As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

The South America Data Center Market report segments the industry into Data Center Size (Large, Massive, Medium, Mega, Small), Tier Type (Tier 1 and 2, Tier 3, Tier 4), Absorption (Non-Utilized, Utilized), and Country (Brazil, Chile, Rest of South America). Get five years of historical data alongside five-year market forecasts.

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 U.S. Coral Reef Task Force. These point data were generated while conducting ground validation during map preparation.

The California Department of Transportation (Caltrans) and the California Energy Commission (CEC) are partnering to implement the federal National Electric Vehicle Infrastructure (NEVI) Program, which allocates $5 billion to the states to create a nationwide, interconnected network of DC fast chargers along the National Highway Systems. California's share will be $384 million over 5 years. This map was developed to help prospective applicants and interested parties identify eligible areas for infrastructure deployment.InstructionsViewers can display Alternative Fuel Corridors, NEVI 2 (GFO-24-606) corridor groups and corridor segments, NEVI 1 (GFO-23-601) corridor groups, electric vehicle (EV) charging stations, Tribal lands, California-designated low-income or disadvantaged communities, metropolitan planning organizations, regional transportation planning agencies, California state legislative districts, counties, Caltrans districts, utility districts, and congressional districts in this interactive map. The map initially displays the corridor groups and corridor segments eligible for California's Round 2 NEVI solicitation. Viewers can toggle individual layers on and off using the map layers menu located to the right of the map. Some layers are organized into groups; viewers can toggle all layers within a group or select specific ones. The legend to the left of the map will show the layers that have been turned on. There is a search tool to the right of the map that enables viewers to type in an address and locate the address on the map. A basemap selector allows viewers to view road detail. Additional information on the map can be found under the information icon. Viewers can download the map files by clicking on the Data and Supplemental Links icon. Map layers include:An Alternative Fuel Corridors layer that shows designated corridors for California's NEVI funding program. Users can click on a corridor segment to view the start and end of each corridor. When selected, a pop-up window will appear that shows the corridor name and description.A NEVI 2 (GFO-24-606) corridor groups layer shows corridor groups eligible for Round 2 of California's NEVI funding program. Note that this layer is only visible when the Alternative Fuels Corridors layer is turned off.NEVI 2 (GFO-24-606) corridor group labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels) and when the Alternative Fuels Corridors layer is turned off. NEVI 2 (GFO-24-606) corridor segment labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels) and when the Alternative Fuels Corridors layer is turned off. A NEVI 1 (GFO-23-601) corridor groups layer that shows corridor groups eligible for Round 1 of California's NEVI funding program. Note that this layer is only visible when the Alternative Fuels Corridors layer is turned off.A layer showing the locations of EV charging stations awarded through Round 1 of California's NEVI funding program that are planned for deployment. A layer showing California-designated disadvantaged or low-income communities. A layer showing California Federally Recognized Tribal Lands. A layer showing Metropolitan Planning Organizations. A layer showing Regional Transportation Planning Agencies. A layer showing California State Senate Districts. A layer showing California State Assembly Districts. A layer showing California Counties. EV charging stations layers (existing DC fast charging stations that are located within one mile of a NEVI-eligible corridor offramp). One layer shows locations of EV charging stations with DC fast charging capabilities that meet the NEVI power level and four-port minimum requirement and could likely become part of the NEVI network if these stations became compliant with other NEVI program requirements such as data reporting. The other layer shows DC fast charging stations that do not meet NEVI power-level or port count requirements but could be upgraded to be NEVI-compliant. Users can click on EV charging stations and a pop-up window will appear with more information on the station (i.e., station address, total port count, minimum NEVI standard, etc.). These data were last updated in March 2024. Please refer to the Department of Energy's Alternative Fuels Data Center and PlugShare for up-to-date existing and planned DC fast charger site information. A layer showing Caltrans Districts. A layer showing Electric Utilities (IOUs and POUs). A layer showing California Congressional Districts. BackgroundThe $5 billion NEVI Program is part of the $1.2 trillion Infrastructure Investment and Jobs Act (IIJA) signed into law by President Biden in November 2021. IIJA commits significant federal funding to clean transportation and energy programs throughout the U.S. to reduce climate changing greenhouse gas emissions. Caltrans is the designated lead agency for NEVI. The CEC is their designated state energy partner. Caltrans and the CEC have partnered to create California's Deployment Plan for the National Electric Vehicle Infrastructure Program that describes how the state plans to allocate its $384 million share of federal NEVI funds to build out a network of modern, high-powered DC fast chargers along federally designated Alternative Fuel Corridors throughout California. California's latest NEVI Deployment Plan was submitted to the Joint Office of Energy and Transportation on August 1, 2023 and approved on September 29, 2023. The Plans must be updated each year over 5 years.NEVI funds must be used initially on federally-designated Alternative Fuel Corridors (shown on the map).Each NEVI-funded DC fast charge station will have a minimum of four 150 kW Combined Charging System (CCS) connectors. Stations will be located no more than 50 miles apart along freeways and highways and no more than 1 mile from a freeway exit or highway roadway. States are required to emphasize equity, with at least 40 percent of NEVI benefits going to disadvantaged, low income, rural and Tribal communities.Data SourcesData are from the Federal Highway Administration's Alternative Fuel Corridors website, the U.S. Department of Energy's Alternative Fuels Data Center Station Data for Alternative Fuel Corridors (as of September 2022), Argonne National Laboratory's Electric Vehicle Charging Justice40 Map, and the California Air Resources Board's Map of California Climate Investments Priority Populations 2022 CES 4.0. ContactPlease submit questions and comments to mediaoffice@energy.ca.gov

THIS MAP IS NOT AUTHORITATIVE. SEE TERMS OF USE BELOW.This web map was developed by the National Oceanic and Atmospheric Administration’s (NOAA) Office for Coastal Management and is featured in the U.S. Great Lakes Collaborative Benthic Habitat Mapping Common Operating Dashboard in support of the Collaborative Benthic Habitat Mapping in the Nearshore Waters of the Great Lakes Basin Project. This multi-year, multi-agency project is funded through the Great Lakes Restoration Initiative (GLRI) and focuses on new bathymetric data (airborne lidar and vessel based sonar) acquisition, validation, and benthic habitat characterization mapping of the nearshore waters (0-80 meters) in the U.S. Great Lakes. This project also contributes to the regional Lakebed 2030 campaign, which aims to have high-density bathymetric data available for the entirety of the Great Lakes by 2030. This web map contains data layers reflecting the current status of bathy data coverage in the nearshore (0-80 meters) of the U.S. Great Lakes, including acquisition (lidar and multibeam sonar), ground-truthing/validation, and benthic habitat mapping and characterization. Acquisition layers include coverage areas that have been acquired and are available for public use (green) as well as those that have been acquired, but are not yet available or are still in progress (orange). The nearshore water depth layers (0-25 and 25-80 meters) were created using the National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts. The 0 to 25 meter nearshore water depth layer represents areas where bathymetric lidar data acquisition could ideally be conducted, depending on water condition and turbidity. The 25 to 80 meter layer shows locations where acoustic data acquisition can occur. See below for information on additional data layers. All data originally projected in the following coordinate system: EPSG:3175, NAD 1983 Great Lakes and St Lawrence Albers.This map will continue to be updated as new information is made available.Source Data for Bathy Coverage Layers - Acquired/Available:Topobathy and Bathy Lidar (NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/). Multibeam Sonar (National Centers for Environmental Information (NCEI) Bathymetric Data Viewer: https://www.ncei.noaa.gov/maps/bathymetry/; NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/; USGS ScienceBaseCatalog: https://www.sciencebase.gov/catalog/item/656e229bd34e7ca10833f950)Source Data for Bathy Coverage Layers - GLRI AOIs (2020-2024):Acquisition: NOAA Office for Coastal ManagementValidation/CMECS Characterizations: NOAA National Centers for Coastal Ocean Science (NCCOS)Source Data for Bathy Coverage Layers - In Progress and Planned:NOAA Office of Coast Survey Plans: https://gis.charttools.noaa.gov/arcgis/rest/services/Hydrographic_Services/Planned_Survey_Areas/MapServer/0NOAA Office for Coastal ManagementSource Data for Nearshore Water Depths:NOAA's National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts: https://www.ncei.noaa.gov/maps/grid-extract/Source Data for Spatial Prioritization Layers:Great Lakes Spatial Priorities Study Results Jun 2021. https://gis.charttools.noaa.gov/arcgis/rest/services/IOCM/GreatLakes_SPS_Results_Jun_2021/MapServerMapping priorities within the proposed Wisconsin Lake Michigan National Marine Sanctuary (2018). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_WILMPrioritizationResults/MapServerThunder Bay National Marine Sanctuary Spatial Prioritization Results (2020). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_TBNMSPrioritizationResults/MapServerSource Data for Supplemental Data Layers:International Boundary Commission U.S./Canada Boundary (version 1.3 from 2018): https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpNational Oceanic and Atmospheric Administration (NOAA) HydroHealth 2018 Survey: https://wrecks.nauticalcharts.noaa.gov/arcgis/rest/services/Hydrographic_Services/HydroHealth_2018/ImageServerNational Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Inventory 2023-2024: https://www.fisheries.noaa.gov/inport/item/69506National Oceanic and Atmospheric Administration (NOAA) National Marine Sanctuary Program Boundaries (2021): https://services2.arcgis.com/C8EMgrsFcRFL6LrL/arcgis/rest/services/ONMS_2021_Boundaries/FeatureServerNational Oceanic and Atmospheric Administration (NOAA) U.S. Bathymetry Gap Analysis: https://noaa.maps.arcgis.com/home/item.html?id=4d7d925fc96d47d9ace970dd5040df0aU.S. Environment Protection Agency (EPA) Areas of Concern: https://services.arcgis.com/cJ9YHowT8TU7DUyn/arcgis/rest/services/epa_areas_of_concern_glahf_viewlayer/FeatureServerU.S. Geological Survey (USGS) Great Lakes Subbasins: https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd Latest update: February 20, 2025

Regional geophysical maps of the Great Basin, USA were generated from new and existing sources to support ongoing efforts to characterize geothermal resource potential in the western US. These include: (1) a provisional regional gravity grid that was produced from data compiled from multiple sources: data collected by the USGS and Utah Geological Survey under various projects, industry sources, and regional compilations derived from two sources: a Nevada state-wide database (Ponce, 1997), and a public domain dataset (Hildenbrand et al., 2002), (2) a regional magnetic grid derived from the North American magnetic compilation map of Bankey et al. (2002) and, (3) a regional depth-to-basement grid derived from Shaw and Boyd (2018). References: Bankey, V., Cuevas, A., Daniels, D., Finn, C.A., Hernandez, I., Hill, P., Kucks, R., Miles, W., Pilkington, M., Roberts, C., Roest, W., Rystrom, V., Shearer, S., Snyder, S., Sweeney, R.E., Velez, J., Phillips, J.D., and Ravat, D.K.A., 2002, Digital data grids for the magnetic anomaly map of North America, U.S. Geological Survey, Open-File Report 2002-414, https://doi.org/10.3133/ofr02414. Hildenbrand, T.G., Briesacher, A., Flanagan, G., Hinze, W.J., Hittelman, A.M., Keller, G.R., Kucks, R.P., Plouff, D., Roest, W., Seeley, J., Smith, D.A., and Webring, M., 2002, Rationale and operational plan to upgrade the U.S. Gravity Database: U.S. Geological Survey Open-File Report 02-463, 12p. [https://pubs.er.usgs.gov/publication/ofr0246; data downloaded from the Pan-American Center for Earth and Environmental Studies (PACES) gravity database in October 2007 from URL http://paces.geo.utep.edu/research/gravmag/gravmag.shtml]. Ponce, D.A., 1997, Gravity data of Nevada, U.S. Geological Survey Digital Data Series DDS-42. https://pubs.usgs.gov/dds/dds-42/. Shah, A.K, and Boyd, O.S., 2018, Depth to basement and thickness of unconsolidated sediments for the western United States—Initial estimates for layers of the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2018–1115, 13 p., https://doi.org/10.3133/ofr20181115.

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

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.

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.

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 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.

The geologic map was created in GSMAP at Socorro, New Mexico by Orin Anderson and Glen Jones and published as the Geologic Map of New Mexico 1:500,000 in GSMAP format in 1994. This graphic file was converted to ARC/INFO format by Greb Green and GlenJones and released as the Geologic Map of New Mexico in ARC/INFO format in 1997.

Hurricane Katrina of August, 2005, is remembered as one of the most destructive and influential storms in United States history. The densely populated city of New Orleans, one of many areas around the Gulf Coast to face catastrophic damage, endured extreme flooding and physical destruction when several levees and other flood prevention features guarding the city broke down. Many evacuated the city and fled to far corners of the country, and a large portion of these evacuees were unable to resettle in New Orleans after the storm. Dealing with the aftermath of Hurricane Katrina involved many immense challenges, but ten years later, one can see the effects of a decade of hard work in restoring this historic city. This series of maps tracks New Orleans through these ten years of change. The story map uses the Esri Story Map Series app, The story was produced by Esri in collaboration with the Smithsonian Institution. The story can also be found on the Smithsonian Website. Data for each map was taken from the following sources:Katrina Diaspora: 2006 American Community Survey 1-year Estimates, State-to-State Migration Flows, NHC, NOAA, NWS. Flooding: Terrestrial lidar datasets of New Orleans levee failures from Hurricane Katrina, August 29, 2005: U.S. Geological Survey Data Series, NASA Earth Observatory, NOAA National Geodetic Survey. Physical Damage: FEMA dataset collection following Hurricane Katrina and transferred to CNO/SHPOPopulation Shift: The Data Center analysis of data from U.S. Census 2000 Summary File 1 (SF1) and U.S. Census 2010 Summary File 1 (SF1)Steady Restoration: The Data Center analysis of Valassis Residential and Business Database Neighborhood Reference Map: City of New Orleans GIS Department For more information on Esri Story Map apps, visit storymaps.arcgis.com.

These ESI data were collected, mapped, and digitized to provide environmental data for oil spill planning and response. The Clean Water Act with amendments by the Oil Pollution Act of 1990 requires response plans for immediate and effective protection of sensitive resources. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. ESI MAPS SHOULD NOT BE USED FOR NAVIGATIONAL PURPOSES. Source data used in the development of these regional atlases range from 1900 to 2005 with much of the data dated from the 1980s, 1990s, to 2005. Source data dates are extensively documented in the included metadata and include the following DE_NJ_PA, data range 1969-1995, compiled 1995, HudsonRiver data range 1942-2005, compiled 2005, Massachusetts data range 1978-1998, compiled 1998, New Hampshire data range 1948-2003, compiled 2003, and RI_CT_NY_NJ data range 1900-2001, compiled 1999.

This atlas update adds data formats to those originally released to accommodate new technologies of digital mapping. The underlying data have not been updated since the atlas publication dates shown. Each ESI atlas listed is provided in a variety of GIS formats, including a personal Geodatabase for use with the ESRI ArcGIS product line. An .mxd file, created in ArcMap 9.3 is also included. This mapping document provides links to all of the data tables and symbolization of the layers using the standardized ESI colors and hatch patterns. Layer files are also supplied. These, together with the associated geodatabase, can be used in other mapping projects to define the symbology and links established in the original ESI .mxd file.

PDF files of the map pages are also included. These PDFS now have the seasonality pages attached to the appropriate map document. This should make it easier to print and distribute individual maps and insure that the supporting information is always included. The GIS data are also provided in ARC Export .e00 format, as shape files with an ArcView 3.x project and in MOSS format. Database files are included in text and .e00 format. Each area directory contains a readme file which shows the area of coverage and gives a bit more description of the various file formats included.

Several regions are represented in this unique collection of earth surface measurements of magnetic field parameters and their related anomalies. The DNAG Magnetics "Super grid" of Magnetic Anomaly Map of North America was created from the four "Original" DNAG Magnetic data sets distributed by The Committee for the Magnetic Anomaly Map of North America, 1987. This development of a super grid involved an extensive task of matching original quadrant information and eliminating overlap. The resulting grid, with x and y step intervals of 2.0 kilometers yields a grid with dimensions (4451 x 4273) containing 19,019,123 values. This process can be thought of as "stitching the grids." The data in this grid are in a Spherical Transverse Mercator projection, the kilometer coordinates of which can be recovered from the indices of a grid point. The Ministry of Geology of the USSR published a mosaic series of 18 maps in 1974, at a scale of 1:2,500,000 showing the residual magnetic intensity over the land mass of the USSR. Much of the source material originated from data collected between 1949-1962, during which time the entire territory of the USSR was surveyed using aerial magnetic survey techniques. These surveys wereadjusted based on many methods including secular variation linked to magnetic observatories. Anomalies were computed with reference to a normal field map for 1964-65 constructed from equally accurate total field measurements along control network strips. Digitization was accomplished in 1982 by the U.S. Naval Oceanographic Office. The "BRIGGS cubic spline" method was used to compute grid values. A one-minute grid was created by properly matching the boundaries of the digitized sub-sections. The units of the original map aremilli-Oersteds and the units of the resulting digital grid are milli-Oersted/100. Corrections to the digital contour file were made by Conoco Inc.in 1993. New Grid files at 2.5 Km and 5.0 Km spacing were created and re-archived by NGDC. These data are available on CD-ROM. World Data Center-A (WDC-A) for Solid Earth Geophysics presently holds Grid data from many U.S. and other regions. These data were contributed by: USGS, MINN G.S. and other Worldwide organizations. Grid intervals vary but are as fine as 213.36m for the NGS Super Grid of the state of Minnesota. Other grids were recreated indigital form from previously published maps and charts. The bulk of these grid data files were contributed to NGDC after 1985. A detailed list of the specific regions is available upon request.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

description: This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information systsem (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps.; abstract: This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information systsem (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps.

The Antarctic Air Operations Planning Maps series is a SCAGI (Standing Committee on Antarctic Geographic Information) product with contributions from Australia, Belgium, Norway, the United Kingdom and the United States of America.

This updated selection of maps from the Australian Antarctic Division incorporates both pre-existing datasets and new data created using Sentinel2 imagery at a scale of 1:25000. Metadata record for new digitised data: https://data.aad.gov.au/metadata/East_Antarctic_25K_topographic_data_2023

Feature types were edited or created within a topology. Line data include Coastline, Grounding line, Ice front. Polygon data include Continent, Island, Ice shelf, Tongue, Iceberg, Rock and Lakes. Grounding lines were derived from ASAID_Grounding_line_continent_Sc_dep (Rignot, E., J. Mouginot, and B. Scheuchl. 2016. MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/IKBWW4RYHF1Q) The ASAID data was edited using ICESat2 and InSAR_GL_Antarctica_v02, Sentinel2 imagery and REMA2 DEM as a guide.

The Antarctic Iceberg Data were sourced from U.S.National Ice Centre (usicecenter.gov/Products/AntarcIcebergs) in CSV format. The CSV data used in this project is dated 3 Feb 2023.

Spot heights, contour and hillshade were derived from The Reference Elevation Model of Antarctica version 2 (REMA 2): Howat, Ian, et al., 2022, “The Reference Elevation Model of Antarctica – Mosaics, Version 2”, https://doi.org/10.7910/DVN/EBW8UC Harvard Dataverse, V1, 2022
DEM(s) courtesy of the Polar Geospatial Center.

Annotation and text: Arial font are specified. As a result of there being such variation in annotation on all current editions of the series, the Geoscience Australia protocols for 1:250 000 maps have been adopted, with the exception of contour values being regular and not italic. They are regular on all previously produced maps in the series.
Natural Features: Italics.
Human-made features: non-Italics (this includes coast or land name, island names).
Hydrology features are Blue Italic text.

The maps are available in the SCAR Map Catalogue:
• AOPM 10: Map Catalogue No. 16053 – https://data.aad.gov.au/map-catalogue/map/16053
• AOPM 17: Map Catalogue No. 16054 – https://data.aad.gov.au/map-catalogue/map/16054
• AOPM 18: Map Catalogue No. 16055 – https://data.aad.gov.au/map-catalogue/map/16055
• AOPM 19: Map Catalogue No. 16056 – https://data.aad.gov.au/map-catalogue/map/16056
• AOPM 20: Map Catalogue No. 16057 – https://data.aad.gov.au/map-catalogue/map/16057
• AOPM 21: Map Catalogue No. 16058 – https://data.aad.gov.au/map-catalogue/map/16058

Historical map of acequias along Rio Grande river between Texas and Mexico. U.S.-Mexican Boundary Survey. The original map is a Xerox of a Photolithographic Copy of Salazar Ilarregui's Original Mexican Steet No. 29, of the Commission of 1853-1855. Accepted and Adopted by both the United States and Mexico.

The Geographic Names Information System (GNIS) actively seeks data from and partnerships with Government agencies at all levels and other interested organizations. The GNIS is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types. See http://geonames.usgs.gov for additional information.