18 datasets found
  1. Population density in Pennsylvania 1960-2018

    • statista.com
    Updated Dec 7, 2024
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    Statista (2024). Population density in Pennsylvania 1960-2018 [Dataset]. https://www.statista.com/statistics/304715/pennsylvania-population-density/
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    Dataset updated
    Dec 7, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States, Pennsylvania
    Description

    This graph shows the population density in the federal state of Pennsylvania from 1960 to 2018. In 2018, the population density of Pennsylvania stood at 286.2 residents per square mile of land area.

  2. 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.

  3. TIGER/Line Shapefile, Current, State, Pennsylvania, Census Tract

    • catalog.data.gov
    Updated Dec 15, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geospatial Products Branch (Point of Contact) (2023). TIGER/Line Shapefile, Current, State, Pennsylvania, Census Tract [Dataset]. https://catalog.data.gov/dataset/tiger-line-shapefile-current-state-pennsylvania-census-tract
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    Dataset updated
    Dec 15, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Pennsylvania
    Description

    This resource is a member of a series. 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.

  4. Panama PA: Population Density: People per Square Km

    • ceicdata.com
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    CEICdata.com, Panama PA: Population Density: People per Square Km [Dataset]. https://www.ceicdata.com/en/panama/population-and-urbanization-statistics/pa-population-density-people-per-square-km
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    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Dec 1, 2006 - Dec 1, 2017
    Area covered
    Panama
    Variables measured
    Population
    Description

    Panama PA: Population Density: People per Square Km data was reported at 55.133 Person/sq km in 2017. This records an increase from the previous number of 54.266 Person/sq km for 2016. Panama PA: Population Density: People per Square Km data is updated yearly, averaging 32.546 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 55.133 Person/sq km in 2017 and a record low of 15.699 Person/sq km in 1961. Panama PA: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

  5. Pennsylvania Population density

    • knoema.de
    csv, json, sdmx, xls
    Updated Jun 28, 2023
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    Knoema (2023). Pennsylvania Population density [Dataset]. https://knoema.de/atlas/Vereinigte-Staaten-von-Amerika/Pennsylvania/Population-density
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    xls, sdmx, json, csvAvailable download formats
    Dataset updated
    Jun 28, 2023
    Dataset authored and provided by
    Knoemahttp://knoema.com/
    Time period covered
    2011 - 2022
    Area covered
    Pennsylvania
    Variables measured
    Population density
    Description

    111,76 (persons per sq. km) in 2022.

  6. 2022 Cartographic Boundary File (KML), Current Census Tract for...

    • catalog.data.gov
    • datasets.ai
    Updated Dec 14, 2023
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    U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Customer Engagement Branch (Point of Contact) (2023). 2022 Cartographic Boundary File (KML), Current Census Tract for Pennsylvania, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2022-cartographic-boundary-file-kml-current-census-tract-for-pennsylvania-1-500000
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    Dataset updated
    Dec 14, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Area covered
    Pennsylvania
    Description

    The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. 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 and beyond, 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.

  7. M

    Pennsylvania - Median Household Income (1984-2023)

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). Pennsylvania - Median Household Income (1984-2023) [Dataset]. https://www.macrotrends.net/4831/pennsylvania-median-household-income
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    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    1984 - 2023
    Area covered
    United States
    Description

    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).

  8. d

    Data from: Quantifying the Size and Geographic Extent of CCTV's Impact on...

    • datasets.ai
    • icpsr.umich.edu
    • +1more
    0
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    Department of Justice, Quantifying the Size and Geographic Extent of CCTV's Impact on Reducing Crime in Philadelphia, Pennsylvania, 2003-2013 [Dataset]. https://datasets.ai/datasets/quantifying-the-size-and-geographic-extent-of-cctvs-impact-on-reducing-crime-in-phila-2003-d9f6e
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    0Available download formats
    Dataset authored and provided by
    Department of Justice
    Area covered
    Philadelphia, Pennsylvania
    Description

    These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

  9. d

    2015 Cartographic Boundary File, Urban Area-State-County for Pennsylvania,...

    • catalog.data.gov
    Updated Jan 13, 2021
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    (2021). 2015 Cartographic Boundary File, Urban Area-State-County for Pennsylvania, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2015-cartographic-boundary-file-urban-area-state-county-for-pennsylvania-1-500000
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    Dataset updated
    Jan 13, 2021
    Area covered
    Pennsylvania
    Description

    The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

  10. a

    PGC Boundaries

    • hub.arcgis.com
    Updated Mar 28, 2025
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    Pennsylvania Game Commission (2025). PGC Boundaries [Dataset]. https://hub.arcgis.com/maps/dcfad5b94e3245ca9a54f6ea1c804905
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    Dataset updated
    Mar 28, 2025
    Dataset authored and provided by
    Pennsylvania Game Commissionhttps://www.pgc.pa.gov/
    Area covered
    Description

    Pennsylvania Game Commission administrative boundaries for public use.PGC Regions and Commissioner Districts are derived from the county boundaries provided by PennDOT's Open Data to insure topologically correct lines.Wildlife Management Units are used to manage all game, except elk, waterfowl, and other migratory game birds. The large-scale units are based on habitat and human-related land characteristics. Human population density, public/private land ownership, recognizable physical features such as major roads and rivers, and land use practices such as agriculture, timber, and development were considered when establishing the physiographic boundaries of Wildlife Management Units. Prior to the implementation of Wildlife Management Units in 2003, game animals were managed using smaller, species-specific management units. Six game species, each with 2 to 67 species-specific management units were originally combined into 21 larger Wildlife Management Units. Though the larger units come with more habitat variability, they provide data sets adequate for management recommendations without added data collection effort, they give hunters larger areas to hunt, and they provide boundaries that are easy to see. Wildlife Management Units are established for the long term and periodically reviewed for adjustments.

  11. f

    Integrating environmental and neighborhood factors in MaxEnt modeling to...

    • plos.figshare.com
    txt
    Updated Jun 7, 2023
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    Daniel Wiese; Ananias A. Escalante; Heather Murphy; Kevin A. Henry; Victor Hugo Gutierrez-Velez (2023). Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania [Dataset]. http://doi.org/10.1371/journal.pone.0223821
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    txtAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Daniel Wiese; Ananias A. Escalante; Heather Murphy; Kevin A. Henry; Victor Hugo Gutierrez-Velez
    License

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

    Area covered
    Pennsylvania
    Description

    Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

  12. 2016 Cartographic Boundary File, 2010 Urban Areas (UA) within 2010 County...

    • data.wu.ac.at
    html, zip
    Updated Jun 5, 2017
    + more versions
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    US Census Bureau, Department of Commerce (2017). 2016 Cartographic Boundary File, 2010 Urban Areas (UA) within 2010 County and Equivalent for Pennsylvania, 1:500,000 [Dataset]. https://data.wu.ac.at/schema/data_gov/NjZiNzA4NmYtZTBmNS00YzhkLWI1MWUtMjFmY2ZhMjhkMTY4
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    html, zipAvailable download formats
    Dataset updated
    Jun 5, 2017
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    United States Department of Commercehttp://www.commerce.gov/
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    512690df1181c97988f3a7dc5fabac0f755528fd
    Description

    The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

    The records in this file allow users to map the parts of Urban Areas that overlap a particular county.

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

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

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

  13. 巴拿马 PA:人口密度:每平方公里人口

    • ceicdata.com
    Updated Dec 15, 2019
    + more versions
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    CEICdata.com (2019). 巴拿马 PA:人口密度:每平方公里人口 [Dataset]. https://www.ceicdata.com/zh-hans/panama/population-and-urbanization-statistics/pa-population-density-people-per-square-km
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    Dataset updated
    Dec 15, 2019
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 1, 2006 - Dec 1, 2017
    Area covered
    巴拿马
    Variables measured
    Population
    Description

    PA:人口密度:每平方公里人口在12-01-2017达55.133Person/sq km,相较于12-01-2016的54.266Person/sq km有所增长。PA:人口密度:每平方公里人口数据按年更新,12-01-1961至12-01-2017期间平均值为32.546Person/sq km,共57份观测结果。该数据的历史最高值出现于12-01-2017,达55.133Person/sq km,而历史最低值则出现于12-01-1961,为15.699Person/sq km。CEIC提供的PA:人口密度:每平方公里人口数据处于定期更新的状态,数据来源于World Bank,数据归类于Global Database的巴拿马 – 表 PA.世界银行:人口和城市化进程统计。

  14. d

    National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human...

    • datadiscoverystudio.org
    • search.dataone.org
    Updated May 20, 2018
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    (2018). National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Pennsylvania. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/a40b1599c6ae42ffa4923b9823bcaaac/html
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    Dataset updated
    May 20, 2018
    Description

    description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Pennsylvania. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Pennsylvania. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) 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. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 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). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Pennsylvania. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7DJ5CMC; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Pennsylvania. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Pennsylvania. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) 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. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 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). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Pennsylvania. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7DJ5CMC

  15. País Vasco Population density

    • knoema.es
    • knoema.de
    csv, json, sdmx, xls
    Updated May 30, 2013
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    Knoema (2013). País Vasco Population density [Dataset]. https://knoema.es/atlas/Espa%C3%B1a/Pa%C3%ADs-Vasco/Population-density
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    xls, csv, json, sdmxAvailable download formats
    Dataset updated
    May 30, 2013
    Dataset authored and provided by
    Knoemahttp://knoema.com/
    Time period covered
    2002 - 2010
    Area covered
    País Vasco
    Variables measured
    Population density
    Description

    297,2 (Persons per sq. km) in 2010.

  16. Models to assess ability to achieve localized areas of reduced white-tailed...

    • zenodo.org
    • datadryad.org
    bin
    Updated Jun 4, 2022
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    Amanda Van Buskirk; Amanda Van Buskirk; Christopher Rosenberry; Bret Wallingford; Emily Just Domoto; Marc McDill; Marc McDill; Patrick Drohan; Duane Diefenbach; Duane Diefenbach; Christopher Rosenberry; Bret Wallingford; Emily Just Domoto; Patrick Drohan (2022). Models to assess ability to achieve localized areas of reduced white-tailed deer density [Dataset]. http://doi.org/10.5061/dryad.m37pvmd18
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    binAvailable download formats
    Dataset updated
    Jun 4, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Amanda Van Buskirk; Amanda Van Buskirk; Christopher Rosenberry; Bret Wallingford; Emily Just Domoto; Marc McDill; Marc McDill; Patrick Drohan; Duane Diefenbach; Duane Diefenbach; Christopher Rosenberry; Bret Wallingford; Emily Just Domoto; Patrick Drohan
    License

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

    Description

    Localized management of white-tailed deer (Odocoileus virginianus) involves the removal of matriarchal family units with the intent to create areas of reduced deer density. However, application of this approach has not always been successful, possibly because of female dispersal and high deer densities. We developed a spatially explicit, agent-based model to investigate the intensity of deer removal required to locally reduce deer density depending on the surrounding deer density, dispersal behavior, and size and shape of the area of localized reduction. Application of this model is illustrated using the example of abundant deer populations in Pennsylvania, USA. Most scenarios required at least 5 years before substantial deer density reductions occurred. Our model indicated that a localized reduction was successful for scenarios in which the surrounding deer density was lowest (30 deer/mi²), localized antlerless harvest rates were 30%, and the removal area was 5 mi² or larger. When the size of the removal area was < 5 mi2, end population density was highly variable and, in some scenarios, exceeded the initial density. The shape of the area of localized reduction had less influence on the ability to reduce deer density than the size. There were no differences in mean deer density in the same size circle or square removal areas. Similarly, increasing the ratio of sides (length : width) in rectangular removal areas had little influence on the ability to locally reduce deer densities. Situations in which deer density was higher (40 or 50 deer/mi2) required antlerless removal rates to exceed 30% and took more than 5 years to considerably reduce density in the localized area regardless of its size. These results indicate that the size of the area of reduction, surrounding deer density, and antlerless harvest rate are the most influential factors in locally reducing deer density. Therefore, localized management likely can be an effective strategy for lower density herds, especially in larger removal areas. For high density herds, the success of this strategy would depend most on the ability of resource managers to achieve consistently high antlerless harvest rates.

  17. d

    2019 Cartographic Boundary KML, 2010 Urban Areas (UA) within 2010 County and...

    • catalog.data.gov
    Updated Jan 15, 2021
    + more versions
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    (2021). 2019 Cartographic Boundary KML, 2010 Urban Areas (UA) within 2010 County and Equivalent for Pennsylvania, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2019-cartographic-boundary-kml-2010-urban-areas-ua-within-2010-county-and-equivalent-for-pennsy
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    Dataset updated
    Jan 15, 2021
    Description

    The 2019 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

  18. Population-level density values for the MDD and Control group networks, from...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Stijn de Vos; Klaas J. Wardenaar; Elisabeth H. Bos; Ernst C. Wit; Mara E. J. Bouwmans; Peter de Jonge (2023). Population-level density values for the MDD and Control group networks, from different model procedures. [Dataset]. http://doi.org/10.1371/journal.pone.0178586.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Stijn de Vos; Klaas J. Wardenaar; Elisabeth H. Bos; Ernst C. Wit; Mara E. J. Bouwmans; Peter de Jonge
    License

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

    Description

    Population-level density values for the MDD and Control group networks, from different model procedures.

  19. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Statista (2024). Population density in Pennsylvania 1960-2018 [Dataset]. https://www.statista.com/statistics/304715/pennsylvania-population-density/
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Population density in Pennsylvania 1960-2018

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Dataset updated
Dec 7, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States, Pennsylvania
Description

This graph shows the population density in the federal state of Pennsylvania from 1960 to 2018. In 2018, the population density of Pennsylvania stood at 286.2 residents per square mile of land area.

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