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.

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 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.

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.

This map illustrates where infrastructure and population could be potentially impacted by a one meter sea level rise by the year 2100. Examples of infrastructure: airports, education establishments, medical facilities, and buildings. The pattern is shown along coastal areas by both tracts and counties. The sea level rise model comes from the Climate Mapping Resilience and Adaptation (CMRA) portal. As you zoom into the map, you can see the pattern by where human settlement exists. This helps illustrate the pattern by where people live.Airport data: Airports (National) - National Geospatial Data Asset (NGDA) AirportsData can be accessed hereOpenStreetMap Data:BuildingsMedical FacilitiesEducation EstablishmentsPopulation data: ACS Table(s): B01001Data downloaded from: Census Bureau's API for American Community Survey Data can be accessed hereHuman Settlement data:WorldPop Population Density 2000-2020 100mData can be accessed hereAbout the CMRA data:The Climate Mapping Resilience and Adaptation (CMRA) portal provides a variety of information for state, local, and tribal community resilience planning. A key tool in the portal is the CMRA Assessment Tool, which summaries complex, multidimensional raster climate projections for thresholded temperature, precipitation, and sea level rise variables at multiple times and emissions scenarios. This layer provides the geographical summaries. What's included?Census 2019 counties and tracts; 2021 American Indian/Alaska Native/Native Hawaiian areas25 Localized Constructed Analogs (LOCA) data variables (only 16 of 25 are present for Hawaii and territories)Time periods / climate scenarios: historical; RCP 4.5 early-, mid-, and late-century; RCP 8.5 early-, mid-, and late-centuryStatistics: minimum, mean, maximumSeal level rise (CONUS only)Original Layers in Living Atlas:U.S. Climate Thresholds (LOCA)U.S. Sea Level Rise Source Data:Census TIGER/Line dataAmerican Indian, Alaska Native, and Native Hawaiian areasLOCA data (CONUS)LOCA data (Hawaii and territories)Sea level rise

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.

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.

This multi-scale map uses dots to represent the population of each race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for tract, block group, and block.The map's colors represent each of the eight race/ethnicity categories have the highest total count. You can adjust the density of dots in your area by choosing "Change Style" for a layer. Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place. National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions. The specific raster datasets included in this publication include: Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel. Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]). Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints. Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel. Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²). Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel. Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²). Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire. Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.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.

A moose population survey was conducted on the Yukon Flats in November/December 2018. This was the first fall survey since 2015. Moose were counted in 97 of 421-5.3mi2 units, of which 63 were stratified high moose density and 34 low moose density. The estimate for the 2,269 mi2 survey area in the western Yukon Flats (Alaska Game Management Unit [GMU] 25D) was 1123 total observable moose (95% CI; 895-1351). Density of moose was 0.49/mi2 or 0.19/km2. The population was comprised of an estimated 908 adults (95% CI; 698-1118) and 199 calves (148-251). Search time averaged 6.0 minutes/mi2. The estimate of total observable moose increased from the lows of 2004-2010. Improved calf survival may have contributed to the population increase in some years. It is unlikely that public harvest of wolves and bears contributed, as harvest intensity is light. Thus, moose density increased in the presence of lightly harvested wolf and bear populations, suggesting that the dynamics of this low density population may sometimes be more complex than previously thought. Moose numbers can fluctuate naturally within a low density equilibrium over a period of approximately a decade, and this fluctuation can be detected with the current survey method.

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

These data were published in Table S1 in Rasher et al., 2020 (see Related Publications section below).

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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.

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.

The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. 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 generalized boundaries for counties and equivalent entities are as of January 1, 2010.

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.

In 2023, with over 80 professionally active dentists per 100,000 population, Massachusetts was the state with the highest dentist to population ratio. This was followed by Alaska. While California had the highest number of dentist, it came fourth in terms of dentist density by state.