The Digital Geomorphic-GIS Map of Cape Lookout National Seashore, North Carolina (1:24,000 scale 2008 mapping) 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 (calo_geomorphology.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 3.X map file (.mapx) file (calo_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 (calo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (calo_geomorphology.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 (calo_geomorphology_metadata_faq.pdf). Please read the calo_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: 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: North 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 (calo_geomorphology_metadata.txt or calo_geomorphology_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 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).

The Digital Geologic-GIS Map of San Miguel Island, California 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 (smis_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 (smis_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 (smis_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 (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_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 (smis_geology_metadata_faq.pdf). Please read the chis_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: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_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).

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

PLEASE NOTE: These data do not include data over Tasmania. Please see links relevant to that area.

GEODATA TOPO 250K Series 3 is a vector representation of the major topographic features appearing on the 1:250,000 scale NATMAPs supplied in KML format and is designed for use in a range of commercial GIS software. Data is arranged within specific themes. All data is based on the GDA94 coordinate system.

GEODATA TOPO 250K Series 3 is available as a free download product in Personal Geodatabase, ArcView Shapefile or MapInfo TAB file formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in kml format for use on Google Earth TM Mapping Service.

Product Specifications

Themes: Cartography, Elevation, Framework, Habitation, Hydrography, Infrastructure, Terrain, Transport, Utility and Vegetation

Coverage: National (Powerlines not available in South Australia)

Currency: Data has a currency of less than five years for any location

Coordinates: Geographical

Datum: Geocentric Datum of Australia (GDA94)

Formats: Personal Geodatabase, kml, Shapefile and MapInfo TAB

Release Date: 26 June 2006

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 Digital Geologic-GIS Map of Big Cypress National Preserve and Vicinity, Florida 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 (bicy_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 (bicy_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 (bicy_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.) A GIS readme file (bicy_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (bicy_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 (bicy_geology_metadata_faq.pdf). Please read the bicy_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: Florida Geological Survey, U.S. Geological Survey and Earthfx Incorporated/BEM Systems Inc.. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (bicy_geology_metadata.txt or bicy_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 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 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).

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

Documented March 19, 2023

!!NEW!!!

GeoDAR reservoirs were registered to the drainage network! Please see the auxiliary data "GeoDAR-TopoCat" at https://zenodo.org/records/7750736. "GeoDAR-TopoCat" contains the drainage topology (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).

Documented April 1, 2022

Citation

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.

Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.

Contact

Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu

Data description and components

Data folder “GeoDAR_v10_v11” (.zip) contains two consecutive, peer-reviewed versions (v1.0 and v1.1) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:

• GeoDAR_v10_dams (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; https://www.icold-cigb.org; last access on March 13th, 2019).
• GeoDAR_v11_dams (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).
• GeoDAR_v11_reservoirs (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.

As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:

• GRanD_v13_issues.csv: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include:
  • “Issue”: main issue(s) of this record
  • “Description”: more detailed explanation of the issue
  • “Lat_corrected”: suggested correction for latitude (if any) in decimal degree
  • “Lon_corrected”: suggested correction for longitude (if any) in decimal degree
  • “Correction_source”: correction source(s)
  • “Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.
• Wada_et_al_2017_harmonized.csv: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include:
  • Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization
  • Revised_lat: Revised latitude in decimal degree
  • Revised_lon: Revised longitude in decimal degree
  • Verification_notes: Description of the issues, verification sources, and other information used for harmonization.

Attribute description

The Digital Geologic-GIS Map of Santa Rosa Island, California 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 (sris_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 (sris_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 (sris_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 (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_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 (sris_geology_metadata_faq.pdf). Please read the chis_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: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sris_geology_metadata.txt or sris_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).

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding.The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population. New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA products which provide coverage for those areas.In the NFHL Viewer, you can use the address search or map navigation to locate an area of interest and the NFHL Print Tool to download and print a full Flood Insurance Rate Map (FIRM) or FIRMette (a smaller, printable version of a FIRM) where modernized data exists. Technical GIS users can also utilize a series of dedicated GIS web services that allow the NFHL database to be incorporated into websites and GIS applications. For more information on available services, go to the NFHL GIS Services User Guide.You can also use the address search on the FEMA Flood Map Service Center (MSC) to view the NFHL data or download a FIRMette. Using the “Search All Products” on the MSC, you can download the NFHL data for a County or State in a GIS file format. This data can be used in most GIS applications to perform spatial analyses and for integration into custom maps and reports. To do so, you will need GIS or mapping software that can read data in shapefile format.FEMA also offers a download of a KMZ (keyhole markup file zipped) file, which overlays the data in Google Earth™. For more information on using the data in Google Earth™, please see Using the National Flood Hazard Layer Web Map Service (WMS) in Google Earth™.

Four digital water-surface profile maps for a 14-mile reach of the Mississippi River near Prairie Island in Welch, Minnesota from the confluence of the St. Croix River at Prescott, Wisconsin to upstream of the United States Army Corps of Engineers (USACE) Lock and Dam No. 3 in Welch, Minnesota, were created by the U.S. Geological Survey (USGS) in cooperation with the Prairie Island Indian Community. The water-surface profile maps depict estimates of the areal extent and depth of inundation corresponding to selected water levels (stages) at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). Current conditions for estimating near-real-time areas of water inundation by use of USGS streamgage information may be obtained on the internet at http://waterdata.usgs.gov/. Water-surface profiles were computed for the stream reach using HEC-GeoRAS software by means of a one-dimensional step-backwater HEC-RAS hydraulic model using the steady-state flow computation option. The hydraulic model used in this study was previously created by the USACE . The original hydraulic model previously created extended beyond the 14-mile reach used in this study. After obtaining the hydraulic model from USACE, the HEC-RAS model was calibrated by using the most current stage-discharge relations at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The hydraulic model was then used to determine four water-surface profiles for flood stages referenced to 37.00, 39.00, 40.00, and 41.00-feet of stage at the USGS streamgage on the Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The simulated water-surface profiles were then combined with a digital elevation model (DEM, derived from light detection and ranging (LiDAR) in Geographic Information System (GIS) data having a 0.35-foot vertical and 1.97-foot root mean square error horizontal resolution) in order to delineate the area inundated at each stage. The calibrated hydraulic model used to produce digital water-surface profile maps near Prairie Island, as part of the associated report, is documented in the U.S. Geological Survey Scientific Investigations Report 2021-5018 (https://doi.org/10.3133/ sir20215018). The data provided in this data release contains three zip files: 1) MissRiverPI_DepthGrids.zip, 2) MissRiverPI_InundationLayers.zip, and 3) ModelArchive.zip. The MissRiverPI_DepthGrids.zip and MissRiverPI_InundationLayers.zip files contain model output water-surface profile maps as shapefiles (.shp) and Keyhole Markup Language files (.kmz) that can be opened using Esri GIS systems (.shp files) or Google Earth (.kmz files), while the ModelArchive.zip contains model inputs, outputs, and calibration data used in creating the water-surface profiles maps.

This dataset consists of map files which show bus routes covering the Greater Manchester area. The dataset is available in MapInfo .tab, Google .kml, and ESRI .shp file formats.

Please acknowledge the source of this information using the following attribution statement:

Contains Transport for Greater Manchester data. Contains OS data © Crown copyright and database right 2021.

LIBRARIES_ISL_IN.SHP is a point shapefile showing the locations of 441 public and public branch libraries in Indiana. Street addresses for each library were originally provided by personnel of the Indiana State Library. IGWS personnel verified each address via online investigations using the Google search engine. Locations were also adjusted manually by visual inspection of aerial imagery in comparison with locations provided by the Google Maps search engine. Attributes include the library name, branch name, address, city, telephone numbers, and Website URLs for each location.

The Digital Geologic-GIS Map of Rocky Mountain National Park and Vicinity, Colorado 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 (romo_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 3.X map file (.mapx) file (romo_geology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 (romo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (romo_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 (romo_geology_metadata_faq.pdf). Please read the romo_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: 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 (romo_geology_metadata.txt or romo_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 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 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 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).

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

The Unpublished Digital Geologic-GIS Map of Virgin Islands National Park, Virgin Islands is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (viis_geology.gdb), a 10.1 ArcMap (.mxd) map document (viis_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (viis_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (viis_geology_gis_readme.pdf). Please read the viis_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). 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 (viis_geology_metadata.txt or viis_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 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). The GIS data projection is NAD83, UTM Zone 20N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Virgin Islands National Park.

The Digital Bedrock Geologic-GIS Map of the North, South, and Heritage Addition Units and Vicinity, Effigy Mounds National Monument, Iowa 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 (efmo_bedrock_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 (efmo_bedrock_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 (efmo_bedrock_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.) a readme file (efmo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (efmo_bedrock_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 (efmo_bedrock_geology_metadata_faq.pdf). Please read the efmo_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: 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: Iowa 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 (efmo_bedrock_geology_metadata.txt or efmo_bedrock_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 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 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).