Provides regional identifiers for county based regions of various types. These can be combined with other datasets for visualization, mapping, analyses, and aggregation. These regions include:Metropolitan Statistical Areas (Current): MSAs as defined by US OMB in 2023Metropolitan Statistical Areas (2010s): MSAs as defined by US OMB in 2013Metropolitan Statistical Areas (2000s): MSAs as defined by US OMB in 2003Region: Three broad regions in North Carolina (Eastern, Western, Central)Council of GovernmentsProsperity Zones: NC Department of Commerce Prosperity ZonesNCDOT Divisions: NC Dept. of Transportation DivisionsNCDOT Districts (within Divisions)Metro Regions: Identifies Triangle, Triad, Charlotte, All Other Metros, & Non-MetropolitanUrban/Rural defined by:NC Rural Center (Urban, Regional/Suburban, Rural) - 2020 Census designations2010 Census (Urban = Counties with 50% or more population living in urban areas in 2010)2010 Census Urbanized (Urban = Counties with 50% or more of the population living in urbanized areas in 2010 (50,000+ sized urban area))Municipal Population - State Demographer (Urban = counties with 50% or more of the population living in a municipality as of July 1, 2019)Isserman Urban-Rural Density Typology

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.

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.

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

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.

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.

Layer Information: -Weather Events: Convection in Las Cruces and the Significant Flood Event of 2006 in El Paso are displayed. Clicking on the icon can give information of the phenomena or event. - Major Cities: Major cities and populations are mapped. The bigger the circle the bigger the population of that city. - Observation/ Data collection sites: This layer contains the location of where atmospheric soundings launched from the surface and where in-stu surface observations are gathered. The later includes Weather, Ocean, Lake, River, Water Quality, and Air Quality. - Köppen-Geiger Climate Divisions: General temperatures, precipitation, and latitude define these climate classes. The World Meteorological Organization (WMO) defined a classic climate record to be 30 years, so this current map is based of off the average weather an area has experienced from 1981 to 2010. New normals will be calculated in 2021. To read more click here. -National Weather Service Forecast Offices (WFO): Locations of the continental United States weather forecast offices, including office contact information. App Information: How to use it: Zooming in and out will turn on and off different layers. A zoomed out map will show the global Koppen climate classification. Zooming in will turn off the climate layer, while enabling the National Weather Service (NWS) Offices, Weather Events and other layers. Clicking on a Weather event or NWS office in the map will bring up a window with more information. - The legends and layers are shown by toggling the menus on via the icons at the bottom of the map.

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).

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

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).

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.)

Context

I needed this dataset to map some countries in the analysis: Advanced Global Warming Analysis with Plotly. Feel free to use this mapping for whatever cool analysis you're doing. :)

Content

• name - Country name in english
• alpha-2 - ISO code formed of 2 letters
• alpha-2 - ISO code formed of 3 letters (use this in your plotly maps ;) )
• country code - unique
• region - the continent of provenience
• sub-region - subcontinent
• intermediate region
• codes for region/ subregion/ intermediate region

Acknowledgements

Dataset was taken from lukes on GitHub: https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes/blob/master/all/all.csv. I made only some small changes to the country names to mach my needs in the dataset (eg. United States of America transformed in United States).

FAF domestic region level datasets and products provide information for states, state portions of large metropolitan areas, and remainders of states. Metropolitan areas consist of Metropolitan Statistical Areas or Consolidated Statistical Areas as defined by the Office of Management and Budget. When a metropolitan area is entirely within a state or when a state's portion of a multi-state metropolitan area is large enough to support the sampling procedures in the Commodity Flow Survey, the area becomes a separate FAF region. Small single-state metropolitan areas and small portions of a multi-state metropolitan area are part of the State or Remainder of State. FAF has two metropolitan areas that are each divided into three FAF regions, four that are each divided into two FAF regions, and several that have small pieces combined with States or Remainders of States.

© United States Department of Transportation, Federal Highway Administration. For more information, see the site http://www.ops.fhwa.dot.gov/freight/freight_analysis/faf/faf3/userguide/index.htm This layer is sourced from maps.bts.dot.gov.

The spatial component of the FAF network is derived from National Highway System Version 2009.11 and contains state primary and secondary roads, National Highway System (NHS), National Network (NN) and several intermodal connectors as appropriate for the freight network modeling. The network consists of over 447,808 miles of equivalent road mileage. The data set covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii. The nominal scale of the data set is 1:100,000 with a maximal positional error of ±80 meters.

© ederal Highway Administration Office of Freight Management and Operations and the Battelle Memorial Institute, Columbus, OH

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.

NERC is an international regulatory authority that works to improve the reliability of the bulk power system in North America. NERC works with many different regional entities to improve the coordination and supply of electricity. This data set of vector layer of polygons represents the boundaries of the regional and subregional entities associated with NERC

This map was created as part of a worldwide series of geologic maps for the U.S. Geological Survey's World Energy Project. These products are available on CD-ROM and through the Internet. The goal of the project is to assess the undiscovered, technically recoverable oil and gas resources of the world. Most of the source data for this map compilation were digitized from the Geologic-Tectonic Map of the Caribbean Region by J.E. Case and T.L. Holcombe, at a scale of 1:2,500,000. For data management purposes, the world was divided into eight energy regions based on political boundaries and corresponding approximately to the economic regions of the world as defined by the U.S. Department of State. Region Six encompasses the Caribbean area, Central America, and South America. Other products are also available related to Region Six, including the Geologic Map of South America (USGS Open File Report 97-470-D). Countries listed below are shown whole or in part within the map extent of the Caribbean region: Anguilla Antigua and Barbuda Aruba Bahamas Barbados Belize British Virgin Islands Cayman Islands Colombia Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guadeloupe Guatemala Guyana Haiti Honduras Jamaica Martinique Mexico Montserrat Netherlands Antilles Nicaragua Panama Puerto Rico St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Trinidad and Tobago Turks and Caicos Islands United States Venezuela Virgin Islands The world was previously divided into geologic provinces for the World Energy Project, of which a subset is shown on the map. Each province has a set of geologic characteristics that distinguish it from surrounding provinces. These characteristics may include dominant lithologies, the age of the strata, and/or structural type. Each province is assigned a unique number and may fall within two or more countries or assessment regions. The World Geographic Coordinate System of 1984 was used for data storage and map display. Other details about the map compilation and data sources are provided in several metadata formats in the data section on this CD-ROM. Various software packages were used to create this map including: Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 8.3, ArcInfo software, Adobe Photoshop CS, Illustrator CS, and Acrobat 6.0.

Analysis of ‘Country Mapping - ISO, Continent, Region’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andradaolteanu/country-mapping-iso-continent-region on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

I needed this dataset to map some countries in the analysis: Advanced Global Warming Analysis with Plotly. Feel free to use this mapping for whatever cool analysis you're doing. :)

Content

• name - Country name in english
• alpha-2 - ISO code formed of 2 letters
• alpha-2 - ISO code formed of 3 letters (use this in your plotly maps ;) )
• country code - unique
• region - the continent of provenience
• sub-region - subcontinent
• intermediate region
• codes for region/ subregion/ intermediate region

Acknowledgements

Dataset was taken from lukes on GitHub: https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes/blob/master/all/all.csv. I made only some small changes to the country names to mach my needs in the dataset (eg. United States of America transformed in United States).

--- Original source retains full ownership of the source dataset ---

This worldwide street map presents highway-level data for the world. Street-level data includes the United States; much of Canada; Mexico; Europe; Japan; Australia and New Zealand; India; South America and Central America; Africa; and most of the Middle East. This comprehensive street map includes highways, major roads, minor roads, one-way arrow indicators, railways, water features, administrative boundaries, cities, parks, and landmarks, overlaid on shaded relief imagery for added context. The map also includes building footprints for selected areas. Coverage is provided down to ~1:4k with ~1:1k and ~1:2k data available in select urban areas. The street map was developed by Esri using Esri basemap data, DeLorme basemap layers, U.S. Geological Survey (USGS) elevation data, Intact Forest Landscape (IFL) data for the world; HERE data for Europe, Australia and New Zealand, North America, South America and Central America, Africa, and most of the Middle East; OpenStreetMap contributors for select countries in Africa; MapmyIndia data in India; and select data from the GIS user community. For more information on this map, including the terms of use, visit us online at http://goto.arcgisonline.com/maps/World_Street_Map

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A Portable Document Format (PDF) file is provided which shows a map of the study area and four analysis regions: (1) the entire South Florida Water Management District (SFWMD), (2) the Lower West Coast (LWC) water supply region, (3) the Lower East Coast (LEC) water supply region, and (4) the Okeechobee plus (OKEE+) water supply meta-region consisting of Lake Okeechobee (OKEE), the Lower Kissimmee (LKISS), Upper Kissimmee (UKISS), and Upper East Coast (UEC) water supply regions in the SFWMD.

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.