GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

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).

Download high-quality, up-to-date Romania shapefile boundaries (SHP, projection system SRID 4326). Our Romania Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

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 USGS data release includes two ESRI polyline shapefiles (file_names.shp) describing the describing the steepest-descent lines calculated at two levels of detail (See Process Step for explanation). To increase access to these data, KMZ (Compressed Keyhole Markup Language) versions of the polyline feature layers are included in this release (file_names.kmz). In addition to these data layers, two supplementary data layers from the Big Island Mapping Project (BIMP) showing lava flows originating on Mauna Loa and Kilauea volcanoes, originally published in Trusdell, Wolfe, and Morris (2006), are included for context and reference. Both ESRI polygon shapefiles and KMZ versions of these files are included, naming conventions are identical as the files in this release. This metadata file provides information for the GIS data files unique to this data release. Below are the files that comprise this release, including the metadata files: Steepest-Descent_lines_3M_m2.shp Steepest-Descent_lines_750K_m2.shp Steepest-Descent_lines_3M_m2.KMZ Steepest-Descent_lines_750K_m2.KMZ Kilauea1983-1996_from_BIMP.shp ML1984_from_BIMP.shp Kilauea1983-1996_from_BIMP.kmz ML1984_from_BIMP.kmz mauna_loa_steepest_descent_lines_FGDC.xml mauna_loa_steepest_descent_lines_FGDC.txt

The NOAFAULTs database of active faults was published in 2013 (versions 1.0 & 1.1). In this datase we present the upgrades comprising the newer version of the database (version 2.1). NOAFAULTs was created towards compiling a digital database of fault geometry and additional attributes (character of faulting, past seismicity etc) primarily to support seismicity monitoring at the National Observatory of Athens (NOA). It has been constructed from published fault maps in peer-reviewed journals since 1972 while the number of the scientific papers that were included is 110. The standard commercial software ARC GIS has been used to design and populate the database. In the new version, details on fault geometry, such as the strike, the dip-angle and the dip direction, and kinematics for each individual fault are included. For well-studied faults, information about the slip rate or the creep or the co-seismic slip is reported. The fault layer was produced by on-screen digitization and is available to the scientific community in ESRI shapefile (SHP), KML/KMZ and TXT formats in WGS84 projection. In this version of the database, we continue to focus on the active faults of the upper (Aegean + Eurasian) plate and the back-arc region of the Hellenic Arc, in general. A number of 2437 faults are now included.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Download high-quality, up-to-date Japan shapefile boundaries (SHP, projection system SRID 4326). Our Japan Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Provide the distribution map file of forest roads in SHP and KML formats, as well as the download link for the interpretation data.

This dataset contains all zip codes in Montgomery County. Zip codes are the postal delivery areas defined by USPS. Zip codes with mailboxes only are not included.

As this is geographic data, SHP and KMZ formats are available for download.

Download high-quality, up-to-date Bhutan shapefile boundaries (SHP, projection system SRID 4326). Our Bhutan Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

The Unpublished Digital Geologic Map of Glen Canyon National Recreation Area and Vicinity, Utah, Arizona is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. These data formats also fully represent all of the features present on a GRI digital map, as well as containing related ancillary information GIS data tables. The data is also available as a 2.2 KMZ/KML file for use in Google Earth. 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: Utah Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (glca_metadata.xml; available at http://nrdata.nps.gov/glca/nrdata/geology/gis/glca_metadata.xml). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (glca_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 12N, 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 Glen Canyon National Recreation Area, as well as Rainbow Bridge National Monument (RABR), Canyonlands National Park (CANY), Capitol Reef National Park (CARE) and Grand Canyon National Park (GRCA).

Download high-quality, up-to-date Mexico shapefile boundaries (SHP, projection system SRID 4326). Our Mexico Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Map data showing Local Link (Demand Responsive Transport) service areas in Greater Manchester. The dataset is available in MapInfo .tab, Google .kmz, 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 2024.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies (China, India, and Brazil).

Our self-hosted geospatial data displays clean shapes with no gaps or overlaps over your base map of choice.

The geospatial data shapes are offered in high-precision resolution and are easily customized on-premise.

Use cases for the Global Postal Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Metadata record for the USFS's Rocky Mountain Region library of geospatial datasets; link to web-page in record. The datasets presented are derived from the USFS Land Status Records System(LSRS) and the USFS infrastructure database (Infra) and processed using ArcMap and Google Earth Pro. The datasets are presented in several formats: ESRI shapefiles SHP (zipped sets), clipped JPEG images JPEG 2000, clipped and oriented Imagine IMG images, visitor map image View (zoomable), Metadata (HTML format), and Virtual Globe KML files (KMZ format). The current version of Google Earth is recommended for proper function and display of KML datasets.