31 datasets found
  1. Population density in the U.S. 2023, by state

    • statista.com
    Updated Dec 3, 2024
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    Statista (2024). Population density in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/183588/population-density-in-the-federal-states-of-the-us/
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    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  2. Population density of the United States 2019

    • statista.com
    Updated Dec 7, 2024
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    Statista (2024). Population density of the United States 2019 [Dataset]. https://www.statista.com/statistics/183475/united-states-population-density/
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    Dataset updated
    Dec 7, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    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.

  3. s

    U.S.: Population Density, by state

    • statistico.com
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    Statistico, U.S.: Population Density, by state [Dataset]. https://www.statistico.com/s/us-population-density-by-state
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    Dataset provided by
    Statistico
    Area covered
    United States
    Description

    The distribution of population density across U.S. states varies greatly, with urban states like New Jersey and New York experiencing high levels of density, while rural states like Alaska and Wyoming have much lower levels of density.

  4. TIGER/Line Shapefile, 2022, State, Alaska, AK, Census Tract

    • catalog.data.gov
    Updated Jan 28, 2024
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch (Point of Contact) (2024). TIGER/Line Shapefile, 2022, State, Alaska, AK, Census Tract [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-2022-state-alaska-ak-census-tract
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    Dataset updated
    Jan 28, 2024
    Dataset provided by
    United States Department of Commercehttp://commerce.gov/
    United States Census Bureauhttp://census.gov/
    Area covered
    Alaska
    Description

    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.

  5. a

    CENSUS 2020 PL94171 MONTANA PLACE

    • hub.arcgis.com
    • ceic-mtdoc.opendata.arcgis.com
    Updated Aug 23, 2021
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    Montana Department of Commerce (2021). CENSUS 2020 PL94171 MONTANA PLACE [Dataset]. https://hub.arcgis.com/datasets/mtdoc::census-2020-pl94171-montana-place/about
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    Dataset updated
    Aug 23, 2021
    Dataset authored and provided by
    Montana Department of Commerce
    Area covered
    Description

    Population and Housing data for Census Places (cities, towns, CDPs) within the State of Montana was compiled from the PL 94-171 Redistricting Summary files released by the U.S. Census Bureau for the 2020 Decennial Census. This data set was created by the Montana Department of Commerce for use by the citizens of Montana and the general public. TIGER shapefiles were joined to the tabular summary file data to create this data set. A subset of variables from the release were selected for this dataset. A description of each variable and calculations are provided here.

    VINTAGE - Decennial Census vintage year - Calculation

    SUMLEV - Geography summary level - Calculation

    GEOID - Geography ID - Calculation

    NAME - Geography Name - Calculation

    AREALAND - Area of land in square meters - Calculation

    AREAWATR - Area of water in square meters - Calculation

    INTPTLAT - Geography point latitude - Calculation

    INTPTLON - Geography point longitude - Calculation

    POPTOT - Population Total - Calculation P0010001

    POPPCAP - Population per square mile - Calculation P0010001 / (AREALAND / 2589988.110336)

    POPWH - Population White alone - Calculation P0010003

    POPBL - Population Black alone - Calculation P0010004

    POPAI - Population American Indian or Alaska Native alone - Calculation P0010005

    POPAS - Population Asian alone - Calculation P0010006

    POPNH - Population Native Hawaiian or Pacific Islander alone - Calculation P0010007

    POPOT - Population Some other Race alone - Calculation P0010008

    POP2MO - Population 2 or more races - Calculation P0010009

    POPWHPCT - Population White alone percent - Calculation P0010003 / P0010001 * 100

    POPBLPCT - Population Black alone percent - Calculation P0010004 / P0010001 * 100

    POPAIPCT - Population American Indian or Alaska Native alone percent - Calculation P0010005 / P0010001 * 100

    POPASPCT - Population Asian alone percent - Calculation P0010006 / P0010001 * 100

    POPNHPCT - Population Native Hawaiian or Pacific Islander alone percent - Calculation P0010007 / P0010001 * 100

    POPOTPCT - Population Some other Race alone percent - Calculation P0010008 / P0010001 * 100

    POP2MOPCT - Population 2 or more races percent - Calculation P0010009 / P0010001 * 100

    POPWHC - Population White alone or in combination - Calculation P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071

    POPBLC - Population Black alone or in combination - Calculation P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071

    POPAIC - Population American Indian or Alaska Native alone or in combination - Calculation P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071

    POPASC - Population Asian alone or in combination - Calculation P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071

    POPNHC - Population Native Hawaiian or Pacific Islander alone or in combination - Calculation P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPOTC - Population Some Other Race alone or in combination - Calculation P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPWHCPCT - Population White alone or in combination percent - Calculation (P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071)/ P0010001 * 100

    POPBLCPCT - Population Black alone or in combination percent - Calculation (P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071)/ P0010001 * 100

    POPAICPCT - Population American Indian or Alaska Native alone or in combination percent - Calculation (P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPASCPCT - Population Asian alone or in combination percent - Calculation (P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPNHCPCT - Population Native Hawaiian or Pacific Islander alone or in combination percent - Calculation (P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPOTCPCT - Population Some Other Race alone or in combination percent - Calculation (P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPHSP - Population Hispanic - Calculation P0020002

    POPNHSP - Population Non-Hispanic - Calculation P0020003

    POPHSPPCT - Population Hispanic percent - Calculation P0020002 / P0010001 * 100

    POPNHSPPCT - Population Non-Hispanic percent - Calculation P0020003 / P0010001 * 100

    POP18OV - Population 18 years and over - Calculation P0030001

    POP18OVPCT - Population 18 years and over percent - Calculation P0030001 / P0010001 * 100

    HUTOT - Housing Units Total - Calculation H0010001

    HUOCC - Housing Units Occupied - Calculation H0010002

    HUVAC - Housing Units Vacant - Calculation H0010003

    HUOCCPCT - Housing Units Occupied percent - Calculation H0010002 / H0010001 * 100

    HUVACPCT - Housing Units Vacant percent - Calculation H0010003 / H0010001 * 100

    POPGQ - Population Group Quarters - Calculation P0050001

    POPGQIN - Population Group Quarters - Institutionalized - Calculation P0050002

    POPGQNI - Population Group Quarters - Non-Institutionalized - Calculation P0050007

    POPGQPCT - Population Group Quarters percent - Calculation P0050001 / P0010001 * 100

    POPGQINPCT - Population Group Quarters - Institutionalized percent - Calculation P0050002 / P0010001 * 100

    POPGQNIPCT - Population Group Quarters - Non-Institutionalized percent - Calculation P0050007 / P0010001 * 100

    POPTOT2010 - Population Total 2010 - Calculation

    POPCHG - Population Change from 2010 to 2020 - Calculation

    POPCHGPCT - Population Percent Change from 2010 to 2020 - Calculation

  6. ARC Population Forecasts series13

    • opendata.atlantaregional.com
    Updated Jun 10, 2015
    + more versions
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    Georgia Association of Regional Commissions (2015). ARC Population Forecasts series13 [Dataset]. https://opendata.atlantaregional.com/datasets/55778658bd3846eb8a4e93c3e4c327cd
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    Dataset updated
    Jun 10, 2015
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission from ARC's Regional Plan Update to show population forecasts by census tract for the Atlanta region.Attributes:GEOID10 = The entire Federal Information Processing Series (FIPS) code for this geography. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.NAME10 = Census tract numberPLNG_REGIO = Planning regionPercent_BA_or_Higher = Percentage of the population that has a bachelor's degree or higherMedian_household_income = Median household incomeSquare_Miles = Total area in square milesHouseholds, 2015 = Total number of households forecasted for 2015Population, 2015 = Total population forecated for 2015Households, 2020 = Total number of households forecasted for 2020Population, 2020 = Total population forecasted for 2020Households, 2025 = Total number of households forecasted for 2025Population, 2025 = Total population forecasted for 2025Households 2030 = Total number of households forecasted for 2030Population, 2030 = Total population forecasted for 2030Households, 2035 = Total number of households forecasted for 2035Population, 2035 = Total population forecasted for 2035Households, 2040 = Total number of households forecasted for 2040Population, 2040 = Total population forecasted for 2040Population Change, 2015-2040 = Forecasted population change between 2015 and 2040Household Change, 2015-2040 = Forecasted change in the number of households between 2015 and 2040Population Change per Square Mile, 2015-2040 = Forecasted population change per square mile between 2015 and 2040Population Change per Square Mile 2000-2010 = Actual change in population per square mile between 2000 and 2010Shape.STArea() = Total area in square feetSource: Atlanta Regional CommissionDate: 2015For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

  7. e

    Data from: Bonanza Creek site, station Fairbanks North Star Borough, Alaska...

    • portal.edirepository.org
    csv
    Updated 2013
    + more versions
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    Christopher Boone; Michael R. Haines; Nichole Rosamilia; Ted Gragson (2013). Bonanza Creek site, station Fairbanks North Star Borough, Alaska (FIPS 2090), study of human population density in units of numberPerKilometerSquared on a yearly timescale [Dataset]. http://doi.org/10.6073/pasta/c477bef77d347ea0d0ce628b16fe5aaf
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    csvAvailable download formats
    Dataset updated
    2013
    Dataset provided by
    EDI
    Authors
    Christopher Boone; Michael R. Haines; Nichole Rosamilia; Ted Gragson
    Time period covered
    1970 - 2000
    Area covered
    Variables measured
    YEAR, S_DEV, S_ERR, ID_OBS, N_TRACE, N_INVALID, N_MISSING, N_EXPECTED, N_OBSERVED, N_ESTIMATED, and 3 more
    Description

    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.

  8. a

    CENSUS 2020 PL94171 MONTANA TRACT

    • hub.arcgis.com
    • ceic-mtdoc.opendata.arcgis.com
    Updated Aug 23, 2021
    + more versions
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    Montana Department of Commerce (2021). CENSUS 2020 PL94171 MONTANA TRACT [Dataset]. https://hub.arcgis.com/maps/mtdoc::census-2020-pl94171-montana-tract
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    Dataset updated
    Aug 23, 2021
    Dataset authored and provided by
    Montana Department of Commerce
    Area covered
    Description

    Population and Housing data for Census Tracts within the State of Montana was compiled from the PL 94-171 Redistricting Summary files released by the U.S. Census Bureau for the 2020 Decennial Census. This data set was created by the Montana Department of Commerce for use by the citizens of Montana and the general public. TIGER shapefiles were joined to the tabular summary file data to create this data set. A subset of variables from the release were selected for this dataset. A description of each variable and calculations are provided here.

    VINTAGE - Decennial Census vintage year - Calculation

    SUMLEV - Geography summary level - Calculation

    GEOID - Geography ID - Calculation

    NAME - Geography Name - Calculation

    AREALAND - Area of land in square meters - Calculation

    AREAWATR - Area of water in square meters - Calculation

    INTPTLAT - Geography point latitude - Calculation

    INTPTLON - Geography point longitude - Calculation

    POPTOT - Population Total - Calculation P0010001

    POPPCAP - Population per square mile - Calculation P0010001 / (AREALAND / 2589988.110336)

    POPWH - Population White alone - Calculation P0010003

    POPBL - Population Black alone - Calculation P0010004

    POPAI - Population American Indian or Alaska Native alone - Calculation P0010005

    POPAS - Population Asian alone - Calculation P0010006

    POPNH - Population Native Hawaiian or Pacific Islander alone - Calculation P0010007

    POPOT - Population Some other Race alone - Calculation P0010008

    POP2MO - Population 2 or more races - Calculation P0010009

    POPWHPCT - Population White alone percent - Calculation P0010003 / P0010001 * 100

    POPBLPCT - Population Black alone percent - Calculation P0010004 / P0010001 * 100

    POPAIPCT - Population American Indian or Alaska Native alone percent - Calculation P0010005 / P0010001 * 100

    POPASPCT - Population Asian alone percent - Calculation P0010006 / P0010001 * 100

    POPNHPCT - Population Native Hawaiian or Pacific Islander alone percent - Calculation P0010007 / P0010001 * 100

    POPOTPCT - Population Some other Race alone percent - Calculation P0010008 / P0010001 * 100

    POP2MOPCT - Population 2 or more races percent - Calculation P0010009 / P0010001 * 100

    POPWHC - Population White alone or in combination - Calculation P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071

    POPBLC - Population Black alone or in combination - Calculation P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071

    POPAIC - Population American Indian or Alaska Native alone or in combination - Calculation P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071

    POPASC - Population Asian alone or in combination - Calculation P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071

    POPNHC - Population Native Hawaiian or Pacific Islander alone or in combination - Calculation P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPOTC - Population Some Other Race alone or in combination - Calculation P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPWHCPCT - Population White alone or in combination percent - Calculation (P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071)/ P0010001 * 100

    POPBLCPCT - Population Black alone or in combination percent - Calculation (P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071)/ P0010001 * 100

    POPAICPCT - Population American Indian or Alaska Native alone or in combination percent - Calculation (P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPASCPCT - Population Asian alone or in combination percent - Calculation (P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPNHCPCT - Population Native Hawaiian or Pacific Islander alone or in combination percent - Calculation (P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPOTCPCT - Population Some Other Race alone or in combination percent - Calculation (P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPHSP - Population Hispanic - Calculation P0020002

    POPNHSP - Population Non-Hispanic - Calculation P0020003

    POPHSPPCT - Population Hispanic percent - Calculation P0020002 / P0010001 * 100

    POPNHSPPCT - Population Non-Hispanic percent - Calculation P0020003 / P0010001 * 100

    POP18OV - Population 18 years and over - Calculation P0030001

    POP18OVPCT - Population 18 years and over percent - Calculation P0030001 / P0010001 * 100

    HUTOT - Housing Units Total - Calculation H0010001

    HUOCC - Housing Units Occupied - Calculation H0010002

    HUVAC - Housing Units Vacant - Calculation H0010003

    HUOCCPCT - Housing Units Occupied percent - Calculation H0010002 / H0010001 * 100

    HUVACPCT - Housing Units Vacant percent - Calculation H0010003 / H0010001 * 100

    POPGQ - Population Group Quarters - Calculation P0050001

    POPGQIN - Population Group Quarters - Institutionalized - Calculation P0050002

    POPGQNI - Population Group Quarters - Non-Institutionalized - Calculation P0050007

    POPGQPCT - Population Group Quarters percent - Calculation P0050001 / P0010001 * 100

    POPGQINPCT - Population Group Quarters - Institutionalized percent - Calculation P0050002 / P0010001 * 100

    POPGQNIPCT - Population Group Quarters - Non-Institutionalized percent - Calculation P0050007 / P0010001 * 100

    POPTOT2010 - Population Total 2010 - Calculation

    POPCHG - Population Change from 2010 to 2020 - Calculation

    POPCHGPCT - Population Percent Change from 2010 to 2020 - Calculation

  9. 2000 Decennial Census: PCT041 | LANGUAGE DENSITY BY LINGUISTIC ISOLATION BY...

    • data.census.gov
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    DEC, 2000 Decennial Census: PCT041 | LANGUAGE DENSITY BY LINGUISTIC ISOLATION BY AGE FOR THE POPULATION 5 YEARS AND OVER IN HOUSEHOLDS [28] (DEC American Indian and Alaska Native Summary File) [Dataset]. https://data.census.gov/table/DECENNIALAIAN2000.PCT041?q=All%20Counties%20within%20United%20States%20and%20Puerto%20Rico%20Creek&t=Language%20Spoken%20at%20Home&g=040XX00US48
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    DEC
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2000
    Area covered
    United States
    Description

    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

  10. Razor clam density estimates 1998 and 2019 in Hallo Bay, Alaska

    • s.cnmilf.com
    • catalog.data.gov
    Updated Oct 16, 2025
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    National Park Service (2025). Razor clam density estimates 1998 and 2019 in Hallo Bay, Alaska [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/razor-clam-density-estimates-1998-and-2019-in-hallo-bay-alaska
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    Dataset updated
    Oct 16, 2025
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Hallo Bay, Alaska
    Description

    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.

  11. Bonanza Creek site, station Fairbanks North Star Borough, Alaska (FIPS...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 10, 2015
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    Christopher Boone; Michael R. Haines; U.S. Bureau of the Census; Ted Gragson; Inter-University Consortium for Political and Social Research; Nichole Rosamilia; EcoTrends Project (2015). Bonanza Creek site, station Fairbanks North Star Borough, Alaska (FIPS 2090), study of percent urban population in units of percent on a yearly timescale [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fecotrends%2F808%2F2
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    Dataset updated
    Mar 10, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Christopher Boone; Michael R. Haines; U.S. Bureau of the Census; Ted Gragson; Inter-University Consortium for Political and Social Research; Nichole Rosamilia; EcoTrends Project
    Time period covered
    Jan 1, 1970 - Jan 1, 2000
    Area covered
    Variables measured
    YEAR, S_DEV, S_ERR, ID_OBS, N_TRACE, N_INVALID, N_MISSING, N_EXPECTED, N_OBSERVED, N_ESTIMATED, and 3 more
    Description

    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.

  12. Wildfire Risk to Communities Housing Unit Density (Image Service)

    • agdatacommons.nal.usda.gov
    • resilience.climate.gov
    • +5more
    bin
    Updated Sep 22, 2025
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    U.S. Forest Service (2025). Wildfire Risk to Communities Housing Unit Density (Image Service) [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Wildfire_Risk_to_Communities_Housing_Unit_Density_Image_Service_/25973110
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    binAvailable download formats
    Dataset updated
    Sep 22, 2025
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    U.S. Forest Service
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  13. d

    Data from: Avian Point-Count Data from Boreal Alaska and Maps of Predicted...

    • catalog.data.gov
    • data.usgs.gov
    Updated Sep 13, 2025
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    U.S. Geological Survey (2025). Avian Point-Count Data from Boreal Alaska and Maps of Predicted Population Density for Lesser Yellowlegs, Olive-sided Flycatcher, and Rusty Blackbird, 2001-2020 [Dataset]. https://catalog.data.gov/dataset/avian-point-count-data-from-boreal-alaska-and-maps-of-predicted-population-density-fo-2001
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    Dataset updated
    Sep 13, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    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.

  14. a

    Small mammal mark-recapture data 2017-2023 (3 sites near Toolik Lake,...

    • arcticdata.io
    • search.dataone.org
    Updated Aug 12, 2024
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    Rebecca Rowe; Natalie Boelman; Kevin Griffin; Laura Gough; Jennie McLaren; Edward Rastetter (2024). Small mammal mark-recapture data 2017-2023 (3 sites near Toolik Lake, Alaska) [Dataset]. http://doi.org/10.18739/A24F1MK9P
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    Dataset updated
    Aug 12, 2024
    Dataset provided by
    Arctic Data Center
    Authors
    Rebecca Rowe; Natalie Boelman; Kevin Griffin; Laura Gough; Jennie McLaren; Edward Rastetter
    Time period covered
    Jan 1, 2017 - Jan 1, 2023
    Area covered
    Variables measured
    Age, Sex, BagWt, DayID, PitID, HairID, SiteID, TimeIn, TrapID, YearID, and 20 more
    Description

    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.

  15. a

    MOA AMATS Boundary

    • gis.data.alaska.gov
    • data-muniorg.hub.arcgis.com
    • +2more
    Updated May 31, 2024
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    Municipality of Anchorage (2024). MOA AMATS Boundary [Dataset]. https://gis.data.alaska.gov/maps/ce3a1dd79d7d41daadbe0d32990b47a9
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    Dataset updated
    May 31, 2024
    Dataset authored and provided by
    Municipality of Anchorage
    Area covered
    Description

    SummaryThis is the 2024 AMATS Area Boundary, showing all areas where AMATS funds can be used. AMATS coordinates transportation improvements in Anchorage. In areas with 50,000 or more people, a Metropolitan Planning Organization (MPO) works to balance transportation goals between state and local needs. AMATS is the MPO for the Anchorage region.DescriptionEvery ten years, with each census, MPO boundaries are reviewed with input from the State and local transportation operators. After the 2020 Census, the AMATS boundary was updated to include all Urbanized Areas identified by the U.S. Census Bureau. The Census defines these areas based on population density, using Block Group lines that can create uneven shapes, which don’t always fit transportation planning needs. Federal law lets States and MPOs adjust these areas for transportation purposes, making the adjusted boundary larger, but not smaller, than the Census Urbanized Areas. The AMATS area includes all of the Municipality of Anchorage except for the smaller communities of the Turnagain Arm region, including Girdwood. The gap in population density between the northern and southern sections of the area is too wide to be included in the boundary according to how census urbanized boundaries are calculated.

  16. a

    Small mammal mark-recapture data 2018-2023 (3 sites near Utqiagvik, Alaska)

    • arcticdata.io
    • dataone.org
    Updated Aug 12, 2024
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    Rebecca Rowe; Natalie Boelman; Kevin Griffin; Laura Gough; Jennie McLaren; Edward Rastetter (2024). Small mammal mark-recapture data 2018-2023 (3 sites near Utqiagvik, Alaska) [Dataset]. http://doi.org/10.18739/A20P0WS61
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    Dataset updated
    Aug 12, 2024
    Dataset provided by
    Arctic Data Center
    Authors
    Rebecca Rowe; Natalie Boelman; Kevin Griffin; Laura Gough; Jennie McLaren; Edward Rastetter
    Time period covered
    Jan 1, 2018 - Jan 1, 2023
    Area covered
    Variables measured
    Age, Sex, BagWt, DayID, PitID, HairID, SiteID, TimeIn, TrapID, YearID, and 20 more
    Description

    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.

  17. a

    CENSUS 2020 PL94171 MONTANA RESERVATION

    • ceic-mtdoc.opendata.arcgis.com
    • hub.arcgis.com
    Updated Aug 23, 2021
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    Montana Department of Commerce (2021). CENSUS 2020 PL94171 MONTANA RESERVATION [Dataset]. https://ceic-mtdoc.opendata.arcgis.com/datasets/census-2020-pl94171-montana-reservation
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    Dataset updated
    Aug 23, 2021
    Dataset authored and provided by
    Montana Department of Commerce
    Area covered
    Description

    Population and Housing data for American Indian Reservations within the State of Montana was compiled from the PL 94-171 Redistricting Summary files released by the U.S. Census Bureau for the 2020 Decennial Census. This data set was created by the Montana Department of Commerce for use by the citizens of Montana and the general public. TIGER shapefiles were joined to the tabular summary file data to create this data set. A subset of variables from the release were selected for this dataset. A description of each variable and calculations are provided here.

    VINTAGE - Decennial Census vintage year - Calculation

    SUMLEV - Geography summary level - Calculation

    GEOID - Geography ID - Calculation

    NAME - Geography Name - Calculation

    AREALAND - Area of land in square meters - Calculation

    AREAWATR - Area of water in square meters - Calculation

    INTPTLAT - Geography point latitude - Calculation

    INTPTLON - Geography point longitude - Calculation

    POPTOT - Population Total - Calculation P0010001

    POPPCAP - Population per square mile - Calculation P0010001 / (AREALAND / 2589988.110336)

    POPWH - Population White alone - Calculation P0010003

    POPBL - Population Black alone - Calculation P0010004

    POPAI - Population American Indian or Alaska Native alone - Calculation P0010005

    POPAS - Population Asian alone - Calculation P0010006

    POPNH - Population Native Hawaiian or Pacific Islander alone - Calculation P0010007

    POPOT - Population Some other Race alone - Calculation P0010008

    POP2MO - Population 2 or more races - Calculation P0010009

    POPWHPCT - Population White alone percent - Calculation P0010003 / P0010001 * 100

    POPBLPCT - Population Black alone percent - Calculation P0010004 / P0010001 * 100

    POPAIPCT - Population American Indian or Alaska Native alone percent - Calculation P0010005 / P0010001 * 100

    POPASPCT - Population Asian alone percent - Calculation P0010006 / P0010001 * 100

    POPNHPCT - Population Native Hawaiian or Pacific Islander alone percent - Calculation P0010007 / P0010001 * 100

    POPOTPCT - Population Some other Race alone percent - Calculation P0010008 / P0010001 * 100

    POP2MOPCT - Population 2 or more races percent - Calculation P0010009 / P0010001 * 100

    POPWHC - Population White alone or in combination - Calculation P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071

    POPBLC - Population Black alone or in combination - Calculation P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071

    POPAIC - Population American Indian or Alaska Native alone or in combination - Calculation P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071

    POPASC - Population Asian alone or in combination - Calculation P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071

    POPNHC - Population Native Hawaiian or Pacific Islander alone or in combination - Calculation P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPOTC - Population Some Other Race alone or in combination - Calculation P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

    POPWHCPCT - Population White alone or in combination percent - Calculation (P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071)/ P0010001 * 100

    POPBLCPCT - Population Black alone or in combination percent - Calculation (P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071)/ P0010001 * 100

    POPAICPCT - Population American Indian or Alaska Native alone or in combination percent - Calculation (P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPASCPCT - Population Asian alone or in combination percent - Calculation (P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPNHCPCT - Population Native Hawaiian or Pacific Islander alone or in combination percent - Calculation (P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPOTCPCT - Population Some Other Race alone or in combination percent - Calculation (P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

    POPHSP - Population Hispanic - Calculation P0020002

    POPNHSP - Population Non-Hispanic - Calculation P0020003

    POPHSPPCT - Population Hispanic percent - Calculation P0020002 / P0010001 * 100

    POPNHSPPCT - Population Non-Hispanic percent - Calculation P0020003 / P0010001 * 100

    POP18OV - Population 18 years and over - Calculation P0030001

    POP18OVPCT - Population 18 years and over percent - Calculation P0030001 / P0010001 * 100

    HUTOT - Housing Units Total - Calculation H0010001

    HUOCC - Housing Units Occupied - Calculation H0010002

    HUVAC - Housing Units Vacant - Calculation H0010003

    HUOCCPCT - Housing Units Occupied percent - Calculation H0010002 / H0010001 * 100

    HUVACPCT - Housing Units Vacant percent - Calculation H0010003 / H0010001 * 100

    POPGQ - Population Group Quarters - Calculation P0050001

    POPGQIN - Population Group Quarters - Institutionalized - Calculation P0050002

    POPGQNI - Population Group Quarters - Non-Institutionalized - Calculation P0050007

    POPGQPCT - Population Group Quarters percent - Calculation P0050001 / P0010001 * 100

    POPGQINPCT - Population Group Quarters - Institutionalized percent - Calculation P0050002 / P0010001 * 100

    POPGQNIPCT - Population Group Quarters - Non-Institutionalized percent - Calculation P0050007 / P0010001 * 100

    POPTOT2010 - Population Total 2010 - Calculation

    POPCHG - Population Change from 2010 to 2020 - Calculation

    POPCHGPCT - Population Percent Change from 2010 to 2020 - Calculation

  18. g

    Energy Information Administration, Energy Consumption by Source and State,...

    • geocommons.com
    Updated May 29, 2008
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    data (2008). Energy Information Administration, Energy Consumption by Source and State, USA, 2005 [Dataset]. http://geocommons.com/search.html
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    Dataset updated
    May 29, 2008
    Dataset provided by
    data
    EIA - Energy Information Administration
    Description

    This dataset displays figures on energy consumption by source and total consumption per Capita. This information is available by state for the year 2005. This information is provided by the Energy Information Administration. Alaska tops the list of total consumption per capita, while Texas ranks highest in consumption for all other categories. Included is figures regarding coal, natural gas, petroleum, and retail electricity sales.

  19. b

    Estimates of island-wide sea otter population density as surveyed with boats...

    • bco-dmo.org
    • datacart.bco-dmo.org
    • +1more
    csv
    Updated Jan 26, 2021
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    James A. Estes; Robert S. Steneck; Douglas B. Rasher (2021). Estimates of island-wide sea otter population density as surveyed with boats circumnavigating nine focal islands within the central and western Aleutian Islands (Alaska) from 1991-2015. [Dataset]. http://doi.org/10.26008/1912/bco-dmo.838077.1
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    csv(7.42 KB)Available download formats
    Dataset updated
    Jan 26, 2021
    Dataset provided by
    Biological and Chemical Data Management Office
    Authors
    James A. Estes; Robert S. Steneck; Douglas B. Rasher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1991 - Dec 31, 2015
    Area covered
    Variables measured
    Year, Island, Density, Latitude, Longitude
    Measurement technique
    Binoculars Handheld
    Description

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

  20. Arctic LTER site, station North Slope Borough, AK (FIPS 2185), study of...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 10, 2015
    + more versions
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    Nichole Rosamilia; Ted Gragson; Inter-University Consortium for Political and Social Research; Michael R. Haines; Christopher Boone; U.S. Bureau of the Census; EcoTrends Project (2015). Arctic LTER site, station North Slope Borough, AK (FIPS 2185), study of percent urban population in units of percent on a yearly timescale [Dataset]. https://search.dataone.org/view/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fecotrends%2F412%2F2
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    Dataset updated
    Mar 10, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Nichole Rosamilia; Ted Gragson; Inter-University Consortium for Political and Social Research; Michael R. Haines; Christopher Boone; U.S. Bureau of the Census; EcoTrends Project
    Time period covered
    Jan 1, 1970 - Jan 1, 2000
    Area covered
    Variables measured
    YEAR, S_DEV, S_ERR, ID_OBS, N_TRACE, N_INVALID, N_MISSING, N_EXPECTED, N_OBSERVED, N_ESTIMATED, and 3 more
    Description

    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 percent urban population measurements in percent units and were aggregated to a yearly timescale.

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Statista (2024). Population density in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/183588/population-density-in-the-federal-states-of-the-us/
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Population density in the U.S. 2023, by state

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28 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Dec 3, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

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.

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