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.

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 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.

Census tracts as of 2018."Census Tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity that are updated by local participants prior to each decennial census as part of the Census Bureau's Participant Statistical Areas Program. The Census Bureau delineates census tracts in situations where no local participant existed or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.Census tract boundaries generally follow visible and identifiable features. They may follow nonvisible 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. Tribal census tracts are a unique geographic entity defined within federally recognized American Indian reservations and off-reservation trust lands and can cross state and county boundaries. Tribal census tracts may be completely different from the census tracts and block groups defined by state and county.Census Tract Codes and Numbers—Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively."- US Census Bureau For information about US Census Bureau geographies, click here. USE CONSTRAINTS: The Alaska Department of Commerce, Community, and Economic Development (DCCED) provides the data in this application as a service to the public. DCCED makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided on this site. DCCED shall not be liable to the user for damages of any kind arising out of the use of data or information provided. DCCED is not the authoritative source for American Community Survey data, and any data or information provided by DCCED is provided "as is". Data or information provided by DCCED shall be used and relied upon only at the user's sole risk.

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

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

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

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.

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 2 (F026) 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, migratory routes, and breeding locales. In addition to data on the survey track and observed environmental conditions (including ice conditions, if encountered), the file contains data for each species sighted. Parameters reported may include total number of individuals, number of pups, number of groups and number of mammals per group. F026 contains data for 1976 only. For further information on these data, contact Karl Schneider (see personnel section). NOTE: In this file type, the geographic _location of each group sighted is not recorded; in F127, locations of group sightings are recorded as well as the beginning and end positions of each station or segment of survey track.

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 Arctic LTER (ARC) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

In 2014, we initiated an investigation into the role of wolf (Canis lupus) and brown bear (Ursus arctos) predation in regulating the population dynamics of moose (Alces alces) on Togiak National Wildlife Refuge (Refuge), BLM Goodnews Block, and adjacent areas. We will relate the predation impact by wolves and bears on moose at varying levels of moose population density. We will use existing population estimates for brown bears, and through the use of radio telemetry, we will estimate the number and composition of wolf packs on the Refuge. We will model wolf and bear predation on moose based on the quantity of wolves and bears and diet composition of both species determined through analysis of carbon (13C) and nitrogen (15N) stable isotopes. To date, we have gathered demographic and isotopic data on 25 wolves from nine wolf packs, and have collected approximately 400 brown bear hair samples. Genetic and isotopic analyses have been successfully performed on 139 bear samples. This reports our progress to date, and identifies the remaining data gaps.

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 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.

Marine Bird Sighting, Ship/Aircraft Census (F033) is one of a group of seven datasets related to Marine Birds from Coastal Alaska and Puget Sound Data (1974 -1983). Each dataset uses the NODC Taxonomic Code to indicate species. Marine Bird Sighting, Ship/Aircraft Census (F033) contains data from field observations of marine birds made along ship or aircraft survey tracks. These data are collected to provide information on population density and distribution. Start and end position, date and elapsed time, speed and course, platform type and observing techniques are reported for each survey. Environmental information may include meteorological and sea surface conditions, distances to the shoreline and shelf break, ice characteristics within and outside each transect and surface debris, including oil slicks. Species data may include age, sex, color, plumage, number of individuals, direction of flight, behavior and food source association. Any number of species may be reported within one observation time span. These data were collected from 1975 - 1982 for coastal Alaska and adjacent North Pacific Ocean.

description: 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.; abstract: 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.

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

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".