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License information was derived automatically
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.
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.
This dataset was produced with support from the MIT Climate & Sustainability Consortium.
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 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. |
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. |
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 |
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 |
I created a dataset to help people create choropleth maps of United States states.
One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.
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
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.
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.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
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This dataset contains useful vector maps to work with with Goode's Homolosine projection. The list of files included are:
These datasets were generated with the open souce programme homolosine-vectors, available at: https://gitlab.com/ldesousa/homolosine-vectors
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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.
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)
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).
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.
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 = ['
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License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
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.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
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/
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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
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.
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.
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
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.
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
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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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)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
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.
This dataset was produced with support from the MIT Climate & Sustainability Consortium.
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 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. |
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. |
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 |
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 |