Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

Ferdinand Magellan was the first European explorer to reach the Pacific Ocean by crossing the Atlantic Ocean when his expedition sailed through an opening, or strait, near the tip of South America in 1520. He named the ocean Mar Pacifico, which means peaceful sea. The strait, which connected the Atlantic and Pacific oceans, was later named for him.

At that point in his journey, Magellan and his fleet had been at sea for more than a year. He had lost two of his five ships. Now he would cross the Pacific Ocean with three ships, looking for the coast of Asia and the Spice Islands. However, he had no idea the Pacific Ocean would be so big!

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at http://www.esri.com/geoinquiries

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

The zoo in your community is so popular and successful that it has decided to expand. After careful research, zookeepers have decided to add an exotic animal to the zoo population. They are holding a contest for visitors to guess what the new animal will be. You will use skills you have learned in classification and analysis to find what part of the world the new animal is from and then identify it.

To help you get started, the zoo has provided a list of possible animals. A list of clues will help you choose the correct answers. You will combine information you have in multiple layers of maps to find your answer.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology. Let's just start with a quick refresher on what exactly GIS is. Click the box below for an amazing overview of GIS provided by Esri, the world leader in geospatial technology. Be sure to explore additonal tabs and live buttons. This site is packed full of information, from the history of GIS, to its applications and career opportunities.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

The 2018 Indiana BRFSS Child Optional Modules report contains prevalence estimates for child health risk behaviors and outcomes collected via an optional module.Topics include childhood asthma prevalence.

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!

The FGGD Digital Atlas consists of more than 100 global maps that allows to analyse food insecurity and poverty in relation to environment. It is subdivided into 6 modules as follows: Module 1 Boundaries and Topography Module 2 Population Module 3 Socio-Economic and Nutrition Indicators Module 4 Environmental Conditions Module 5 Land Use Patterns and Land Cover Module 6 Land Productivity Potential

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package. This course will prepare you to take more advanced geospatial science courses. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

Python Notebooks are a tool that has become vital in the Python and Data Science communities to enhance your workflows for GIS data management, analysis, and visualization. This workshop will introduce how to use Python Notebooks within ArcGIS Pro. The learning outcome is to gain an understanding of the basics for working with Python Notebooks to describe and document workflows, execute Python code, and visualize data and analysis outputs. There will be a focus on integrating with more advanced geospatial capabilities of ArcGIS Pro and ArcGIS Online via Python modules including ArcPy and ArcGIS.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

A VIADAT module; VIADAT-GIS connects the platform with maps.

Developed in cooperation with ÚSD AV ČR and NFA.

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.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. states and Canadian provinces for mapping.

      Citation for the full documentation of this database:


      Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M.
      Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K.
      Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan,
      J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A.
      Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a
      multi-scaled geospatial temporal ecology database from disparate data
      sources: Fostering open science and data reuse. GigaScience 4:28
      doi:10.1186/s13742-015-0067-4


      Citation for the data paper for this database:


      Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell,
      C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr,
      K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M.Collins, J.D.
      Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk,
      M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C.
      Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K.
      Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S.
      King, S.A. Kishbaugh, J.F. Lapierre, B. Lathrop, J.A. Latimore, Y.
      Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B.
      Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson,
      A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry,
      K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K.
      Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L.
      Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P.
      Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E.
      Webster, J.D. White, M.K. Wilmes, S. Yuan. In Review. LAGOS-NE: A
      multi-scaled geospatial and temporal database of lake ecological
      context and water quality for thousands of U.S. lakes. In Review at
      GigaScience. Submitted April 2017.

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

This resource contains data inputs and an iPython Jupyter Notebook used to simulate semi-distributed variable source area runoff generation in a tributary to the Logan River. This resource is part of the HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about.

In this activity, the student learns how to (1) calculate the topographic wetness index using digital elevation models (DEMs) following up on a previous module on DEMs and GIS in Hydrology; (2) apply TOPMODEL concepts and equations to estimate soil moisture deficit and runoff generation across a watershed given necessary watershed and storm characteristics; and (3) critically assess concepts and assumptions to determine if and why TOPMODEL is an appropriate tool given information about a specific watershed.

Please note that this exercise sets up the data needed to estimate runoff in the Spawn Creek watershed using TOPMODEL. Spawn Creek is a tributary of the Logan River, Utah. This exercise uses some of the same data as the Logan River Exercise in Digital Elevation Models and GIS in Hydrology at https://www.hydroshare.org/resource/9c4a6e2090924d97955a197fea67fd72/. If running the TOPMODEL for other study sites, you need to prepare a DEM TIF file and an outlet shapefile for the area of interest. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS.

These modules have some GIS embedded within in them.The modules have been aligned to Level 4-6 Achievement Objectives from the Science Learning Area of the New Zealand Curriculum and are accompanied by a Teacher Guide to support their use in the classroom. Each module comes with a downloadable and printable activity sheet and finishes with a quiz to test students learning. Answers to the activity sheets are included in the Teacher Guide.Please note: The What’s On Our Plates? learning modules are best viewed on a desktop, laptop or tablet, they are not suitable for viewing on mobile phones or small screens. We recommend using Google Chrome, the maps will not work in Internet Explorer.If your school needs access to an ArcGIS Online Subscription to finish the Story Map , GIS components of the modules, please email gisinschools@eagle.co.nz with your request.

Purpose Of The ItemA feature layer is used to store emergency incident locations and related information. It plays a crucial role in the geographic coordinates, natures, and details of incidents like natural disasters, accidents, and other emergencies. AudienceThis web map's primary audience includes emergency managers, first responders, and local government officials responsible for disaster response and recovery in DuPage County, Illinois. Data SourceThe data stored in this feature layer includes points, lines, and areas for field observations and emergency response teams.

The Asia-Pacific Gas Insulated Switchgear market is booming, projected to reach [estimated 2033 market size] by 2033, driven by renewable energy growth and infrastructure development. Explore market trends, key players (Hitachi, Schneider Electric, GE), and regional insights in this comprehensive analysis. Recent developments include: In May 2022, German start-up Nuventura sought partners in China to commercialize a replacement for sulfur hexafluoride (SF6), the world's most harmful greenhouse gas widely used in electricity distribution grids. The Asian Development Bank-backed firm plans to transfer its technology to makers of medium voltage gas-insulated switchgear (GIS) in China that cannot develop a replacement for SF6., In February 2022, Bharat Heavy Electricals LTD (BHEL) developed a Bus Potential Transformer Module for Gas Insulated Switchgear (GIS) at 33 kV. As the first to be developed by an Indian manufacturer, the transformer module eliminates the need for a separate Potential Transformer (PT) panel in GIS switchboards, making it the first of its kind.. Notable trends are: High Voltage Level Segment Expected to Dominate the Market.