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.

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.

Curb lines for the city of Chicago. Curb lines mark the points where curbs meet the edge of the street pavement. To view or use these files, special GIS software such as Google Earth is required. To download, right-click the "Download" link above and choose "Save link as." This is a KMZ zipped file, and therefore upzipping software, such as 7-Zip, is required to convert to KML.

With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them.

Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format.

A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out.

Macros must be enabled for the tool to function.

When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names.

In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window.

To run a choropleth, select the menu option 'Run Choropleth' to get this form.

To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button.

The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time.

The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author.

Acknowledgments

Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

Supported file formats:- Avaatech XRF Core Scanner Spectrum (SPE). Example: hdl:10013/epic.42747.d011- DShip ActionLog Events to PANGAEA event import file and cruise report station list. Example: hdl:10013/epic.42747.d014- TRiDaS (Tree Ring Data Standard, http://www.tridas.org). Example: hdl:10013/epic.42747.d001- IMMA (International Maritime Meteorological Archive). Used by the project CLIWOC (García-Herrera et al. 2007, doi:10.1594/PANGAEA.743343)- NOAA IOAS (International Ocean Atlas Series). Example: hdl:10013/epic.42747.d008- NYA upper air soundings files. Format developed by Marion Maturilli. Example: hdl:10013/epic.42747.d013- SOCAT (Surface Ocean CO2 Atlas, Bakker et al. 2014, doi:10.1594/PANGAEA.811776). Example: hdl:10013/epic.42747.d012- CHUAN (Comprehensive Historical Upper-Air Network, Stickler et al. 2013, doi:10.1594/PANGAEA.821222). Example: hdl:10013/epic.42747.d003- Thermosalinograph (TSG) data. Format developed by Gerd Rohardt. Example: hdl:10013/epic.42747.d002- Columus GPS Data Logger V-900 format to KML or GPX. Example: hdl:10013/epic.42747.d006

Please note: this is an experimental dataset, due to untested conversion from original KML files to ESRI geodatabase format.

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

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).

Please note: this is an experimental dataset, due to untested conversion from original KML files to ESRI geodatabase format.

The Florida Wiildlife Corridor (layer name Florida_Wildlife_Corridor_2021.shp): This vector layer was created from the original raster grid 2021 version of the Florida Ecological Greenways Network (FEGN) by combining the Priority 1, Priority 2, and Priority 3 values in the raster layer and converting to a shapefile using the Raster to Polygon command with the simplify option to remove the jagged edges of the original raster layer, reduce file size, and the make conversion to a kml file feasible. The Florida Wildlife Corridor is now part of a new state law intended to protect the corridor through enhanced land protection planning and funding. The Florida Wildlife Corridor is defined in the state law as The Florida Wildlife Corridor represents the most important opportunities to protect a functionally connected statewide system of public and private conservation lands essential for protecting Florida's native biodiversity, water resources, and other ecosystem services while providing a sustainable natural resource economy including a variety of resource-based recreational activities.The FEGN guides OGT ecological greenway conservation efforts and promotes public awareness of the need for and benefits of a statewide ecological greenways network. It is also used as the primary data layer to inform the Florida Forever and other state and regional land acquisition programs regarding the location of the most important wildlife and ecological corridors and large, intact landscapes in the state. The FEGN identifies areas of opportunity for protecting a statewide network of ecological hubs (large areas of ecological significance) and linkages designed to maintain large landscape-scale ecological functions including priority species habitat and ecosystem services throughout the state. Inclusion in the FEGN means the area is either part of a large landscape-scale “hub”, or an ecological corridor connecting two or more hubs. Hubs indicate core landscapes that are large enough to maintain populations of wide-ranging or fragmentation-sensitive species including black bear or panther and areas that are more likely to support functional ecosystem services. Highest priorities indicate the most significant hubs and corridors in relation to completing a functionally connected statewide ecological network, but all priority levels have conservation value. FEGN Priorities 1, 2, and 3 are the most important for protecting a ecologically functional connected statewide network of public and private conservation lands, and these three priority levels (P1, P2, and P3) are now called the Florida Wildlife Corridor as per the Florida Wildlife Corridor legislation passed and signed into law by the Florida Legislature and Governor and 2021, which makes protection of these wildlife and ecological hubs and corridors a high priority as part of a strategic plan for Florida’s future. To accomplish this goal, we need robust state, federal, and local conservation land protection program funding for Florida Forever, Rural and Family Lands Protection Program, Natural Resources Conservation Service easements and incentives, federal Land and Waters Conservation Fund, payments for ecosystem services, etc.

The V1 ('flying bomb' or 'doodlebug') and V2 (a ballistic missile) were two new weapons developed by Nazi Germany. In 1944 and 1945 thousands were fired at London. They killed thousands of people and injured many more.

This dataset includes all impact sites for V1s and V2s within the London County Council boundary. These were manually compiled from bomb maps published in 'The London County Council Bomb Damage Maps 1939-1945' by Laurence Ward (Thames and Hudson, 2015). The original LCC Bomb Damage maps are held at the London Archives.

**Please note that this is not a comprehensive dataset of all V1s and V2s. Only those within the London County Council boundary are included.**

The impact sites can also be viewed as a layer on Google Maps. Data is separated into two layers: V1 sites and V2 sites. Each point represents an impact site, with the closest street name (to help with possible cross-reference) and page number in the LCC Bomb Damage Maps: https://www.google.com/maps/d/viewer?mid=1VwyxV_e_LAwzbyJPCAF-C7aCRVNA5W7N&ll=51.509018493447314%2C-0.05324588962980492&z=14

We previously analysed this dataset in 'The flying bomb and the actuary', Significance (2019). doi: 10.1111/j.1740-9713.2019.01315.x

The Geological Survey of Canada (Atlantic and Pacific) has collected marine survey field records on marine expeditions for over 50 years. This release makes available the results of an ongoing effort to scan and convert our inventory of analog marine survey field records (seismic, sidescan and sounder) to digital format. These records were scanned at 300 dpi and converted into JPEG2000 format. Typically, each of these files was between 1 to 2 gbyte in size before compression and compressed by a factor of 10:1. Empirical tests with a number of data sets suggest that there is minimal visual distortion of the scanned data at this level of compression. In this KML file, scanned data are available in a reduced-scale thumbnail format and a compressed full-resolution JPEG2000 format.

This release makes available the West Canadian Coast part of the results of an ongoing effort to scan and convert all our inventory of analog marine survey field records (seismic, sidescan and sounder) to digital format. These records have been scanned at 300 dpi and were converted into JPEG2000 format. Typically each of these files were from 1 to 2 gbyte in size before compression, and were compressed by a factor of 10:1. Empirical tests with a number of data sets suggest that there is minimal visual distortion of the scanned data at this level of compression. In this KML file, scanned data are available in a reduced-scale thumbnail format and a compressed full-resolultion JPEG2000 format.

This release makes available the West Canadian Coast part of the results of an ongoing effort to scan and convert all our inventory of analog marine survey field records (seismic, sidescan and sounder) to digital format. These records have been scanned at 300 dpi and were converted into JPEG2000 format. Typically each of these files were from 1 to 2 gbyte in size before compression, and were compressed by a factor of 10:1. Empirical tests with a number of data sets suggest that there is minimal visual distortion of the scanned data at this level of compression. In this KML file, scanned data are available in a reduced-scale thumbnail format and a compressed full-resolultion JPEG2000 format.

117 original plate boundaries from Esri Data and Maps (2007) edited to better match 10 years of earthquakes, land forms and bathymetry from Mapping Our World's WSI_Earth image from module 2. Esri Canada's education layer of plate boundaries and the Smithsonian's ascii file from the download section of the 'This Dynamic Planet' site plate boundaries were used to compare the resulting final plate boundaries for significant differences.