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.

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.

Hiking trails in Södertälje municipality. The data set consists of two different files where the KML and GeoJSON files contain the names of the hiking trails and coordinates according to the WGS84 format. Some of the hiking trails appear several times in the material. This is due to the fact that some hiking trails are divided into several sections and that there are sometimes several alternative sections or connecting sections, for example from a nearby urban area to the main section of the trail.

• Description: Notifications from citizens in public space. The shape and geojson files contain the notifications whose location is indicated using the nearest address, whose coordinates are known. This concerns about half of the total number of reports. The reports go back to about 1 year. The CSV and Excel files contain all reports, including those without an exact location indication, and date back to January 1, 2013 * Source: management system of the municipality of The Hague * Purpose of registration: reports of citizens in public space * ** Restrictions:** This dataset is not suitable for legal or surveying purposes * Features: This dataset is suitable for analysis and providing insight into the location on the map * Coordinate system: WGS84

This Data Release contains version 3.0 of two related earthquake geology databases for use in the 2023 U.S. National Seismic Hazard Model. The databases are: 1) A fault sections database (“NSHM23_FSD_v3”), which depicts the geometry of faults capable of hosting independent earthquakes, and 2) an earthquake geology site information database (“NSHM23_EQGeoDB_v3”), which contains fault slip rate constraints at points. These databases cover the 12 western U.S. states: Washington, Oregon, California, Idaho, Nevada, Arizona, Montana, Wyoming, Colorado, New Mexico and Texas. Datasets containing crustal fault information for Alaska and the central and eastern United States were prepared by Bender and others, 2021 and Thompson Jobe and others, 2023 in separate efforts. The two databases are broken into separate child items from this landing page. The databases are provided as geospatial data (.SHP, .KML, and GeoJSON file formats) and tables (.CSV format). Reference information, including change log, version notes, and a README, are included as "Attached Files" below this Summary. Versioning These databases are updated as of December 2023 (version 3.0), which supersede the databases release in February 2022 (version 2.0) and the January 2021 (version 1.0) preliminary datasets. After significant testing by many user groups, this version 3.0 data release contains minor changes. The specific changes made in the fault sections database (FSD) from version 2.0 (2022) to version 3.0 (2023; this release) are outlined in "NSHM23_FSD_v2-v3_VersionChanges.txt." The changes to the EQGeoDB involve fixing typos and further populating the reference list to include UCERF3 references; the authors acknowledge Scott Marshall (Appalachian State University) for uncovering these additional references. Note on the Cheraw fault: At the time of original compilation (2020-2021), the Cheraw fault of Colorado was included in the western U.S. fault sections database. During model implementation, the Cheraw fault was instead treated as a central and eastern U.S. fault. To maintain consistency with earlier releases, we retain the Cheraw fault geometry and attributes in this table. For more information, please review Shumway and others., in press manuscript about CEUS fault implementation. For more information on how these datasets were compiled, please refer to our manuscript publication, Hatem and others, 2022. References Cited Bender, A.M., Haeussler, P.J. and Powers, P.M., 2021, Geologic inputs for the 2023 Alaska update to the U.S. National Seismic Hazard Model (NSHM) (ver. 2.0, February 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P97NRR0F Hatem, A.E., Collett, C.M., Briggs, R.W., Gold, R.D., Angster, S.J., Field, E.H., Powers, P.M. and the Earthquake Geology Working Group, 2022, Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for US NSHM 2023. Scientific data, 9(1), 506. https://doi.org/10.1038/s41597-022-01609-7 Shumway, A.M., Petersen, M.D., Powers, P.M., Toro, G., Altekruse, J. M., Herrick, J.A., Rukstales, K.S., Thompson Jobe, J.A., Hatem, A.E., and Girot, D.L., in press, Earthquake Rupture Forecast Model Construction for the 2023 U.S. 50-State National Seismic Hazard Model Update: Central and Eastern U.S. Fault-Based Source Model. Seismological Research Letters. Thompson Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Earthquake geology inputs for the National Seismic Hazard Model (NSHM) 2023 (central and eastern United States), version 1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94HLE5G

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.

When a natural disaster or disease outbreak occurs there is a rush to establish accurate health care location data that can be used to support people on the ground. This has been demonstrated by events such as the Haiti earthquake and the Ebola epidemic in West Africa. As a result valuable time is wasted establishing accurate and accessible baseline data. Healthsites.io establishes this data and the tools necessary to upload, manage and make the data easily accessible. Global scope The Global Healthsites Mapping Project is an initiative to create an online map of every health facility in the world and make the details of each location easily accessible. Open data collaboration Through collaborations with users, trusted partners and OpenStreetMap the Global Healthsites Mapping Project will capture and validate the location and contact details of every facility and make this data freely available under an Open Data License (ODBL). Accessible The Global Healthsites Mapping Project will make the data accessible over the Internet through an API and other formats such as GeoJSON, Shapefile, KML, CSV. Focus on health care location data The Global Healthsites Mapping Project's design philosophy is the long term curation and validation of health care location data. The healthsites.io map will enable users to discover what healthcare facilities exist at any global location and the associated services and resources.

List of publicly owned sports complexes censused in Umbria. Data also available in LOD format expressed according to the ontology http://dbpedia.org/ontology/sportFacility and related to Linked Data Istat as regards municipality and toponym (fraction) if present. A geo-location was carried out starting from the address of each complex which is also made available in kml and geojson format.

The data set includes the car sharing stations of operators active in Wuppertal in the Wuppertal city area (as of September 2021 25 stations from cambio, 3 from Flinkster, 1 from RUHRAUTOe). The data was first collected in December 2017 as point geometries with some technical attributes. The data set is updated every six months on the basis of an updated station list provided by cambio. The data set is available in Shape, KML and GeoJSON format under an open data license (CC BY 4.0).

The data set contains the punctiform signage locations of the 164 30 km/h zones in the Wuppertal city area. It was created in the period from 08/2008 to 09/2008 by the "Roads and Traffic" department in a concerted effort by digitizing the existing analogue files and plans, in particular the traffic regulations. The attribute "BEZEICH" contains a typification of the signs with which the 30 km/h zones are marked ("1": beginning of a zone, "2": end of a zone, "3": double-sided sign beginning / end of a zone) . The digital base map DGK was used as the map basis for the digitization of the signage locations. The database is subject to very rare changes, since the potential areas for designating 30 km/h zones have been exhausted for a number of years. Changes to the 30 km/h zones and the associated signage are initiated by the Roads and Traffic department, which promptly communicates such changes to the data-carrying department of Surveying, Cadastral Office and Geodata. There the data record is updated with a geographic information system. Since the beginning of 2016, the official base map ABK has been used as the basis for digitization. The ESRI shapefiles, KML and GeoJSON files provided as open data are updated weekly in an automated process.

The polygons of the Wuppertal residential areas form the thematic layer for the residential area map of the city of Wuppertal, which has been updated and approved annually by the expert committee for property values ​​​​in the city of Wuppertal since 2018 after it was first created in 2016. The delimitations currently published here were decided on March 15, 2023 with effect from January 1, 2023. The map of residential areas classifies the Wuppertal residential areas into four grades (simple, medium, good and exclusive residential area) according to the predominant character of a coherent residential area. (The location quality of individual properties may vary.) The current residential area map is a basis for the qualified rent index for the city of Wuppertal, which has been available since December 2016. The dataset of the Wuppertal residential areas is provided in the formats ESRI Shapefile, KML and GeoJSON under an open data license (data license Germany - attribution - version 2.0).

The data set of the climate locations Wuppertal includes the descriptions of (as of 08/2021) 118 punctually modelled best practice examples for climate protection in Wuppertal. They were collected by the Coordination Office for Climate Protection of the City of Wuppertal in the period Q4/2020 to Q2/2021 as a data basis for the interactive map application "Klimaortkarte Wuppertal". The climate location map also presents the line-shaped railway paths from the open data data set "Radrouten Wuppertal". These are not included in the dataset of the climatic locations. The data set assigns one or more thematically categorized offers to the locations of organizations, facilities and facilities. The categorization uses a two-step model (topic/category). Several sites may be located in the same geographical position, e.g. in a building where several climate protection organisations reside. The continuation of the dataset takes place irregularly, in each case promptly after identification of a new or change of an existing climatic location. The dataset is available in Shape, KML and GeoJSON formats under an Open Data license (CC BY 4.0).

2020 Census Tract data for use with GIS mapping software, databases, and web applications are from Caliper Corporation. Available for Maptitude or in any format such as shapefile, KML, KMZ, GeoJSON.

Dataset CA2

List of the archaeological sites in different file formats (geoJSON, kml, csv, shp).

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.

The data set of the Wuppertal city parking areas includes the delimitations and names of (as of 05/2023) 61 modeled city parking areas in the Wuppertal urban area, maintained by the Roads and Transport Department. “City parking areas” are those areas within a city parking area (also referred to as a city parking zone) where cars with a city parking permit valid for the respective area may be parked. The application for such a city parking permit can be submitted digitally via the Wuppertal service portal. The boundaries of the city parking areas were vectorized in February 2023 using available PDF map representations from the responsible specialist team. For each surrounding polygon of a city parking area, the data set also contains the name of the associated city parking area (Aue, Barmen or Elberfeld). As of 05/2023, the parking areas are distributed as follows across the three parking areas: Barmen 46, Elberfeld 14, Aue 1. The data set is updated irregularly by the Roads and Traffic Department, in each case promptly after a change regarding the city parking areas and -Parking areas. The digital continuation process is currently under construction. The open data data set, which is available in Shape, KML and GeoJSON format under an open data license (CC BY 4.0), is updated automatically on a fixed, weekly basis.

We present the dataset of mills from 1880 and 1920s-1930s in the area of the former Galicia (78,500 km2), now in Ukraine and Poland. We found 4,022 mill locations for 1880 and 3,588 for the 1920s-1930s. We present them as shapefile, GML, GeoJSON, KML formats with attributes for seven types of mills for 1880 and ten types of mills for 1920s-1930s, and mills counted in a 10 km grid.

Our data contains two point layers and six grid layers (10 km side squares). All data is available in an open shapefile, GML, GeoJSON, KML formats, commonly used in Geographic Information Systems. Point layers contain the following attributes for each of the mills: auto-numbered numeric identifier (ID), type (Type), map sheet date (Map_year), longitude (Long), and latitude (Lat). According to the legend of these maps and explanations, the following types of mills can be distinguished for 1880:

1 – Gristmill (ger. Fruchtmühle), 2 – Sawmill (ger. Sägemühle), 3 – Paper mill (ger. Papiermühle), 4 – Powder mill (ger. Pulvermühle), 5 – Fulling mill (ger. Walkmühle), 6 – Windmill (ger. Windmühle), 7 – Ship mill, (ger. Schiffmühle).

For the 1920s-1930s, the following types of mills were distinguished according to the legend of these maps and explanations.

1 – Watermill, 2 – Steam mill, 3 – Sawmill, 4 – Sawmill with water wheel, 5 – Motor sawmill, 6 – Steam sawmill, 7 – Steam mill, 8 – Windmill, 9 – Wind turbine, 10 – Ship mill.

A reference grid designed by the European Environment Agency (EEA) in the ETRS 1989 LAEA projection (EPSG 9820) was used to create the grid layers, consisting of cells with sides of 10 km. In the set we provide, it contains the following attributes: auto-numbered numeric identifier of the cell (FID), cell code (CellCode), east (EofOrigin) and north (NofOrigin) cell start coordinates and an attribute (Count) in which aggregated mill types are counted for each cell: gristmills, sawmills, windmills

The data can be used in economic, demographic and environmental reconstructions, e.g. to estimate historical anthropopressure related to settlement, agriculture and forestry. Mills are often associated with river structures such as floodgates, dams, and millraces and therefore they are a good example of human interference in river ecosystems. They can also be one criteria for identifying areas where the local population used traditional environmental knowledge. It can be useful for a contemporary assessment of the environment’s suitability for devices using renewable energy sources. Finally, the data on the remains of former mills is suitable for the protection of cultural heritage sites that are technical monuments related to traditional food processing and industry.

This research was funded by the Ministry of Science and Higher Education, Republic of Poland under the frame of “National Programme for the Development of Humanities” 2015–2021, as a part of the GASID project (Galicia and Austrian Silesia Interactive Database 1857–1910, 1aH 15 0324 83)

The data set includes the (as of 06/2020) 9 free public battery charging stations for electric bicycles in the Wuppertal city area. The data were collected for the first time in December 2014 as part of the EmoTal joint project (http://www.emotal.de). The locations of the stations were recorded as freely digitised point geometries with a position accuracy of a few meters. Since the end of the EmoTal project (31.08.2017), the data are kept by the responsible performance unit (Coordinating Unit Climate Protection in the Division Office of the GB3) and updated promptly if necessary (creation of a new charging station or relevant modification of an existing station). Since 05/2020, a specialised procedure has been used within the Wuppertal navigation and data management system Wunda. In addition to the location coordinates for each station, the data set includes some descriptive attributes including a hyperlink to a photo of the installation. The ESRI shape files, KML and GeoJSON files provided as open data under the CC BY 4.0 license are updated weekly in an automated process. The data set includes the (as of 06/2020) 9 free public battery charging stations for electric bicycles in the Wuppertal city area. The data were collected for the first time in December 2014 as part of the EmoTal joint project (http://www.emotal.de). The locations of the stations were recorded as freely digitised point geometries with a position accuracy of a few meters. Since the end of the EmoTal project (31.08.2017), the data are kept by the responsible performance unit (Coordinating Unit Climate Protection in the Division Office of the GB3) and updated promptly if necessary (creation of a new charging station or relevant modification of an existing station). Since 05/2020, a specialised procedure has been used within the Wuppertal navigation and data management system Wunda. In addition to the location coordinates for each station, the data set includes some descriptive attributes including a hyperlink to a photo of the installation. The ESRI shape files, KML and GeoJSON files provided as open data under the CC BY 4.0 license are updated weekly in an automated process.

Download high-quality, up-to-date Taiwan shapefile boundaries (SHP, projection system SRID 4326). Our Taiwan 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.

This data release documents proposed updates to geologic inputs (faults) for the upcoming 2023 National Seismic Hazard Model (NSHM). This version (1.0) conveys differences between 2014 NSHM fault sources and those recently released in the earthquake geology inputs for the U.S. National Seismic Hazard Model (NSHM) 2023, version 1.0 data release by Hatem et al. (2021). A notable difference between the 2014 and 2023 datasets is that slip rates are provided at points for 2023 instead of generalized along the entire fault section length as in 2014; consequently, slip rates are not provided for fault sections in the draft 2023 dataset. Geospatial data (shapefile, kml and geojson) are provided in this data release with each fault distinguished as either “addition” for newly added faults, “revised” for faults that have undergone geometry changes, or “no change” for fault geometries that have remained the same as in the 2014 NSHM.