Census Current (2022) Legal and Statistical Entities Web Map Service; January 1, 2022 vintage.

Census Regions are groupings of states and the District of Columbia that subdivide the United States for the presentation of census data. There are four census regions-Northeast, Midwest, South, and West. Each of the four census regions is divided into two or more census divisions. Puerto Rico and the Island Areas are not part of any census region or census division.

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Divisions are groupings of states within a census geographic region, established by the Census Bureau for the presentation of census data. The current nine divisions (East North Central, East South Central, Middle Atlantic, Mountain, New England, Pacific, South Atlantic, West North Central, and West South Central) are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions.

This layer is a component of ENOW_Counties.

This map service presents spatial information about the Economics: National Ocean Watch (ENOW) data in the Web Mercator projection. The ENOW data provides time-series data on the ocean and Great Lakes economy, which includes six economic sectors dependent on the oceans and Great Lakes, and measures four economic indicators: Establishments, Employment, Wages, and Gross Domestic Product (GDP). The annual time-series data are available for about 400 coastal counties, 30 coastal states, 8 regions, and the nation. The service was developed by the National Oceanic and Atmospheric Administration (NOAA), but may contain data and information from a variety of data sources, including non-NOAA data. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. The NOAA Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (http://coast.noaa.gov/digitalcoast/publications/subscribe). For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management

This data set delineates the boundaries of the U.S. Fish and Wildlife Service geographic Regions. The dataset was created as a geographic representation of the Regional administrative boundaries of the US Fish and Wildlife Service at a very coarse scale. The boundaries were created using the ArcGIS shoreline dataset from approximately 1995. This dataset should not be used for legal purposes or at small scales and does not accurately denote the shorelines of the united states. The Regional Boundaries data set is managed by the FWS Headquarters Information Resources and Technology Management, Branch of Geospatial Data Management. The complete data and metadata can be accessed here: https://catalog.data.gov/dataset/us-fish-and-wildlife-service-regional-boundaries. This data set is a graphical representation and has limitations of accuracy as determined by, among others, the source, scale and resolution of the data. DOI Interior Regions / Regional Boundaries (https://fws.maps.arcgis.com/home/item.html?id=309aa728d6c041ceaefc1526a409b5d1).

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.

Northeastern United States State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

The 2023 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Divisions are groupings of states within a census geographic region, established by the Census Bureau for the presentation of census data. The current nine divisions (East North Central, East South Central, Middle Atlantic, Mountain, New England, Pacific, South Atlantic, West North Central, and West South Central) are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions.

NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical map of the NW suburbs of the Twin Cities.

Minnesota River: 3-D topographical map of the Minnesota River Valley highlighting the river bend in Mankato.

St. Croix River: 3-D topographical map of the St. Croix extending from Taylors Falls to the Mississippi confluence.

Mississippi River, Lake Pepin: 3-D topographical map of the confluence of Chippewa Creek and the Mississippi River.

Red Wing, MN: 3-D topographical map of Redwing, MN on the Mississippi River.

Winona, Minnesota: 3-D topographical map of Winona, MN highlighting the Mississippi River.

Cannon Falls, MN: 3-D topographical map of Cannon Falls area.

Rochester, MN: 3-D topographical map of Rochester and the surrounding area.

Northfield, MN: 3-D topographical map of Northfield and the surrounding area.

St. Louis River, MN: 3-D map of the St. Louis River and Duluth, Minnesota.

Lake Itasca, MN: 3-D map of the source of the Mississippi River.

Elmore, MN: 3-D topographical map of Elmore, MN in south-central Minnesota.

Glencoe, MN: 3-D topographical map of Glencoe, MN.

New Prague, MN: 3-D topographical map of the New Prague in south-central Minnesota.

Plainview, MN: 3-D topographical map of Plainview, MN.

Waterville-Morristown: 3-D map of the Waterville-Morris area in south-central Minnesota.

Eau Claire, WI: 3-D map of Eau Claire highlighting abandon river channels.

Dubuque, IA: 3-D topographical map of Dubuque and the Mississippi River.

Londonderry, NH: 3-D topographical map of Londonderry, NH.

Santa Cruz, CA: 3-D topographical map of Santa Cruz, California.

Crater Lake, OR: 3-D topographical map of Crater Lake, Oregon.

Mt. Rainier, WA: 3-D topographical map of Mt. Rainier in Washington.

Grand Canyon, AZ: 3-D topographical map of the Grand Canyon.

District of Columbia: 3-D map highlighting the confluence of the rivers and the Mall.

Ireland: 3-D grayscale map of Ireland.

New Jersey: 3-D grayscale map of New Jersey.

SP Crater, AZ: 3-D map of random craters in the San Francisco Mountains.

Mars Water Features: 3-D grayscale map showing surface water features from Mars.

This layer is sourced from maritimeboundaries.noaa.gov.

The ENC_General map service displays ENC data within the scale range of 1:600,001 and 1:1,500,000. The ENC data will be updated weekly. This map service is not intended for navigation purpose.

description: This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer; abstract: This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The U.S. Geological Survey (USGS) previously identified 62 Principal Aquifers (PAs) in the U.S., with 57 located in the conterminous states. The USGS characterized areas outside of PAs as “other rocks;” other rocks account for about 40% of the area of the conterminous states. This paper subdivides the large area identified as other rocks into Secondary Hydrogeologic Regions (SHRs). SHRs are defined as areas of other rock within which the rocks are of comparable geologic age, lithology, and relationship to the presence or absence of underling PAs or overlying glacial deposits. A total of 69 SHRs were identified. SHRs were identified in two phases. In the first phase, Other Rock Regions (ORRs) were defined as regions underlain by geologic units of comparable age, lithology, and geologic or physiographic setting. ORRs were an intermediate product. In the second phase, ORRs were evaluated relative to the presence of PAs that may underlie the ORRs and (or) glacial deposits that may overly the ORRs. The presence or absence of stream-valley aquifers overlying an SHR was not considered, which is consistent with the identification of PAs. Identification and mapping of ORRs and SHRs was facilitated using digital databases and geographic information system tools. The SHRs were classified using three criteria: (1) presence or absence of underlying PAs or overlying glacial deposits, (2) primary lithology, and (3) geologic province and subprovince. The number and size of SHRs identified in this paper are comparable to the number and size of PAs previously identified by the USGS. With the identification of SHRs, all areas of the conterminous U.S. belong to an internally consistent mapped feature, thus providing a comprehensive framework for assessing groundwater at regional and national scales. Figures 1-3 are included for reference. Files are provided in Portable Network Graphic (PNG) format: Figure 1. Map showing the 69 Secondary Hydrogeologic Regions of the conterminous United States. Figure 2. Maps showing Secondary Hydrogeologic Regions (SHRs) classified by (A) Type; (B) Lithologic class, and (C) Geologic province and subprovince. Figure 3. Map showing Community Supply Wells (Price and Maupin, 2014) classified by the type of Secondary Hydrogeologic Region (SHR) that the well plots within. Type refers to the presence of a Principal Aquifer beneath (first letter) or glacial deposits above a SHR (second letter); Y= yes, present, and N = no, not present. There are about 143,000 wells shown: 9% are in SHRs classified as Type NN, 13% in Type NY, 3% in type YN, and 4% in Type YY; 71% are in Principal Aquifers.

USDA/NRCS SSURGO: This layer shows the Soil Survey Geographic (SSURGO) by the United States Department of Agriculture’s Natural Resources Conservation Service. SSURGO digitizing duplicates the original soil survey maps. This level of mapping is designed for use by landowners, townships, and county natural resource planning and management. The user should be knowledgeable of soils data and their characteristics. The soil units are symbolized by Esri to show the dominant condition for the 12 soil orders according to Soil Taxonomy. Dominant condition was determined by evaluating each of the components in a map unit; the percentage of the component that each soil order represented was accumulated for all the soil orders present in the map unit. The soil order with the highest accumulated percentage is then characterized as the dominant condition for that unit. If a tie was found between soil orders, a “tie-break” rule was applied. The tie-break was based on the component’s “slope_r” attribute value, which represents the Slope Gradient – Representative Value. The slope_r values were accumulated in the same fashion as the soil order attributes, i.e., by soil order, and the order with the lowest slope_r value was selected as dominant because that represented the lower slope value, and therefore we assumed the soils were more likely to be staying in that area or being deposited in that area. USDA/NRCS STATSGO This layer shows the U.S. General Soil Map of general soil association units by the United States Department of Agriculture’s Natural Resources Conservation Service. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad-based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The soil units are symbolized by Esri to show the dominant condition for the 12 soil orders according to Soil Taxonomy. Dominant condition was determined by evaluating each of the components in a map unit; the percentage of the component that each soil order represented was accumulated for all the soil orders present in the map unit. The soil order with the highest accumulated percentage is then characterized as the dominant condition for that unit. If a tie was found between soil orders, a “tie-break” rule was applied. The tie-break was based on the component’s “slope_r” attribute value, which represents the Slope Gradient – Representative Value. The slope_r values were accumulated in the same fashion as the soil order attributes, i.e., by soil order, and the order with the lowest slope_r value was selected as dominant because that represented the lower slope value, and therefore we assumed the soils were more likely to be staying in that area or being deposited in that area. USDA/NRCS GLOBAL SOIL REGIONS This layer shows the Global Soil Regions map by the United States Department of Agriculture’s Natural Resources Conservation Service. The data and symbology are based on a reclassification of the FAO-UNESCO Soil Map of the World combined with a soil climate map. The soils data is symbolized to show the distribution of the 12 soil orders according to Soil Taxonomy. For more information on this map, including the terms of use, visit us online.Website Link: https://www.nrcs.usda.gov/wps/portal/nrcs/site/national/home/

This map features the Soil Survey Geographic (SSURGO) by the United States Department of Agriculture's Natural Resources Conservation Service. It also shows data that was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. SSURGO digitizing duplicates the original soil survey maps. This level of mapping is designed for use by landowners, townships, and county natural resource planning and management. The user should be knowledgeable of soils data and their characteristics. The smallest scale map shows the Global Soil Regions map by the United States Department of Agriculture’s Natural Resources Conservation Service.The web map combines with the soil survey with the terrain basemap and a hydro overlay layer for reference purposes. This basemap is ideal for display of thematic data such as the soil survey map, providing a neutral terrain background with an overlay layer for reference purposes.

This dataset captures in digital form the results of previously published U.S. Geological Survey (USGS) Water Mission Area studies related to water resource assessment of Cenozoic strata and unconsolidated deposits within the Mississippi Embayment and the Gulf Coastal Plain of the south-central United States. The data are from reports published from the late 1980s to the mid-1990s by the Gulf Coast Regional Aquifer-System Analysis (RASA) studies and in 2008 by the Mississippi Embayment Regional Aquifer Study (MERAS). These studies, and the data presented here, describe the geologic and hydrogeologic units of the Mississippi embayment, Texas coastal uplands, and the coastal lowlands aquifer systems, south-central United States. The Mississippi embayment, Texas coastal uplands, and coastal lowlands aquifer systems underlie about 487,000 km2 in parts of Alabama, Arkansas, Florida, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee, and Texas from the Rio Grande on the west to the western part of Florida on the east. The previously published investigations divided the Cenozoic strata and unconsolidated deposits within the Mississippi Embayment and the Gulf Coastal Plain into 11 major geologic units, typically mapped at the group level, with several additional units at the formational level, which were aggregated into six hydrogeologic units within the Mississippi embayment and Texas coastal uplands and into five hydrogeologic units within the Coastal Lowlands aquifer system. These units include the Mississippi River Valley alluvial aquifer, Vicksburg-Jackson confining unit (contained within the Jackson Group), the upper Claiborne aquifer (contained within the Claiborne Group), the middle Claiborne confining unit (contained within the Claiborne Group), the middle Claiborne aquifer (contained within the Claiborne Group), the lower Claiborne confining unit (contained within the Claiborne Group), the lower Claiborne aquifer (contained within the Claiborne Group), the middle Wilcox aquifer (contained within the Wilcox Group), the lower Wilcox aquifer (contained within the Wilcox Group), and the Midway confining unit (contained within the Midway Group). This dataset includes structure contour and thickness data digitized from plates in two reports, borehole data compiled from two reports, and a geologic map digitized from a report plate. Structure contour and thickness maps of hydrogeologic units in the Mississippi Embayment and Texas coastal uplands had been previously digitized by a USGS study from georeferenced images of altitude and thickness contours in USGS Professional Paper 1416-B (Hosman and Weiss, 1991). These data, which were stored on the USGS Water Mission Area’s NSDI node, were downloaded, reformatted, and attributed for present dataset. Structure contour maps of geologic units in the Mississippi Embayment and Texas coastal uplands were digitized and attributed from georeferenced images of altitude and thickness contours in USGS Professional Paper 1416-G (Hosman, 1996) for this data release. Borehole data in this data release include data compiled for USGS Gulf Coast RASA studies in which a scanned version of a USGS report (Wilson and Hosman, 1987) was converted through optical character recognition and then manipulated to form a data table, and from borehole data compiled for the subsequent MERAS study (Hart and Clark, 2008) where an Excel workbook was downloaded and manipulated for use in a GIS and as part of this dataset. The digital geologic map was digitized from Plate 4 of USGS Professional Paper 1416-G (Hosman, 1996) and then attributed according to the USGS National Cooperative Geologic Mapping Program’s GeMS digital geologic map schema. The digital dataset a digital geologic map with contacts and faults and geologic map polygons distributed as separate feature classes within a geographic information system geodatabase. The geologic map database is a digital representation of the geologic compilation of the Guld Coast region originally published as Plate 4 of USGS Professional Paper 1416-G (Hosman, 1996). The dataset includes a second geographic information system geodatabase that contain digital structure contour and thickness data as polyline feature classes for all of the hydrogeologic units contoured in USGS Professional Paper 1416-B (Hosman and Weiss, 1991) and all of the geologic units contoured in USGS Professional Paper 1416-G (Hosman, 1996). The geodatabase also contains separate point feature classes that portray borehole location and the depth to hydrogeologic units penetrated downhole for all boreholes compiled for the USGS RASA sturdies by Wilson and Hosman (1987) and for the subsequent USGS MERAS study (Hart and Clark, 2008). Borehole data are provided in Microsoft Excel spreadsheet that includes separate TABs for well location and tabulation of the depths to top and base of hydrogeologic units intercepted downhole, in a format suitable for import into a relational database. Each of the geographic information system geodatabases include non-spatial tables that describe the sources of geologic or hydrogeologic information, a glossary of terms, and a description of units. Also included is a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. To maximize usability, spatial data are also distributed as shapefiles and tabular data are distributed as ascii text files in comma separated values (CSV) format.

Connecticut Planning Region Index is a general purpose index map of Connecticut Planning Regions based on mapped information compiled at 1:125,000 scale (1 inch equals approximately 2 miles) and a list of towns in each region available from the State of Connecticut, Office of Policy and Management. The layer is designed to be used to depict Connecticut Planning Regions at small scales or on small maps printed on regular size (8.5 x 11 inch) paper, for example. This Planning Region Index layer does not accurately represent planning region boundaries because it was digitized at 1:125,000 scale. Do not display, map or analyze this index layer with information collected at larger scales. To depict more accurate 1:24,000-scale Connecticut state, county, town, and planning region boundaries on a map, use the layer named Town, which is also published by the State of Connecticut Department of Energy & Environmental Protection. The 2012 Edition reflects consolidation of two organizations into the Lower Connecticut River Council of Governments.

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are included in this data release, allowing comparison of the influence of measurement confidence: all wells are equal-weight, and two different published categorizations of measurement quality were used to de-emphasize low-quality measurements. Each map is an estimate of background conductive heat flow as a function of assumed data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the well’s departure from estimated background heat flow conditions, and the value of residual may be useful in identifying hydrothermal or groundwater influence on conductive heat flow. References Cleveland, W. S., Grosse, E., Shyu, W. M, 1992, Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole. Sass, J. H., S.S. Priest, A.H. Lachenbruch, S.P. Galanis, Jr., T.H. Moses, Jr., J.P. Kennelly, Jr., R.J. Munroe, E.P. Smith, F.V. Grubb, R.H. Husk, Jr., and C.W. Mase, 2005, Summary of supporting data for USGS regional heat flow studies of the Great Basin, 1970-1990, USGS Open file Report, 2005-1207. SMU Regional Heat Flow Database, retrieved from http://geothermal.smu.edu on March 29, 2021.

USGS map quandrangle boundaries with names and unique identifiers for the 1:24,000 (7.5 minute) quadrangles. Additional attributes provide unique identifiers and hierarchical relationships between these quadrangles and the enclosing 1:100,000 (30 x 60 minute) and 1:250,000 (1 x 2 degree) quadrangles.

Feature layer generated from running the Derive New Locations tool in ArcGIS Online, selecting Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, New York, Connecticut, New Jersey, Pennsylvania, West Virginia, Virginia, Delaware, Maryland, Kentucky. Expression and source layers below: Expression United States State Boundaries 2018 intersects North_Atlantic_Appalachian_Region (from DOI Interior Regions / Regional Boundaries). This layer is to be used in maps and associated StoryMaps to specifically highlight the states within the U.S. Fish and Wildlife Service's North Atlantic-Appalachian Region and provide visual background.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The SPATIAL LOCATION of railroads/ is based upon locations as given in the National Transportation Atlas Database (United States Department of Transportation, Bureau of Transportation Statistics) and contemporary and historical U.S. topographical maps (United States Department of the Interior, U.S. Geological Survey)./The EXISTENCE of a railroad serving locations at a specific date (see variable "InOpBy") was determined using the following resources: 1911: state maps from William D. Whitney and Benjamin E. Smith (eds) The Century dictionary and cyclopedia, with a new atlas of the world, New York: Century Co., 1911 (using scanned images from http://www.goldbug.com); 1903: regional maps from Rand McNally, Rand McNally & Co.'s Enlarged Business Atlas And Shippers' Guide ... Showing In Detail The Entire Railroad System ... Accompanied By A New And Original Compilation And Ready Reference Index…, Chicago: Rand McNally & Company, 1903 (using images 2844006, 2844007 and 2844008 from http://www.davidrumey.com); 1898: regional maps from Rand McNally, United States. Rand, McNally & Co., Map Publishers and Engravers, Chicago, 1898. Rand, McNally & Co.'s New Business Atlas Map of the United States…, Chicago: Rand McNally & Company, 1898 (using images 0772003, 0772004 and 0772005 from http://www.davidrumey.com); 1893: state maps from Rand McNally and Company, Rand, McNally & Co.'s enlarged business atlas and shippers guide ; containing large-scale maps of all the states and territories in the United States, of the Dominion of Canada, the Republic of Mexico, Central America, the West Indies and Cuba. Chicago: Rand McNally, 1893 (images courtesy of Murray Hudson, www.antiquemapsandglobes.com) except for Louisiana, Maryland/Delaware, Michigan, and Mississippi which were taken from Rand McNally, Universal Atlas of the World, Chicago: Rand McNally, 1893 (images courtesy of the University of Alabama Cartographic Lab) and Texas which was digitized by Amanda Gregg from Rand McNally & Co. Indexed county and railroad pocket map and shippers' guide of Texas : accompanied by a new and original compilation and ready reference index, showing in detail the entire railroad system ...Chicago: Rand McNally & Co., c1893 (Yale University Beinecke Library, Call Number: Zc52 893ra); 1889: state maps from Rand McNally, Rand, McNally & Co.'s enlarged business atlas and shippers guide…, Chicago: Rand McNally & Co., 1889 (using images 2094016 through 2094062 from http://www.davidrumey.com); 1881: state maps from Rand McNally, New Indexed Business Atlas and Shippers Guide, Chicago: Rand McNally & Co., 1881 (photographed by Amanda Gregg from a copy in the Yale University Beinecke Library, 2009 Folio 63); 1877: state maps from Rand McNally and Company, Rand McNally & Co’s Business Atlas, Chicago: Rand McNally & Co., 1877 (digitized by Matthew Van den Berg from a copy in the Library of Congress, Call no. G1200 .R3358 1877); 1872: regional maps from Warner & Beers, Atlas of the United States, Chicago: Warner & Beers, 1872 (using images 2585069 through 2585078 from http://www.davidrumey.com);1868: national map by J. T. Lloyd, Lloyd's New Map of the United States The Canadas and New Brunswick From The Latest Surveys Showing Every Railroad & Station Finished … 1868, New York: J. T. Lloyd, 1868 (using image 2859002 from http://www.davidrumey.com)1863: national map by J. T. Lloyd, Lloyd's New Map of the United States The Canadas And New Brunswick From the latest Surveys Showing Every Railroad & Station Finished to June 1863, New York: J. T. Lloyd, 1863 (using image 2591002 from http://www.davidrumey.com)1861: regional maps by G. R. Taylor and Irene D. Neu, The American Railroad Network 1861-1890, Cambridge, Mass: Harvard University Press, 1956;1858: national map by Hugo Stammann, J. Sage & Son's new & reliable rail road map comprising all the railroads of the United States and Canadas with their stations and distances, Buffalo, NY: J Sage & Sons, 1858 using image rr000360 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr000360;1856: national map by Richard S. Fisher, Dinsmore's complete map of the railroads & canals in the United States & Canada carefully compiled from authentic sources by Richard S. Fisher, editor of the American Rail Road & Steam Navigation Guide, New York, 1856 using image rr000300 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr000300;1854: national map by E. D. Sanford, H. V. Poor's rail road map showing particularly the location and connections of the North East & South West Alabama Rail Road, by E. D. Sanford, Civil Engineer, n.p.: 1854 using image rr004950 from the Library of Congress at http://hdl.loc.gov/loc.gmd/g3701p.rr004950;1852: national map by J. H. Colton, Colton's Map Of The United States, The Canadas &c. Showing The Rail Roads, Canals & Stage Roads: With Distances from Place to Place, New York: J. H. Colton, 1852 (using image 0172002 from http://www.davidrumey.com)1850 and earlier dates: Curran Dinsmore, Dinsmore & Company's new and complete map of the railway system of the United States and Canada; compiled from official sources, under the direction of the editor of the "American Railway Guide.", New York: 1850, the early railroad database assembled by Professor Milton C. Hallberg (deceased, Pensylvania State University) and appearing on http://oldrailhistory.com/, various railroad histories, on-line google search results and Wikipedia entries for specific railroads appearing in Hallberg’s database. Digitized maps were geo-referenced using ArcGIS 10’s spline algorithm against the National Historical Geographic Information System’s 2009 TIGER-based historical state and county boundary files (see www.nhgis.org) and the U.S. National Atlas’s database of cities and town.No effort was made to identify or preserve double tracking. Sidings, yards, and turnouts, etc., were deleted whenever possible absent any knowledge as to when these features were constructed.See Jeremy Atack "Procedures and Issues Relating to the Creration of Historical Transportation Shapfiles of Navigabale Rivers, Canals, and Railroads in the United States" available at https://my.vanderbilt.edu/jeremyatack/files/2015/09/HistoricalTransportationSHPfilesDocumenation.pdf. Also Jeremy Atack, "On the Use of Geographic Informations Systems in Economic History" Journal of Economic History, 73:2 (June 2013): 313-338. Also available at https://my.vanderbilt.edu/jeremyatack/files/2011/08/EHAPresidentialAddress.pdfRevision History: Edited = 1 ==> minor modifications by Jeremy Atack, September 20, 2015 amending dates for "InOpBy" and/or endpoints to fix microfractures and inconsistencies,1861 or earlier.= 2 ==> JA; 9/21/2015 switched dates and names (1861-1903) on Charleston & Savannah RR just west of Ashley River to accurately reflect LOC map for this RR= 3 ==> JA: 12/22/2015 modification to RR dates and locations around Baltimore, New York city, Philadelphia and Washington DC reflecting (some but not all) of the 1860 mapping by C. Baer et al., Canals and Railroads of the Mid-Atlantic States, 1800-1860 (Hagley Foundation 1981)SHP file edited 5/9/2016 to fix error message in ArcCatalog caused by 4 "phantom" features (InOpBy=blank/zero) that had no geometry associated with them.