15 datasets found
  1. d

    Country Polygons as GeoJSON

    • datahub.io
    Updated Sep 1, 2017
    + more versions
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    (2017). Country Polygons as GeoJSON [Dataset]. https://datahub.io/core/geo-countries
    Explore at:
    Dataset updated
    Sep 1, 2017
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    geodata data package providing geojson polygons for all the world's countries

  2. d

    Dengue Clusters (GEOJSON)

    • data.gov.sg
    Updated Jul 9, 2025
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    National Environment Agency (2025). Dengue Clusters (GEOJSON) [Dataset]. https://data.gov.sg/datasets/d_dbfabf16158d1b0e1c420627c0819168/view
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    National Environment Agency
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_dbfabf16158d1b0e1c420627c0819168/view

  3. d

    Geospatial Data | Global Map data | Administrative boundaries | Global...

    • datarade.ai
    .json, .xml
    Updated Jul 4, 2024
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    GeoPostcodes (2024). Geospatial Data | Global Map data | Administrative boundaries | Global coverage | 245k Polygons [Dataset]. https://datarade.ai/data-products/geopostcodes-geospatial-data-global-map-data-administrati-geopostcodes-a4bf
    Explore at:
    .json, .xmlAvailable download formats
    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    GeoPostcodes
    Area covered
    United Kingdom, United States
    Description

    Overview

    Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

    Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

    The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

    Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

    • In-depth spatial analysis

    • Clustering

    • Geofencing

    • Reverse Geocoding

    • Reporting and Business Intelligence (BI)

    Product Features

    • Coherence and precision at every level

    • Edge-matched polygons

    • High-precision shapes for spatial analysis

    • Fast-loading polygons for reporting and BI

    • Multi-language support

    For additional insights, you can combine the map data with:

    • Population data: Historical and future trends

    • UNLOCODE and IATA codes

    • Time zones and Daylight Saving Time (DST)

    Data export methodology

    Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

    All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

    Why companies choose our map data

    • Precision at every level

    • Coverage of difficult geographies

    • No gaps, nor overlaps

    Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

  4. d

    Other PA Networks (GEOJSON)

    • data.gov.sg
    Updated Jun 6, 2024
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    People's Association (2024). Other PA Networks (GEOJSON) [Dataset]. https://data.gov.sg/datasets/d_ddae2233aec5ca47e1d485b54b37fd34/view
    Explore at:
    Dataset updated
    Jun 6, 2024
    Dataset authored and provided by
    People's Association
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from People's Association. For more information, visit https://data.gov.sg/datasets/d_ddae2233aec5ca47e1d485b54b37fd34/view

  5. d

    E-waste Recycling (GEOJSON)

    • data.gov.sg
    Updated Jun 6, 2024
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    National Environment Agency (2024). E-waste Recycling (GEOJSON) [Dataset]. https://data.gov.sg/datasets/d_db40d004afeb5a7f0f555fdcc34934cc/view
    Explore at:
    Dataset updated
    Jun 6, 2024
    Dataset authored and provided by
    National Environment Agency
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_db40d004afeb5a7f0f555fdcc34934cc/view

  6. Geolocet | Points of Interest (POI) data | Europe | Stores, Restaurants,...

    • datarade.ai
    Updated Nov 3, 2023
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    Geolocet (2023). Geolocet | Points of Interest (POI) data | Europe | Stores, Restaurants, Supermarkets, Schools, Hospitals, and more | Fully customizable format [Dataset]. https://datarade.ai/data-products/geolocet-points-of-interest-poi-data-europe-stores-r-geolocet
    Explore at:
    .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Nov 3, 2023
    Dataset authored and provided by
    Geolocet
    Area covered
    Luxembourg, Germany
    Description

    Geolocet's POI Data spans the entire European continent, offering a wealth of information about Points of Interest in all countries. The extensive database covers a wide spectrum of sectors, providing valuable insights into the retail landscape, healthcare facilities, educational institutions, and much more. Whether seeking insights into markets, healthcare services, or educational access, Geolocet's POI data offers access to comprehensive information.

    šŸ” Uncover the Essence of Localities

    Geolocet's POI Data allows exploration into the unique characteristics of various localities. With information available for more than 2,500 types of Points of Interest (POIs), including businesses, services, and amenities within specific regions, Geolocet provides valuable aggregated insights. Alternatively, for those seeking precise locations, Geolocet can provide the exact coordinates of individual POIs. This granularity offers the flexibility to craft insightful profiles of local communities or pinpoint specific POIs, aiding in tailored strategies and decisions for specific areas.

    šŸŒ Customizable Data Solutions

    At Geolocet, we recognize the significance of tailored solutions, which is why our POI Data is entirely customizable to meet your specific requirements. Whether you need data for a single region, or multiple countries, Geolocet's flexible data solutions empower you to select and acquire precisely the information you need.

    Tailored Selection: Our platform allows users to choose the sectors and geographic regions that align most closely with their objectives.

    Preferred Formats: Data can be received in your preferred formats, whether it's Shapefile, GeoJSON, or any other compatible format.

    Moreover, we provide two distinct lists of available attributes to cater to your diverse data needs:

    For Customers Requiring Points Data: - ID - Name - Category - Location Latitude - Location Longitude - Address (available for 50% of records) - Phone Number - Email Address - Website - Opening Hours - Brand - Operator - Wheelchair Accessibility - Uber Grid Cell IDs

    Please note that data availability within the above list of attributes may vary depending on the POI category.

    For Customers Needing Aggregated Data:

    • ID of the Area
    • Name of the Area
    • Number of Clothing Stores
    • Number of Restaurants
    • Number of Cafes
    • Number of Bars
    • Number of Cinemas
    • Number of Schools
    • Number of Universities
    • Number of Hospitals

    It's important to emphasize that the attributes listed for the Aggregated datasets serve as examples. Geolocet offers complete flexibility, allowing you to customize attributes to suit your specific needs.

    Reach out to Geolocet today to explore how our POI Data can enhance your decision-making processes and provide invaluable insights for your success.

    šŸ”„ Regular Data Updates

    To maintain current and relevant insights, Geolocet's POI data undergoes regular updates. Our subscription models provide access to the latest information, enabling users to stay ahead in analyses and strategies. Recognizing the importance of up-to-date data in today's fast-paced world, Geolocet supports ongoing data needs.

    🌐 Integration Potential

    Geolocet's POI Data seamlessly integrates with other data offerings, including Administrative Boundaries Spatial Data and Demographic Data. This integration enriches insights and provides a holistic understanding of regions. Combining POI data with administrative boundaries and demographic information empowers data-driven decisions that consider the broader context.

    šŸ” Craft Informed Strategies

    Geolocet's POI Data goes beyond numbers, uncovering the essence of each locality and understanding its unique characteristics. Whether in retail, healthcare, education, or any other sector, the data equips users with the insights needed to craft informed strategies, optimize resource allocation, and make decisions that resonate with the target audiences.

    šŸ” Customized Data Solutions with DaaS

    Geolocet's Data as a Service (DaaS) offers flexibility tailored to needs. The transparent pricing model ensures cost-efficiency, allowing payment solely for the required data. Whether a startup is exploring a local market or a multinational corporation is analyzing multiple regions, Geolocet provides solutions that align with those objectives.

    Contact Geolocet today to explore how the POI Data can elevate decision-making processes and provide valuable insights for success in those endeavors.

  7. GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer...

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated May 28, 2025
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    Danika Eamer; Danika Eamer; Micah Borrero; Micah Borrero; Noman Bashir; Noman Bashir (2025). GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE) [Dataset]. http://doi.org/10.5281/zenodo.13207716
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 28, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Danika Eamer; Danika Eamer; Micah Borrero; Micah Borrero; Noman Bashir; Noman Bashir
    License

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

    Description

    Summary

    Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

    Relevant Links

    Link to the online version of the tool (requires creation of a free user account).

    Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

    Funding

    This dataset was produced with support from the MIT Climate & Sustainability Consortium.

    Original Data Sources

    These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

    Filename(s)Description of Original Data Source(s)Link(s) to Download Original Data
    License and Attribution for Original Data Source(s)

    faf5_freight_flows/*.geojson

    trucking_energy_demand.geojson

    highway_assignment_links_*.geojson

    infrastructure_pooling_thought_experiment/*.geojson

    Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

    Shapefile for FAF5 Regions

    Shapefile for FAF5 Highway Network Links

    FAF5 2022 Origin-Destination Freight Flow database

    FAF5 2022 Highway Assignment Results

    Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

    License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

    Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

    Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

    Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

    Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREETĀ® 2022. United States. https://doi.org/10.2172/1891644

    grid_emission_intensity/*.geojson

    Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

    eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

    eGRID database

    Shapefile with eGRID subregion boundaries

    Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

    Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

    US_elec.geojson

    US_hy.geojson

    US_lng.geojson

    US_cng.geojson

    US_lpg.geojson

    Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

    These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

    daily_grid_emission_profiles/*.geojson

    Hourly emission intensity data obtained from ElectricityMaps.

    Original data can be downloaded as csv files from the ElectricityMaps United States of America database

    Shapefile with region boundaries used by ElectricityMaps

    License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

    Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

    Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

    gen_cap_2022_state_merged.geojson

    trucking_energy_demand.geojson

    Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

    U.S. state boundaries obtained from "https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United

  8. InterAgencyFirePerimeterHistory All Years View

    • wifire-data.sdsc.edu
    Updated Oct 5, 2022
    + more versions
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    National Interagency Fire Center (2022). InterAgencyFirePerimeterHistory All Years View [Dataset]. https://wifire-data.sdsc.edu/dataset/interagencyfireperimeterhistory-all-years-view
    Explore at:
    kml, zip, csv, html, arcgis geoservices rest api, geojsonAvailable download formats
    Dataset updated
    Oct 5, 2022
    Dataset provided by
    National Interagency Fire Centerhttps://www.nifc.gov/
    Description

    Historical Fires

    Last updated on 06/17/2022

    Overview

    The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.

    WFIGS, NPS and CALFIRE data now include Prescribed Burns.

    Data Input

    Several data sources were used in the development of this layer:

    • Alaska fire history
    • USDA FS Regional Fire History Data
    • BLM Fire Planning and Fuels
    • National Park Service - Includes Prescribed Burns
    • Fish and Wildlife Service
    • Bureau of Indian Affairs
    • CalFire FRAS - Includes Prescribed Burns
    • WFIGS - BLM & BIA and other S&L
    Data Limitations

    Fire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.

    Attributes
    This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.
    https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdf

    IRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)

    INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.

    FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).

    AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.

    SOURCE - System/agency source of record from which the perimeter came.

    DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.

    MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.
    GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other

    GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9

    UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001

    LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.

    UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP

    COMMENTS - Additional information describing the feature. Free Text.

    FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown

    GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).

    Cross-Walk from sources (GeoID) and other processing notes
    • AK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed.
    • CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)
    • FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.
    • BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.
    • NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.
    • BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)
    • USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.

    Relevant Websites and References
  9. Pacific island region spatial data

    • pacific-data.sprep.org
    • pacificdata.org
    • +13more
    geojson
    Updated Nov 2, 2022
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    SPREP Environmental Monitoring and Governance (EMG) (2022). Pacific island region spatial data [Dataset]. https://pacific-data.sprep.org/dataset/pacific-island-region-spatial-data
    Explore at:
    geojson(79634032), geojson(3898830)Available download formats
    Dataset updated
    Nov 2, 2022
    Dataset provided by
    Pacific Regional Environment Programmehttps://www.sprep.org/
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Area covered
    Pacific islands region
    Description

    Dataset includes various regional-scale spatial data layers in geojson format.

  10. d

    Designated Smoking Areas (GEOJSON)

    • data.gov.sg
    Updated Jun 6, 2024
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    National Environment Agency (2024). Designated Smoking Areas (GEOJSON) [Dataset]. https://data.gov.sg/datasets/d_d0fa8f07ef80ab23feaa3b870323bf27/view
    Explore at:
    Dataset updated
    Jun 6, 2024
    Dataset authored and provided by
    National Environment Agency
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Description

    Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_d0fa8f07ef80ab23feaa3b870323bf27/view

  11. World Transportation

    • wifire-data.sdsc.edu
    csv, esri rest +4
    Updated Jun 9, 2021
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    Esri (2021). World Transportation [Dataset]. https://wifire-data.sdsc.edu/dataset/world-transportation
    Explore at:
    zip, html, csv, kml, geojson, esri restAvailable download formats
    Dataset updated
    Jun 9, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Area covered
    World
    Description

    This map presents transportation data, including highways, roads, railroads, and airports for the world.

    The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

    You can add this layer on top of any imagery, such as the Esri World Imagery map service, to provide a useful reference overlay that also includes street labels at the largest scales. (At the largest scales, the line symbols representing the streets and roads are automatically hidden and only the labels showing the names of streets and roads are shown). Imagery With Labels basemap in the basemap dropdown in the ArcGIS web and mobile clients does not include this World Transportation map. If you use the Imagery With Labels basemap in your map and you want to have road and street names, simply add this World Transportation layer into your map. It is designed to be drawn underneath the labels in the Imagery With Labels basemap, and that is how it will be drawn if you manually add it into your web map.

  12. d

    Manufacturing Company Data | API | Dataset | CSV | JSON | 4,289,762...

    • datarade.ai
    .json, .csv
    Updated May 6, 2024
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    HitHorizons (2024). Manufacturing Company Data | API | Dataset | CSV | JSON | 4,289,762 Companies | 50 European Countries | Data Enrichment | Monthly Updated | GDPR [Dataset]. https://datarade.ai/data-products/hithorizons-manufacturing-company-data-api-csv-json-hithorizons
    Explore at:
    .json, .csvAvailable download formats
    Dataset updated
    May 6, 2024
    Dataset authored and provided by
    HitHorizons
    Area covered
    Europe, Sweden, Czech Republic, Guernsey, Austria, Isle of Man, Uzbekistan, Serbia, Kazakhstan, Bosnia and Herzegovina, Ukraine
    Description

    HitHorizons Manufacturing Company Data API gives access to aggregated firmographic data on 4,289,762 manufacturing companies from the whole of Europe and beyond.

    Company registration data: company name national identifier and its type registered address: street, postal code, city, state / province, country business activity: SIC code, local activity code with classification system year of establishment company type location type

    Sales and number of employees data: sales in EUR, USD and local currency (with local currency code) total number of employees sales and number of employees accuracy local number of employees (in case of multiple branches) companies’ sales and number of employees market position compared to other companies in a country / industry / region

    Industry data: size of the whole industry size of all companies operating within a particular SIC code benchmarking within a particular country or industry regional benchmarking (EU 27, state / province)

    Contact details: company website company email domain (without person’s name)

    Invoicing details available for selected countries: company name company address company VAT number

  13. D

    Dataset Alerts - Open and Monitoring

    • datasf.org
    • data.sfgov.org
    • +1more
    application/rdfxml +5
    Updated Jun 20, 2025
    + more versions
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    (2025). Dataset Alerts - Open and Monitoring [Dataset]. https://datasf.org/opendata/
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    json, application/rssxml, csv, tsv, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 20, 2025
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

  14. B

    UNI-CEN Boundaries (CBF-Original Shorelines) - Census Division (CD) - 1861 -...

    • borealisdata.ca
    • search.dataone.org
    Updated Apr 4, 2023
    + more versions
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    UNI-CEN Project (2023). UNI-CEN Boundaries (CBF-Original Shorelines) - Census Division (CD) - 1861 - Esri Shapefile format (WGS84 / EPSG:4326) [Dataset]. http://doi.org/10.5683/SP3/2AFGSW
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 4, 2023
    Dataset provided by
    Borealis
    Authors
    UNI-CEN Project
    License

    https://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/2AFGSWhttps://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/2AFGSW

    Time period covered
    Jan 1, 1861
    Area covered
    Canada
    Description

    The UNI-CEN Digital Boundary File Series facilitates the mapping of UNI-CEN census data tables. Boundaries are provided in multiple formats for different use cases: Esri Shapefile (SHP), geoJson, and File Geodatabase (FGDB). SHP and FGDB files are provided in two projections: NAD83 CSRS for print cartography and WGS84 for web applications. The geoJson version is provided in WGS84 only. The UNI-CEN Standardized Census Data Tables are readily merged to these boundary files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

  15. d

    US B2B Contact Data | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON

    • datarade.ai
    .json, .csv
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    Forager.ai, US B2B Contact Data | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON [Dataset]. https://datarade.ai/data-products/us-b2b-contact-data-180m-records-bi-weekly-updates-csv-forager-ai
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    .json, .csvAvailable download formats
    Dataset provided by
    Forager.ai
    Area covered
    United States of America
    Description

    US B2B Contact Database | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON Elevate your sales and marketing efforts with America's most comprehensive B2B contact data, featuring over 200M+ verified records of decision-makers, from CEOs to managers, across all industries. Powered by AI and refreshed bi-weekly, this dataset ensures you have access to the freshest, most accurate contact details available for effective outreach and engagement.

    Key Features & Stats:

    200M+ Decision-Makers: Includes C-level executives, VPs, Directors, and Managers.

    95% Accuracy: Email & Phone numbers verified for maximum deliverability.

    Bi-Weekly Updates: Never waste time on outdated leads with our frequent data refreshes.

    50+ Data Points: Comprehensive firmographic, technographic, and contact details.

    Core Fields:

    Direct Work Emails & Personal Emails for effective outreach.

    Mobile Phone Numbers for cold calls and SMS campaigns.

    Full Name, Job Title, Seniority for better personalization.

    Company Insights: Size, Revenue, Funding data, Industry, and Tech Stack for a complete profile.

    Location: HQ and regional offices to target local, national, or international markets.

    Top Use Cases:

    Cold Email & Calling Campaigns: Target the right people with accurate contact data.

    CRM & Marketing Automation Enrichment: Enhance your CRM with enriched data for better lead management.

    ABM & Sales Intelligence: Target the right decision-makers and personalize your approach.

    Recruiting & Talent Mapping: Access CEO and senior leadership data for executive search.

    Instant Delivery Options:

    JSON – Bulk downloads via S3 for easy integration.

    REST API – Real-time integration for seamless workflow automation.

    CRM Sync – Direct integration with your CRM for streamlined lead management.

    Enterprise-Grade Quality:

    SOC 2 Compliant: Ensuring the highest standards of security and data privacy.

    GDPR/CCPA Ready: Fully compliant with global data protection regulations.

    Triple-Verification Process: Ensuring the accuracy and deliverability of every record.

    Suppression List Management: Eliminate irrelevant or non-opt-in contacts from your outreach.

    US Business Contacts | B2B Email Database | Sales Leads | CRM Enrichment | Verified Phone Numbers | ABM Data | CEO Contact Data | US B2B Leads | US prospects data

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

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(2017). Country Polygons as GeoJSON [Dataset]. https://datahub.io/core/geo-countries

Country Polygons as GeoJSON

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2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 1, 2017
License

ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically

Description

geodata data package providing geojson polygons for all the world's countries

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