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

The North Carolina State and County Boundary vector polygon data provides location information for North Carolina State and County Boundary lines derived from the best available survey and/or Geographic Information System (GIS) data. Sources for information are the North Carolina Geodetic Survey (NCGS), NC Department of Transportation (NCDOT), United States Geological Survey (USGS), and field surveys conducted by licensed surveyors in North Carolina and neighboring states that have been approved and recorded in their respective counties. North Carolina Geodetic Survey assists counties on a cooperative basis (NC General Statute 153A-18) in defining and monumenting the location of uncertain or disputed boundaries as established by law. Some counties have completed boundary surveys for at least a portion of their county boundary. However, the majority of county boundaries have not been surveyed and are represented by the best currently available data from GIS sources, including NCDOT county maps (which originally came from the USGS) and updated county parcel maps.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Kenya counties GeoJSON map from HumData. Version as of July 2022.

Counties in California intended for the NEVI Map.

Polygon for counties in the state of Texas.

U.S. Counties (Generalized) represents the counties of the United States in the 50 states and the District of Columbia.

This layer is sourced from maps.bts.dot.gov.

Kenya sub-counties GeoJSON map from HumData. Version as of July 2022.

1 Overview World Administrative Boundaries are available from various sources (UN, WHO, Global Administrative Areas [GADM], Natural Earth, World Bank). We would like to have the most accurate one with a reasonable size for an interactive world map in a Data Exploration Application, called CLIMINET. We provide a complete Geospatial Data that covers at least all 249 countries in the international ISO 3166-1 standard. We aim to maintain a reasonable data size, with countries' boundaries as accurate as possible, to ensure FLUIDITY in data visualization applications. The data are optimized for efficient performance and smooth interactions in interactive world maps for the best possible user experience. 2. Data Overview Number of Spatial Features: 275 countries/territories Data Sources: Compiled from multiple sources to ensure completeness and precision (WHO, Global Administrative Areas [GADM]) CRS Options: WGS84 [EPSG:4326] World Robinson (1963) [ESRI:54030] World Winkel-Tripel (Winkel III) - (1921) [ESRI:54042] Data Level: Level 0 (Countries) File Format: GeoJSON File Size: WGS84 [EPSG:4326]: 18.86 MB World Robinson (1963) [ESRI:54030]: 30.91 MB World Winkel-Tripel (Winkel III) - (1921) [ESRI:54042]: 30.90 MB 3. Data Revision Date The data were last updated on 2024-12-19. For further information on data structure and implementation, refer to the metadata files.

The VA_TOWN dataset is a feature class component of the Virginia Administrative Boundaries dataset from the Virginia Geographic Information Network (VGIN). VA_COUNTY represents the best available city and county boundary information to VGIN.VGIN initially sought to develop an improved locality and town boundary dataset in late 2013, spurred by response of the Virginia Administrative Boundaries Workgroup community. The feature class initially started from the locality boundaries from the Census TIGER dataset for Virginia. VGIN solicited input from localities in Virginia through the Road Centerlines data submission process as well as through public forums such as the Virginia Administrative Boundaries Workgroup and VGIN listservs. Data received were analyzed and incorporated into the VA_COUNTY feature class where locality data were a superior representation of the city or county boundary.

© Virginia Geographic Information Network (VGIN), and the Census and Localities and Towns submitting data to the project

This layer is a component of Feature classes representing locality (county, city, and town) boundaries in the Commonwealth of Virginia..

Feature layer of the Counties in California as used by Cal OES. Columns designating CalOES Regions, Fire and Law Mutual Aid Regions, and county name.

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

This dataset contains vector GIS files of boundaries of incorporated places (state, counties, cities, towns, and villages) and Indian territories in New York State.

This layer contains Legal City boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shape file of these city boundaries for download at its Spatial Information Library.Note: This boundary layer will not line up with the Thomas Brothers city layer. Principal attributes include:CITY_NAME: represents the city's name.CITY_TYPE: may be used for definition queries; "Unincorporated" or "City".FEAT_TYPE: contains the type of feature each polygon represents:Land - Use this value for your definition query if you want to see only land features on your map.Pier - One example is the Santa Monica Pier. Man-made features may be regarded as extensions of the coastline.Breakwater - Examples include the breakwater barriers that protect the Los Angeles Harbor.Water - Polygons with this attribute value represent internal navigable waters. Examples of internal waters are found in the Long Beach Harbor and in Marina del Rey.3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles (a nautical mile is 1852 meters) from the coastline.

Countries layers that contains all teh countries in the world

Cartographic Boundary Map provided the State of Texas Open Data Portal here: https://data.texas.gov/dataset/Texas-Counties-Cartographic-Boundary-Map/sw7f-2kkd/about_data Terms of Use This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been provided by the City of Austin via the US Census Bureau for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness.

Boundary polygons for Alabama counties.

This dataset was created by TxDOT for internal purposes. TxDOT is not the authority for county boundary data for the state. These features were digitized by TxDOT from georeferenced USGS topo maps to enable the classification of roadway attributes for the purposes of satisfying federal and state reporting requirements, and to serve as a base layer for TxDOT's cartographic products. This version utilizes a generalized boundary along the coast, which is sometimes necessary for analysis in which it is important to encompass segments of roadways that travel over water. Roadways on bridges or causeways that span intracoastal waterways are not covered by detailed polygons that precisely follow the coastline, therefore a generalized boundary is needed for some types of analysis where it is important to preserve such relationships.Use at your own risk. Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

This layer is sourced from gisprpxy.itd.state.ma.us.

This 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.