Geographic layers

Geographic layers on which the different data records are geo-referenced (e.g. mobility, COVID-19 cases). The different layers can be grouped into those that cover the whole territory of pain (e.g. municipalities) and those that are restricted to a specific region (Table1). Among those that cover the full territory of Spain, the record accounts for the first four levels of administrative division, that is, autonomous communities, provinces, municipalities and districts.

Visit https://flowmaps.life.bsc.es/flowboard/data for more information about the data.

Layers (geo-json format):

• cnig_ccaa : Comunidades Autónomas CNIG
• cnig_provincias : Provincias CNIG
• cnig_municipios : Municipios CNIG
• ine_sec : Secciones censales INE
• mitma_mov : Áreas de movilidad MITMA
• zbs_07 : Zonas Básicas de Salud de Cy
• abs_09 : Àrees Bàsiques de Salut GenCat
• zon_bas_13 : Zonas básicas sanitarias de Madrid
• oe_16 : Osasun Eremuak (Zonas de Salud) Euskadi
• zbs_15 : Zonas Básicas de Salud del Servicio Navarro de Salud

This layer is sourced from gis.wim.usgs.gov.

1.27.2014 --ESM-- created for DOI building location map.

Interesting, largely unexplored data analysis and information retrieval opportunities exist for GIS data. In their current form, traditional data usage patterns for data persisted in shapefiles or spatially-enabled relational databases are limited. Opportunities exist to achieve ESIP’s Winter 2019 theme of ‘increasing the use and value of Earth science data and information’ by transforming geospatial data from their original formats into their Resource Description Framework (RDF) manifestation. This work establishes an innovative workflow enabling the publication for Geospatial data persisted in geospatially enabled databases (PostGIS and MonetDB), ESRI shapefiles and XML, GML, KML, JSON, GeoJSON and CSV documents as graphs of linked open geospatial data. This affords the capability to identify implicit connections between related data that wasn't previously linked e.g. automating the detection of features present within large hydrography datasets as well as smaller regional examples and resolving features in a consistent fashion. This previously unavailable capability is achieved through the use of a semantic technology stack which leverages well matured standards within the Semantic Web space such as RDF as the data model, GeoSPARQL as the data access language and International Resource Identifier’s (IRI) for uniquely identifying and referencing entities such as rivers, streams and other water bodies. In anticipation of NASA’s forthcoming Surface Water Ocean Topography (SWOT – https://swot.jpl.nasa.gov) mission, which once launched in 2021 will make NASA’s first-ever global survey of Earth’s surface water, this work uses Hydrography data products (USGS’s National Hydrography Dataset and other topically relevant examples) as the topic matter. The compelling result is a new, innovative data analysis and information retrieval capability which will increases the use and value of Earth science data (GIS) and information. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.

Publication Date: APR 2018. A polygon layer of all city boundaries in New York State. The city features and attributes in this layer are the same as those in the Cities_Towns layer in this service. The data was originally a compilation of U.S. Geological Survey 1:100,000-scale digital vector files and NYS Department of Transportation 1:24,000-scale and 1:75,000-scale digital vector files. Boundaries were revised to 1:24,000-scale positional accuracy and selectively updated based on municipal boundary reviews, court decisions and NYS Department of State Local Law filings for annexations, dissolutions, and incorporations. Currently, boundary changes are made based on NYS Department of State Local Law filings (http://locallaws.dos.ny.gov/). Additional updates and corrections are made as needed in partnership with municipalities. Additional metadata, including field descriptions, can be found at the NYS GIS Clearinghouse: http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=927.

© NYS Office of Information Technology Services GIS Program Office (GPO) This layer is a component of New York State Civil Boundaries.

Contains results of full FeatureServer crawl of the US Fisheries and Wildlife Open Data site (). Each dataset contains an item.json with basic metadata, though many do not expose direct GIS file downloads through their ArcGIS data sources. For those that do, a GeoJSON file has been downloaded for each layer, as that is the only format available through USFW FeatureServers. Datasets are sorted by category if available, if not they are within the Misc folder, and are then sorted by tag. Includes US Fish and Wildlife Service Open Data.csv, a record of all datasets present.

Hospitals in Harris County

This layer is sourced from www.gis.hctx.net.

Locations of Harris County hospitals used for demonstration and increasing the capibilities of ITC development staff.

© ITC repository

The Canada Basemap – Transportation (CBMT) is a vector tile service that provides spatial reference context with an emphasis on transportation networks across Canada. It is designed especially for use as a background layer in a web mapping application or geographic information system (GIS). Access: Access is free of charge under the terms of the Open Government Licence - Canada. Data Sources: Data for the CBMT is sourced from multiple datasets. - Topographic data of Canada - CanVec Series. - “Automatically Extracted Buildings” GeoBase (a raw digital product in vector format automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources). - Open Street Map (OSM) data available under the Open Database License (https://www.openstreetmap.org/copyright). - Official names from the Canadian Geographical Names Database (CGNDB). Geographic Coverage: CBMT has complete coverage of the world, with full datasets in Canada and only partial data in other parts of the world including boundaries, Country Names, and major cities. Data Update Frequency: Updates are applied monthly to reflect the latest updates in the source datasets. Projection: Data is provided in the EPSG:3857 (WGS84 Pseudo-Mercator) projected coordinate system. Layer Access: - CBMT is accessible via the ArcGIS Online item link with the applied style or it can also be accessed directly with the default style using the following Vector Tile Server: https://tiles.arcgis.com/tiles/HsjBaDykC1mjhXz9/arcgis/rest/services/CBMT_CBCT_3857_V_OSM/VectorTileServer - In QGIS or other applications that require the style JSON, the following link can be used: https://arcgis.com/sharing/rest/content/items/800d755712e8415aab301b9d55bc2800/resources/styles/root.json Use Cases: This layer is suitable for use in any map as a basemap layer and can be modified to meet the needs of the project by editing the JSON style in the Vector Tile Style editor. Additional Versions: - A geometry-only version (CBMT3857GEOM) and a text-only version (CBMT3857TXT) are available. - French versions of the basemap are accessible via the Carte de base du Canada - Transport 3857 V (CBCT3857).

Custom JSON File created for download

The Water Level sensor collect telemetry at set intervals throughout the day. This sensor is located at Wharf Street Basin in the City of Canning, Western Australia. Contact us at opendata@canning.wa.gov.au for more details on the location of sensors or for a larger data set (The data is supplied is the sensor reading for 30 days).

This tool takes the state of a web application (for example, included services, layer visibility settings, and client-side graphics) and returns a printable page layout or basic map of the specified area of interest., The input for Export Web Map is a piece of text in JavaScript object notation (JSON) format describing the layers, graphics, and other settings in the web map. The JSON must be structured according to the ExportWebMap specification in the ArcGIS Help., This tool is shipped with ArcGIS Server to support web services for printing, including the preconfigured service named PrintingTools. The ArcGIS web APIs for JavaScript, Flex and Silverlight use the PrintingTools service to generate images for simple map printing.

NB: The source map is only updated if a "snow event" is declared; if snowfall is relatively minor it may be cleared without the progress being reflected on the map.

This dataset provides a tabular representation of the data used to populate the map at https://gis.edmonton.ca/portal/apps/webappviewer/index.html?id=c69d03c2d216415e820c45d7ea2566bf.

It is obtained by paging through the data exposed by the ArcGIS REST API, e.g. https://gis.edmonton.ca/arcgishosting/rest/services/Hosted/SNIC_Routes_2020/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&resultOffset=0&resultRecordCount=500&f=geojson.

To know how many times we need to page through the data to extract all of the rows, the total row count is obtained thus: https://gis.edmonton.ca/arcgishosting/rest/services/Hosted/SNIC_Routes_2020/FeatureServer/0/query?where=1%3D1&returnCountOnly=true&f=json.

This is a connection to the Allegheny County's Geographic Information Systems Group's Open Data Portal. They are pleased to share some of our most comprehensive data sets with the public. You can solve important local issues by exploring and downloading relevant open data, analyzing and combining the datasets using maps, and discovering and building apps.

These datasets are available in a number of formats. You can choose to download them, use REST APIs, or view them directly in an interactive web map. API's provide access as REST, HTML, JSON, GeoJSON, etc.

Please contact Allegheny for any questions or suggestions on datasets at GISHelp@AlleghenyCounty.US

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).

Micro - climate sensors collect telemetry at set intervals throughout the day. Sensors are located at various locations in the City of Canning, Western Australia and each sensor has a unique ID. Contact us at opendata@canning.wa.gov.au for a larger data set (The data is supplied is the sensor reading for 30 days). The following lists the locations of each sensor:18zua9muwbb is located at Wharf Street Basin - Pavilion2hq3byfebne is located at The City’s Civic and Administration Building uu90853psl is located at Wharf Street Basin - Leila Street entrancexd2su7w05m is located at Wharf Street Basin - Nature Play Area

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.

In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles.

© U.S. Census Bureau This layer is sourced from gis.flhealth.gov.

Want to keep the data in your Hosted Feature Service current? Not interested in writing a lot of code?Leverage this Python Script from the command line, Windows Scheduled Task, or from within your own code to automate the replacement of data in an existing Hosted Feature Service. It can also be leveraged by your Notebook environment and automatically managed by the MNCD Tool!See the Sampler Notebook that features the OverwriteFS tool run from Online to update a Feature Service. It leverages MNCD to cache the OverwriteFS script for import to the Notebook. A great way to jump start your Feature Service update workflow! RequirementsPython v3.xArcGIS Python APIStored Connection Profile, defined by Python API 'GIS' module. Also accepts 'pro', to specify using the active ArcGIS Pro connection. Will require ArcGIS Pro and Arcpy!Pre-Existing Hosted Feature ServiceCapabilitiesOverwrite a Feature Service, refreshing the Service Item and DataBackup and reapply Service, Layer, and Item properties - New at v2.0.0Manage Service to Service or Service to Data relationships - New at v2.0.0Repair Lost Service File Item to Service Relationships, re-enabling Service Overwrite - New at v2.0.0'Swap Layer' capability for Views, allowing two Services to support a View, acting as Active and Idle role during Updates - New at v2.0.0Data Conversion capability, able to invoke following a download and before Service update - New at v2.0.0Includes 'Rss2Json' Conversion routine, able to read a RSS or GeoRSS source and generate GeoJson for Service Update - New at v2.0.0Renamed 'Rss2Json' to 'Xml2GeoJSON' for its enhanced capabilities, 'Rss2Json' remains for compatability - Revised at v2.1.0Added 'Json2GeoJSON' Conversion routine, able to read and manipulate Json or GeoJSON data for Service Updates - New at v2.1.0Can update other File item types like PDF, Word, Excel, and so on - New at v2.1.0Supports ArcGIS Python API v2.0 - New at v2.1.2RevisionsSep 29, 2021: Long awaited update to v2.0.0!Sep 30, 2021: v2.0.1, Patch to correct Outcome Status when download or Coversion resulted in no change. Also updated documentation.Oct 7, 2021: v2.0.2, workflow Patch correcting Extent update of Views when Overwriting Service, discovered following recent ArcGIS Online update. Enhancements to 'datetimeUtil' Support script.Nov 30, 2021: v2.1.0, added new 'Json2GeoJSON' Converter, enhanced 'Xml2GeoJSON' Converter, retired 'Rss2Json' Converter, added new Option Switches 'IgnoreAge' and 'UpdateTarget' for source age control and QA/QC workflows, revised Optimization logic and CRC comparison on downloads.Dec 1, 2021: v2.1.1, Only a patch to Conversion routines: Corrected handling of null Z-values in Geometries (discovered immediately following release 2.1.0), improve error trapping while processing rows, and added deprecation message to retired 'Rss2Json' conversion routine.Feb 22, 2022: v2.1.2, Patch to detect and re-apply case-insensitive field indexes. Update to allow Swapping Layers to Service without an associated file item. Added cache refresh following updates. Patch to support Python API 2.0 service 'table' property. Patches to 'Json2GeoJSON' and 'Xml2GeoJSON' converter routines.Sep 5, 2024: v2.1.4, Patch service manager refresh failure issue. Added trace report to Convert execution on exception. Set 'ignore-DataItemCheck' property to True when 'GetTarget' action initiated. Hardened Async job status check. Update 'overwriteFeatureService' to support GeoPackage type and file item type when item.name includes a period, updated retry loop to try one final overwrite after del, fixed error stop issue on failed overwrite attempts. Removed restriction on uploading files larger than 2GB. Restores missing 'itemInfo' file on service File items. Corrected false swap success when view has no layers. Lifted restriction of Overwrite/Swap Layers for OGC. Added 'serviceDescription' to service detail backup. Added 'thumbnail' to item backup/restore logic. Added 'byLayerOrder' parameter to 'swapFeatureViewLayers'. Added 'SwapByOrder' action switch. Patch added to overwriteFeatureService 'status' check. Patch for June 2024 update made to 'managers.overwrite' API script that blocks uploads > 25MB, API v2.3.0.3. Patch 'overwriteFeatureService' to correctly identify overwrite file if service has multiple Service2Data relationships.Includes documentation updates!

Xverum’s Global GIS & Geospatial Data is a high-precision dataset featuring 230M+ verified points of interest across 249 countries. With rich metadata, structured geographic attributes, and continuous updates, our dataset empowers businesses, researchers, and governments to extract location intelligence and conduct advanced geospatial analysis.

Perfectly suited for GIS systems, mapping tools, and location intelligence platforms, this dataset covers everything from businesses and landmarks to public infrastructure, all classified into over 5000 categories. Whether you're planning urban developments, analyzing territories, or building location-based products, our data delivers unmatched coverage and accuracy.

Key Features: ✅ 230M+ Global POIs Includes commercial, governmental, industrial, and service locations - updated regularly for accurate relevance.

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✅ Bulk Delivery for GIS Platforms Available in .json - delivered via S3 Bucket or cloud storage for easy integration into ArcGIS, QGIS, Mapbox, and similar systems.

✅ Continuous Discovery & Refresh New POIs added and existing ones refreshed on a regular refresh cycle, ensuring reliable, up-to-date insights.

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The "GIS Boundary Information" data table provides the SPEN licence area boundaries for our SP Distribution (SPD), SP Manweb (SPM) and SP Transmission (SPT) licence areas. Please note that the SPT licence area is essentially the same as the SPD licence area so is not included as a separate feature in the data table.The table gives the following information: SPD: Licenced Distribution Network Operator (DNO) for Central Belt and South of Scotland up to and including the 33kV network.SPM: Licenced Distribution Network Operator (DNO) for North Wales, Merseyside, Cheshire and North Shropshire up to and including the 132kV network.SPT: Licenced Transmission Network Owner (TNO) for the Central Belt and South of Scotland for network operating greater than or equal too 132kV.For additional information on column definitions, please click the Dataset schema link below.
DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the GIS Boundary Information dataset.To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page.Download dataset metadata (JSON)

Column names currently coded as follows:r = application numberd = permit typed2 = descriptiona = addressi = application datey = address latitudex = address longitudep = project estimated costcn = contractor name