100+ datasets found
  1. a

    Computer and Broadband Internet Access (by Atlanta Neighborhood Planning...

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • opendata.atlantaregional.com
    • +2more
    Updated Feb 26, 2021
    + more versions
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    Georgia Association of Regional Commissions (2021). Computer and Broadband Internet Access (by Atlanta Neighborhood Planning Unit S, T, and V) 2019 [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/6e618a4f53a642609ce04a9021d207f9
    Explore at:
    Dataset updated
    Feb 26, 2021
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

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

    Area covered
    Description

    This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

  2. Viewshed

    • africageoportal.com
    • rwanda.africageoportal.com
    • +4more
    Updated Jul 4, 2013
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    Esri (2013). Viewshed [Dataset]. https://www.africageoportal.com/content/1ff463dbeac14b619b9edbd7a9437037
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    Dataset updated
    Jul 4, 2013
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

  3. a

    How to Submit Rebuttals

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Apr 8, 2024
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    Vermont Community Broadband Board (2024). How to Submit Rebuttals [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/documents/67cf8ce9ce864e19be65a3a563739d77
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    Dataset updated
    Apr 8, 2024
    Dataset authored and provided by
    Vermont Community Broadband Board
    Description

    The document contains detailed instructions of the registration process and steps for submitting rebuttals in the Vermont Broadband Equity, Access, and Deployment (BEAD) Rebuttal Process. Registrants receive a Challenger ID and an ArcGIS Online login to access both the Rebuttal Portal and the Challenge Portal. This document includes links to relevant resources and contact information for technical and program support.

  4. d

    ArcGIS Online: Map Viewer

    • fed.dcceew.gov.au
    Updated Apr 3, 2023
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    Dept of Climate Change, Energy, the Environment & Water (2023). ArcGIS Online: Map Viewer [Dataset]. https://fed.dcceew.gov.au/datasets/arcgis-online-map-viewer
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    Dataset updated
    Apr 3, 2023
    Dataset authored and provided by
    Dept of Climate Change, Energy, the Environment & Water
    Description

    This Guide is designed to assist you with using ArcGIS Online (AGOL)'s Map Viewer.An ArcGIS web map is an interactive display of geographic information. Web maps are made by adding and combining layers. The layers are made from data, they are logical collections of geographic data.Map Viewer can be used to view, explore and create web maps in ArcGIS Online.

  5. a

    ARC Survey Hub terms of use v2021.1

    • amphibian-survey.arc-trust.org
    • monitoring.arc-trust.org
    • +1more
    Updated Jun 7, 2021
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    data.officer (2021). ARC Survey Hub terms of use v2021.1 [Dataset]. https://amphibian-survey.arc-trust.org/documents/67ffef7cf361448088a50af0543d633a
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    Dataset updated
    Jun 7, 2021
    Dataset authored and provided by
    data.officer
    Description

    The terms of use for community users setting up ARC Survey Hub ArcGIS Online accounts.

  6. ACL-ARC dataset

    • figshare.com
    zip
    Updated May 30, 2023
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    TONG ZENG (2023). ACL-ARC dataset [Dataset]. http://doi.org/10.6084/m9.figshare.12573872.v1
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    zipAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    TONG ZENG
    License

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

    Description

    The dataset used in the experiments on the paper "Modeling citation worthiness by using attention‑based bidirectional long short‑term memory networks and interpretable models"For the pre-processing of the dataset, please refer to the paper Bonab et al., 2018 (http://doi.org/10.1145/3209978.3210162)We downloaded a copy of that dataset, adjusted some fields. The data are stored in jsonl format (each row is an json object), we list a couple of rows as example:{"cur_sent":"the nespole uses a client server architecture to allow a common user who is initially browsing through the web pages of a service provider on the internet to connect seamlessly to a human agent of the service provider who speaks another language and provides speech to speech translation service between the two parties","cur_scaled_len_features":{"type":1,"values":[0.06936542669584245,0.07202216066481995]},"cur_has_citation":1}
    For the code using this dataset to modeling citation worthiness, please refer to https://github.com/sciosci/cite-worthiness

  7. Electric Pocket Lighter Market By Distribution channel (online and offline...

    • zionmarketresearch.com
    pdf
    Updated Jul 12, 2025
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    Zion Market Research (2025). Electric Pocket Lighter Market By Distribution channel (online and offline stores), By Product type (coil lighter, dual arc, single arc, and others) And By Region: - Global And Regional Industry Overview, Market Intelligence, Comprehensive Analysis, Historical Data, And Forecasts, 2024-2032 [Dataset]. https://www.zionmarketresearch.com/report/electric-pocket-lighter-market
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    pdfAvailable download formats
    Dataset updated
    Jul 12, 2025
    Dataset provided by
    Authors
    Zion Market Research
    License

    https://www.zionmarketresearch.com/privacy-policyhttps://www.zionmarketresearch.com/privacy-policy

    Time period covered
    2022 - 2030
    Area covered
    Global
    Description

    Electric Pocket Lighter Market was valued at $680.5 Mn in 2023, and is projected to reach $USD 951.9 Mn by 2032, at a CAGR of 3.80% from 2023 to 2032.

  8. Pedon Vault

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • ngda-soils-geoplatform.hub.arcgis.com
    • +1more
    Updated Aug 1, 2016
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    USDA NRCS ArcGIS Online (2016). Pedon Vault [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/nrcs::pedon-vault-1/data
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    Dataset updated
    Aug 1, 2016
    Dataset provided by
    Natural Resources Conservation Servicehttp://www.nrcs.usda.gov/
    Authors
    USDA NRCS ArcGIS Online
    Area covered
    Description

    The Pedon Vault form will record soil exposures across the country for agency and citizen scientists to provide a means of population for a national database of sites to visit in the field for good exposures of soil profiles to be used by educators, scientists, interested public, and universities classes and soil judging teams. It will require that users have an ArcGIS Online account. It’s a great first step toward citizen science involvement with the national soils program.

  9. a

    USpga050 1:5M arc

    • hifld-geoplatform.opendata.arcgis.com
    Updated Mar 22, 2021
    + more versions
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    GeoPlatform ArcGIS Online (2021). USpga050 1:5M arc [Dataset]. https://hifld-geoplatform.opendata.arcgis.com/datasets/seismic-ground-motion-hazards-with-10-percent-probability?layer=64
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    Dataset updated
    Mar 22, 2021
    Dataset authored and provided by
    GeoPlatform ArcGIS Online
    Area covered
    Description

    Probabilistic seismic-hazard maps were prepared for the conterminous United States portraying peak horizontal acceleration and horizontal spectral response acceleration for 0.2- and 1.0-second periods with probabilities of exceedance of 10 percent in 50 years and 2 percent in 50 years. This particular data set is for peak horizontal acceleration with a 10 percent probability of exceedance in 50 years. All of the maps were prepared by combining the hazard derived from spatially smoothed historic seismicity with the hazard from fault-specific sources. The acceleration values contoured are the random horizontal component. The reference site condition is firm rock, defined as having an average shear-wave velocity of 760 m/sec in the top 30 meters corresponding to the boundary between NEHRP (National Earthquake Hazards Reduction program) site classes B and C.

    For more information online visit: http://pubs.usgs.gov/sim/3195/, http://pubs.usgs.gov/of/2008/1128/, and http://earthquake.usgs.gov/hazards/

  10. d

    Arckaringa basin - ARC

    • data.gov.au
    • cloud.csiss.gmu.edu
    • +3more
    zip
    Updated Apr 13, 2022
    + more versions
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    Bioregional Assessment Program (2022). Arckaringa basin - ARC [Dataset]. https://data.gov.au/data/dataset/ddb8400d-1b76-43b1-8c1a-427fe1297884
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    zipAvailable download formats
    Dataset updated
    Apr 13, 2022
    Dataset authored and provided by
    Bioregional Assessment Program
    License

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

    Area covered
    Arckaringa Basin
    Description

    Abstract

    This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

    Interpretation of the extent of the Arckaringa Basin at time of issue.

    Purpose

    The data file is used to indicate the location and understood outer extent of the Arckaringa Basin at the time of issue. Basement highs (or inliers) that puncture the extent of Arckaringa Basin sediments are not displayed in this file.

    Dataset History

    The outer extent of the Arckaringa Basin has been interpreted from outcrop geology mapping as provided by the South Australian Government Department of State Development (DSD) from there online SARIG database, seismic data that was either collected by or provided to the DSD for record-keeping and regulatory requirements, as well as vetted downhole geological logging data as stored in the South Australian Government managed geoscientific database SA_Geodata.

    Dataset Citation

    SA Department of Environment, Water and Natural Resources (2015) Arckaringa basin - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/ddb8400d-1b76-43b1-8c1a-427fe1297884.

  11. King Cove, Alaska 8 arc-second Coastal Digital Elevation Model

    • datadiscoverystudio.org
    esri arc ascii v.1
    Updated Jan 2, 2014
    + more versions
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    DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce (2014). King Cove, Alaska 8 arc-second Coastal Digital Elevation Model [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/bb7f5d0723a7441aa4d3d36a73555592/html
    Explore at:
    esri arc ascii v.1(165), esri arc ascii v.1Available download formats
    Dataset updated
    Jan 2, 2014
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    National Centers for Environmental Informationhttps://www.ncei.noaa.gov/
    United States Department of Commercehttp://www.commerce.gov/
    National Environmental Satellite, Data, and Information Service
    National Tsunami Hazard Mitigation Program (NTHMP)
    Area covered
    Description

    NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) to support individual coastal States as part of the National Tsunami Hazard Mitigation Program's (NTHMP) efforts to improve community preparedness and hazard mitigation. These integrated bathymetric-topographic DEMs are used to support tsunami and coastal inundation mapping. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to various vertical and horizontal datums depending on the specific modeling requirements of each State. For specific datum information on each DEM, refer to the appropriate DEM documentation. Cell sizes also vary depending on the specification required by modelers in each State, but typically range from 8/15 arc-second (~16 meters) to 8 arc-seconds (~240 meters).This is an ArcGIS image service showing color shaded relief visualizations of high-resolution digital elevation models (DEMs) of U.S. coastal regions. NOAA's National Geophysical Data Center (NGDC) builds and distributes high-resolution coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. DEMs included in this visualization: High-resolution DEMs of select U.S. coastal communities and surrounding areas. Most are at a resolution of 1/3 to 1 arc-second (approx 10-30 m); U.S. Coastal Relief Model: A 3 arc-second (approx 90 m) comprehensive view of the conterminous U.S. coastal zone, Puerto Rico, and Hawaii; Southern Alaska Coastal Relief Model: A 24 arc-second (approx. 500 m) model of Southern Alaska, spanning the Bering Sea, Aleutian Islands, and Gulf of Alaska. This map service can be used as a basemap. It has a transparent background, so it can also be shown as a layer on top of a different basemap. Please see NGDC's corresponding DEM Footprints map service for polygon footprints and more information about the individual DEMs used to create this composite view.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Geophysical Data Center. NOAA's National Geophysical Data Center (NGDC) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NGDC, and elsewhere on the web); Layers 6-11: NGDC DEM Projects (DEMs hosted at NGDC, color-coded by project); Layer 12: All NGDC Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NGDC).

  12. a

    Computer and Broadband Internet Access (by ARC 20 County) 2017

    • opendata.atlantaregional.com
    Updated Jun 26, 2019
    + more versions
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    Georgia Association of Regional Commissions (2019). Computer and Broadband Internet Access (by ARC 20 County) 2017 [Dataset]. https://opendata.atlantaregional.com/datasets/d4bef0650de1415793074dd0b3e90835
    Explore at:
    Dataset updated
    Jun 26, 2019
    Dataset provided by
    The Georgia Association of Regional Commissions
    Authors
    Georgia Association of Regional Commissions
    License

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

    Area covered
    Description

    This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show populations with computer and internet access by ARC 20 County in the Atlanta region.

    The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

    The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

    For further explanation of ACS estimates and margin of error, visit Census ACS website.

    Naming conventions:

    Prefixes:

    None

    Count

    p

    Percent

    r

    Rate

    m

    Median

    a

    Mean (average)

    t

    Aggregate (total)

    ch

    Change in absolute terms (value in t2 - value in t1)

    pch

    Percent change ((value in t2 - value in t1) / value in t1)

    chp

    Change in percent (percent in t2 - percent in t1)

    Suffixes:

    None

    Change over two periods

    _e

    Estimate from most recent ACS

    _m

    Margin of Error from most recent ACS

    _00

    Decennial 2000

    Attributes:

    SumLevel

    Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

    GEOID

    Census tract Federal Information Processing Series (FIPS) code

    NAME

    Name of geographic unit

    Planning_Region

    Planning region designation for ARC purposes

    Acres

    Total area within the tract (in acres)

    SqMi

    Total area within the tract (in square miles)

    County

    County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

    CountyName

    County Name

    TotalHH_e

    # Total households, 2017

    TotalHH_m

    # Total households, 2017 (MOE)

    WithAComputer_e

    # Households with a computer, 2017

    WithAComputer_m

    # Households with a computer, 2017 (MOE)

    pWithAComputer_e

    % Households with a computer, 2017

    pWithAComputer_m

    % Households with a computer, 2017 (MOE)

    WithBroadband_e

    # Households with broadband Internet, 2017

    WithBroadband_m

    # Households with broadband Internet, 2017 (MOE)

    pWithBroadband_e

    % Households with broadband Internet, 2017

    pWithBroadband_m

    % Households with broadband Internet, 2017 (MOE)

    last_edited_date

    Last date the feature was edited by ARC

    Source: U.S. Census Bureau, Atlanta Regional Commission

    Date: 2013-2017

    For additional information, please visit the Census ACS website.

  13. h

    Water level data @ Arc

    • hydrosos.com
    • sobos.at
    • +1more
    Updated Nov 30, 2022
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    SOBOS GmbH, Shared Environment (2022). Water level data @ Arc [Dataset]. https://www.hydrosos.com/river.php?river=Arc
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    Dataset updated
    Nov 30, 2022
    Dataset authored and provided by
    SOBOS GmbH, Shared Environment
    License

    https://creativecommons.org/publicdomain/by/4.0/https://creativecommons.org/publicdomain/by/4.0/

    Area covered
    Description

    Online Service für Wasserstandsüberwachung in den USA, CA, BE, NL, UK, IE, DE, AT, CH und Südtirol.

  14. A

    US1hz050 1:5M arc

    • data.amerigeoss.org
    • communities-amerigeoss.opendata.arcgis.com
    • +2more
    csv, esri rest +5
    Updated Nov 9, 2018
    + more versions
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    AmeriGEO ArcGIS (2018). US1hz050 1:5M arc [Dataset]. https://data.amerigeoss.org/dataset/us1hz050-1-5m-arc21
    Explore at:
    kml, geojson, zip, ogc wms, esri rest, html, csvAvailable download formats
    Dataset updated
    Nov 9, 2018
    Dataset provided by
    AmeriGEO ArcGIS
    Description

    Probabilistic seismic-hazard maps were prepared for the conterminous United States portraying peak horizontal acceleration and horizontal spectral response acceleration for 0.2- and 1.0-second periods with probabilities of exceedance of 10 percent in 50 years and 2 percent in 50 years. This particular data set is for horizontal spectral response acceleration for 1.0-second period with a 10 percent probability of exceedance in 50 years. All of the maps were prepared by combining the hazard derived from spatially smoothed historic seismicity with the hazard from fault-specific sources. The acceleration values contoured are the random horizontal component. The reference site condition is firm rock, defined as having an average shear-wave velocity of 760 m/sec in the top 30 meters corresponding to the boundary between NEHRP (National Earthquake Hazards Reduction program) site classes B and C.

    For more information online visit: http://pubs.usgs.gov/sim/3195/, http://pubs.usgs.gov/of/2008/1128/, and http://earthquake.usgs.gov/hazards/

  15. Northern Gulf 1 arc-second NAVD 88 Coastal Digital Elevation Model

    • datadiscoverystudio.org
    • gimi9.com
    • +3more
    netcdf v.4 classic
    Updated Dec 31, 2010
    + more versions
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    DOC/NOAA/NESDIS/NGDC > National Geophysical Data Center, NESDIS, NOAA, U.S. Department of Commerce (2010). Northern Gulf 1 arc-second NAVD 88 Coastal Digital Elevation Model [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/467926f6b4394c33b2f7a77cf98df74d/html
    Explore at:
    netcdf v.4 classicAvailable download formats
    Dataset updated
    Dec 31, 2010
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    United States Department of Commercehttp://www.commerce.gov/
    National Environmental Satellite, Data, and Information Service
    Authors
    DOC/NOAA/NESDIS/NGDC > National Geophysical Data Center, NESDIS, NOAA, U.S. Department of Commerce
    Area covered
    Description

    NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions in the Gulf of Mexico. These integrated bathymetric-topographic DEMs were developed for NOAA Coastal Survey Development Laboratory (CSDL) through the American Recovery and Reinvestment Act (ARRA) of 2009 to evaluate the utility of the Vertical Datum Transformation tool (VDatum), developed jointly by NOAA's Office of Coast Survey (OCS), National Geodetic Survey (NGS), and Center for Operational Oceanographic Products and Services (CO-OPS). Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. Coastal Services Center (CSC), the U.S. Office of Coast Survey (OCS), the U.S. Army Corps of Engineers (USACE), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of North American Vertical Datum of 1988 (NAVD 88), Mean High Water (MHW) or Mean Lower Low Water (MLLW) and horizontal datum of North American Datum of 1983 (NAD 83). Cell size ranges from 1/3 arc-second (~10 meters) to 1 arc-second (~30 meters). The NOAA VDatum DEM Project was funded by the American Recovery and Reinvestment Act (ARRA) of 2009 (http://www.recovery.gov/).The DEM Global Mosaic is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), along with the global GEBCO_2014 grid: http://www.gebco.net/data_and_products/gridded_bathymetry_data. NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service is a general-purpose global, seamless bathymetry/topography mosaic. It combines DEMs from a variety of near sea-level vertical datums, such as mean high water (MHW), mean sea level (MSL), and North American Vertical Datum of 1988 (NAVD88). Elevation values have been rounded to the nearest meter, with DEM cell sizes going down to 1 arc-second. Higher-resolution DEMs, with greater elevation precision, are available in the companion NAVD88: http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042 and MHW: http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799 mosaics. By default, the DEMs are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Please see NCEI's corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. In this visualization, the elevations/depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Centers for Environmental Information (NCEI). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NCEI, and elsewhere on the web); Layers 6-11: NCEI DEM Projects (DEMs hosted at NCEI, color-coded by project); Layer 12: All NCEI Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NCEI).This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), with vertical units referenced to mean high water (NAVD88). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service provides data from many individual DEMs combined together as a mosaic. By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the 'Lock Raster 'mosaic method in ArcMap. This is one of three services displaying collections of DEMs that are referenced to common vertical datums: North American Vertical Datum of 1988 (NAVD88): http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042, Mean High Water (MHW): http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799, and Mean Higher High Water: http://noaa.maps.arcgis.com/home/item.html?id=9471f8d4f43e48109de6275522856696. In addition, the DEM Global Mosaic is a general-purpose global, seamless bathymetry/topography mosaic containing all the DEMs together. Two services are available: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff Elevation Values: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff and Color Shaded Relief: http://noaa.maps.arcgis.com/home/item.html?id=feb3c625dc094112bb5281c17679c769. Please see the corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. This service has several server-side functions available. These can be selected in the ArcGIS Online layer using 'Image Display ', or in ArcMap under 'Processing Templates '. None: The default. Provides elevation/depth values in meters relative to the NAVD88 vertical datum. ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png. GrayscaleHillshade: A simple grayscale hillshade visualization. SlopeMapRGB: Slope in degrees, visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/SlopeMapLegend_V7b.png. SlopeNumericValues: Slope in degrees, returning the actual numeric values. AspectMapRGB: Orientation of the terrain (0-360 degrees), visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/AspectMapLegendPie_V7b.png. AspectNumericValues: Aspect in degrees, returning the actual numeric values.

  16. d

    Mount Toondina Crater - ARC

    • data.gov.au
    • researchdata.edu.au
    • +1more
    zip
    Updated Apr 13, 2022
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    Bioregional Assessment Program (2022). Mount Toondina Crater - ARC [Dataset]. https://data.gov.au/data/dataset/groups/4e160c80-2cbd-4acd-aef1-51c3a2813234
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 13, 2022
    Dataset authored and provided by
    Bioregional Assessment Program
    License

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

    Description

    Abstract

    This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

    Earth impact structure location.

    Purpose

    Understanding the geological complexity in the vicinity.

    Dataset History

    Coordinate point taken from unknown source however Mount Toondina is a well known topographic feature and will be on any number of topographic and geologic map sheets. Given it is an astrobleme its co-ordinates will be on any number of meteorite impact crater web pages and books.

    Dataset Citation

    SA Department of Environment, Water and Natural Resources (2015) Mount Toondina Crater - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/4e160c80-2cbd-4acd-aef1-51c3a2813234.

  17. Arc Fault Detection Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Arc Fault Detection Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-arc-fault-detection-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Arc Fault Detection Market Outlook



    The global Arc Fault Detection market size was estimated to be USD 3.2 billion in 2023 and is expected to reach USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2032. This growth is driven by increasing safety concerns and stringent government regulations to prevent electrical fires.



    One of the primary growth factors for the Arc Fault Detection market is the rising incidence of electrical fires, which has led to an increased focus on safety measures across residential, commercial, and industrial sectors. Governments and regulatory bodies around the world are implementing stringent safety standards that mandate the use of arc fault detection devices. The National Electrical Code (NEC) in the United States, for example, has made the installation of Arc Fault Circuit Interrupters (AFCIs) mandatory in many types of new construction, which has significantly boosted market demand.



    Technological advancements are another crucial driver for market growth. The integration of smart technologies and Internet of Things (IoT) in arc fault detection systems has improved the efficiency and reliability of these devices. Smart AFCIs can now be monitored and controlled remotely, providing real-time data and diagnostics that further enhance safety measures. Such advancements are attracting investments from both established players and new entrants, accelerating market expansion.



    Economic development in emerging markets, particularly in Asia Pacific and Latin America, is also contributing significantly to the growth of the Arc Fault Detection market. The rapid urbanization and industrialization in these regions are leading to increased construction activities, which in turn is driving the demand for arc fault detection devices. Governments in these regions are also becoming more aware of the importance of electrical safety, leading to the adoption of stricter regulations and standards, thereby fueling market growth.



    The Faulted Circuit Indicating FCI System Sales have been gaining traction as a vital component in enhancing electrical safety and reliability. These systems are designed to quickly identify and locate faults in electrical circuits, thereby minimizing downtime and preventing potential hazards. With the increasing complexity of electrical grids and the growing emphasis on smart grid technologies, the demand for advanced FCI systems is on the rise. These systems not only improve operational efficiency but also contribute to the overall safety of electrical infrastructures. As utilities and industries strive to modernize their electrical networks, the adoption of FCI systems is expected to see significant growth, further driving market expansion.



    Regionally, North America and Europe are currently the leading markets for arc fault detection, owing to the stringent safety regulations and high awareness levels. However, the Asia Pacific region is expected to witness the highest growth over the forecast period, driven by rapid urbanization, increasing construction activities, and growing awareness about electrical safety. Latin America and the Middle East & Africa are also expected to show substantial growth, albeit at a slower pace compared to Asia Pacific.



    Product Type Analysis



    The product type segment of the Arc Fault Detection market includes Combination Arc Fault Circuit Interrupters (CAFCI), Branch/Feeder Arc Fault Circuit Interrupters (BCAFCI), and Outlet Circuit Arc Fault Circuit Interrupters (OCAFCI). Each of these product types serves distinct applications and has unique advantages that cater to specific needs in the market.



    Combination Arc Fault Circuit Interrupters (CAFCI) are designed to provide protection against both parallel and series arc faults. These devices are highly versatile and are increasingly being installed in residential and commercial buildings. The increasing preference for CAFCIs is attributed to their dual protection capability, which significantly reduces the risk of electrical fires. The growing awareness about electrical safety in residential areas is expected to drive the demand for CAFCIs, making them one of the fastest-growing segments in the market.



    Branch/Feeder Arc Fault Circuit Interrupters (BCAFCI) are specifically designed to protect branch circuits and feeders from arc faults. These devices are commonly used in commercial a

  18. Rivers of the Near East

    • data.amerigeoss.org
    • data.apps.fao.org
    http, png, show, wms +1
    Updated Mar 5, 2022
    + more versions
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    Food and Agriculture Organization (2022). Rivers of the Near East [Dataset]. https://data.amerigeoss.org/dataset/337ebe2b-318c-4c17-9b40-c07ab03e9019
    Explore at:
    zip, wms, show, png, httpAvailable download formats
    Dataset updated
    Mar 5, 2022
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    License

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

    Area covered
    Near East
    Description

    The rivers of the Near East dataset is derived from the World Wildlife Fund's (WWF) HydroSHEDS drainage direction layer and a stream network layer. The source of the drainage direction layer was the 15-second Digital Elevation Model (DEM) from NASA's Shuttle Radar Topographic Mission (SRTM). The raster stream network was determined by using the HydroSHEDS flow accumulation grid, with a threshold of about 1000 km² upstream area.

    The stream network dataset consists of the following information: the origin node of each arc in the network (FROM_NODE), the destination of each arc in the network (TO_NODE), the Strahler stream order of each arc in the network (STRAHLER), numerical code and name of the major basin that the arc falls within (MAJ_BAS and MAJ_NAME); - area of the major basin in square km that the arc falls within (MAJ_AREA); - numerical code and name of the sub-basin that the arc falls within (SUB_BAS and SUB_NAME); - area of the sub-basin in square km that the arc falls within (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows that the arc falls within (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea). The attributes table now includes a field named "Regime" with tentative classification of perennial ("P") and intermittent ("I") streams.

    Supplemental Information:

    This dataset is developed as part of a GIS-based information system on water resources for the Near East. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

    Contact points:

    Metadata contact: AQUASTAT FAO-UN Land and Water Division

    Contact: Jippe Hoogeveen FAO-UN Land and Water Division

    Contact: Livia Peiser FAO-UN Land and Water Division

    Data lineage:

    The linework of the map was obtained by converting the stream network to a feature dataset with the Hydrology toolset in ESRI ArcGIS.The Flow Direction and Stream Order grids were derived from hydrologically corrected elevation data with a resolution of 15 arc-seconds.The elevation dataset was part of a mapping product, HydroSHEDS, developed by the Conservation Science Program of World Wildlife Fund.Original input data had been obtained during NASA's Shuttle Radar Topography Mission (SRTM).

    Online resources:

    Download - Rivers of the Near East (ESRI shapefile)

    For general information regarding the HydroSHEDS data product

    For HydroSHEDS dataset download and technical information

    Hydrological basins in the Near East

  19. Z

    Dataset related to article "Impact of the Extremities Positioning on the...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Oct 27, 2023
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    Castiglioni, Isabella (2023). Dataset related to article "Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment" [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10046460
    Explore at:
    Dataset updated
    Oct 27, 2023
    Dataset provided by
    Brioso, Ricardo Coimbra
    Carlo-Stella, Carmelo
    loiacono, Daniele
    Galdieri, Carmela
    Bramanti, Stefania
    Scorsetti, Marta
    Dei, Damiano
    De Philippis, Chiara
    Crespi, Leonardo
    Lambri, Nicola
    Antonetti, Simone Leopoldo
    Castiglioni, Isabella
    Reggiori, Giacomo
    Rusconi, Roberto
    Tomatis, Stefano
    Bellu, Luisa
    Navarria, Pierina
    Clerici, Elena
    Mancosu, Pietro
    Description

    Abstract Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup.

  20. V

    Address

    • data.virginia.gov
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Jun 30, 2025
    + more versions
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    Town of Blacksburg GIS Data (2025). Address [Dataset]. https://data.virginia.gov/dataset/address
    Explore at:
    zip, csv, kml, arcgis geoservices rest api, geojson, htmlAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset provided by
    Town of Blacksburg - ArcOnline
    Authors
    Town of Blacksburg GIS Data
    Description

    GIS Address Points, Road Centerlines, and Building Footprints provide base data information for many purposes, including call routing and emergency response within the New River Valley 911 Authority.

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Georgia Association of Regional Commissions (2021). Computer and Broadband Internet Access (by Atlanta Neighborhood Planning Unit S, T, and V) 2019 [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/6e618a4f53a642609ce04a9021d207f9

Computer and Broadband Internet Access (by Atlanta Neighborhood Planning Unit S, T, and V) 2019

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Dataset updated
Feb 26, 2021
Dataset provided by
The Georgia Association of Regional Commissions
Authors
Georgia Association of Regional Commissions
License

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

Area covered
Description

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

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