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.

This graph shows the population density of the United States of America from 1790 to 2019. In 2019, the population density was approximately 92.9 residents per square mile of land area. Population density in the United States Population density has been tracked for over two hundred years in the United States. Over the last two centuries, the number of people living in the United States per square mile has grown from 4.5 in 1790 to 87.4 in 2010. After examining the data in detail, it becomes clear that a major population increase started around 1870. Population density was roughly 11 at the time and has doubled in the last century. Since then, population density grew by about 16 percent each decade. Population density doubled in 1900, and grew in total by around 800 percent until 2010.

The population density of the United States varies from state to state. The most densely populated state is New Jersey, with 1,208 people per square mile living there. Rhode Island is the second most densely populated state, with slightly over 1,000 inhabitants per square mile. A number of New England states follow at the top of the ranking, making the northeastern region of the United States the most densely populated region of the country.

The least populated U.S. state is the vast territory of Alaska. Only 1.3 inhabitants per square mile reside in the largest state of the U.S.

Compared to other countries around the world, the United States does not rank within the top 50, in terms of population density. Most of the leading countries and territories are city states. However, the U.S. is one of the most populous countries in the world, with a total population of over 327 million inhabitants, as of 2018.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This data package contains 1) field data and 2) predicted distributions of three species of boreal-nesting birds in interior Alaska: Lesser Yellowlegs (Tringa flavipes), Olive-sided Flycatcher (Contopus cooperi), and Rusty Blackbird (Euphagus carolinus). The data are compiled from several monitoring programs: Alaska Landbird Monitoring Survey, Alaska Off-road Point-count Program, Susitna-Watana Hydroelectric Project, Tetlin Forest Inventory Analysis, and surveys on Department of Defense lands by the U.S. Fish and Wildlife Service. Each program conducted avian point-count surveys in some or all years (2001-2020) at locations in Alaska. This dataset includes only the locations within Bird Conservation Region 4 (BCR4; Bird Studies Canada and NABCI 2014) and south-central Alaska. The dataset includes a number of remotely-sensed covariates that were compiled from other sources for the survey locations (Alaska Department of Transportation 2018, Alaska Wildland Fire Coordinating Group 2020, Alaska Center for Conservation Science 2017, Porter et al. 2018, PRISM Climate Group 2018a,b). The data package also includes shapefiles showing the predicted population density of each species across BCR4 in Alaska, where predictions were developed by relating observations to covariates to estimate density and then predicting density based on values of covariates across the landscape; and shapefiles delineating hotspots for each species, where a hotspot is defined as a grid cell whose mean predicted density exceeds the means of 90% of other grid cells in BCR4.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Bonanza Creek (BNZ) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

We conducted mark-recapture surveys of small mammals in polygonal tundra at three sites near Utqiagvik Alaska (AK) (71.2906 North (N), 156.7885 West (W), 3 meters (m) above sea level). In 2018, we established a mark-recapture grid at each of three sites (greater than; 500 meter (m) apart) in low polygonal tundra within the Barrow Environmental Observatory (Cake Eater (CE), NOAA (NO), and Shed (SH)). Each grid consisted of 120 trap stations set in a 15 x 8 arrangement with 10 m spacing, and encompassed an area of 1.2 hectares (ha). Mark-recapture surveys were conducted twice during the summer season in late June or early July (shortly after snowmelt) and in late August in 2018 - 2023. In each session, grids were surveyed for four consecutive days with traps checked every eight hours. Due to COVID-19 access and quarantine policies sites were only trapped once (in early August for 3 days) in 2020. In addition, in 2019 one site (NO) was monitored for only 3 consecutive days in both June and August. A single Sherman live trap was set to sign within three meters of each station, baited with a mixture of peanut butter and bird seed, and insulated with nestlets. Waterproof tar paper was pinned over each trap. Captured individuals were identified to species, weighed, sexed, aged, and marked with a PIT tag (9 millimeter (mm), Passive Integrated Transponder, Biomark, Inc, Boise, Idaho) to track recaptures. Upon the first capture of an individual in a survey period, a small hair sample was taken from the rump and a small sample of ear tissue was taken. Fecal samples were collected from the trap. Traps which had captured individuals were removed and replaced with clean traps. This dataset is part of a collaborative project examining herbivore effects on vegetation and nutrient cycling. At each site, one mark-recapture grid was paired with a fenced exclosure (8 m x 8 m) and unfenced control plot (8 m x 8 m) as well as two experimental herbivory treatments. The experimental Press treatment (PR) treatment was an enclosure (20m x 20m) that was stocked with up to 4 voles from spring snowmelt through September in each year from 2018-2023 and the experimental Pulse treatment (PU) was a 20 m x 20 m enclosure that was stocked with up to 4 voles from spring snowmelt through September in 2019 only, after which voles were removed and the fences served to exclude voles from 2020-2023. In addition, over-winter herbivore activity (e.g., surface nests, latrines, runways) was quantified each year in control plots, enclosures, and on each mark-recapture monitoring grid following the ITEX herbivory protocol.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Bonanza Creek (BNZ) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

We conducted mark-recapture surveys of small mammals in tussock tundra at three sites near Toolik Lake Alaska (AK) (68.6 North (N), 149.5 West (W), 760 meters (m) above sea level). In 2017, we established a mark-recapture grid at each of three sites (Imnavait (IM), Pipeline (PL), and South Toolik (ST)). Each grid consisted of 120 trap stations set in a 15 x 8 arrangement with 10 m spacing, and encompassed an area of 1.2 hectares (ha). Mark-recapture surveys were conducted twice during the summer season in early June and late August in 2017 - 2019 and then truncated to once a year in 2020 - 2023 due to COVID-19 access and quarantine policies. Also as a result of COVID-19 access policies, the Pipeline grid was not surveyed in 2020. A single Sherman live trap was set within one meter of each station, baited with a mixture of peanut butter and bird seed, and insulated with polyester batting. Grids were surveyed for four consecutive days with traps checked every six to eight hours. On occasion heavy rain truncated sampling or delayed the timing of sampling. Captured individuals were identified to species, weighed, sexed, aged (based on weight and reproductive status; Batzli and Henttonen 1990), and marked with a PIT tag (9 millimeter (mm), Passive Integrated Transponder, Biomark, Inc, Boise, Idaho) to track recaptures. Upon the first capture of an individual in a survey period, a small hair sample was taken from the rump and a small sample of ear tissue was taken. Fecal samples were collected from the trap. Traps which had captured individuals were removed and replaced with clean traps. This dataset is part of a collaborative project examining herbivore effects on vegetation and nutrient cycling. At each site, one mark-recapture grid was paired with a fenced exclosure (8 m x 8 m) and unfenced control plot (8 m x 8 m) as well as two experimental herbivory treatments. The experimental Press treatment (PR) treatment was an enclosure (20m x 20m) that was stocked with up to 4 voles from spring snowmelt through September in each year from 2018-2023 and the experimental Pulse treatment (PU) was a 20 m x 20 m enclosure that was stocked with up to 4 voles from spring snowmelt through September in 2019 only, after which voles were removed and the fences served to exclude voles from 2020-2023. In addition, over-winter herbivore activity (e.g., surface nests, latrines, runways) was quantified each year in control plots, enclosures, and on each mark-recapture monitoring grid following the ITEX herbivory protocol.

NOTE: Data based on a sample. For information on confidentiality.protection, sampling error, nonsampling error, definitions, and.count corrections see http://www.census.gov/prod/cen2000/doc/aiansf.pdf

NODC maintains data in three NODC Standard Format Marine Mammal Data Sets: Marine Mammal Sighting and Census (F127); Marine Mammal Specimens (F025); Marine Mammal Sighting 2 (F026). These data type formats are designed to support studies of biological populations and ecosystems that are subject to impact from oil and gas development, marine pollution and other environmental disturbances. Information on marine animal populations, activities, migratory routes and breeding locales are obtained from either surface ship or aircraft surveys. The Marine Mammal Sighting and Census (F127) data type contains data from field observations of marine mammals. Obtained from ship or aircraft surveys, the data are collected to provide information on population density and distribution, mammal behavior and activity, migratory routes and breeding locales. In addition to data on the survey track and observed environmental conditions (including ice conditions, if encountered), F127 data may contain data from each species sighted. Parameters reported may include group size; total number of individuals, adults (males and females), subadults and pups; and mammal activity. F127 contains data for 1979 - 1991.

One of the most difficult challenges for wildlife managers is reliably estimating wildlife populations. Camera traps combined with spatial capture-recapture (SCR) models are a popular tool for population estimation. They have limitations, however, including long data processing times. Drones with thermal imagery are an emerging tool for estimating wildlife populations, but how they compare to other methods remains poorly studied. We compared the use of camera traps and SCR models to drone surveys for estimating population densities of Sitka black-tailed deer (Odocoileus hemionus sitkensis) on Afognak Island, Alaska. We deployed 26 camera traps from 1 September until 6 October 2022 and individually identified males using antler characteristics, for the SCR model. At the same site, we conducted three drone surveys between October and December 2022, identified sex composition, and obtained deer counts. The estimated density from the SCR model was 3.7 males ± 0.8 (SE) /km2, and 14.1 ± 3.1 adults/km2 of clear-cut forest. Results from the drone survey produced similar estimates with 2.1 ± 0.9 males/km2 and 13.4 ± 1.6 adults / km2. The similarity in estimates suggests that both methods converged on an accurate representation of the population in this habitat, but these methods diverge in levels of sampling effort, duration, and financial cost. Camera traps offer further insights on behavior and home-range size but require longer data processing times, can be subject to malfunctions, and are difficult to deploy and maintain in remote areas. Drones are subject to legal restrictions, have difficulty in closed canopy habitats, and can be initially costly, but they provide results faster and require less data analysis. Camera traps and drones are useful for determining population dynamics but are subject to their limitations. Wildlife managers should make survey decisions based on their specific goals, habitat type, focal species ecology, and financial limitations. Methods We placed 26 camera traps (Browning Strike Force HD- 20MP) at un-baited sites, spaced on average 400 m apart along inactive logging roads. Roads here are heavily used by deer as the adjacent clear-cut areas are comprised heavily of slash piles, making movement more difficult (Finnegan et al. unpublished data). Roads and trails have been used for camera locations in previous studies focused on deer densities in Central America (Soria-Díaz et al. 2015). Cameras were fixed to poles or naturally occurring stumps at an average height of approximately 100 cm off the ground. We removed any vegetation in front of the camera view shed to reduce false triggers. Cameras were active from 1 September until 6 October 2022 and were set to operate 24 hours a day with a three-photo capture series upon trigger, and a three-second delay between trigger events. SCR models Within the SCR modelling framework, focal animal detections should be independent of each other, therefore we used a six-minute cutoff between deer capture events at a given camera based on prior information on deer activity at camera sites (Macaulay et al. 2020). For each encounter, we identified the sex of individuals when possible (bucks, does, does with fawns) and recorded group size, date, time, and camera location. One observer individually identified bucks manually based on antler characteristics (e.g., shape, length, number of points, brow point morphology) and other natural markings such as body scars (Hinojo et al. 2022; Fig.2). These identifications were then confirmed by a second independent observer to reduce potential observer bias. We attempted to identify does where possible with natural markings (Macaulay et al. 2020). For both the camera trapping and drone surveys we determined the sex ratio (buck:doe) as the number of independent encounters of bucks divided by the sum of independent encounters of does and bucks (Hinojo et al. 2022). We analysed camera trap data using spatially explicit photographic capture-recapture models (SCR) following the methodology described to estimate roe deer densities in Hinojo et al. (2022). SCR is a set of methods for modelling animal capture-recapture data collected with an array of detectors (Efford 2020), in our case camera traps. Animal density is directly estimated using information on capture histories in combination with spatial locations of captures (Efford 2020). We used the “secr” package (Efford 2020) in Program R (R Core Team 2019) and the input data of individual male Sitka black-tailed deer encounters (i.e., site number, date, time of the encounter, male individual identification), the location of the camera sites, and camera deployment details including dates when cameras were active. We defined a sampling occasion as a time frame of 24 hours starting at noon, which results in 32 sampling occasions.

Marine Mammal Sighting and Census data were collected from Coastal Alaska. Data were collected by Alaska Department of Fish and Game from 05 May 1985 to 13 June 1985. Data were processed by NODC to the NODC standard F127 Marine Mammal Sighting and Census format. This file format is used for data from field observations of marine animals. Data may be reported either for individual, random sightings or for sightings made as part of systematic ship or aircraft surveys along specified tracks. These data provide information on animal population densities and distributions, activities, migratory routes and breeding locales. Cruise or survey information, start and end positions, start and end times, and platform speed, direction, and altitude are reported for each observation or series of observations. Position, date and time are reported for each sighting location, along with a code indicating presence or absence of animals and, if present, their distance to the observer, shoreline, and ice edge and heading direction. For each sighting location, animal sighting data are reported by species for all observed species. Species identification, total number of individuals, and counts by age group (adults, subadults, juveniles, unknown) may be reported in summary for all animals sighted or by subgroups distinguished by sex, behavior, markings, or other characteristics. A text record is available for comments.

.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

The boundaries for counties and equivalent entities are as of January 1, 2010.

A study to estimate the kill rate of wolves on moose was initiated in eastern interior Alaska. This study is the first to examine kill rates in a system with such a low prey density (0.08 moose/km2) and the presence of only a single prey species. Wolves were radio tracked daily during early February and March to locate kills. The estimated kill rate was 0.019 moose/wolf/day (95% CI: 0.011-0.028). This estimate was intermediary relative to previous work. The management implications of this result are discussed.

This CSV file contains landscape factors representing anthropogenic disturbances to stream habitats summarized within 6th level Hydrologic Unit Code (HUC12) watersheds of the Watershed Boundary Dataset. The source datasets compiled and attributed to spatial units were identified as being: (1) meaningful for assessing fluvial fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) broadly representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among HUC12 units. Variables summarized at the HUC12 scale include measures of anthropogenic land uses, population density, roads, dams, mines, culverts, 303d listed waterbodies, railroads, pipelines, airports, and point-source pollution sites. In this data set, variable summaries are linked to HUC12 watersheds developed for the Watershed Boundary Dataset downloaded on March 18, 2015. The final spatial dataset ca ...

The data were collected to estimate Pacific razor clam density in the region of Hallo Bay, Alaska in Katmai National Park and Preserve. The region was originally sampled in the 1970's (Kaiser, R.J., Konigsberg, D., 1977. Razor clam (Siliqua patula Dixon) distribution and population assessment study. Alaska Department of Fish and Game. 78 pages). Tom Smith with USGS resampled Hallo Bay plots in 1998 and 1999 and the NPS, lead by Heather Coletti, revisited a subset of Smith's plots in 2019. Plots were sampled using the same methodology across all three time-periods and estimates were used to assess declines in densities over time.

Human Footprint data was extracted for thirteen regions of Alaska for years 1993 and 2009 for the State of Alaska Salmon and People (SASAP) project. Methods and code for the regional extraction can be found in "HumanFootprint.ipynb" in this package. Human Footprint data is based on an original dataset "Global terrestrial Human Footprint maps for 1993 and 2009" acquired here https://doi.org/10.1038/sdata.2016.67, by Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, Possingham HP, Laurance WF, Wood P, Fekete BM, Levy MA, Watson JE (2016). The Human Footprint index is based on remotely-sensed and bottom-up survey information on eight variables: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Original data is distributed by the Dryad Digital Repository (https://doi.org/10.5061/dryad.052q5.2). To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.

Snowshoe hares (Lepus americanus) are a keystone herbivore in the boreal forests of Canada and Alaska, and are cyclical over an approximately 8 to 11 year period. Monitoring hare populations can provide predictive information for other species. We monitored snowshoe hare abundance and trend during two time periods (1990-1994 and 2000-2004) at permanent pellet transects within the Tetlin National Wildlife Refuge (Tetlin Refuge) in eastern Alaska. Data gathered during 1990-1994 coincided with other information suggesting the population density peaked in 1990-1991; results from the 2000-2004 time period did not demonstrate a similar pattern. Recommendations are made for future survey efforts.

description: This report is a progress report on the survey of waterfowl populations and habitat on Nelson Island in Alaska. Aerial surveys and ground surveys are covered. Results cover breeding populations, habitat analysis, nesting studies, density and location and distribution & production.; abstract: This report is a progress report on the survey of waterfowl populations and habitat on Nelson Island in Alaska. Aerial surveys and ground surveys are covered. Results cover breeding populations, habitat analysis, nesting studies, density and location and distribution & production.

This CSV file contains landscape factors representing anthropogenic disturbances to stream habitats summarized within local and network stream catchments of Southeast Alaska. The source datasets compiled and attributed to spatial units were identified as being: (1) meaningful for assessing fluvial fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) broadly representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. Variables summarized at the catchment scale include measures of anthropogenic land uses, population density, roads, dams, mines, culverts, 303d listed waterbodies, railroads, pipelines, airports, and point-source pollution sites. In this data set, variable summaries are linked to catchments developed for the National Hydrography Dataset using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). Spatial data can be downloaded at https://doi.org/10.5066/F7TT4P4X. This version of the data (v1.1) addresses an issue detected in the pipeline and railroad variables found in Version 1. Updated values are documented in the file "change_log.csv".