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.

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). {"references": ["Bolch, T., Kulkarni, A., K\u00e4\u00e4b, A., Huggel, C., Paul, F., Cogley, J. G., Stoffel, M. (2012). The state and fate of Himalayan glaciers. Science, 336, 310-314. https...

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.

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 data set is a conversion of Califonia building footprint file from GeoJSON format to shapefile format. The California building footprint file which contains 10,988,525 computer generated building footprints in California state is extracting from US building footprint dataset by Microsoft (2018).

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 data includes offices where Forest Service employees work or where IT equipment is housed. There is no Personally Identifiable Information (PII) data in this dataset, nor telework locations. It includes owned, leased and shared offices. Shared offices are buildings owned or leased by another entity (i.e. a university, other federal agency, etc.) but one or more Forest Service employee(s) work at the building or IT equipment is housed at the building.Depicts the spatial locations for Office locations from the Forest Service CIO Asset Management Office. It includes owned, leased and shared offices. Data is collected, maintained and stewarded by the CIO Asset Management Office. EDW data loading tools extract the office location data from the CIO Asset Mgt. database. Latitude and longitude values are validated and then converted to spatial point data. Spatial point data and associated attributed data describing the office location are inserted into the Office Location Feature class in the Enterprise Data Warehouse. Changes to the Office Location data are checked daily by EDW data loading tools. Data is updated weekly. Data is visible at all scales and zoom levels. Metadata and Downloads.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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

The Unpublished Digital Geologic-GIS Map of Virgin Islands National Park, Virgin Islands is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (viis_geology.gdb), a 10.1 ArcMap (.mxd) map document (viis_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (viis_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (viis_geology_gis_readme.pdf). Please read the viis_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. 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: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (viis_geology_metadata.txt or viis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, UTM Zone 20N, 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 Virgin Islands National Park.

These web layers display discrete tidal zoning generated by the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS). Zoned tides, relative to a tidal datum, can be constructed by applying time and range correctors to observed water level data from a NOAA tide station. These correctors and the recommended tide stations are contained within discrete tide zones. Each zone was constructed with an ideal uncertainty of less than 0.45m at 95% Confidence Interval.Content: Discrete Tide Zones: Discrete tide zones delineate geographic areas of similar tidal characteristics. For each discrete zone, a tide curve can be constructed by applying a time (AvgTimeCorr) and range (RangeRatio) corrector to the observed water level data for the zone's assigned control water level station. Zones are grouped by geographic region. Table attributes contain tidal information for each zone:ControlStn: Operating water level station referenced by the zone AvgTimeCorr: Average of high and low tide time corrections in 6 minute intervalsRangeRatio: Range ratio (multiplier used to scale the tidal value read for the observation file)ControlStn2 (where available): Alternate operating water level station referenced by the zoneAvgTimeCorr2 (where available): Alternate average of high and low tide time corrections in 6 minute intervalsRangeRatio2 (where available): Alternate range ratio (multiplier used to scale the tidal value read for the observation file)Download Layers to Shapefile, CSV, FGDB, GeoJSON or Feature Collection by clicking on the "Export To" drop-down menu below.(Copy/paste the following link into your browser to access the authoritative data source: ftp://tidepool.nos.noaa.gov/pub/outgoing/HPT/CO-OPS_Regional_Zoning/)Note: Please be aware that you must create a free ArcGIS Online account before you can download the data.Tide Zone Water Level Correction for Hydrographic Data: To create a tide zone correction file for use with hydrographic processing software you will need the following information from the shapefiles contained in this map:Name (OBJECTID) and number of vertices for each zone that overlaps your area of coverageCoordinates of all vertices within each tide zone polygonReference/Control tide station (ControlStn or ControlStn2) for each zoneAverage tide time corrector (AvgTimeCorr or AvgTimeCorr2) and tide zone range ratio (RangeRatio or RangeRatio2)Reference/Control tide station name and coordinatesSix minute preliminary and verified water level data may be retrieved in one month increments over the internet from the CO-OPS web services at https://opendap.co-ops.nos.noaa.gov/axis/ by clicking on “Six Minute Data”.More Resources: https://tidesandcurrents.noaa.gov/hydro.html

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.

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. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Vector polygon map data of property parcels from Sarasota County, Florida containing 285,291 features.

Property parcel GIS map data consists of detailed information about individual land parcels, including their boundaries, ownership details, and geographic coordinates.

Property parcel data can be used to analyze and visualize land-related information for purposes such as real estate assessment, urban planning, or environmental management.

Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

The Unpublished Digital Geologic-GIS Map of Navajo National Monument and Vicinity, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (nava_geology.gdb), a 10.1 ArcMap (.mxd) map document (nava_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (nava_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (nava_geology_gis_readme.pdf). Please read the nava_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. 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: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (nava_geology_metadata.txt or nava_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 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. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm). 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 Navajo National Monument.

Xtract.io's airport location data provides a game-changing resource for the aviation and transportation industries. By offering precise geographical coordinates and boundary information for over 150 airports across the United States and Canada, this dataset enables comprehensive spatial analysis. Urban planners can optimize infrastructure, airlines can strategize route planning, and researchers can conduct in-depth studies on airport ecosystems. The polygon data allows for accurate geofencing, supporting security, navigation, and development initiatives. With centimeter-level precision, these datasets transform how organizations understand and interact with airport infrastructure.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

There are two types of boundary files: cartographic and digital. Cartographic boundary files portray the geographic areas using only the major land mass of Canada and its coastal islands. Digital boundary files portray the full extent of the geographic areas, including the coastal water area.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a homeowner or business.

The data formats downloadable from the Minnesota Geospatial Commons contain just the Minnesota turbines. Data, maps and services accessed from the USWTDB website provide nationwide turbines.

The regularly updated database has wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), the American Wind Energy Association (AWEA), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single data set.

Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in Esri ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated and a confidence is recorded for both. None of the data are field verified.

The U.S. Wind Turbine Database website provides the national data in many different formats: shapefile, CSV, GeoJSON, web services (cached and dynamic), API, and web viewer. See: https://eerscmap.usgs.gov/uswtdb/

The web viewer provides many options to search; filter by attribute, date and location; and customize the map display. For details and screenshots of these options, see: https://eerscmap.usgs.gov/uswtdb/help/

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This metadata record was adapted by the Minnesota Geospatial Information Office (MnGeo) from the national version of the metadata. It describes the Minnesota extract of the shapefile data that has been projected from geographic to UTM coordinates and converted to Esri file geodatabase (fgdb) format. There may be more recent updates available on the national website. Accessing the data via the national web services or API will always provide the most recent data.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['railway'] IN ('rail','station')

Features may have these attributes:

• name
• name:en
• railway
• ele
• operator:type
• layer
• addr:full
• addr:city
• source
• name:hi
• name:ta

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Dutch Distribution Centres 2021 Geodata The Geodataset includes 26.951 logistics buildings in the Netherlands, built before November 2021, as well as the business estates they are in, and the outline of the East-Southeast freight corridor of the Netherlands - the busiest logistics area of the country. The used definition of the logistics sector includes trade, import, export, wholesale, transportation, and warehousing activities. To avoid irrelevant buildings such as small, dedicated office buildings and electrical installations, only logistics buildings larger than 500 m2 were considered in the data collection. Additionally, 4.533 large buildings with retail centres on industrial sites were included, where consumers ‘pick their own orders’, such as construction materials stores. As well as 782 buildings with logistics co-activity, for example a hospital with a logistics entity for medical materials. Manufacturing and recycling facilities, which often include logistics activities, are not included unless a separate logistics company is registered in the building. Several of the analyses by the author are exclusively focused on XL (> 20.000 m2) and XXL DCs (>40.000 m2), used by traders and logistics service providers. Depending on the analysis, therefore, subsets of certain functions and size classes of DCs are used. The dataset contains a Geopackage with equivalents in ESRI shapefile format. The buildings layer with attributes is also provided as a Geojson file and CSV data table (without geometry). Besides this README file, a method describing the compilation of the data is provided (Pdf).An earlier version of this dataset, covering only the area within the East-Southeast corridor, was released in 2021 (DOI:10.4121/14791686). The new dataset substitutes the former. An interactive zoomable map with timeline slider is available on: http://mertennefs.eu/landscapes-of-trade/Some interactive metrics, showing the extent and potential of the dataset, are shown in this RShiny app: https://mertennefs.shinyapps.io/distributioncentres_geodata_app/ ## CollaborationThe data were gathered and compiled by Merten Nefs, in collaboration with Thomas Bonte and Carlijn Ligterink (Vrije Universiteit Amsterdam), Vera Loefs and Ana Luisa Moura (Deltametropolis Association); with the help and data provision by Frank van Oort (Erasmus School of Economics) and Hans van Amsterdam (Netherlands Environmental Assessment Agency, PBL). The data have been used for research, media articles and academic work such as the PhD research project Landscapes of Trade, by Merten Nefs, at TUDelft and Erasmus School of Economics. More information at www.landscapesoftrade.nl ## DisclaimerThe buildings were selected by and joined with employment data of Lisa 2020, and therefore reasons back in time from this point and assumes the same function as well for 2021. Discrepancies between earlier data (Lisa 2017) and 2020 can be viewed and filtered out via the 'Current' attribute below. Another important aspect is the overlap of logistics buildings over time: a small portion of these has replaced former buildings on the same site that have been demolished. Such 'brownfield' developments amount to ca. 10% of the dataset - as established by a test sample - whereas ca. 90% are 'greenfield' developments. More information on the data collection and compilation method is found in the accompanying pdf file.

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Liste des défibrillateurs automatiques externes (DAE) en France métropolitaine et dans les territoires d'outre-mer.

Licence Ces données sont issues du crowdsourcing effectué par les contributeurs au projet OpenStreetMap (OSM). Elles sont sous licence ODbL qui impose un partage à l'identique et la mention obligatoire d'attribution doit être "© les contributeurs d'OpenStreetMap sous licence ODbL" conformément à la licence détaillée sur la page http://osm.org/copyright.

Exploitation des données Dans OpenStreetMap les défibrillateurs sont notés emergency=defibrillator et toutes les données associées sont documentées sur la page dédiée du wiki d’OpenStreetMap. Dans ce jeu de données seules les informations les plus utiles ont été extraites. Les données brutes OpenStreetMap contiennent donc plus de détails. Les données suivent le format défini par l'arrêté du 29 octobre 2019 relatif au fonctionnement de la base de données nationale des défibrillateurs automatisés externes (DAE).

Les informations contenues dans ces fichiers sont les suivantes : * gid : identifiant du DAE dans la base nationale GéoDAE (attribut ref:FR:GeoDAE dans OpenStreetMap) * nom : nom donné au DAE * lat_coor1 : coordonnée de latitude (WGS84, les chiffres après la 5e décimale ne sont pas significatifs) * long_coor1 : coordonnée de longitude (WGS84, les chiffres après la 5e décimale ne sont pas significatifs) * adr_num : numéro de la voie et, le cas échéant, suffixe d'implantation du DAE * adr_voie : type et nom de la voie ou lieu-dit d'implantation du DAE * com_cp : Code postal de la commune d'implantation du DAE * com_insee : Code Insee de la commune d'implantation du DAE * com_nom : Nom de la commune d'implantation du DAE * acc : Environnement d'accès du DAE (intérieur ou extérieur) * acc_lib : Accès libre du DAE (oui ou non) * acc_pcsec : présence d'un poste de sécurité * acc_acc : présence d'un accueil public * acc_etg : étage d'accessibilité du DAE * acc_complt : Complément d'information sur l'accès au DAE * photo1 : photo du DAE dans son environnement * photo2 : absent de cet export de données * disp_j : Jours d'accessibilité de l'appareil * disp_h : Heures d'accessibilité de l'appareil * disp_complt : Complément d'information sur la disponibilité du DAE (suivant la syntaxe d'horaires d'ouverture d'OpenStreetMap) * tel1 : Numéro de téléphone 1 sur le site d'implantation du DAE * tel2 : absent de cet export de données * site_email : Adresse email de contact du site où le DAE a été implanté * date_instal : Date d'installation du DAE * etat_fonct : absent de cet export de données (noté comme Inconnu) * fab_siren : Numéro SIREN du fabricant du DAE * fab_raison : Raison sociale du fabricant du DAE * mnt_siren : absent de cet export de données * mnt_raison : absent de cet export de données * num_serie : Numéro de série du DAE * id_euro : Identifiant unique du dispositif (IUD européen) * lc_ped : Présence d'électrodes pédiatriques * dtpr_lcped : absent de cet export de données * dtpr_lcad : absent de cet export de données * dtpr_bat : absent de cet export de données * freq_mnt : absent de cet export de données * dispsurv : Dispositif de surveillance à distance du DAE * dermnt : absent de cet export de données * expt_siren : Numéro SIREN de l'exploitant * expt_rais : Raison sociale de l'exploitant, personne morale * expt_tel1 : Numéro de téléphone 1 de l'exploitant * expt_tel2 : absent de cet export de données * expt_email : Adresse électronique de l'exploitant

La communauté OpenStreetMap OpenStreetMap est « le Wikipédia de la cartographie », une communauté mondiale coordonnée, auto-organisée, créant des données librement exploitables. OpenStreetMap est aujourd'hui considérée comme la base de données cartographique ouverte la plus exhaustive au Monde.

La communauté OpenStreetMap s'efforce de décrire de manière précise et publique les spécifications de cartographie de toutes les données créées dans le wiki OpenStreetMap. Néanmoins aucune garantie de qualité ou d'exhaustivité n'est apportée. Si vous identifiez des manques ou des données qui devraient être corrigées, vous êtes le bienvenu(e) pour le faire vous-même.

En France, l'association OpenStreetMap France soutient la communauté. OpenStreetMap repose en grande partie sur du travail bénévole. Si ce jeu de données vous a été utile, vous pouvez soutenir l'association OpenStreetMap France en faisant un don.

Exports de données OpenStreetMap Il existe plusieurs autres manière pour télécharger les données depuis la base de données d'OpenStreetMap : 1. Chercher les jeux de données publiés par OpenStreetMap sur data.gouv.fr. La majorité des données sont mises à jour quotidiennement directement depuis OSM. 2. Télécharger les données pour un sujet et pour une collectivité Française en particulier avec GeoDataMine. Formats CSV, GeoJSON, XLSX, Shapefile. Exemple : Les défibrillateurs présents sur le territoire du Grand Paris. Le jeu de données des défibrillateurs publié sur data.gouv.fr a été réalisé depuis l'outil GeoDataMine. 3. Utiliser overpass turbo pour préciser exactement la données à télécharger, dans le Monde entier. Formats CSV, GeoJSON, GPX, KML, OSM et plus. Une solution plus complète mais plus technique. La documentation est disponible sur le wiki d'OpenStreetMap. 4. Utiliser une autre solution. De très nombreuses possibilités d'exports existent et sont listées sur la page du wiki d'OpenStreetMap.

Contact Pour toute question concernant les export de données OpenStreetMap, vous pouvez contacter les bénévoles de l'association OpenStreetmap France : exports@openstreetmap.fr - https://www.openstreetmap.fr - Twitter : @OSM_FR - Mastodon : @osm_fr

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Mises à jour - 22/08/2020 : première publication du jeu de données.