimate" shown here, with similar distributions by region.

Contained within the 4th Edition (1974) of the Atlas of Canada is a map that shows the distribution of population for 1961 by census division. Supplementary charts show the percentages of rural population, urban population in places with populations of 5000 or more and urban populations that are between 1000 to 5000 people. A supplementary text listing urban complexes and centres with populations of 5000 or more accompanies this map.

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.

Contained within the 1st Edition (1906) of the Atlas of Canada is a plate that shows two maps. The maps show the density of population per square mile for every township in Manitoba, Saskatchewan, British Columbia, Alberta, circa 1901. The statistics from the 1901 census are used, yet the population of Saskatchewan and Alberta is shown as confined within the vicinity of the railways, this is because the railways have been brought up to date of publication, 1906. Cities and towns of 5000 inhabitants or more are shown as black dots. The size of the circle is proportionate to the population. The map uses eight classes, seven of which are shades of brown, more densely populated portions are shown in the darker tints. Numbers make it clear which class is being shown in any one township. Major railway systems are shown. The map also displays the rectangular survey system which records the land that is available to the public. This grid like system is divided into sections, townships, range, and meridian from mid-Manitoba to Alberta.

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.

Contained within the 5th Edition (1978 to 1995) of the National Atlas of Canada is a map that shows distribution of Indians and Inuit using several types of symbols to represent population in 1976.

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.

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.

This map shows the direct influence of humans on terrestrial ecosystems across North America. The Human Influence Index (HII) is based on population density, built-up areas, roads, railroads, navigable rivers, coastlines, land use/land cover, and nighttime lights.HII values range from 0 to 64, with 0 representing no human influence and 64 representing maximum human influence, based on all eight measures of human influence. The data layer was compiled by the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN).Source: The Last of the Wild, Version Two. 2005. Global human influence index (HII). Wildlife Conservation Society (WCS), and Center for International Earth Science Information Network (CIESIN).Files Download

The Pseudo-Household Demographic Distribution is a geospatial representative distribution of demographic data (population and households) derived from the Canadian Census from Statistics Canada. Demography is distributed within Dissemination Blocks along roadways, providing a more accurate geospatial distribution while still aligning with published Census geographies.

Pseudo-household demographics are currently used to calculate broadband Internet service availability, but are equally applicable to other disciplines requiring a spatial distribution of households or population.

About this dataset

Context

The dataset tabulates the population of North Bend by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of North Bend across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 57.75% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the North Bend is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of North Bend total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for North Bend Population by Race & Ethnicity. You can refer the same here

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander, Plethodon cinereus. Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization and failure to accommodate imperfect detection. We present results from a range-wide monitoring network that accounts for detection uncertainty using systematic survey protocols and robust quantitative models. We analyzed mark-recapture data from 19 study areas across the range. Estimated salamander densities ranged from 1950 to 34300 salamanders/ha, with a median of 9965 salamanders/ha. We compare these results to previous estimates for P. cinereus and other abundant terrestrial vertebrates. We demonstrate that overall biomass of P. cinereus, a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer and Peromyscus mice, and 2-3 orders of magnitude greater than common high-biomass omnivorous species and other secondary consumer species. Our results add empirical evidence that P. cinereus specifically, and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems.

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.

About this dataset

Context

The dataset tabulates the population of North Harmony town by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of North Harmony town across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.05% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the North Harmony town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of North Harmony town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for North Harmony town Population by Race & Ethnicity. You can refer the same here

Carrying Capacity EstimationsThe carrying capacity for individuals in this dataset is based on estimated population density (people per km²) for forager societies, as derived from Kavanagh et al. Using a fitted piecewise structural equation model, the authors estimated global population density at a 0.5° × 0.5° resolution.The corresponding raster dataset used in our simulation model is provided in this repository. This raster file is a spatial object in .asc format, where each cell value represents the estimated population density (people per km²) for that location. The file is named:4K_popD_raster_Dec.ascKavanagh, P. H. et al. Hindcasting global population densities reveals forces enabling the origin of agriculture. Nature human behaviour 2, 478 (2018).Language Diversity Data for North AmericaThe empirical richness pattern of language diversity in North America, as used in our simulation, is based on established literature and previously published language range maps.The final dataset is provided as a shapefile, containing:The name of each languageThe corresponding Glottolog identification codeThis shapefile can be found in this repository under the name:NAM_Continent.shpHaynie, H. J. & Gavin, M. C. Modern language range mapping for the study of language diversity. (2019).

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios