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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 this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.
Annual electricity generation by state
Net summer capacity by state
Shapefile with U.S. state boundaries
Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.
electricity_rates_by_state_merged.geojson
Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.
Electricity rate by state
Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.
demand_charges_merged.geojson
demand_charges_by_state.geojson
Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.
Historical demand charge dataset
The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').
Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.
eastcoast.geojson
midwest.geojson
la_i710.geojson
h2la.geojson
bayarea.geojson
saltlake.geojson
northeast.geojson
Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.
The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.
The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.
Shapefile for Bay Area country boundaries
Shapefile for counties in Utah
Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.
Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.
Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.
License for Utah boundaries: Creative Commons 4.0 International License.
incentives_and_regulations/*.geojson
State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.
Data was collected manually from the State Laws and Incentives database.
Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. 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.
costs_and_emissions/*.geojson
diesel_price_by_state.geojson
trucking_energy_demand.geojson
Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.
In
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TwitterWorld Countries Generalized provides a generalized basemap layer for the countries of the world. It has fields for official names and country codes. The generalized boundaries improve draw performance and effectiveness at global and continental levels.This layer is best viewed out beyond a maximum scale (zoomed in) of 1:5,000,000.The sources of this dataset are Esri, Garmin, and U.S. Central Intelligence Agency (The World Factbook). It is updated every 12-18 months as country names or significant borders change.
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TwitterFire Department Connections (FDC's) points within Fuquay-Varina. These are primarily privately owned and maintained. Mapping of FDC's primarily began from 2015 and later from as-built information provided by new developments, so this should be considered a very limited dataset. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)
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TwitterThis dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.
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TwitterThe North American Rail Network (NARN) Rail Lines dataset was created in 2016 and was updated on July 18, 2025 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528950
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This layer contains all active Iowa DOT Plow Trucks that are traveling more than 3 MPH. This data is updated every 2 minutes; 24 hours a day/7 days a week. Real-Time Data Formats: GeoJSON | JSONNotesFor Date/Time Queries: Use MODIFIEDDT as it is a valid UTC timestamp. There is a MODIFIEDDT_UTC_OFFSET that can be used if needed. LOGDT is in Local-Iowa time.When viewing in ArcGIS Online or Open Data, your local timezone offset is applied AUTOMATICALLY. This will make the UTC time appear correctly, while the local Iowa time will appear earlier than the UTC time.ArcGIS Open Data file formats will CACHE data. If you are looking to pull data to show real-time data, you will need to use the REST service. Further documentation can be found here.
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TwitterFrom the US Census Bureau: "The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping."
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TwitterWater Valves within Fuquay-Varina. Please note that some of these valves are privately owned and maintained. Pay attention to the WValve_Subtype field and OWNEDBY fields. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)
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TwitterSewer system valve points in Fuquay-Varina. These are primarily forced sewer pipe valves. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersWater Meter VaultsRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This data is a graphic representation of natural gas utility service territories. The file has not been certified by a Professional Surveyor. This data is not suitable for legal purposes. The purpose of this data is to provide a generalized statewide view of electric service territories. The data does not include individual or commercial releases. A release is an agreement between adjoining utilities that release customers from one utility to be served by the adjoining utility. A customer release does not change the territory boundary. The file has been compiled from numerous sources and as such contains errors. The data only contains the electric utility service territories and the name of the utility.The data was derived from ESRI zipcode boundary and utility companies.
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TwitterSewer System Manhole points in Fuquay-Varina. These are primarily publicly-maintained gravity sewer manhole points. However, there are also some privately-owned and privately-maintained manholes included in this dataset; they are mapped for modeling and informational purposes to provide more context for the complete Town sewer network.Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)
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TwitterThis layer contains the boundaries for California’s 58 counties. County features are derived from the US Census Bureau's TIGER/Line database and have been clipped to the coastal boundary line and designed to overlay with the California Department of Education’s (CDE) educational boundary layers.
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TwitterIncluded HIFLD Open Layers:
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TwitterThe North American Roads dataset was compiled on October 27, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset contains geospatial information regarding major roadways in North America. On March 31, 2025, the errant records with a value of 2 in the "NHS" field were corrected to have a value of 7 (Other NHS). The data set covers the 48 contiguous United States plus the District of Columbia, Alaska, Hawaii, Canada and Mexico. The nominal scale of the data set is 1:100,000. The data within the North American Roads layer is a compilation of data from Natural Resources Canada, USDOT’s Federal Highway Administration, and the Mexican Transportation Institute. North American Roads is a digital single-line representation of major roads and highways for Canada, the United States, and Mexico with consistent definitions by road class, jurisdiction, lane counts, speed limits and surface type.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The National Highway System consists of a network of roads important to the economy, defense and mobility. On October 1, 2012 the existing National Highway System (NHS) was expanded to include all existing Principal Arterials (i.e. Functional Classifications 1, 2 and 3) to the new Enhanced NHS.
Under MAP-21, the Enhanced NHS is composed of rural and urban roads nationwide serving major population centers, international border crossings, intermodal transportation facilities, and major travel destinations.The NHS includes:
The Interstate System.
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License information was derived automatically
This data portrays the FSTopo quad footprint. Quadrangles with a Vintage (greater than zero) make up the FSTopo area of interest.Within the FSTopo database, features are represented as lines, points, or polygons, with descriptive subtype attribute codes attached to describe the cartographic symbology characteristics of features. Annotation features are represented as stand-alone map text collected relative to the scale of the topographic quadrangle. The FSTopo database was originally populated with Cartographic Feature File (CFF) data which was digitized from either the Primary Base Series (PBS) quadrangles or U.S. Geological Survey (USGS) topographic map series quadrangles. Over time, the legacy CFF data is being replaced (at least partially) with data from nationally standardized sources. Data completeness reflects the content of the original source graphic, digital correction guide information, stereoscopic source, monoscopic source, supplemented with cadastral source information. Forests and Quadrangles may have undergone revision at varying dates. The update revision uses a variety of sources, including Digital Orthophoto Quad (DOQ) imagery, NAIP imagery, cadastral information, other vector data sources, and field-prepared correction guides in hardcopy or digital format.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.
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TwitterIncluded HIFLD Open Layers:
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TwitterAttribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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Summary:
The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.
These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.
Terms of Use:
The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.
Associated Files:
As of this release, the specific files included here are:
GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.
Column Information for the datasets:
Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.
fid - Unique identifier
bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
feat_code - Code describing the type of building, pulled from the Building Footprint Data.
groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
address - Address based on MapPLUTO, joined to the dataset based on bbl.
borough - Borough abbreviation pulled from MapPLUTO.
ownertype - Owner type field pulled from MapPLUTO.
zonedist1 - Zoning District 1 type pulled from MapPLUTO.
spdist1 - Special District 1 pulled from MapPLUTO.
bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)
For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):
xcoord - Longitude in decimal degrees.
ycoord - Latitude in decimal degrees.
Acknowledgements:
This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.
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TwitterThis city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.
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TwitterThis layer contains detailed outlines of Maryland counties. The Maryland land county boundaries were built using political county boundaries and the National Hydrology Data (NHD). Land boundaries are a key geographic featue in our mapping process.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Last Updated: UnknownFeature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PhysicalBoundaries/FeatureServer/0
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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 this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.
Annual electricity generation by state
Net summer capacity by state
Shapefile with U.S. state boundaries
Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.
electricity_rates_by_state_merged.geojson
Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.
Electricity rate by state
Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.
demand_charges_merged.geojson
demand_charges_by_state.geojson
Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.
Historical demand charge dataset
The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').
Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.
eastcoast.geojson
midwest.geojson
la_i710.geojson
h2la.geojson
bayarea.geojson
saltlake.geojson
northeast.geojson
Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.
The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.
The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.
Shapefile for Bay Area country boundaries
Shapefile for counties in Utah
Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.
Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.
Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.
License for Utah boundaries: Creative Commons 4.0 International License.
incentives_and_regulations/*.geojson
State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.
Data was collected manually from the State Laws and Incentives database.
Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. 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.
costs_and_emissions/*.geojson
diesel_price_by_state.geojson
trucking_energy_demand.geojson
Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.
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