As of November 2025, there were a reported 4,165 data centers in the United States, the most of any country worldwide. A further 499 were located in the United Kingdom, while 487 were located in Germany. What is a data center? Data centers are facilities designed to store and compute vast amounts of data efficiently and securely. Growing in importance amid the rise of cloud computing and artificial intelligence, data centers form the core infrastructure powering global digital transformation. Modern data centers consist of critical computing hardware such as servers, storage systems, and networking equipment organized into racks, alongside specialized secondary infrastructure providing power, cooling, and security. AI data centers Data centers are vital for artificial intelligence, with the world’s leading technology companies investing vast sums in new facilities across the globe. Purpose-built AI data centers provide the immense computing power required to train the most advanced AI models, as well as to process user requests in real time, a task known as inference. Increasing attention has therefore turned to the location of these powerful facilities, as governments grow more concerned with AI sovereignty. At the same time, rapid data center expansion has sparked a global debate over resource use, including land, energy, and water, as modern facilities begin to strain local infrastructure.

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

This data set is a shapefile for coastal network infrastructure in the US. This data set was used to conduct the study of how sea water inundation over the next 100 years will affect US Internet infrastructure (refer to R. Durairajan, C. Barford and P. Barford. "Lights Out: Climate Change Risk to Internet Infrastructure", In Proceedings of the ACM/IRTF/ISOC Applied Networking Research Workshop, July, 2018.). The shapefile provides detailed locations of nodes (e.g, co-location centers) and fiber conduits.

This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; U.S. Geological Survey; National Park Service; and the National Geophysical Data Center to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the...

Abstract: The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations. The 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low. Data are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP.

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

This multi-scale map shows counts of the total population the US. Data is from U.S. Census Bureau's 2020 PL 94-171 data for county, tract, block group, and block.County and metro area highlights:The largest county in the United States in 2020 remains Los Angeles County with over 10 million people.The largest city (incorporated place) in the United States in 2020 remains New York with 8.8 million people.312 of the 384 U.S. metro areas gained population between 2010 and 2020.The fastest-growing U.S. metro area between the 2010 Census and 2020 Census was The Villages, FL, which grew 39% from about 93,000 people to about 130,000 people.72 U.S. metro areas lost population from the 2010 Census to the 2020 Census. The U.S. metro areas with the largest percentage declines were Pine Bluff, AR, and Danville, IL, at -12.5 percent and -9.1 percent, respectively.View more 2020 Census statistics highlights on local populations changes.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

Abstract: The MODIS Mosaic of Antarctica 2008-2009 (MOA2009) Image Map consists of two cloud-free digital image maps that show mean surface morphology and a quantitative measure of optical snow grain size on the Antarctic continent and surrounding islands using 260 orbit swaths from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the NASA EOS Aqua and Terra satellites.

Medical Emergency Response StructuresThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays hospitals, medical centers, ambulance services, fire stations and EMS stations in the U.S. Per the USGS, "Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations."Greendale Fire DepartmentData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Medical & Emergency Response) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 135 (USGS National Structures Dataset - USGS National Map Downloadable Data Collection)OGC API Features Link: (Medical Emergency Response Structures - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: The National MapFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Theme CommunityThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

These maps show for the first time an accurate georeferenced mosaic of the Marshall Islands, the Federated States of Micronesia, the Republic of Palau and their respective corresponding shallow water areas. Shallow-water (generally, less than 30 meters) bank and land areas in these areas were identified through analysis of Landsat 7 ETM+ satellite imagery. The mosaics are laid over ETOPO2 Bathymetric Data to provide an enhanced understanding of how the Atolls and Islands fit together. In addition selected islands and atolls are shown next to the mosaic. This project was conducted in support of the U.S. Coral Reef Task Force.

Data in this accession are best used with appropriate Geographic Information System (GIS) software.

The Barrow Area Information Database (BAID) data collection is comprised of geospatial data for the research hubs of Barrow, Atqasuk and Ivotuk on Alaska's North Slope. Over 9600 research plots and instrument locations are included in the BAID research sites database. Updates to the project tracking database are ongoing through field mapping of new research locations and extant sampling sites dating back to the 1940s. Many ancillary data layers are also compiled to facilitate research activities and science communication. These geospatial data sets have been compiled through BAID and related NSF efforts. Geospatial data unique to this project are currently browseable via the BAID archive and include shapefiles of research information (sampling sites and instrumentation, the NOAA-CMDL clean air sector), administrative units (Barrow Environmental Observatory Science Research District plus adjacent federal lands, village districts, zoning, tax parcels, and the Ukpeagvik Inupiat Corporation boundary), infrastructure (power poles, snow fences, roads), erosion data for Elson Lagoon and imagery (declassified military imagery, air photo mosaics, IKONOS, Landsat, Quickbird, SAR and flight line indexes). Related data sets can be browsed via BAID’s web mapping tools and downloaded via the “Related links” section below. In addition, the BAID Internet Map Server (BAID-IMS) provides browse access to a number of additional layers which are available for download through catalog pages at the National Snow and Ice Data Center (NSIDC), the Alaska Geospatial Data Clearinghouse at USGS and the Alaska State Geo-Spatial Data Clearinghouse. Some layers are proprietary and are only available for browse access in BAID-IMS through special agreement. BAID provides a suite of user interfaces (Internet Map Server, Google Earth and Adobe Flex) and Open Geospatial Consortium web services for accessing the research plots and instrument locations. For more information on...

Find a NYC Department of Small Business Services NYC Business Solutions Center, Workforce1 Career Center, or Employment Works Center. Click here to view a map- https://maps.nyc.gov/sbs/

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Rang ...

This map was created for the US National Science Foundation Land-Atmosphere-Ice Interactions (LAII) Flux Study and the Arctic Transitions in the Land-Atmosphere System (ATLAS) Study (OPP-9318530 and OPP-9415554). The map covers all of northern Alaska, from the Brooks Range divide to the coast. It is a raster (tif) map, with 50 m pixels, and 9 land cover categories. It is based on an unsupervised classification of a Landsat Multispectral Scanner (MSS) composite created by the National Mapping Division, U.S. Geological Survey EROS data center, Anchorage, Alaska. Geobotanical maps and earlier Landsat-derived maps of the region were used to interpret the spectral classes. References Muller, S. V., A. E. Racoviteanu, and D. A. Walker. 1999. Landsat MSS-derived land-cover map of northern Alaska: Extrapolation methods and a comparison with photo-interpreted and AVHRR-derived maps. International Journal of Remote Sensing 20:2921-2946.

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

Currently, the most widely used approach to sensitive environment mapping in the United States is the NOAA Environmental Sensitivity Index or ESI. This approach systematically compiles information in standard formats for coastal shoreline sensitivity, biological resources, and human-use resources. ESI maps are useful for identifying sensitive resources before a spill occurs so that protection priorities can be established and cleanup strategies designed in advance. Using ESIs in spill response and planning reduces the environmental consequences of the spill and cleanup efforts. NOAA has undertaken a wide-ranging program to promote open ESI standards and to develop digital ESI databases for high-priority coastal areas in partnerships with states and other Federal agencies. NOTE: MAPS ARE NOT TO BE USED FOR NAVIGATIONAL PURPOSES

This ESI Atlas Guam and the Northern Mariana Islands: Maps and Geographic Systems Data contains geographic shape files e.g., ArcView 3.x project and supporting shape and .dbf files, of the locale and identified sensitive areas and biota. Accompanying detailed Adobe Acrobat .PDF ESI maps, extensive metadata, a user guide, and files in MOSS Map Overlay and Statistical System format are also provided; in addition, files have been provided for users of ArcGIS 9.x. Biological and geographical data utilized in this atlas date from 1999 to 2005 and data were compiled 2004-2005.

A digital data re-release from April 2007 is also archived at the NODC under accession number 0033616.

however, they are responsible for its appropriate application. Digital data files are periodically updated. Files are dated and users are responsible for obtaining the latest revisions of the data. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made by the agency regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. A copy of this map is presented on the CAPS Version 1.0 CD-ROM, June 1998.