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.

The plate contains four maps of 24 hour rainfalls (in millimetres) for a 2 year return period, a 5 year return period, a 10 year return period and a 25 year return period. Each map has a detailed inset of the Vancouver area. These four maps were not analyzed for the mountainous parts of Canada in British Columbia and the Yukon because of the limited number of stations, the non-representative nature of the valley stations and the variability of precipitation owing to the orographic effects. From the incomplete data, it is impossible to draw accurate isolines of short duration rainfall amounts on maps of national scale. Point values for all stations west of the Rocky Mountain range and in the Yukon have been plotted for durations of less than 24 hours. For the Vancouver metropolitan area, recording rain gauges have been in operation for several years. For some of these stations point rainfall data have been plotted on inset maps. The density of climatological stations varies widely as does population density. In general, the accuracy of the analysis increases with station density. North of latitude 55 degrees North, there are only five stations. Therefore, the isoline analyses represent extrapolations beyond the station values. Whenever sufficient data were available for interpretation, isolines were drawn as solid lines. The scale of the map used for Canada dictates the use of an isoline interval of 12 millimetres.

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 record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

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

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.

'The U.S. Geological Survey (USGS) has a long history of involvement in multi-scale, and multi-temporal land cover characterization and mapping of the world. During the 1970\'s, the Anderson System for land use and land cover classification system was developed and the conterminous United States (US) was mapped using aerial photographs. During 1980\'s, 75% of state of Alaska was mapped using Landsat satellite data. During the 1990\'s, (i) land cover characteristics database concept was demonstrated, (ii) Multi-Resolution Land Characteristics (MRLC) consortium was formed with Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and US Forest Service (USFS), (iii) Global Land Cover Characteristics database was completed, and (iv) land cover and vegetation databases of the U.S. using Landsat TM data were completed. During 2000-2002, a forest canopy density map was produced as a part of Forest Resources Assessment 2000 for the Food and Agriculture Organization of the United Nations (FAO), and MRLC-2001 dataset was released. In 2003, land cover mapping of North America was carried out as a contribution to the Global Land Cover 2000 project being implemented by the Joint Research Center (JRC) of European Commission (EC). '

These data represent predicted common raven (Corvus corax) density (ravens/square-km) derived from random forest models given field site unit-specific estimates of raven density that were obtained from hierarchical distance sampling models at 43 field site units within the Great Basin region, USA. Fifteen landscape-level predictors summarizing climate, vegetation, topography and anthropogenic footprint were used to predict average raven density at each unit. A raven density of greater than or equal to 0.40 ravens/square-km corresponds to below-average survival rates of sage-grouse (Centrocercus urophasianus) nests. We mapped areas which exceed this threshold within sage-grouse concentration areas to determine where ravens may be impacting sage-grouse populations.

Sugar pine (Pinus lambertiana) is a five-needle pine of great economical and ecological importance in western North America. Genome-wide analyses in the species have been limited by the absence of a chromosome-scale reference genome. The genome of the species is one of the largest in any plant diploid species, with a size of 31 Gb, making assembly and annotation very challenging.

Here we present high-density linkage maps that were used to help scaffolding a new chromosome-scale genome of sugar pine (assembly version 2.0, unpublished).

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs (https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Land Cover Mapping and Estimation (GLanCE (https://sites.bu.edu/measures/)) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids (https://measures-glance.github.io/glance-grids/) that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class (https://sites.bu.edu/measures/project-overview/methods/), the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule. Known Issues Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

A rich legacy of geochemical data produced since the early 1960s covers the great expanse of Alaska; careful treatment of such data may provide significant and revealing geochemical maps that may be used for landscape geochemistry, mineral resource exploration, and geoenvironmental investigations over large areas. To maximize the spatial density and extent of data coverage for statewide mapping of element distributions, we compiled and integrated analyses of more than 175,000 sediment and soil samples from three major, separate sources: the U.S. Geological Survey, the National Uranium Resource Evaluation program, and the Alaska Division of Geological & Geophysical Surveys geochemical databases. Various types of heterogeneity and deficiencies in these data presented major challenges to our development of coherently integrated datasets for modeling and mapping of element distributions. Researchers from many different organizations and disparate scientific studies collected samples that were analyzed using highly variable methods throughout a time period of more than 50 years, during which many changes in analytical techniques were developed and applied. Despite these challenges, the U.S. Geological Survey has produced a new systematically integrated compilation of sediment and soil geochemical data with an average sample site density of approximately 1 locality per 10 square kilometers (km2) for the entire State of Alaska, although density varies considerably among different areas. From that compilation, we have modeled and mapped the distributions of 68 elements, thus creating an updated geochemical atlas for the State.

Maps of the moments of the trait distributions using presence–absence weighting in the FIA plots in 1° by 1° grid cells in eastern North America.

The results of a new EGS geothermal resource assessment of the eastern US, focused on the Northeastern US and based on use of Bottom Hole Temperatures (BHT), are summarized. A total of 5,800 heat flow points are now available for the area as opposed to the 323 used to produce the 2004 Geothermal Map of North America. The challenge is determining heat flow and subsurface temperature in areas where no data or limited conventional heat flow data exist in the previous assessments (most of the eastern 2/3 of the US). The techniques used to allow large scale use of BHT data for heat flow calculations are described. The process for the temperature-at-depth calculation is updated to better accommodate the use of BHT data. The geophysical data are also utilized as an ancillary predictor to the heat flow determination process in areas with limited or no thermal data. This study uses the same process to calculate heat storage when the thermal properties and temperature at depth are known described in the Future of Geothermal Energy report. Heat-in-place values have been updated for the northeastern US. Because of the higher data density the new temperature at depth maps show more localized temperature anomalies then the older maps and are a first step in the identification of site specific geothermal anomalies for further research and development. An important result is the identification and delineation of a significant thermal anomaly in eastern West Virginia.

AbstractIn species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, we developed a linkage map relying on 21,570 SNP makers in Sitka spruce (Picea sitchensis [Bong.] Carr.), a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for P. sitchensis and P. glauca based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species. MethodsThe data included in this dataset is genotypic data for two full-sib families of Sitka spruce (Picea sitchensis) in the United Kingdom and resulting linkage maps for the species. Samples for DNA extraction and genotyping were collected from two full-sib genetic field trials as described in the accompanying publication. A SNP Chip array was developed for this work using exome capture. A subset of the samples had been genotyped using RAD Seq from a previous project (Fuentes-Utrilla et al 2017). The dataset includes information on the SNP array developed for the project and genotype data that has been filtered for missingness and minor allele frequency. Final results are in the form of linkage maps stored in csv files. Further information on collection methods and processing are detailed in the accompanying manuscript and scripts for data processing are available on GitHub (https://github.com/HayleyTumas/SitkaLinkageMap). Usage notesAll files should be able to be opened using open access, freely available software. All tabular data are CSV or text files for the larger genotype data files. Code files are stored as bash script and can be opened using any text editor or in .R files that can be opened using the freely available R software.

Carnivores have life histories that can render them susceptible to roads, such as low population growth rates and great mobility. However, little is known about the effect of roads on population viability. In this study we determined which carnivore species are more affected by roads at the global level, and the spatial match between the number of species affected and road density. We used a reaction-diffusion model describing population dynamics to predict the impact of a road network on a population including the following parameters: dispersal distance, growth rate in favorable natural habitat patches, and growth rate in unfavorable habitats (roads). We applied this approach to 230 carnivore species at a global level. To rank the species most affected by roads we used maximum road density, and the minimum size of the patches between roads, above or below which populations cannot persist. We addressed the following tasks: 1) for each species we computed the maximum road density and the minimum patch size between roads that allow species to occur, using species-specific life histories and road mortality data; 2) we obtained the road density, and the number and size of the patches between roads that are observed within each species range, by intersecting each species IUCN range map with roads (density) map from openstreetmap; 3) we computed for each species the ratio of maximum to observed road density and the number and area of patches that are bigger than the minimum patch size; 4) we selected the species within the 5% percentile for these quantities as the most affected species. We found that family Ursidae has the highest percentage (43%) of species within the 5% most affected species, followed by family Felidae and family Canidae. We also found that 54% of the most affected species are not threatened by roads according to the IUCN, including 10 species that currently have an IUCN “Least Concern” status. The highest numbers of species affected by roads are found in Europe, North and Central America, South of Asia and China, and central-east Africa. However, while in Europe this high number of species is matched by high road density, this is not necessarily the case in the other regions, indicating that species can be affected even at low road densities. Our approach can be extended to any species for which the necessary life history data can be obtained, and can assist in developing conservation and mitigation measures. Furthermore, it can be applied at different spatial or temporal scales, such as projecting the impact of future road network development.

This web map was created to help tell the story about deep-sea corals and sponges found within West Coast National Marine Sanctuaries, including Greater Farallones, Cordell Bank, Monterey Bay and Channel Islands National Marine Sanctuaries. Learn more about deep-sea corals and sponges found within national marine sanctuaries in our storymap entitled, Corals & Sponges of West Coast National Marine Sanctuaries.This web map contains three data layers, including a heat map showing relative density of coral and sponge observations along the West Coast. Data presented is from the Deep-Sea Coral Research and Technology Program (DSCRTP) and its partners.The second hosted feature layer shows deep-sea coral and sponge observations along the West Coast. These data were extracted from NOAA's Deep-Sea Coral Data Portal from the Deep-Sea Coral Research and Technology Program (DSCRTP) and its partners. Data was extracted from the data portal on January 03, 2018.The third data layer highlights the locations of 4 marine protected areas managed by The Office of National Marine Sanctuaries, located along the West Coast of North America. Data are derived from the MPA Inventory, managed by NOAA's Marine Protected Areas Center.

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer. The DEM includes the 'best available' lidar data known to exist at the time of DEM creation that meets project specifications for those counties within the boundary of the Houston/Galveston TX Weather Forecast Office (WFO), as defined by the NOAA National Weather Service. The counties within this boundary are: Jackson, Matagorda, Brazoria (portion), Harris (portion), Galveston, and Chambers. For all the counties listed, except for Harris, the DEM is derived from LiDAR data sets collected for the Texas Water Development Board (TWDB) in 2006 with a point density of 1.4 m GSD. LiDAR data for Harris County was collected in October 2001 by the Harris County Flood Control District Tropical Storm Allison Recovery Project (TSARP) with a point density of 2.0 m GSD. Hydrographic breaklines used in the creation of the DEM were delineated using LiDAR intensity imagery generated from the data sets. The DEM is hydro flattened such that water elevations are less than or equal to 0 meters.The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 10 meters.