This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

This map contains the 4 Regional Areas: Border and Permian Basin, Central Texas, Coastal and East Texas, North Central and West Texas and the 16 Regions of the TCEQ. The areas for this data was obtained from TXDOT county boundaries (no coastal detail). General purpose use is to delineate TCEQ Region boundaries on maps and other products. Originating feature class was digitized by TXDOT at 1:24,000 using DRGs (USGS Topos) in the NAD 83 Datum.

This data release documents the digital data used to produce flood-inundation maps for a range of gage heights (stages) for the Sabinal River near Utopia, Tex. The simulated flood-inundation maps correspond to a range in stage from 7 to 24 feet (ft) at U.S. Geological Survey (USGS) streamgage 08197970 Sabinal River at Utopia, Tex. at intervals of 0.5-ft. The maps were created for a 10-mile reach of the Sabinal River extending from USGS streamgage 08197936 Sabinal River below Mill Creek near Vanderpool, Tex. to USGS streamgage 08197970 Sabinal River at Utopia, Tex. (hereinafter referred to as the “Utopia gage”) and 7-mile reach of the West Sabinal River were created by the USGS in cooperation with the Bandera County River Authority and Groundwater District and with the Texas Water Development Board. Stage data are collected every 5 minutes and used for estimating areas of inundation near the Utopia gage; the stage data are available from the USGS National Water Information System ( ...

Hurricane Harvey made landfall near Rockport, Texas on August 25 as a category 4 hurricane with wind gusts exceeding 150 miles per hour. As Harvey moved inland the forward motion of the storm slowed down and produced tremendous rainfall amounts to southeastern Texas and southwestern Louisiana. Historic flooding occurred in Texas and Louisiana as a result of the widespread, heavy rainfall over an 8-day period in Louisiana in August and September 2017. Following the storm event, U.S. Geological Survey (USGS) hydrographers recovered and documented 2,123 high-water marks in Texas, noting location and height of the water above land surface. Many of these high-water marks were used to create flood-inundation maps for selected communities of Texas that experienced flooding in August and September, 2017.
The mapped area boundary, flood inundation extents, depth rasters, and coastal surge layer were created to provide an estimated extent of flood inundation in Coastal basins including East and West Matagorda Bay Subbasins, East and West San Antonio Bay Subbasins, and Aransas Bay Subbasin, Texas. The mapped area of the Coastal basins were separated into three sections based on the availability and location of high-water marks. The maps of the eastern part of the East Matagorda Bay Subbasin include a 17-mi reach of Peyton Creek and a 16-mi reach of Big Boggy Creek, and flood-inundation map for 6-mi reach of Little Boggy Creek in Matagorda County. The maps of the western part of East Matagorda Bay Subbasin include a 13.5-mi reach of West Carancahua Creek, 14.5-mi reach of East Carancahua Creek, and 9.6-mi reach of Keller Creek within Matagorda, Jackson, and Calhoun Counties. The maps of the middle part of the East Matagorda Bay Subbasin are for a 21-mi reach of the Tres Palacios River within Matagorda County. These geospatial data include the following items: 1. bnd_emb1, bnd_emb2, and bnd_tres_palacios; shapefiles containing the polygon showing the mapped area boundary for the Coastal basins flood maps, 2. hwm_emb_1, hwm_emb2, and hwm_tres_palacios; shapefiles containing high-water mark points used for inundation maps, 3. polygon_emb1, polygon_emb_2, and polygon_tres_palacios; shapefiles containing mapped extent of flood inundation for the Coastal basins, derived from the water-surface elevation surveyed at high-water marks, 4. depth_emb1, depth_emb2, and depth_tres; raster files for the flood depths derived from the water-surface elevation surveyed at high-water marks, and 5. coastal_surge.lyr; a layer file generated from the depth raster depicting water height above ground recorded at the high-water marks. The upstream and downstream mapped area extent is limited to the upstream-most and downstream-most high-water mark locations. In areas of uncertainty of flood extent, the mapped area boundary is lined up with the flood inundation polygon extent. The mapped area boundary polygon was used to extract the final flood inundation polygon and depth raster from the water-surface elevation raster file. Depth raster files were created using the "Topo to Raster" tool in ArcMap (ESRI, 2012). The HWM elevation data from the USGS Short-tern Network (STN) was used to create the flood water-surface raster file (U.S. Geological Survey [USGS], 2018, Short-Term Network Data Portal: USGS flood information web page, accessed February 13, 2018, at https://water.usgs.gov/floods/FEV.). The water-surface raster was the basis for the creation of the final flood inundation polygon and depth layer to support the development of flood inundation map for the Federal Emergency Management Agency's (FEMA) response and recovery operations.

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

This is the compilation of the most significant events of 2020 that occurred across the county warning area (CWA) for NWS Midland. The CWA encompasses 26 counties across West Texas and Southeast New Mexico. These major events include snow and ice storms, severe weather, high wind events, and record-breaking temperatures. While this story map does not include every weather event from 2020, it does comprise of the biggest events as determined by the employees at NWS Midland. The most important details of each event are mentioned in this story map, but further information on each event can be found at: https://www.weather.gov/maf/Many photos and videos within this story map were shared by local media and/or the public.Snow Map: https://noaa.maps.arcgis.com/apps/mapviewer/index.html?webmap=86a96b13e0194a108cefee9defb6b7eb

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24, 000-scale) and the 15-minute (1:48, 000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 400,000-plus point and polygon mine symbols from approximately 51,000 maps of 17 western states (AZ, CA, CO, ID, KS, MT, ND, NE, NM, NV, OK, OR, SD, UT, WA, WY and western TX) has been completed. The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

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.

Reason for Selection Native grasslands and savannas are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands and savannas have significantly declined from their historical extent. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland and savanna birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential habitat. Input Data

Texas Ecological Mapping Systems: statewide raster, accessed 12-2023
Oklahoma Ecological Systems Map: download the raster, accessed 12-2023
Protected Areas Database of the United States (PAD-US): PAD-US 3.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement; PAD-US 4.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement
National Land Cover Database (NLCD): 2021 Land Cover, 2021 U.S. Forest Service (USFS) Tree Canopy Cover, 2013 Land Cover, and 2013 USFS Tree Canopy Cover
2020 LANDFIRE Biophysical Settings (BPS) [LF 2.2.0]
Southeast Blueprint 2024 landscape condition indicator
Southeast Blueprint 2024 extent

Known grasslands

Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University in Oct 2020 (available on request by emailing rua_mordecai@fws.gov); this is an improved version of the Known Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO) layer
Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailing rua_mordecai@fws.gov).
Grassland polygons from the Catawba Indian Nation, provided by Aaron Baumgardner, Natural Resources Director, in July 2023 (for more information email rua_mordecai@fws.gov)
Grassland polygons from two iNaturalist projects in Texas: erwin-park-prairie-restoration-area, stella-rowan-prairie
Southeastern Grasslands Institute polygons from selected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: 
    allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades, folder-six-glades

Mapping Steps

Combine all known grasslands polygons and convert to raster, assigning them a value of 7.
From the 2021 and 2013 NLCD landcover, create rasters that only include classes likely to have grasslands and savannas. The classes included are based on NLCD classes that overlap known grassland and savanna polygons. Any class that covered >1% of known grasslands and savannas is included: 31 Barren Land, 41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, 52 Scrub/Shrub, 71 Grassland/Herbaceous, 81 Pasture/Hay.
For those 2021 and 2013 selected landcover rasters, remove forest with ≥ 60% canopy cover using NLCD USFS Tree Canopy Cover for the corresponding year. This results in potential grassland and savanna rasters for 2021 and 2013.
Make a single potential grassland and savanna raster that only includes pixels that are potential grasslands and savannas in both 2013 and 2021. This removes temporary grasslands and savannas that result from clearcuts.
From the Texas and Oklahoma ecological systems maps, extract classes that predict areas invaded by mesquite, a non-native tree that spreads aggressively in the grasslands and savannas of the Southwest and disrupts natural ecosystems through its heavy water consumption. For Oklahoma, this is VegName = 'Ruderal Mesquite Shrubland'. For Texas, this is CommonName = 'Native Invasive: Mesquite Shrubland'. Combine these and use them to remove areas that are no longer grassland and savanna due to mesquite invasion. The resulting layer represents potential grasslands.
To identify potential grasslands and savannas in natural landscapes, use values 5 and 6 from the landscape condition indicator. Assign a value of 3 to any potential grassland pixel that receives a landscape condition score of 5 or 6. Assign all other potential grassland pixels a value of 2.
To identify likely grasslands and savannas, overlay the potential grasslands and savannas raster with select polygons from PAD-US 4.0. To pull out types of protected lands that commonly manage grasslands and savannas, we used GAP status, designation type, manager name, and easement holder. We also identified a number of protected areas directly by name that had important areas of grassland and savanna but weren’t captured by the other rules.
    GAP status (GAP_sts) 1 or 2: Gap status 1 and 2 refer to areas managed for biodiversity that are not subject to extractive uses like logging and mining. GAP status 2 is technically intended to encompass areas where disturbance events are suppressed, but in practice, most protected areas in the Southeast that are actively managing grasslands and savannas are classified as GAP status 2.
    Designation type (Des_Tp) of ‘NWR’, ‘MIL’, ‘NF’, or ‘NG’ (i.e. National Wildlife Refuge, military installation, National Forest, or National Grassland)
    Manager name (Mang_Name) of ‘RWD’ (i.e. Regional Water District)
    Local manager name (Loc_Mang) of 'Ducks Unlimited (Wetlands America Trust)'
    Easement holder (EsmtHldr) of 'Tall Timbers Research Station & Land Conservancy'
    Unit name (Unit_Nm) of ‘Point Washington State Forest’, ‘Pine Log State Forest’, ‘M. C. Davis - Seven Runs Creek Conservation Easement’, ‘Nokuse Plantation Conservation Easements’, ‘Tate's Hell State Forest’, ‘Box-R Wildlife Management Area’, ‘Aucilla Wildlife Management Area’, ‘Snipe Island Unit’, ‘Big Bend Wildlife Management Area’, ‘Goethe State Forest’, ‘Amelia Wildlife Management Area’, ‘Powhatan Wildlife Management Area’, ‘Cumberland State Forest’, ‘Appomattox-Buckingham State Forest’, ‘Haw River State Park’, ‘R. Wayne Bailey - Caswell Game Land’, ‘Medoc Mountain State Park’, ‘Embro Game Land’, ‘Dupont State Forest’, ‘Hanging Rock State Park’, ‘Bladen Lakes State Forest’, ‘Whitehall Plantation Game Land’, ‘Suggs Mill Pond Game Land’, ‘Bushy Lake State Natural Area’, ‘Pondberry Bay Plant Conservation Preserve’, ‘Green Swamp Game Land’, ‘Holly Shelter Game Land’, ‘Chowan Swamp Game Land’, ‘Brookgreen Gardens’, ‘Cary State Forest’, ‘Suwannee Ridge Mitigation Park Wildlife and Environmental Area’, ‘Adams-Alapha Ag & Conservation Easement’, ‘Twin Rivers State Forest’, ‘Chattahoochee Fall Line Wildlife Management Area’, ‘Enon Plantation’, or ‘Georgia-Alabama Land Trust Easement #214’, ‘Covington Wildlife Management Area’, ’ Magnolia Branch Wildlife Reserve’, ‘Little River State Forest’, or ‘Susan Turner Plantation’, or have the local name (Loc_Nm) 'Sandhills Game Land', 'Blackwater River State Forest', 'Three Lakes Wildlife Management Area', 'Herky Huffman/Bull Creek Wildlife Management

Max Speed limit values in miles per hour. This data is an extract from the Geospatial Roadway Inventory Databse (GRID), which is TxDOT's system for managing roadway assets in Texas.Note: Extracts from GRID are made on a regular basis and reflect the state of the data at that moment. Assets on routes that are in the process of being edited may be affected.Update Frequency: 1 MonthsSource: Geospatial Roadway Inventory Database (GRID)Security Level: PublicOwned by TxDOT: TrueRelated LinksData Dictionary PDF [Generated 2025/04/24]