73 datasets found
  1. 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
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    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 GREET® 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

  2. USA states GeoJson

    • kaggle.com
    Updated Aug 18, 2020
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    Kate Gallo (2020). USA states GeoJson [Dataset]. https://www.kaggle.com/pompelmo/usa-states-geojson/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 18, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Kate Gallo
    Area covered
    United States
    Description

    Context

    I created a dataset to help people create choropleth maps of United States states.

    Content

    One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.

    Inspiration

    I think the best way to use this dataset is in joining it with other data. For example, I used this dataset to plot police killings using the data from https://www.kaggle.com/jpmiller/police-violence-in-the-us

  3. r

    Data from: geojson

    • redivis.com
    Updated Nov 17, 2022
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    Knight Sustainability Accelerator (2022). geojson [Dataset]. https://redivis.com/datasets/fzrm-20cdfr3h9/files
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    Dataset updated
    Nov 17, 2022
    Dataset authored and provided by
    Knight Sustainability Accelerator
    Description

    This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

  4. d

    UNI-CEN Boundaries (CBF-Harmonized Shorelines) - Census Subdivision (CSD) -...

    • search.dataone.org
    Updated Dec 28, 2023
    + more versions
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    UNI-CEN Project (2023). UNI-CEN Boundaries (CBF-Harmonized Shorelines) - Census Subdivision (CSD) - 1871 - geojson format (WGS84 / EPSG:4326) [Dataset]. http://doi.org/10.5683/SP3/ZOXF59
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    UNI-CEN Project
    Time period covered
    Jan 1, 1871
    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.

  5. Vectors for Goode's Homolosine projection

    • zenodo.org
    • explore.openaire.eu
    • +1more
    bin
    Updated Jan 24, 2020
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    Luís Moreira de Sousa; Luís Moreira de Sousa (2020). Vectors for Goode's Homolosine projection [Dataset]. http://doi.org/10.5281/zenodo.1841302
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Luís Moreira de Sousa; Luís Moreira de Sousa
    License

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

    Description

    This dataset contains useful vector maps to work with with Goode's Homolosine projection. The list of files included are:

    • CounterDomain.geojson - a polygonal approximation of the Homolosine projection counter-domain. This can be used to fix vectors wrongly projected by programmes that consider the counter-domain to be infinite. It can also be used to represent the seas in global mapping.
    • ParallelsMeridians.geojson - a set of meridians and parallels to be used in the creation of global maps.
    • Homolosine.crs - the PROJ string defining the Homolosine projection (referenced by the GeoJSON slides)
    • LICENCE - full text of the licence (EUPL-1.2)

    These datasets were generated with the open souce programme homolosine-vectors, available at: https://gitlab.com/ldesousa/homolosine-vectors

  6. Z

    Data from: 3DHD CityScenes: High-Definition Maps in High-Density Point...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jul 16, 2024
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    Fingscheidt, Tim (2024). 3DHD CityScenes: High-Definition Maps in High-Density Point Clouds [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7085089
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    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Plachetka, Christopher
    Fricke, Jenny
    Fingscheidt, Tim
    Sertolli, Benjamin
    Klingner, Marvin
    License

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

    Description

    Overview

    3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

    Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

    Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

    Python tools to read, generate, and visualize the dataset,

    3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

    The DevKit is available here:

    https://github.com/volkswagen/3DHD_devkit.

    The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

    When using our dataset, you are welcome to cite:

    @INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

    Acknowledgements

    We thank the following interns for their exceptional contributions to our work.

    Benjamin Sertolli: Major contributions to our DevKit during his master thesis

    Niels Maier: Measurement campaign for data collection and data preparation

    The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

    The Dataset

    After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

    1. Dataset

    This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

    During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

    To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

    import json

    json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

    1. HD_Map

    Map items are stored as lists of items in JSON format. In particular, we provide:

    traffic signs,

    traffic lights,

    pole-like objects,

    construction site locations,

    construction site obstacles (point-like such as cones, and line-like such as fences),

    line-shaped markings (solid, dashed, etc.),

    polygon-shaped markings (arrows, stop lines, symbols, etc.),

    lanes (ordinary and temporary),

    relations between elements (only for construction sites, e.g., sign to lane association).

    1. HD_Map_MetaData

    Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

    Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

    1. HD_PointCloud_Tiles

    The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

    x-coordinates: 4 byte integer

    y-coordinates: 4 byte integer

    z-coordinates: 4 byte integer

    intensity of reflected beams: 2 byte unsigned integer

    ground classification flag: 1 byte unsigned integer

    After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

    import numpy as np import pptk

    file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

  7. H

    Mozambique: COD Admin 0 geojson simplified geometries from gistmaps live...

    • data.humdata.org
    • data.amerigeoss.org
    geojson
    Updated Jul 26, 2025
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    ITOS (inactive) (2025). Mozambique: COD Admin 0 geojson simplified geometries from gistmaps live service source [Dataset]. https://data.humdata.org/dataset/ddadcacd-3329-426d-ba36-6e0bff80e883?force_layout=desktop
    Explore at:
    geojsonAvailable download formats
    Dataset updated
    Jul 26, 2025
    Dataset provided by
    ITOS (inactive)
    License

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

    Area covered
    Mozambique
    Description

    This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is.

  8. H

    COD Admin 2 geojson simplified geometries from gistmaps live service source

    • data.humdata.org
    geojson
    Updated Mar 1, 2023
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    ITOS (2023). COD Admin 2 geojson simplified geometries from gistmaps live service source [Dataset]. https://data.humdata.org/dataset/3dd7c273-3fb2-4b41-80b1-55df10efe1c1
    Explore at:
    geojsonAvailable download formats
    Dataset updated
    Mar 1, 2023
    Dataset provided by
    ITOS
    License

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

    Description

    This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

  9. A

    COD Admin 1 Benin geojson simplified geometries from gistmaps live service...

    • data.amerigeoss.org
    geojson
    Updated Jun 4, 2025
    + more versions
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    UN Humanitarian Data Exchange (2025). COD Admin 1 Benin geojson simplified geometries from gistmaps live service source [Dataset]. https://data.amerigeoss.org/tl/dataset/activity/cod-admin-1-benin-geojson-simplified-geometries-from-gistmaps-live-service-source
    Explore at:
    geojsonAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset provided by
    UN Humanitarian Data Exchange
    License

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

    Description

    This dataset is of simplified geometries from COD live services deployed August 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

  10. I

    TerriaJS Map Catalog in JSON Format

    • ihp-wins.unesco.org
    • data.dev-wins.com
    json
    Updated Jul 25, 2025
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    Pablo Rojas (2025). TerriaJS Map Catalog in JSON Format [Dataset]. https://ihp-wins.unesco.org/dataset/terriajs-map-catalog-in-json-format
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 25, 2025
    Dataset provided by
    Pablo Rojas
    Description

    This dataset contains a collection of JSON files used to configure map catalogs in TerriaJS, an interactive geospatial data visualization platform. The files include detailed configurations for services such as WMS, WFS, and other geospatial resources, enabling the integration and visualization of diverse datasets in a user-friendly web interface. This resource is ideal for developers, researchers, and professionals who wish to customize or implement interactive map catalogs in their own applications using TerriaJS.

  11. w

    GeoJSON of Bulgarian municipalities

    • data.wu.ac.at
    bin
    Updated Oct 10, 2013
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    Bulgaria (2013). GeoJSON of Bulgarian municipalities [Dataset]. https://data.wu.ac.at/odso/datahub_io/Y2U3ZDhlNWYtZTEwNS00NTlkLTk0MmMtZjMzZmVhMjVmY2My
    Explore at:
    bin(257323.0)Available download formats
    Dataset updated
    Oct 10, 2013
    Dataset provided by
    Bulgaria
    License

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

    Description

    This is a GeoJson of local municipalities in Bulgaria. The properties contain a unique ID, name and province name in Bulgarian. The features are extracted from the world definition of OpenStreetMap. The file can be used directly for generating overlays in maps.

    Wiki page on municipalities: http://en.wikipedia.org/wiki/Municipalities_of_Bulgaria

    Example of how it can be used: http://opendata.yurukov.net/educ/map/

  12. f

    Simplified shape file for mapping

    • figshare.com
    txt
    Updated May 31, 2023
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    Ziwang Deng; Huaiping Zhu; Jingliang Liu; Xin Qiu; Xiaolan Zhou; Xiaoyun Chen (2023). Simplified shape file for mapping [Dataset]. http://doi.org/10.6084/m9.figshare.10312097.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    figshare
    Authors
    Ziwang Deng; Huaiping Zhu; Jingliang Liu; Xin Qiu; Xiaolan Zhou; Xiaoyun Chen
    License

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

    Description

    In this directory, there are 6 geojson files which were used for mapping.1. Lake_Arc_simplify: Boundary of Lakes in and around Ontario2. Ontario_arc: Boundary lines of Ontario3. Municipal_Arc_simplify: Boundary lines of municipalities4. Municipal_Polygon: Polygons of the municipalities5. Stations151: Locations of 151 weather stations (municipalites)6.polygon9864: Rectangle areas centered at the 9864 grid pointssource:https://github.com/LAMPSYORKU/OntarioClimateDataPortal/tree/master/shapefiles

  13. ACS Youth School and Work Activity Variables - Centroids-State

    • anrgeodata.vermont.gov
    Updated Nov 20, 2018
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    Esri JSAPI (2018). ACS Youth School and Work Activity Variables - Centroids-State [Dataset]. https://anrgeodata.vermont.gov/datasets/652b78a18f794cb5840dd16a0697c83d
    Explore at:
    Dataset updated
    Nov 20, 2018
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri JSAPI
    Area covered
    Description

    This layer shows youth (age 16-19) school enrollment and employment status. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work). This layer is symbolized by the count of total youth and the percentage of youth who were disconnected. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2012-2016ACS Table(s): B14005 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: October 16, 2018National Figures: American Fact FinderThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This dataset is updated automatically when the most current vintage of ACS data is released each year. The service contains the ACS data as of the current vintage listed. Tabular data is updated annually with the Census Bureau's release schedule. This may alter data values, fields, and boundaries. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

  14. d

    UNI-CEN Boundaries (CBF-Original Shorelines) - Census Division (CD) - 1951 -...

    • dataone.org
    Updated Dec 28, 2023
    + more versions
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    UNI-CEN Project (2023). UNI-CEN Boundaries (CBF-Original Shorelines) - Census Division (CD) - 1951 - geojson format (WGS84 / EPSG:4326) [Dataset]. http://doi.org/10.5683/SP3/7O5QLO
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    UNI-CEN Project
    Time period covered
    Jan 1, 1951
    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. s

    GLOBE Observer MHM Measurements Updated Daily

    • geospatial.strategies.org
    Updated May 27, 2021
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    Institute for Global Environmental Strategies (2021). GLOBE Observer MHM Measurements Updated Daily [Dataset]. https://geospatial.strategies.org/maps/56d0567731664e69a42a31d2e2b4da6c
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    Dataset updated
    May 27, 2021
    Dataset authored and provided by
    Institute for Global Environmental Strategies
    Area covered
    Description

    MHM Data from GLOBE API URL:https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=mosquito_habitat_mapper&startdate=2017-05-01&enddate={yesterday}&geojson=FALSE&sample=FALSEData is updated daily- refer to the "Updated" date.Modifications:Only includes data submitted via the GLOBE Observer App.Coordinate geometry defined by mosquitohabitatmapperMeasurementLatitude and mosquitohabitatmapperMeasurementLongitude coordinates, rather than the MGRS coordinates used in the GeoJSON generated by the GLOBE API

  16. a

    Concurrent LC MHM Polygons

    • globe-data-igestrategies.hub.arcgis.com
    • geospatial.strategies.org
    Updated Jan 7, 2023
    + more versions
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    Institute for Global Environmental Strategies (2023). Concurrent LC MHM Polygons [Dataset]. https://globe-data-igestrategies.hub.arcgis.com/datasets/concurrent-lc-mhm-polygons
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    Dataset updated
    Jan 7, 2023
    Dataset authored and provided by
    Institute for Global Environmental Strategies
    Area covered
    Description

    This feature layer consists of paired GLOBE Observer Mosquito Habitat Mapper (MHM) and GLOBE Observer Land Cover (LC) observation data resulting from the following processing steps:MHM
    GEOJSON Data was pulled from this GLOBE API URL: https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=mosquito_habitat_mapper&startdate=2017-05-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App"
    As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude". All instances of duplicate photos have been removed from the dataset.LC
    GEOJSON Data was pulled from this GLOBE API URL:https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=land_covers&startdate=2018-09-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App"
    As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude".ConcurrenceThese two layers were then combined using a spatiotemporal join with the following conditions: Tool: Geoanalytics Desktop Tools -> Join Features Target Layer: LC Join Type: one to many Join Layer: MHM Coordinate fields used: MeasurementLatitude, MeasurementLongitude Time fields used: MeasuredAt (UTC time) Spatial Proximity: 100 meters (NEAR_GEODESIC) Temporal Proximity: 60 minutes (NEAR) Attribute match: UserIDThe result is a dataset consisting of all paired instances where the same observer (Userid) collected a Mosquito Habitat Mapper observation within 100 meters and 1 hour of collecting a Land Cover observation.Additional fields include:lc_mhm_obsID_pair': A string representing the two paired observations- {lc_LandCoverId}_{mhm_MosquitoHabitatMapperId}'lc_latlon': A string representing the coordinates of the LC observation - "({lc_MeasurementLatitude}, {lc_MeasurementLongitude})"'mhm_latlon': A string representing the coordinates of the MHM observation - "({mhm_MeasurementLatitude}, {mhm_MeasurementLongitude})"'spatialDistanceMeters': Numeric value representing the distance between the two paired observations in meters'temporalDistanceMinutes': Numeric value representing the time delta between the two paired observations in minutes'squareBuffer': A polygon string representing a 100m square centered on the LC observation coordinates. This may be used in conjunction with additional map layers to evaluate the land cover types near the observation coordinates. (n.b. This is not the buffer used in calculating spatiotemporal concurrence)For the purposes of this visualization, geometry is a 100m x 100m square centered on the Land Cover observation coordinates.

  17. s

    GLOBE Observer Land Cover Measurements Updated Daily

    • geospatial.strategies.org
    • hub.arcgis.com
    • +1more
    Updated Nov 4, 2020
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    Institute for Global Environmental Strategies (2020). GLOBE Observer Land Cover Measurements Updated Daily [Dataset]. https://geospatial.strategies.org/datasets/ff0dda11e84141c0a630c47e2a8203bf
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    Dataset updated
    Nov 4, 2020
    Dataset authored and provided by
    Institute for Global Environmental Strategies
    Area covered
    Description

    Land Cover Data from GLOBE API URL:https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=land_covers&startdate=2018-09-01&enddate={yesterday}&geojson=FALSE&sample=FALSEData is updated daily- refer to the "Updated" dateModifications:Only includes data submitted via the GLOBE Observer App.Coordinate geometry defined by landcoversMeasurementLatitude and landcoversMeasurementLongitude coordinates, rather than the MGRS coordinates used in the GeoJSON generated by the GLOBE API

  18. f

    data_code_MeasuringGeodiversity1

    • figshare.com
    zip
    Updated Jul 14, 2021
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    Aaron Wesley; Timothy Matisziw (2021). data_code_MeasuringGeodiversity1 [Dataset]. http://doi.org/10.6084/m9.figshare.13318271.v1
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    zipAvailable download formats
    Dataset updated
    Jul 14, 2021
    Dataset provided by
    figshare
    Authors
    Aaron Wesley; Timothy Matisziw
    License

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

    Description

    Datasets, code and results for paper 'Measuring Geodiversity in Large Overhead Imagery Datasets'1) Synthetic shapes dataset .zip (Count:3)Used to test GeoFID and GeoIS computations in the paper.Contains 3 separate zip files for 'polygon', 'star', and 'ellipse' shape classes. Each .zip file contains 2,000 images in .png format, 1,000 of which are randomized images along with 1,000 control images as described in the paper. Image filenames are referenced in external polygon GIS files (.geojson format) to facilitate research experiments in the paper.2) Synthetic shapes dataset creation script .py (Count:1)Used to create the dataset in (1) with the pycairo package3) Sample locations .csv (Count:3)Used to specify XY coordinate locations (WGS1984) for each shape class of the synthetic shapes dataset in (1)4) Polygon sub-regions creation script .py (Count:1)Used to create subregion grids in GeoJSON format with the shapely package5) Polygon sub-region grids .geojson (Count:12)Used to link images in synthetic dataset to geospatial regions based on point locations in (3). Used to store GeoFID and GeoIS values calculated for each subregion grid6) GeoFID/GeoIS implementation Jupyter Notebook .pynb (Count:1)Used in conjunction with Google Cloud Services to train deep learning models & calculate GeoFID/GeoIS values stored in (5)

  19. Z

    Extracted patterns about transport from the French Great National Debate...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 22, 2022
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    Sautot, Lucile (2022). Extracted patterns about transport from the French Great National Debate (Grand Débat National) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4074991
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    Dataset updated
    Jun 22, 2022
    Dataset provided by
    Hilal, Mohamed
    Fize, Jacques
    Sautot, Lucile
    Lentschat, Martin
    Journaux, Ludovic
    License

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

    Area covered
    French, France
    Description

    This data set is composed by 5 geojson files, that can be used to generate maps of mainland France :

    motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes

    bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

    trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

    au2010_carto.geojson : categorized urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011

    communesimportantes.geojson : the main cities of mainland France

    The data set is in French.

    Ce jeu de données est composé de 5 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :

    motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes

    bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html

    trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html

    au2010_carto.geojson : aires urbaines catégorisées en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011

    communesimportantes.geojson : principales villes de France métropolitaine

  20. o

    Global Healthsites Mapping Project - building an open data commons of health...

    • data.opendatascience.eu
    Updated May 13, 2021
    + more versions
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    (2021). Global Healthsites Mapping Project - building an open data commons of health facility data with OpenStreetMap [Dataset]. https://data.opendatascience.eu/geonetwork/srv/search?keyword=health
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    Dataset updated
    May 13, 2021
    Description

    When a natural disaster or disease outbreak occurs there is a rush to establish accurate health care location data that can be used to support people on the ground. This has been demonstrated by events such as the Haiti earthquake and the Ebola epidemic in West Africa. As a result valuable time is wasted establishing accurate and accessible baseline data. Healthsites.io establishes this data and the tools necessary to upload, manage and make the data easily accessible. Global scope The Global Healthsites Mapping Project is an initiative to create an online map of every health facility in the world and make the details of each location easily accessible. Open data collaboration Through collaborations with users, trusted partners and OpenStreetMap the Global Healthsites Mapping Project will capture and validate the location and contact details of every facility and make this data freely available under an Open Data License (ODBL). Accessible The Global Healthsites Mapping Project will make the data accessible over the Internet through an API and other formats such as GeoJSON, Shapefile, KML, CSV. Focus on health care location data The Global Healthsites Mapping Project's design philosophy is the long term curation and validation of health care location data. The healthsites.io map will enable users to discover what healthcare facilities exist at any global location and the associated services and resources.

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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
Organization logo

GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE)

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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 GREET® 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

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