This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

Stream network data originated from USGS National Hydrologic Database (NHD). While the NHD is a very useful and spatially accurate dataset, it is missing one attribute that is commonly referenced as a method to classify and stratify streams, the Strahler Stream Order. Stream order information was available on the Surface Waters Information Management System (SWIMS), digitized from 1:100,000 scale maps. ArcGIS was used to convert the SWIMS vectors to points, spaced at 100 meter intervals, and then to calculate the distance to nearest point (NHD stream to SWIMS points). For each arc segment, the attributes of the nearest point were then appended to the attribute table. While this process successfully added the stream order to the NHD arcs, there were some errors. There were instances of the nearest point to a segment actually belonging to a tributary, and being incorrectly assigned to the wrong stream segment. Also, since the NHD data is much more detailed in its inclusion of smaller streams, the origin for the calculation of 1st order and 2nd order streams is different. Efforts were made to address and correct both of these issues, but users should recognize that not all errors were corrected.The stream network was manually scanned for inconsistencies in stream order flow (jumping from a 3rd order to 1st order, and then back to a 3rd order) and corrected. Emphasis was placed on correcting the larger (3rd order and greater) steams first, and many (but not all) of the 1st and 2nd order. Instances of stream beginnings being mislabeled as an order greater than 1st order were corrected by searching for all dangling arcs (stream beginnings) and then recoding them to a order of 1. This process corrected 1,199 arcs that had been incorrectly coded. One last issue users should be aware of is that since the NHD includes streams not used in calculating the Strahler order in the SWIMS dataset, there are inconsistencies in the labeling of 1st and 2nd order streams. Some corrections were made where obvious lager gaps were in the SWIMS database, but for the most part the original SWIMS stream order was transferred directly. Where adjustments were made, they were only made to lower (less then 4th order) streams.Last Updated October 2013.

Stream network data originated from USGS National Hydrologic Database (NHD). While the NHD is a very useful and spatially accurate dataset, it is missing one attribute that is commonly referenced as a method to classify and stratify streams, the Strahler Stream Order. Stream order information was available on the Surface Waters Information Management System (SWIMS), digitized from 1:100,000 scale maps. ArcGIS was used to convert the SWIMS vectors to points, spaced at 100 meter intervals, and then to calculate the distance to nearest point (NHD stream to SWIMS points). For each arc segment, the attributes of the nearest point were then appended to the attribute table. While this process successfully added the stream order to the NHD arcs, there were some errors. There were instances of the nearest point to a segment actually belonging to a tributary, and being incorrectly assigned to the wrong stream segment. Also, since the NHD data is much more detailed in its inclusion of smaller streams, the origin for the calculation of 1st order and 2nd order streams is different. Efforts were made to address and correct both of these issues, but users should recognize that not all errors were corrected.The stream network was manually scanned for inconsistencies in stream order flow (jumping from a 3rd order to 1st order, and then back to a 3rd order) and corrected. Emphasis was placed on correcting the larger (3rd order and greater) steams first, and many (but not all) of the 1st and 2nd order. Instances of stream beginnings being mislabeled as an order greater than 1st order were corrected by searching for all dangling arcs (stream beginnings) and then recoding them to a order of 1. This process corrected 1,199 arcs that had been incorrectly coded. One last issue users should be aware of is that since the NHD includes streams not used in calculating the Strahler order in the SWIMS dataset, there are inconsistencies in the labeling of 1st and 2nd order streams. Some corrections were made where obvious lager gaps were in the SWIMS database, but for the most part the original SWIMS stream order was transferred directly. Where adjustments were made, they were only made to lower (less then 4th order) streams.Last Updated October 2013.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. An areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell. Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. Geospatial data provided in an ArcGIS shapefile are described herein. Areal reduction factors (ARF) and their standard deviation have been calculated for each region. For each model grid cell closest to each NOAA Atlas 14 station, return levels for extreme precipitation depth are adjusted from the area-scale to the station-scale by dividing by the areal reduction factor (ARF) for the ARF region where the station is located. See Areal_reduction_factors.xlsx and Table 1 of Datasets_station_information.xlsx for the ARF data by ARF region, event duration, and model grid-cell area.

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

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.

The geopusher extension for CKAN automatically converts KML and Shapefile resources uploaded to a CKAN instance into GeoJSON resources. This conversion process allows users to easily access and utilize geospatial data in a modern, web-friendly format without needing to manually reformat the files. The extension operates as a celery task, meaning it can be configured to run automatically when resources are added or updated within CKAN. Key Features: Automatic GeoJSON Conversion: Converts KML and Shapefile resource uploads into GeoJSON format, increasing data usability and accessibility. Celery Task Integration: Operates as a Celery task, enabling asynchronous and automatic conversion upon resource creation or update and allowing other asynchronous operations to be processed at defined times. Batch Conversion: Provides functionality to convert all Shapefile resources on a CKAN instance or a specific subset of datasets at once. Technical Integration: The geopusher extension integrates with CKAN by listening to resource update events. The celery daemon needs to be running for automatic conversion to occur. The extension requires GDAL to be installed on the server to handle the geospatial data conversion. The README shows that the installation and usage involve updating the CKAN configuration Benefits & Impact: By automatically converting geospatial data into GeoJSON, the geopusher extension simplifies the use of KML and Shapefile data within web applications. This automation reduces manual effort, increases accessibility, and helps users to more readily integrate CKAN data into mapping and analysis tools. The automatic conversion ensures that when geospatial data is uploaded to a CKAN repository, users are able to immediately access the data in a suitable format for a wide range of web-based mapping applications, supporting improved data dissemination and collaboration.

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. An areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell. Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. Geospatial data provided in an ArcGIS shapefile are described herein. Areal reduction factors (ARF) and their standard deviation have been calculated for each region. For each model grid cell closest to each NOAA Atlas 14 station, return levels for extreme precipitation depth are adjusted from the area-scale to the station-scale by dividing by the areal reduction factor (ARF) for the ARF region where the station is located. See Areal_reduction_factors.xlsx and Table 1 of Datasets_station_information.xlsx for the ARF data by ARF region, event duration, and model grid-cell area.

The structure contours were created using biostratigraphic data in the Paleo-Data, Inc., Tenroc Regional Geologic Database. The depths of the microfossil locations were associated with the wells data provided by the Louisiana Department of Natural Resources. Because of the proprietary nature of the Tenroc database, no actual data can be shown and only those data points contained in the Louisiana State wells database are included in the control points layer. Contouring was accomplished in Dynamic Graphics, Inc., EarthVision modeling software (v.5) using minimum tension gridding. Three custom programs were used to convert contour lines generated from grids in EarthVision to Arc/Info coverages and then to shapefiles. The data are provided as both lines and polygons (mmtoplg.shp and mmtoppg.shp), and the public wells that penetrate the top of the Middle Miocene (MM) sequence are provided in a point shapefile (mmtopptg.shp). These datasets contain basic data and interpretations developed and compiled by the U.S. Geological Survey's Framework Studies and Assessment of the Gulf Coast Project. Other major sources of data include publicly available information from state agencies as well as publications of the U.S. Geological Survey and other scientific organizations. In cases where company proprietary data were used to produce various derivatives such as contour surfaces, the source is cited but the data are not displayed.

This is a point shapefile of Designated Wildlife Lakes in Minnesota. This shapefile was created by converting lake polygons from the Designated Wildlife Lakes polygon shapefile into points.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Converting delineations to a digital format involved four main procedures: a) preparation of manuscript maps b) input of the spatial data: c) populating of the attribute tables, and d) conversion to GIS. Each step included many quality control steps. Maps were prepared by pin-registering a clean sheet of mylar to each photo and transferring delineations to the new overlay in ink. Each manuscript map was edgematched to the adjoining sheet. Each photo was numbered, and each polygon on the photo was numbered in sequence. An attribute table was created containing a field each for the photo number, polygon sequence number, land use code, layer number, stature type, height, density, and floristic composition attributes.

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. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads.

The rivers of the Near East dataset is derived from the World Wildlife Fund's (WWF) HydroSHEDS drainage direction layer and a stream network layer. The source of the drainage direction layer was the 15-second Digital Elevation Model (DEM) from NASA's Shuttle Radar Topographic Mission (SRTM). The raster stream network was determined by using the HydroSHEDS flow accumulation grid, with a threshold of about 1000 km² upstream area.

The stream network dataset consists of the following information: the origin node of each arc in the network (FROM_NODE), the destination of each arc in the network (TO_NODE), the Strahler stream order of each arc in the network (STRAHLER), numerical code and name of the major basin that the arc falls within (MAJ_BAS and MAJ_NAME); - area of the major basin in square km that the arc falls within (MAJ_AREA); - numerical code and name of the sub-basin that the arc falls within (SUB_BAS and SUB_NAME); - area of the sub-basin in square km that the arc falls within (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows that the arc falls within (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea). The attributes table now includes a field named "Regime" with tentative classification of perennial ("P") and intermittent ("I") streams.

Supplemental Information:

This dataset is developed as part of a GIS-based information system on water resources for the Near East. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

Contact points:

Metadata contact: AQUASTAT FAO-UN Land and Water Division

Contact: Jippe Hoogeveen FAO-UN Land and Water Division

Contact: Livia Peiser FAO-UN Land and Water Division

Data lineage:

The linework of the map was obtained by converting the stream network to a feature dataset with the Hydrology toolset in ESRI ArcGIS.The Flow Direction and Stream Order grids were derived from hydrologically corrected elevation data with a resolution of 15 arc-seconds.The elevation dataset was part of a mapping product, HydroSHEDS, developed by the Conservation Science Program of World Wildlife Fund.Original input data had been obtained during NASA's Shuttle Radar Topography Mission (SRTM).

Online resources:

Download - Rivers of the Near East (ESRI shapefile)

For general information regarding the HydroSHEDS data product

For HydroSHEDS dataset download and technical information

Hydrological basins in the Near East

description: As suggested by the GIS Technical Advisory Committees DASC subcommittee, the DASC staff has imported and converted the U.S. Bureau of Census 2000 TIGER Line files and Summary File 1 (SF1) data into ESRI's ArcView shapefile format. The purpose of this conversion was to derive a subset of shapefiles from the TIGER files that would be of wide use by DASC clientele. These shapefiles reflect only a portion of the possible themes that could be created from TIGER and serve as a common ground for all to work from and should be customized for each user's individual needs. Additional documentation is provided for any shapefiles that have been edited from the original TIGER line files. DASC TIGER shapefiles are provided "as-is" - please submit any corrections or errors to DASC so the dataset can be corrected.; abstract: As suggested by the GIS Technical Advisory Committees DASC subcommittee, the DASC staff has imported and converted the U.S. Bureau of Census 2000 TIGER Line files and Summary File 1 (SF1) data into ESRI's ArcView shapefile format. The purpose of this conversion was to derive a subset of shapefiles from the TIGER files that would be of wide use by DASC clientele. These shapefiles reflect only a portion of the possible themes that could be created from TIGER and serve as a common ground for all to work from and should be customized for each user's individual needs. Additional documentation is provided for any shapefiles that have been edited from the original TIGER line files. DASC TIGER shapefiles are provided "as-is" - please submit any corrections or errors to DASC so the dataset can be corrected.

The Office of the Geographer’s Global Large Scale International Boundary Detailed Polygons file combines two datasets, the Office of the Geographer’s Large Scale International Boundary Lines and NGA shoreline data. The LSIB is believed to be the most accurate worldwide (non- W. Europe) international boundary vector line file available. The lines reflect U.S. government (USG) policy and thus not necessarily de facto control. The 1:250,000 scale World Vector Shoreline (WVS) coastline data was used in places and is generally shifted by several hundred meters to over a km. There are no restrictions on use of this public domain data. The Tesla Government PiX team performed topology checks and other GIS processing while merging data sets, created more accurate island shoreline in numerous cases, and worked closely with the US Dept. of State Office of the Geographer on quality control checks.

Methodology: Tesla Government’s Protected Internet Exchange (PiX) GIS team converted the LSIB linework and the island data provided by the State Department to polygons. The LSIB Admin 0 world polygons (Admin 0 polygons) were created by conflating the following datasets: Eurasia_Oceania_LSIB7a_gen_polygons, Africa_Americas_LSIB7a_gen_polygons, Africa_Americas_LSIB7a, Eurasia_LSIB7a, additional updates from LSIB8, WVS shoreline data, and other shoreline data from United States Government (USG) sources. The two simplified polygon shapefiles were merged, dissolved, and converted to lines to create a single global coastline dataset. The two detailed line shapefiles (Eurasia_LSIB7a and Africa_Americas_LSIB7a) were merged with each other and the coastlines to create an international boundary shapefile with coastlines. The dataset was reviewed for the following topological errors: must not self overlap, must not overlap, and must not have dangles. Once all topological errors were fixed, the lines were converted to polygons. Attribution was assigned by exploding the simplified polygons into multipart features, converting to centroids, and spatially joining with the newly created dataset. The polygons were then dissolved by country name. Another round of QC was performed on the dataset through the data reviewer tool to ensure that the conversion worked correctly. Additional errors identified during this process consisted of islands shifted from their true locations and not representing their true shape; these were adjusted using high resolution imagery whereupon a second round of QC was applied with SRTM digital elevation model data downloaded from USGS. The same procedure was performed for every individual island contained in the islands from other USG sources.
After the island dataset went through another round of QC, it was then merged with the Admin 0 polygon shapefile to form a comprehensive world dataset. The entire dataset was then evaluated, including for proper attribution for all of the islands, by the Office of the Geographer.

This dataset shows the tiling grid and their IDs for Sentinel 2 satellite imagery. The tiling grid IDs are useful for selecting imagery of an area of interest. Sentinel 2 is an Earth observation satellite developed and operated by the European Space Agency (ESA). Its imagery has 13 bands in the visible, near infrared and short wave infrared part of the spectrum. It has a spatial resolution of 10 m, 20 m and 60 m depending on the spectral band. Sentinel-2 has a 290 km field of view when capturing its imagery. This imagery is then projected on to a UTM grid and made available publicly on 100x100 km2 tiles. Each tile has a unique ID. This ID scheme allows all imagery for a given tile to be located. Provenance: The ESA make the tiling grid available as a KML file (see links). We were, however, unable to convert this KML into a shapefile for deployment on the eAtlas. The shapefile used for this layer was sourced from the Git repository developed by Justin Meyers (https://github.com/justinelliotmeyers/Sentinel-2-Shapefile-Index). Why is this dataset in the eAtlas?: Sentinel 2 imagery is very useful for the studying and mapping of reef systems. Selecting imagery for study often requires knowing what the tile grid IDs are for the area of interest. This dataset is intended as a reference layer. The eAtlas is not a custodian of this dataset and copies of the data should be obtained from the original sources. Data Dictionary: Name: UTM code associated with each tile. For example 55KDV

Natural land resources in Brazil have been subject to strong pressure from agricultural expansion over the past two decades. This map identifies and classifies deforestation hotspots in the Southern American country. Moreover, it hints to land use change dynamics such as leakage effects in tropical areas. The map represents the period between 2005-2012, and classifies deforestation hotspots in three categories: a) reduced, b) increased, and c) new. Quality/Lineage: Land cover information from Global Forest Watch (https://data.globalforestwatch.org/) was used to identify deforested pixels per year. ArcGIS 10 was used to create spatial statistics of yearly information. R and RStudio were used to classify each grid cell as a hotspot and its type, and to convert the resulting cover information into a shapefile.

This dataset consists of shapefiles that correspond to the model grids used in the CSIRO eReefs hydrodynamic and biogeochemical models. These models store their results in multi-dimensional NetCDF files using a curvilinear grid. This dataset corresponds to an extract from these files converting the curvilinear grid into polygons in a shapefile. This dataset only captures the structure of the grid, not the time series data generated by the model. It contains shapefiles of the 4 km model grid (GBR4) and the 1 km grid (GBR1) as well as shapefiles for the bounding polygon of all the 'wet' cells in the model. This dataset is useful for visualising the extent of the various CSIRO eReefs models.

This dataset contains shapefiles for the 1 km and 4 km eReefs grids, derived from version 2.0 of the eReefs Hydrodynamic model. It contains shapefiles of the individual grid cells and the bounds. It also includes a low resolution version of the bounds suitable for detecting whether locations are inside the eReefs model extent.

The grid shapefile contains polygons representing each of the grid cells. An attribution is associated with each polygon corresponding to the depth used in the model. This can be used to show where the model has 'wet' cells.


Methods:

1. Representative data files for the GBR1 and GBR4 hydrodynamic version model were downloaded from the public repository of eReefs model data on NCI. The two common grids GBR1 and GBR4 are used over the model time series and for the both the hydrodynamic and biogeochemical models. We therefore just chose one model NetCDF for each model resolution. These were taken from the hydrodynamic model version 2.

2. The grid was converted to shapefiles using an R script that calculated the coordinates corners of each curvilinear pixel in the grid based on the centroids of the neighbouring pixels.

3. The grid boundary shapefiles were calculated using the merge GIS operation in QGIS after selecting all the 'wet' cells, where the depth was greater than 0.

Full step-by-step instructions and scripts are available to reproduce this dataset from github (https://github.com/eatlas/GBR_AIMS_eReefs-grid-shapefiles).


Format:

Shapefile


Data Dictionary:

SP_ID: Row and column indices in the NetCDF grid joined together
depth: Depth used in the eReefs model in metres. This is based on the botz variable in the original NetCDF eReefs model data file.
row: Row index in the NetCDF tables for this pixel.
col: Column index in the NetCDF tables for this pixel.


Data Location:

This dataset is filed in the eAtlas enduring data repository at: X:\data\custodian\2018-22-eReefs\GBR_AIMS_eReefs-grid-shapefiles
Source code for reproducing this dataset is available on github (https://github.com/eatlas/GBR_AIMS_eReefs-grid-shapefiles).