23 datasets found
  1. Population density in Michigan 1960-2018

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). Population density in Michigan 1960-2018 [Dataset]. https://www.statista.com/statistics/588903/michigan-population-density/
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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States, Michigan
    Description

    This graph shows the population density in the federal state of Michigan from 1960 to 2018. In 2018, the population density of Michigan stood at 176.8 residents per square mile of land area.

  2. M

    Michigan Population 1900-2024

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Michigan Population 1900-2024 [Dataset]. https://www.macrotrends.net/states/michigan/population
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    csvAvailable download formats
    Dataset updated
    May 31, 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

    Area covered
    Michigan
    Description

    Chart and table of population level and growth rate for the state of Michigan from 1900 to 2024.

  3. Michigan Population density

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

    68,20 (persons per sq. km) in 2022.

  4. Data from: Kellogg Biological Station site, station Allegan County, MI (FIPS...

    • dataone.org
    • portal.edirepository.org
    Updated Mar 11, 2015
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    Inter-University Consortium for Political and Social Research; Nichole Rosamilia; U.S. Bureau of the Census; Christopher Boone; Ted Gragson; Michael R. Haines; EcoTrends Project (2015). Kellogg Biological Station site, station Allegan County, MI (FIPS 26005), study of human population density in units of numberPerKilometerSquared on a yearly timescale [Dataset]. https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fecotrends%2F9152%2F2
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    Dataset updated
    Mar 11, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Inter-University Consortium for Political and Social Research; Nichole Rosamilia; U.S. Bureau of the Census; Christopher Boone; Ted Gragson; Michael R. Haines; EcoTrends Project
    Time period covered
    Jan 1, 1880 - 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 Kellogg Biological Station (KBS) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

  5. a

    Michigan Association of Regions

    • gis-egle.hub.arcgis.com
    • gis-michigan.opendata.arcgis.com
    • +2more
    Updated May 2, 2023
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    Michigan Dept. of Environment, Great Lakes, and Energy (2023). Michigan Association of Regions [Dataset]. https://gis-egle.hub.arcgis.com/maps/egle::michigan-association-of-regions
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    Dataset updated
    May 2, 2023
    Dataset authored and provided by
    Michigan Dept. of Environment, Great Lakes, and Energy
    Area covered
    Description

    This data is used in the Materials Management Facilities Web App (Item Details). From the Michigan Association of Regions (MAR) website: "The Michigan Association of Regions is a state association of the fourteen (14) regional councils in Michigan. MAR consists of a policy board of local elected and appointed officials that meets periodically to discuss regional policy issues and programs, and adopts legislative positions. MAR also has an Executive Directors Committee that meets monthly. Member services consists of advocacy of regional programs, training and education, research, membership surveys, networking, as well as liaison to national associations, including the National Association of Regional Councils (NARC) and the National Association of Development Organizations (NADO).State Designated Planning and Development Regions are voluntary organizations comprised of local governments dedicated to serving the regional planning needs of multi-county areas in all parts of Michigan. They are a form of local government voluntarily created by their members, which are largely representative of local governments in the region; although membership also includes road authorities, nonprofit organizations and representatives of the business community in many regions.The land area of Michigan is divided into 14 planning & development regions with counties as the organizing unit. They range widely in size. Five have only three counties, while one has fourteen counties. The two smallest are only 1,711-13 square miles each in size, while the largest is 8,735 square miles in size. Population served varies from 57,510 persons to 4,833,493 based on Census estimates in 2000. Population density ranges from under 14 persons/square mile in Region 13 (Western U.P.), to over 1,043 persons/square mile in Region 1 (Southeast Michigan). The oldest of today’s regions, Tri-County Regional Planning Commission (Region 6 in Lansing, formed in 1956), and the three county Detroit Metropolitan Area Regional Planning Commission (formed in 1947and subsequently replaced by the Southeast Michigan Council of Governments in 1968 (SEMCOG, which covers seven counties in SE Michigan), originated out of a desire by local officials to coordinate transportation infrastructure planning and to serve as a forum for other regional issues."These boundaries are static and were digitized from boundaries shared on the Michigan Association of Regions (MAR) website in March 2023. They were digitized for inclusion on the Materials Management Division's facilities web map. For questions or comments, reach out to EGLE-Maps@Michigan.gov.

  6. d

    2019 Cartographic Boundary Shapefile, 2010 Urban Areas (UA) within 2010...

    • catalog.data.gov
    Updated Dec 3, 2020
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    (2020). 2019 Cartographic Boundary Shapefile, 2010 Urban Areas (UA) within 2010 County and Equivalent for Michigan, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2019-cartographic-boundary-shapefile-2010-urban-areas-ua-within-2010-county-and-equivalent-for-12
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    Dataset updated
    Dec 3, 2020
    Description

    The 2019 cartographic boundary shapefiles 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.

  7. d

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

    • catalog.data.gov
    Updated Jan 13, 2021
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    (2021). 2015 Cartographic Boundary File, Urban Area-State-County for Michigan, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2015-cartographic-boundary-file-urban-area-state-county-for-michigan-1-5000001
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    Dataset updated
    Jan 13, 2021
    Area covered
    Michigan
    Description

    The 2015 cartographic boundary shapefiles 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.

  8. M

    Michigan - Median Household Income (1984-2023)

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). Michigan - Median Household Income (1984-2023) [Dataset]. https://www.macrotrends.net/4757/michigan-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).

  9. d

    Data from: High-density genomic data reveal fine-scale population structure...

    • search.dataone.org
    • data.niaid.nih.gov
    • +2more
    Updated May 17, 2025
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    Yue Shi (2025). High-density genomic data reveal fine-scale population structure and pronounced islands of adaptive divergence in lake whitefish (Coregonus clupeaformis) from Lake Michigan [Dataset]. http://doi.org/10.5061/dryad.r4xgxd2gq
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    Dataset updated
    May 17, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Yue Shi
    Time period covered
    Jan 1, 2022
    Area covered
    Lake Michigan
    Description

    Understanding patterns of genetic structure and adaptive variation in natural populations is crucial for informing conservation and management. Past genetic research using 11 microsatellite loci identified six genetic stocks of lake whitefish (Coregonus clupeaformis) within Lake Michigan, USA. However, ambiguity in genetic stock assignments suggested those neutral microsatellite markers did not provide adequate power for delineating lake whitefish stocks in this system, prompting calls for a genomics approach to investigate stock structure. Here, we generated a dense genomic dataset to characterize population structure and investigate patterns of neutral and adaptive genetic diversity among lake whitefish populations in Lake Michigan. Using Rapture sequencing, we genotyped 829 individuals collected from 17 baseline populations at 197,588 SNP markers after quality filtering. Although the overall pattern of genetic structure was similar to the previous microsatellite study, our genomic da...

  10. d

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

    • datadiscoverystudio.org
    • search.dataone.org
    Updated May 12, 2018
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    (2018). National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Michigan. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/2a46b696dd574320b0e53af943c4198e/html
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    Dataset updated
    May 12, 2018
    Description

    description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Michigan. 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 Michigan. 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 Michigan. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7GH9FZG; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Michigan. 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 Michigan. 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 Michigan. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7GH9FZG

  11. o

    National Neighborhood Data Archive (NaNDA): Socioeconomic Status and...

    • openicpsr.org
    Updated May 14, 2020
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    Robert Melendez; Philippa Clarke; Anam Khan; Iris Gomez-Lopez; Mao Li; Megan Chenoweth (2020). National Neighborhood Data Archive (NaNDA): Socioeconomic Status and Demographic Characteristics of Census Tracts, United States, 2008-2017 [Dataset]. http://doi.org/10.3886/E119451V2
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    Dataset updated
    May 14, 2020
    Dataset provided by
    University of Michigan Institute for Social Research
    University of Michigan. Institute for Social Research
    Authors
    Robert Melendez; Philippa Clarke; Anam Khan; Iris Gomez-Lopez; Mao Li; Megan Chenoweth
    License

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

    Time period covered
    2008 - 2017
    Area covered
    United States
    Description

    This dataset contains measures of socioeconomic and demographic characteristics by US census tract for the years 2008-2017. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.A curated version of this data is available through ICPSR at http://dx.doi.org/10.3886/ICPSR38528.v1.

  12. l

    TblPopsum

    • geohub.lacity.org
    • visionzero.geohub.lacity.org
    Updated Sep 30, 2016
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    Los Angeles Department of Transportation (2016). TblPopsum [Dataset]. https://geohub.lacity.org/datasets/ladot::tblpopsum
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    Dataset updated
    Sep 30, 2016
    Dataset authored and provided by
    Los Angeles Department of Transportation
    Description

    Abstract: Dataset includes population, by age group, within ¼ mile buffer around each intersection. This can be used to calculate population density at each intersection, or compare the distribution of age around a particular intersection. These values were derived using the proportional sum calculation on census tracts. Relations: Join to the Intersection Table using the “boeint_fkey” field. Source: ACS 2014 5-Year Estimatesboeint_fkeyUnique identifier for the intersection as part of the Bureau of Engineering’s Centerline networkpopsum_aunder5Population under 5 years of age within ¼ mi. of the intersectionpopsum_a5to9Population between 5 and 9 years of age within ¼ mile of the intersectionpopsum_a10to17Population between 10 and 17 years of age within ¼ mi. of the intersectionpopsum_a18to29Population between 18 and 29 years of age within ¼ mi. of the intersectionpopsum_a30to61Population between 30 and 61 years of age within ¼ mi. of the intersectionpopsum_a62to69Population between 62 and 69 years of age within ¼ mi. of the intersection popsum_a70upPopulation 70 and greater years of age within ¼ mi. of the intersectionpopsum_totalTotal population within ¼ mi. of the intersection

  13. 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 Michigan, 1:500,000 [Dataset]. https://catalog.data.gov/dataset/2019-cartographic-boundary-kml-2010-urban-areas-ua-within-2010-county-and-equivalent-for-michig
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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.

  14. US cities 2022

    • kaggle.com
    Updated Nov 4, 2023
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    Frank Schindler (2023). US cities 2022 [Dataset]. https://www.kaggle.com/datasets/frankschindler1/us-cities-2022-population-coordinates-etc
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 4, 2023
    Dataset provided by
    Kaggle
    Authors
    Frank Schindler
    License

    Attribution-ShareAlike 3.0 (CC BY-SA 3.0)https://creativecommons.org/licenses/by-sa/3.0/
    License information was derived automatically

    Area covered
    United States
    Description

    This dataset includes basic data about all US cities with a population over 100.000 (333 cities)

    Source: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population

    Coordinates of cities have been geocoded using https://rapidapi.com/GeocodeSupport/api/forward-reverse-geocoding/

    Rows description:

    City: Name of city State: Name of state Latitude, Longitude, Population_estimate_2022: Estimated population in 2022 Population_2020: Population figure from 2020 census Change_population: % change in population between 2022 and 2020 Land_area: City land area in sq. mi. Population_density_2020: density of population per sq. mi. in 2020

  15. Kellogg Biological Station site, station Barry County, MI (FIPS 26015),...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 11, 2015
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    Inter-University Consortium for Political and Social Research; Christopher Boone; Ted Gragson; U.S. Bureau of the Census; Michael R. Haines; Nichole Rosamilia; EcoTrends Project (2015). Kellogg Biological Station site, station Barry County, MI (FIPS 26015), 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%2F9162%2F2
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    Dataset updated
    Mar 11, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Inter-University Consortium for Political and Social Research; Christopher Boone; Ted Gragson; U.S. Bureau of the Census; Michael R. Haines; Nichole Rosamilia; EcoTrends Project
    Time period covered
    Jan 1, 1840 - 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 Kellogg Biological Station (KBS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

  16. Data from: North Temperate Lakes site, station Gogebic County, MI (FIPS...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 11, 2015
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    Inter-University Consortium for Political and Social Research; Christopher Boone; Nichole Rosamilia; Ted Gragson; Michael R. Haines; U.S. Bureau of the Census; EcoTrends Project (2015). North Temperate Lakes site, station Gogebic County, MI (FIPS 26053), 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%2F11067%2F2
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    Dataset updated
    Mar 11, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Inter-University Consortium for Political and Social Research; Christopher Boone; Nichole Rosamilia; Ted Gragson; Michael R. Haines; U.S. Bureau of the Census; EcoTrends Project
    Time period covered
    Jan 1, 1890 - 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 North Temperate Lakes (NTL) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

  17. Data from: Kellogg Biological Station site, station Cass County, MI (FIPS...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 11, 2015
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    Inter-University Consortium for Political and Social Research; U.S. Bureau of the Census; Michael R. Haines; Christopher Boone; Ted Gragson; Nichole Rosamilia; EcoTrends Project (2015). Kellogg Biological Station site, station Cass County, MI (FIPS 26027), 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%2F9195%2F2
    Explore at:
    Dataset updated
    Mar 11, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Inter-University Consortium for Political and Social Research; U.S. Bureau of the Census; Michael R. Haines; Christopher Boone; Ted Gragson; Nichole Rosamilia; EcoTrends Project
    Time period covered
    Jan 1, 1830 - 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 Kellogg Biological Station (KBS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

  18. o

    National Neighborhood Data Archive (NaNDA): Urbanicity by Census Tract,...

    • openicpsr.org
    • icpsr.umich.edu
    • +1more
    Updated Jan 11, 2021
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    Stephanie Miller; Robert Melendez; Megan Chenoweth (2021). National Neighborhood Data Archive (NaNDA): Urbanicity by Census Tract, United States, 2010 [Dataset]. http://doi.org/10.3886/E130542V1
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    Dataset updated
    Jan 11, 2021
    Dataset provided by
    University of Michigan
    University of Michigan. Institute for Social Research
    Authors
    Stephanie Miller; Robert Melendez; Megan Chenoweth
    License

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

    Time period covered
    2010
    Area covered
    United States
    Description

    This dataset contains measures of the urban/rural characteristics of each census tract in the United States. These include proportions of urban and rural population, population density, rural/urban commuting area (RUCA) codes, and RUCA-based four- and seven- category urbanicity scales. A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38606/versions/V1

  19. n

    Evaluating consumptive and nonconsumptive predator effects on prey density...

    • data.niaid.nih.gov
    • datadryad.org
    • +1more
    zip
    Updated Dec 20, 2018
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    John A. Marino Jr.; Scott D. Peacor; David B. Bunnell; Henry A. Vanderploeg; Steve A. Pothoven; Ashley K. Elgin; James R. Bence; Jing Jiao; Edward L. Ionides; D.B. Bunnell; J.A. Marino; E.L. Ionides; S.A. Pothoven; A.K. Elgin; H.A. Vanderploeg; S.D. Peacor; J.R. Bence (2018). Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data [Dataset]. http://doi.org/10.5061/dryad.bh688ft
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    zipAvailable download formats
    Dataset updated
    Dec 20, 2018
    Dataset provided by
    University of Michigan
    United States Geological Survey
    Great Lakes Science Center
    National Oceanic and Atmospheric Administration
    Bradley University
    Michigan State University
    Authors
    John A. Marino Jr.; Scott D. Peacor; David B. Bunnell; Henry A. Vanderploeg; Steve A. Pothoven; Ashley K. Elgin; James R. Bence; Jing Jiao; Edward L. Ionides; D.B. Bunnell; J.A. Marino; E.L. Ionides; S.A. Pothoven; A.K. Elgin; H.A. Vanderploeg; S.D. Peacor; J.R. Bence
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Lake Michigan, 086° 34.19’ W, 086° 34.19’, 43° 11.99’ N, 43° 11.99’
    Description

    Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the mechanism underlying the B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore highlights the utility of SSMs to enhance inference for species interactions from time series data. In particular, our findings provide evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.

  20. Data from: Kellogg Biological Station site, station Kalamazoo County, MI...

    • search.dataone.org
    • portal.edirepository.org
    Updated Mar 11, 2015
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    Christopher Boone; Michael R. Haines; U.S. Bureau of the Census; Nichole Rosamilia; Inter-University Consortium for Political and Social Research; Ted Gragson; EcoTrends Project (2015). Kellogg Biological Station site, station Kalamazoo County, MI (FIPS 26077), 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%2F9228%2F2
    Explore at:
    Dataset updated
    Mar 11, 2015
    Dataset provided by
    Long Term Ecological Research Networkhttp://www.lternet.edu/
    Authors
    Christopher Boone; Michael R. Haines; U.S. Bureau of the Census; Nichole Rosamilia; Inter-University Consortium for Political and Social Research; Ted Gragson; EcoTrends Project
    Time period covered
    Jan 1, 1840 - 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 Kellogg Biological Station (KBS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

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Statista (2024). Population density in Michigan 1960-2018 [Dataset]. https://www.statista.com/statistics/588903/michigan-population-density/
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Population density in Michigan 1960-2018

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Dataset updated
Jul 5, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States, Michigan
Description

This graph shows the population density in the federal state of Michigan from 1960 to 2018. In 2018, the population density of Michigan stood at 176.8 residents per square mile of land area.

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