100+ datasets found
  1. a

    Figure 7.3. Overlay analysis biological layers

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • hub.arcgis.com
    Updated Jan 14, 2022
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    Greenland Institute of Natural Resources (2022). Figure 7.3. Overlay analysis biological layers [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/nature::rbuoverlay?layer=1
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    Dataset updated
    Jan 14, 2022
    Dataset authored and provided by
    Greenland Institute of Natural Resources
    Area covered
    Description

    Figure 7.3. Result of overlay analysis of 34 map layers with mainly biologically relevant information (see column “Sub-analysis, biology” in Table 7.1 for included map layers).

  2. a

    Figure 7.1. Overlay analysis all layers

    • hub.arcgis.com
    Updated Jan 14, 2022
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    Greenland Institute of Natural Resources (2022). Figure 7.1. Overlay analysis all layers [Dataset]. https://hub.arcgis.com/datasets/nature::rbuoverlay/explore?layer=0&showTable=true
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    Dataset updated
    Jan 14, 2022
    Dataset authored and provided by
    Greenland Institute of Natural Resources
    Area covered
    Description

    Figure 7.1. Result of overlay analysis of all 51 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are 14, reflecting that in these cells features from 14 different map layers overlap.

  3. e

    RBA Sigguup Nunaa - Figure 7.3 Overlay analysis for human use layers

    • rba.eamra.gl
    • hub.arcgis.com
    Updated May 16, 2023
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    Greenland Institute of Natural Resources (2023). RBA Sigguup Nunaa - Figure 7.3 Overlay analysis for human use layers [Dataset]. https://rba.eamra.gl/maps/df84ea755a534a548db3c37072c57387
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    Dataset updated
    May 16, 2023
    Dataset authored and provided by
    Greenland Institute of Natural Resources
    Area covered
    Description

    Result of overlay analysis of all 28 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are nine, reflecting that in these cells features from nine different map layers overlap. The summary analysis was performed as a so-called GIS overlay analysis using custom-made Python scripts in ArcGIS Pro 3.0.2. In principle, the different map layers presented in Chapters 4-6 were simply stacked on top of each other, and for each 250x250 m cell in a grid system covering the entire AOI, the number of map layers with features present in the cell were counted. Thus, a resulting cell value of e.g., 3 indicates that at the centre of the cell three different map layers have features present. In rare cases, an individual layer may have several features present at the cell centre, e.g., two cultural heritage zone 3 areas, but the layer will still only add a value of one to the overlay. Thus, it is the number of different layer with features present that is summarised, not the number of individual features.

  4. S

    Xinjiang Bazhou based on GIS spatial overlay analysis ‘Korla Fragrant Pear’...

    • scidb.cn
    Updated Dec 9, 2024
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    Wang Lei; Dilichati Borhan; Li Xiaoting; li xi guang; Liu Liguo; Wang Wenjie; Gao Jian (2024). Xinjiang Bazhou based on GIS spatial overlay analysis ‘Korla Fragrant Pear’ industrial resource data set [Dataset]. http://doi.org/10.57760/sciencedb.j00001.00966
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 9, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Wang Lei; Dilichati Borhan; Li Xiaoting; li xi guang; Liu Liguo; Wang Wenjie; Gao Jian
    License

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

    Area covered
    Korla, Xinjiang
    Description

    The Bayinguoleng Mongolian Autonomous Prefecture of Xinjiang is a continental arid climate with abundant light and heat resources. ‘Korla fragrant pear’ has become a pillar industry of economic forest and fruit in Bazhou. With the continuous expansion of planting scale, the disadvantages of industrial planting have become increasingly prominent, which has greatly hindered the sustainable green development of fragrant pear. In this study, GIS spatial overlay analysis and three-phase fruit resource data were used to explore the industrial resources of ‘Korla Fragrant Pear’ in Bazhou. This data set consists of six types of data : forest fruit resource data, meteorological data, pest data, elevation data, soil data and planting management data in Bazhou area. This data set provides a scientific theoretical basis for exploring the current situation of ‘Korla Fragrant Pear’ industry, promoting the quality and efficiency of fruit industry, and realizing the high-quality development of digital management of fruit industry in Xinjiang.

  5. The data for "Multi-Criteria Overlay Analysis for Identifying Preferred...

    • zenodo.org
    zip
    Updated Jul 10, 2025
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    Yousef Mashal; Yousef Mashal; MOHAMED RAMY EL-MAARRY; MOHAMED RAMY EL-MAARRY; Maurizio Pajola; Maurizio Pajola; Ioannis Kourakis; Ioannis Kourakis (2025). The data for "Multi-Criteria Overlay Analysis for Identifying Preferred Exploration Zones on Mars" [Dataset]. http://doi.org/10.5281/zenodo.15854213
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    zipAvailable download formats
    Dataset updated
    Jul 10, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Yousef Mashal; Yousef Mashal; MOHAMED RAMY EL-MAARRY; MOHAMED RAMY EL-MAARRY; Maurizio Pajola; Maurizio Pajola; Ioannis Kourakis; Ioannis Kourakis
    License

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

    Time period covered
    Jun 3, 2025
    Description

    This record contains data products associated with the paper with Mashal et al,. "Multi-Criteria Overlay Analysis for Identifying Preferred Exploration Zones on Mars" accepted by The Planetary Science Journal.

    The folder includes the following files:

    • Readme.txt : Detalied information about the files.
    • MCOA-CONSTRAINTS.tif : The final MCOA result map discussed in the manuscript (Figure 6).
    • _C1.tif to _C8.tif: Input criterion maps used in the overlay analysis, as referenced in the manuscript Equations 1 and 2.
    • Mars_2000_Equidistant_Cylindrical_sphere.wkt : Well-known file for the projection used.
    • MCOA_PROJECT.qgz: A ready-to-use QGIS project file that loads all layers with the correct projection and layout for ease of viewing.
    • shaded_relief.tif : hillshade raster used as a background layer in the QGIS project to enhance the visual interpretation of the MCOA result.

    If you use this dataset, please cite both the article (10.3847/PSJ/ade30e) and this Zenodo record.

  6. e

    RBA Sigguup Nunaa - Figure 7.1-2 Overlay analysis all layers

    • rba.eamra.gl
    Updated May 16, 2023
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    Greenland Institute of Natural Resources (2023). RBA Sigguup Nunaa - Figure 7.1-2 Overlay analysis all layers [Dataset]. https://rba.eamra.gl/datasets/overlay-analysis-all-layers
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    Dataset updated
    May 16, 2023
    Dataset authored and provided by
    Greenland Institute of Natural Resources
    Area covered
    Description

    Figure 7.1. Result of overlay analysis of all 28 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are nine, reflecting that in these cells features from nine different map layers overlap.

  7. f

    Appendix C. Contingency tables from spatial overlay analyses.

    • wiley.figshare.com
    html
    Updated Jun 3, 2023
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    Christof Bigler; Dominik Kulakowski; Thomas T. Veblen (2023). Appendix C. Contingency tables from spatial overlay analyses. [Dataset]. http://doi.org/10.6084/m9.figshare.3525434.v1
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    htmlAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Wiley
    Authors
    Christof Bigler; Dominik Kulakowski; Thomas T. Veblen
    License

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

    Description

    Contingency tables from spatial overlay analyses.

  8. l

    Overlay Cluster Analysis of COVID-19 Cases and Vaccination Coverage

    • visionzero.geohub.lacity.org
    Updated Oct 19, 2022
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    County of Los Angeles (2022). Overlay Cluster Analysis of COVID-19 Cases and Vaccination Coverage [Dataset]. https://visionzero.geohub.lacity.org/documents/75dc3062f11243de9eaabf8682be0144
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    Dataset updated
    Oct 19, 2022
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Map author: Misbah Rashad, MPH Affiliation: Los Angeles County Department of Public HealthData Sources: LACDPH Acute Communicable Disease Control & Vaccine Preventable Disease ControlAreas with high percent positivity (%p) and conversely low vaccination coverage (VC) may be at increased risk for community transmission of the SARS-CoV-2 virus. County-wide data on %p and VC (November 1, 2021 - November 30, 2021) was aggregated to the census tract level. Univariate local Moran’s I analysis was used to identify statistically significant geographic clustering of %p and VC. Signaling census tracts characterized by high %p and low VC were identified in the overlay analysis as potential high-risk areas for virus transmission. These results can inform further analysis on community/population demographics (e.g. area poverty, race/ethnicity) that may contribute to disparities in infection risk.This analysis was originally an abstract submission presented at the 2022 Council of State and Territorial Epidemiologists Conference. A link to the full abstract is provided below:

    https://cste.confex.com/cste/2022/abstract/papers/viewonly.cgi?password=119602&username=15967

  9. f

    Data_Sheet_3_Gaps in Protection of Important Ocean Areas: A Spatial...

    • frontiersin.figshare.com
    pdf
    Updated Jun 4, 2023
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    Natasha J. Gownaris; Christine M. Santora; John B. Davis; Ellen K. Pikitch (2023). Data_Sheet_3_Gaps in Protection of Important Ocean Areas: A Spatial Meta-Analysis of Ten Global Mapping Initiatives.pdf [Dataset]. http://doi.org/10.3389/fmars.2019.00650.s003
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    pdfAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Natasha J. Gownaris; Christine M. Santora; John B. Davis; Ellen K. Pikitch
    License

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

    Description

    To safeguard biodiversity effectively, marine protected areas (MPAs) should be sited using the best available science. There are numerous ongoing United Nations and non-governmental initiatives to map globally important marine areas. The criteria used by these initiatives vary, resulting in contradictions in the areas identified as important. Our analysis is the first to overlay these initiatives, quantify consensus, and conduct gap analyses at the global scale. We found that 55% of the ocean has been identified as important by one or more initiatives, and that individual areas have been identified by as many as seven overlapping initiatives. Using our overlay map and data on current MPA coverage, we highlight gaps in protection of important areas of the ocean. We considered any area identified by two to four initiatives to be of moderate consensus. Over 14% of the ocean fell under this category and most of this area (88%) is not yet protected. The largest concentrations of medium-consensus areas without protection were found in the Caribbean Sea, Madagascar and the southern tip of Africa, the Mediterranean Sea, and the Coral Triangle. Areas of high consensus (identified by five to seven initiatives) were almost always within MPAs, but their no-take status was often unreported. We found that nearly every marine province and nearly every exclusive economic zone contained area that has been identified as important but is not yet protected. Much of the identified area lies within contiguous stretches of >100,000 km2; it is unrealistic to expect that all this area be protected. Nonetheless, our results on areas of consensus provide initial insight into opportunities for further ocean protection.

  10. c

    Parcels with overlay attributes

    • s.cnmilf.com
    • data.sfgov.org
    • +1more
    Updated Jun 29, 2025
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    data.sfgov.org (2025). Parcels with overlay attributes [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/parcels-with-overlay-attributes
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    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.sfgov.org
    Description

    A. SUMMARY This dataset is derived from parcels and several other overlay administrative boundaries (listed below). The dataset was developed by DataSF as a convenience for matching parcels to districts where appropriate. This can be simpler than running a geospatial process every time you want to join parcels to a boundary. The districts provided here run along streets and are non-overlapping so that the parcels will be contained within a single district. The boundaries included are: 1. Analysis Neighborhoods 2. Supervisor Districts 3. Police Districts 4. Planning Districts B. HOW THE DATASET IS CREATED A script runs daily that overlays parcels with each of the boundaries to produce the composite dataset. C. UPDATE PROCESS Updated daily by a script based on the upstream parcels dataset which is also updated daily. D. HOW TO USE THIS DATASET You can use this dataset to match to administrative districts provided here to datasets that contain a parcel number. This can be a simpler process than running these joins spatially. In short, we pre-process the spatial overlays to make joins simpler and more performant.

  11. I

    Impregnated Overlay Papers Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jan 5, 2025
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    Data Insights Market (2025). Impregnated Overlay Papers Report [Dataset]. https://www.datainsightsmarket.com/reports/impregnated-overlay-papers-259349
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    pdf, doc, pptAvailable download formats
    Dataset updated
    Jan 5, 2025
    Dataset authored and provided by
    Data Insights Market
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Impregnated Overlay Papers Market Overview: The global Impregnated Overlay Papers market size is estimated to be 186 million in 2025, and is projected to grow at a CAGR of 1.6% from 2025 to 2033. Impregnated Overlay Papers are papers used in the production of high-pressure laminates (HPL) and low-pressure laminates (LPL). The market is driven by the growth of the construction industry. Drivers, Trends, Restraints: The market is driven by the increasing demand for decorative and durable materials in the construction industry. The rising demand for HPLs and LPLs for applications such as flooring, countertops, and furniture is also contributing to the market growth. The trend towards using sustainable and environmentally friendly materials is expected to drive the demand for impregnated overlay papers made from recycled fibers. However, the market is restrained by factors such as the volatility of raw material prices and the competition from alternative materials such as metals and plastics. This comprehensive market report provides an in-depth analysis of the global impregnated overlay papers industry, covering key market insights, trends, segmentation, challenges, driving forces, and key players.

  12. f

    General information of different soil types.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Feng Zhang; Shihang Wang; Mingsong Zhao; Falv Qin; Xiaoyu Liu (2023). General information of different soil types. [Dataset]. http://doi.org/10.1371/journal.pone.0245040.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Feng Zhang; Shihang Wang; Mingsong Zhao; Falv Qin; Xiaoyu Liu
    License

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

    Description

    General information of different soil types.

  13. H

    Zoning (Hawaii County)

    • opendata.hawaii.gov
    • geoportal.hawaii.gov
    • +2more
    Updated Sep 5, 2024
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    Office of Planning (2024). Zoning (Hawaii County) [Dataset]. https://opendata.hawaii.gov/dataset/zoning-hawaii-county
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    arcgis geoservices rest api, pdf, kml, geojson, zip, ogc wfs, ogc wms, csv, htmlAvailable download formats
    Dataset updated
    Sep 5, 2024
    Dataset provided by
    Hawaii Statewide GIS Program
    Authors
    Office of Planning
    Area covered
    Hawaii County, Hawaii
    Description

    [Metadata] Description: Hawaii County Zoning as of November 2023. Source: County of Hawaii, Planning Dept., November 8, 2023.


    Use for overlay analysis in determining approximate boundary delineation to the County of Hawaii, Zoning district classification. This Polygon feature data set was created by the County of Hawaii, Planning Department for approximate Zoning boundary location illustration use in permit reviews by the Planning Department.

    For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/cty_zoning_haw.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

    The County of Hawaii, Planning Department GIS data is intended to be used as a guide for planning purposes only and should not be used for boundary interpretations or other spatial analysis beyond the limitations of the data.

    Information shown on these maps are derived from public records that are constantly undergoing change and do not replace a site survey, and is not warranted for content or accuracy. The County does not guarantee the positional or thematic accuracy of the GIS data. The GIS data or cartographic digital files are not a legal representation of any of the features in which it depicts,and disclaims any assumption of the legal status of which it represents. This digital version is not the official map therefore users are advised to contact the County of Hawaii, Planning Dept. for zoning verification.

    For more detailed metadata information, please refer to the PDF text metadata document that is distributed with the GIS data.

  14. Data from: A site suitability analysis for castor (Ricinus communis L.)...

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    • +1more
    Updated Apr 21, 2025
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    Agricultural Research Service (2025). Data from: A site suitability analysis for castor (Ricinus communis L.) production during Brazil's second harvest accounting for potential disease [Dataset]. https://catalog.data.gov/dataset/data-from-a-site-suitability-analysis-for-castor-ricinus-communis-l-production-during-braz-964a7
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Agricultural Research Servicehttps://www.ars.usda.gov/
    Description

    Raster data providing site suitability results for the production of castor throughout Brazil. The pixel value range from 1 (currently not suitable) to 10 (highly suitable) for a suitability ranking in the given pixel location. The site suitability for castor was conducted using data associated with agronomic and disease characteristics. The various characteristics were subject to a weighted overlay analysis in conjunction with an analytical hierarchy process. The raster was the result of these analytics.

  15. A

    ‘VT Data - Overlay District 20170109, Ryegate’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 9, 2017
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2017). ‘VT Data - Overlay District 20170109, Ryegate’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-vt-data-overlay-district-20170109-ryegate-f5bf/latest
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    Dataset updated
    Jan 9, 2017
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘VT Data - Overlay District 20170109, Ryegate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a375a091-3b26-4200-b41a-eabb231d23f2 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    {{default.description}}

    --- Original source retains full ownership of the source dataset ---

  16. A

    ‘VT Data - Scenic Overlay District 20110711, Burke’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jul 11, 2011
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2011). ‘VT Data - Scenic Overlay District 20110711, Burke’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-vt-data-scenic-overlay-district-20110711-burke-489c/39904ec7/?iid=000-410&v=presentation
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    Dataset updated
    Jul 11, 2011
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘VT Data - Scenic Overlay District 20110711, Burke’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a6f6fd37-848f-45cc-afa0-3d60221fbba0 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    {{default.description}}

    --- Original source retains full ownership of the source dataset ---

  17. c

    Jefferson County KY Zoning Overlays

    • s.cnmilf.com
    • data.lojic.org
    • +2more
    Updated Apr 13, 2023
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    Louisville Metro Planning & Design (2023). Jefferson County KY Zoning Overlays [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/jefferson-county-ky-zoning-overlays
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    Dataset updated
    Apr 13, 2023
    Dataset provided by
    Louisville Metro Planning & Design
    Area covered
    Kentucky, Jefferson County
    Description

    The zoning overlay layer is suited for overlay analysis and is appropriate for use on small scale cartographics, general query and overlay operations. Overlay Districts are administrative district created by city ordinance. Sets within these districts require special approvals before issuance of any building permits. View detailed metadata.

  18. A

    ‘VT Data - Northfield Zoning - Fluvial Erosion Hazard Zone Overlay’ analyzed...

    • analyst-2.ai
    Updated Nov 21, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘VT Data - Northfield Zoning - Fluvial Erosion Hazard Zone Overlay’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-vt-data-northfield-zoning-fluvial-erosion-hazard-zone-overlay-9e27/9ae1066e/?iid=001-343&v=presentation
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    Dataset updated
    Nov 21, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘VT Data - Northfield Zoning - Fluvial Erosion Hazard Zone Overlay’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1f633c80-0976-44ff-ac39-5fcf6d8ac88c on 21 November 2021.

    --- Dataset description provided by original source is as follows ---

    This data includes the Fluvial Erosion Hazard Zone for Northfield, Vermont adopted in 2010 and included in the Town's 2017 Land Use Regulations. This overlay zone represents those areas within town that are vulnerable to damage from erosion during a flooding event. Data was created in 2009 using the State of Vermont Stream Geomorphic Assessment Protocols and approved by the Vermont Department of Environmental Conservation, River Management Program.

    --- Original source retains full ownership of the source dataset ---

  19. O

    Overlay Wear Plate Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jan 25, 2025
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    Data Insights Market (2025). Overlay Wear Plate Report [Dataset]. https://www.datainsightsmarket.com/reports/overlay-wear-plate-271950
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Jan 25, 2025
    Dataset authored and provided by
    Data Insights Market
    License

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

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global overlay wear plate market is projected to reach a value of USD 1.2 billion by 2033, growing at a CAGR of 5.2% from 2025 to 2033. The market growth is primarily driven by the increasing demand for wear-resistant materials in various end-use industries such as mining, power generation, cement, metallurgy, and paper and pulp. The rising demand for overlay wear plates is attributed to their ability to enhance the durability and lifespan of equipment components subjected to abrasive and corrosive environments. The increasing investment in infrastructure projects and the growing adoption of automation in manufacturing processes are key factors contributing to the market growth. Furthermore, the development of advanced overlay wear plates with improved wear resistance and corrosion protection is expected to drive the market expansion. The market is segmented by application, type, and region. Mining is the largest application segment, accounting for over 30% of the global market share, with power generation, cement, and metallurgy being other significant segments. Chromium carbide overlay plates are the most widely used type, followed by complex borocarbide overlay plates. North America and Asia Pacific are the dominant regional markets, with Europe also holding a significant market share.

  20. Z

    An analysis of the current overlay journals

    • data.niaid.nih.gov
    Updated Oct 18, 2022
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    Laakso, Mikael (2022). An analysis of the current overlay journals [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6420517
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    Dataset updated
    Oct 18, 2022
    Dataset provided by
    Laakso, Mikael
    Rousi, Antti M.
    License

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

    Description

    Research data to accommodate the article "Overlay journals: a study of the current landscape" (https://doi.org/10.1177/09610006221125208)

    Identifying the sample of overlay journals was an explorative process (occurring during April 2021 to February 2022). The sample of investigated overlay journals were identified by using the websites of Episciences.org (2021), Scholastica (2021), Free Journal Network (2021), Open Journals (2021), PubPub (2022), and Wikipedia (2021). In total, this study identified 34 overlay journals. Please see the paper for more details about the excluded journal types.

    The journal ISSN numbers, manuscript source repositories, first overlay volumes, article volumes, publication languages, peer-review type, licence for published articles, author costs, publisher types, submission policy, and preprint availability policy were observed by inspecting journal editorial policies and submission guidelines found from journal websites. The overlay journals’ ISSN numbers were identified by examining journal websites and cross-checking this information with the Ulrich’s periodicals database (Ulrichsweb, 2021). Journals that published review reports, either with reviewers’ names or anonymously, were classified as operating with open peer-review. Publisher types defined by Laakso and Björk (2013) were used to categorise the findings concerning the publishers. If the journal website did not include publisher information, the editorial board was interpreted to publish the journal.

    The Organisation for Economic Co-operation and Development (OECD) field of science classification was used to categorise the journals into different domains of science. The journals’ primary OECD field of sciences were defined by the authors through examining the journal websites.

    Whether the journals were indexed in the Directory of Open Access Journals (DOAJ), Scopus, or Clarivate Analytics’ Web of Science Core collection’s journal master list was examined by searching the services with journal ISSN numbers and journal titles.

    The identified overlay journals were examined from the viewpoint of both qualitative and quantitative journal metrics. The qualitative metrics comprised the Nordic expert panel rankings of scientific journals, namely the Finnish Publication Forum, the Danish Bibliometric Research Indicator and the Norwegian Register for Scientific Journals, Series and Publishers. Searches were conducted from the web portals of the above services with both ISSN numbers and journal titles. Clarivate Analytics’ Journal Citation Reports database was searched with the use of both ISSN numbers and journal titles to identify whether the journals had a Journal Citation Indicator (JCI), Two-Year Impact Factor (IF) and an Impact Factor ranking (IF rank). The examined Journal Impact Factors and Impact Factor rankings were for the year 2020 (as released in 2021).

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Greenland Institute of Natural Resources (2022). Figure 7.3. Overlay analysis biological layers [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/nature::rbuoverlay?layer=1

Figure 7.3. Overlay analysis biological layers

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Dataset updated
Jan 14, 2022
Dataset authored and provided by
Greenland Institute of Natural Resources
Area covered
Description

Figure 7.3. Result of overlay analysis of 34 map layers with mainly biologically relevant information (see column “Sub-analysis, biology” in Table 7.1 for included map layers).

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