This layer is a component of SC Statewide Address Points and Centerlines.

© SC DOT, SC Counties, SC Geographic Information Council

The Digital Geologic-GIS Map of the Chesnee Quadrangle, South Carolina is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (ches_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (ches_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (cowp_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (cowp_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ches_geology_metadata_faq.pdf). Please read the cowp_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: South Carolina Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ches_geology_metadata.txt or ches_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

SCDHEC ArcGIS Hub - GIS Content and Core Team have ability to edit applications and data.

© Greenville County GIS Division, Greenville, South Carolina

This layer is a component of SC Statewide Building Footprints.

© SC DOT, SC Counties, SC Geographic Information Council

The Digital Geologic-GIS Map of Fort Sumter and Fort Moultrie National Historical Park and Vicinity, South Carolina is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (fosu_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (fosu_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (fosu_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (fosu_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (fosu_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (fosu_geology_metadata_faq.pdf). Please read the fosu_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (fosu_geology_metadata.txt or fosu_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Health facilities and services currently licensed by the South Carolina Department of Health (SC DHEC) Bureau of Health Facilities Licensing. Updated daily.

These data are meant to supply US Census geographical areas and some general Census information for mapping purposes only.

Statewide Imagery flown in 2023 for South Carolina. This layer references service published by SC Revenue and Fiscal Affairs.Imagery collected in 2023 by Kucera International. Imagery is managed by Adam DeMars, South Carolina State GIS Coordinator and hosted by Esri.South Carolina Aerial photography captured between January - February 2023.6 Inch, 4-Band (R, G, B, NIR) imagery. Cloud free conditions with a sun angle of 30 Degrees or higher to reduce shadows as much as possible. Additionally, the northing and easting offsets cannot exceed 3 pixels or 1.5ft Ground Sampled Distance.

The U.S. Geological Survey South Atlantic Water Science Center, in cooperation with the South Carolina Department of Transportation, implemented a South Carolina StreamStats application in 2018. This shapefile dataset contains vector lines representing streams, rivers, and ditches that were used in preparing the underlying data for the South Carolina StreamStats application. Data were compiled from multiple sources, but principally represent lidar-derived linework from the South Carolina Department of Natural Resources and the South Carolina Lidar Consortium.The South Carolina hydrography lines were created from elevation rasters that ranged from 4 to 10 ft resolution, to produce a product of approximately 1:6,000-scale. Other sources include the 1:24,000 scale high resolution National Hydrography Dataset streamlines [for streamlines in Georgetown County (SC), NC, and GA] and the 1:4,800 scale local-resolution North Carolina Stream Mapping Project lines (mountain counties). These ...

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The boundaries of most legal MCDs are as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Collection of various datasets in esri shapefile format for download; includes hydrography and geology data.

South Carolina building outline dataset.

Geospatial data about Horry County, South Carolina County Owned Parcels. Export to CAD, GIS, PDF, CSV and access via API.

Abstract: Annual average wind resource potential for the state of South Carolina at a 50 meter height.

Purpose: Provide information on the wind resource development potential within the state of South Carolina.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a WGS 84 projection system.

Other Citation Details: The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

description: The Digital Geologic Map of Fort Sumter National Monument and vicinity, South Carolina is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (fosu_metadata.txt; available at http://nrdata.nps.gov/fosu/nrdata/geology/gis/fosu_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (fosu_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 17N. That data is within the area of interest of Fort Sumter National Monument, Charles Pinckney National Historic Site and Fort Moultrie National Monument.; abstract: The Digital Geologic Map of Fort Sumter National Monument and vicinity, South Carolina is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (fosu_metadata.txt; available at http://nrdata.nps.gov/fosu/nrdata/geology/gis/fosu_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (fosu_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 17N. That data is within the area of interest of Fort Sumter National Monument, Charles Pinckney National Historic Site and Fort Moultrie National Monument.

Best Chance Network (BCN) South Carolina's Breast and Cervical Cancer Early Detection Program. The contents of this feature class are provided and maintained by the Bureau of Community Health & Chronic Disease Prevention's Cancer Prevention & Control Division at South Carolina's Department of Health and Environmental Control.

Points represent the location of permitted agricultural facilites, including animal houses, burial sites and centroids of potential manure utilization areas (MUA). Although a MUA is permitted for land application, the permittee may never have actually used the field for land application. Use records are maintained by the permittee.

Geospatial data about Anderson County, South Carolina Parcels. Export to CAD, GIS, PDF, CSV and access via API.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.