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 dataset contains spatial boundaries for the DevWA Area relating to the City of Perth Planning Scheme No.2.Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes. Show full description

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.

This dataset contains Mining Leases issued under the Minerals Development Act (Northern Ireland) 1969 by the Department for the Economy. These data geometry are polygons showing the extent of the lease. The attribute table identifies the Lease ID, the Lease Holder, the date the lease was issued and the date the lease is due to expire. These data are provided in the following formats: Geodatabase, GeoJSON, Geopackage, KMZ. GeoJSON and KML data are in Lat Long, WGS84. GeoDatabase, GeoPackage are in Irish National Grid.

Data showing current city plans in Södertälje municipality. Type of plan, plan name, file designation and coordinates in WGS84 format. The data set contains four files. The GeoJSON and KML files show all data. The XLSX and CSV files do not contain coordinates and are intended to make it easier to understand the amount of data.

With a detailed plan, the municipality regulates how land and water are to be used and what the buildings are to look like. The detailed plan therefore talks about what you and others may and may not do for construction measures within the planning area. A detailed plan is displayed as a specific area on a plan map. The detailed plan map includes a plan description, which explains the purpose and content of the plan. Sometimes other documents are also included, such as an illustration map or an environmental impact statement.

https://www.boverket.se/sv/samhallsplanering/sa-planeras-sverige/kommunal-planering/detaljplanering/

Countywide datasets are available as zipped Esri geodatabases. Sets of the 5-foot-interval contours at township-level extents are available as zipped shapefiles in addition to geodatabases. (None of the data are available in GeoJSON or KML format.) Note that the zipped files are exceptionally large.All files are compressed in the open-source 7-Zip format (external link to 7-zip.org). Other utilities which can extract zipped files will work in most cases, but some of these data files might extract with 7-Zip only.

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.

Data containing the municipal border in Södertälje municipality. Two different file formats, GeoJSON and .KML Note that in addition to the main municipal border, there are some small areas belonging to Södertälje municipality that are surrounded by Nykvarn municipality. These are listed as separate areas in the same files.

This dataset is of simplified geometries from COD live services deployed June 2019. Simplification methods applied from ESRI libraries using Python, Node.js and Mapshaper.js and based on adapted procedures for best outcomes preserving shape, topology and attributes. These data are not a substitute for the original COD data sets used in GIS applications. No warranties of any kind are made for any purpose and this dataset is offered as-is. Versions of topojson, kml and csv are also available. For a list of other simplified CODs see the address list: https://github.com/UGA-ITOSHumanitarianGIS/mapservicedoc/raw/master/Data/AWSDeploymentURLlist.xlsx

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

• 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

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

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

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

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 dataset contains public transport stop data for buses, trains and trams in shapefile, geojson and kml format. The dataset contains public transport stop data for buses, trains and trams in shapefile, geojson and kml format.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only trails with the symbol value of 5-12, 16, 17 are Forest Service System trails and contain data concerning their availability for motorized use. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the EDW. Click this link for full metadata description: Metadata _This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

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)

XLS camaras-trafico.xls 다운로드 Traffic Cameras (XLS) KML camaras-trafico.kml 다운로드 Traffic Cameras (KML) JSON camaras-trafico.json 다운로드 Traffic Cameras (JSON) JSON camaras-trafico.geojson

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