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.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

This dataset holds all materials for the Inform E-learning GIS course

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

A community's permit file must have an official record that shows new buildings and substantial improvements in all identified Special Flood Hazard Areas (SFHAs)are properly elevated. This elevation information is needed to show compliance with the floodplain management ordinance. FEMA encourages communities to use the Elevation Certificate developed by FEMA to fulfill this requirement since it also can be used by the property owner to obtain flood insurance. Communities participating in the Community Rating System (CRS) are required to use the FEMA Online Elevation Certificate, FEMA Form FF-206-FY-22-152 (formerly 086-0-33).

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.

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

A public feature layer view used to share natural spaces set aside for recreation or the protection of wildlife or natural habitats.

The spatial analysis software market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market's value is estimated at $5 billion in 2025, demonstrating significant expansion from its historical period (2019-2024). A Compound Annual Growth Rate (CAGR) of 15% is projected from 2025 to 2033, indicating a substantial market expansion to an estimated $15 billion by 2033. Key drivers include the rising need for location intelligence in business decision-making, the increasing availability of geospatial data, and advancements in cloud computing and artificial intelligence (AI) that enhance spatial analysis capabilities. Furthermore, the integration of spatial analysis with other technologies, such as big data analytics and machine learning, is fostering innovation and expanding applications across various industries. The market is segmented by application (e.g., urban planning, environmental monitoring, transportation logistics) and by software type (e.g., GIS software, remote sensing software, spatial statistics software). Leading companies are continuously investing in research and development, leading to the emergence of more sophisticated and user-friendly solutions. Market restraints include the high cost of software licenses and implementation, the complexity of using advanced spatial analysis tools, and the shortage of skilled professionals capable of effectively leveraging these technologies. However, the expanding availability of open-source spatial analysis tools and online training programs is gradually mitigating these barriers. The regional breakdown shows strong growth across North America and Europe, fueled by significant technological advancements and substantial public and private sector investments. The Asia-Pacific region is also poised for significant expansion, driven by rapid urbanization and economic growth. The consistent growth across different segments and regions ensures long-term market stability and offers significant opportunities for both established players and new entrants. The continued convergence of spatial analysis with other technologies will remain a central theme, driving innovation and unlocking further value across numerous sectors.

This dataset is OBSOLETE as of 11/18/2024 and will be removed from ArcGIS Online on 11/18/2025.An updated version of this dataset is available at Certified Sustainable Buildings | Open Data Portal | City of Cambridge.A map of the updated data can be found in two places:Certified Sustainable Buildings Map | Open Data Portal | City of CambridgeSustainable Buildings Map - City of Cambridge, MAThis point layer shows the location of sustainable buildings in Cambridge. For inclusion in this layer, a building must do at least one of the following: qualify for the City’s Article 22 regulatory process; be certified by Passive House; be certified by Enterprise Green Communities; or be certified by LEEDunder a LEED version that requires the whole building to meet sustainability standards. Some buildings meet two or more of these criteria. Additionally, this layer contains information about other certifications (Energy Star, Fitwel, and WELL) that may apply to the included buildings. If an included building participates in the City’s BEUDO regulatory process, this layer provides two key emissions figures for the building. Information provided about the applicable sustainable building programs for qualifying buildings includes certification levels, certification types, ratings, or scores. This layer includes data from City and non-City sources.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription BldgID type: Stringwidth: 50precision: 0 Unique ID for database from GIS.

Latitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Longitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Article22_SystemLevelEquivalenc type: Stringwidth: 150precision: 0

Article22 type: Stringwidth: 3precision: 0 "Yes" indicates Article 22 building.

BEUDO_TotalGHGEmissionsIntensit type: Doublewidth: 8precision: 38

BEUDO type: Stringwidth: 3precision: 0 "Yes" indicates BUEDO building.

BEUDO_SourceEUI type: Doublewidth: 8precision: 38 A critical variable for reporting about BEUDO.

EnergyStar type: Stringwidth: 3precision: 0 "Yes" indicates EnergyStar building.

EnergyStar_CountYearsCert type: SmallIntegerwidth: 2precision: 5 Number of years certified. EnergyStar certification may be renewed annually.

EnergyStar_LastYearCert type: Stringwidth: 4precision: 0 Year of last certification.

EnergyStar_LastCertScore type: SmallIntegerwidth: 2precision: 5 Most recent EnergyStar score.

EnterpriseGC type: Stringwidth: 3precision: 0 "Yes" indicates Enterprise Green Communities building.

EnterpriseGC_CertTemplate type: Stringwidth: 100precision: 0 Certification version.

EnterpriseGC_PointsAchieved type: SmallIntegerwidth: 2precision: 5 Enterprise Green Communities score.

Fitwel type: Stringwidth: 3precision: 0 "Yes" indicates Fitwel building.

Fitwel_StarRating type: SmallIntegerwidth: 2precision: 5 Numerical Fitwel rating.

LEED type: Stringwidth: 3precision: 0 "Yes" indicates LEED building.

LEED_TotalCerts type: SmallIntegerwidth: 2precision: 5 Number of certifications applying to the whole building. The LEED fields contain details about certifications that are "whole-building," not referring to one part of the building only or or to building operations.

LEED_LastCertDate type: Datewidth: 8precision: 0 Date of last certification applying to the whole building.

LEED_LastSystemVersion type: Stringwidth: 100precision: 0 Certification version and rating system.

LEED_LastCertLevel type: Stringwidth: 50precision: 0 LEED certifictation level at which whole building is certified. Certified/Silver/Gold/Platinum: Does not not include "registered" buildings.

PassiveHouse type: Stringwidth: 3precision: 0 "Yes" indicates Passive House building.

PassiveHouse_CertVersion type: Stringwidth: 100precision: 0 Certification version.

WELL type: Stringwidth: 3precision: 0 "Yes" indicates WELL building.

WELL_Version type: Stringwidth: 50precision: 0 Certification version.

WELL_ProjectType type: Stringwidth: 150precision: 0 WELL project type.

WELL_CertLevel type: Stringwidth: 50precision: 0 Certification level. Certified Pilot/Compliance/Bronze/Silver/Gold/Platinum or Health-Safety Rated: Does not include "registered" or "precertified" buildings.

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

Web Map illustration of Garbage Service Area Boundary described in the certificated authority issued to Solid Waste Companies by Utilities and Transportation Commission (UTC). For additional information and questions pertaining to solid waste certificate coverage and issuance please, contact the UTC Licensing Section, 360-664-1223,

SONG, Carol X., Rosen Center for Advanced Computing, Purdue University, 155 South Grant Street, Young Hall, West Lafayette, IN 47907

Science gateways are becoming an integral component of modern collaborative research. They find widespread adoption by research groups to share data, code and tools both within a project and with the broader community. Sustainability beyond initial funding is a significant challenge for a science gateway to continue to operate, update and support the communities it serves. MyGeoHub.org is a geospatial science gateway powered by HUBzero. MyGeoHub employs a business model of hosting multiple research projects on a single HUBzero instance to manage the gateway operations more efficiently and sustainably while lowering the cost to individual projects. This model allows projects to share the gateway’s common capabilities and the underlying hardware and other connected computing resources, and continued maintenance of their sites even after the original funding has run out allowing time for acquiring new funding. MyGeoHub has hosted a number of projects, ranging from hydrologic modeling and data sharing, plant phenotyping, global and local sustainable development, climate variability impact on crops, and most recently, modeling of industry processes to improve reuse and recycling of materials. The shared need to manage, visualize and process geospatial data across the projects has motivated the Geospatial Data Building Blocks (GABBs) development funded by NSF DIBBs. GABBs provides a “File Explorer” type user interface for managing geospatial data (no coding is needed), a builder for visualizing and exploring geo-referenced data without coding, a Python map library and other toolkits for building geospatial analysis and computational tools without requiring GIS programming expertise. GABBs can be added to an existing or new HUBzero site, as is the case on MyGeoHub. Teams use MyGeoHub to coordinate project activities, share files and information, publish tools and datasets (with DOI) to provide not only easy access but also improved reuse and reproducibility of data and code as the interactive online tools and workflows can be used without downloading or installing software. Tools on MyGeoHub have also been used in courses, training workshops and summer camps. MyGeoHub is supporting more than 8000 users annually.

The National Flood Hazard Layer (NFHL) is a compilation of GIS data that comprises a nationwide digital Flood Insurance Rate Map. The GIS data and services are designed to provide the user with the ability to determine the flood zone, base flood elevation, and floodway status for a particular location. It also has information about the NFIP communities, map panels, cross sections, hydraulic structures, Coastal Barrier Resource System, and base maps such as road, stream, and public land survey data. Through flood studies, FEMA produces Flood Insurance Study Reports, FIRM Panels, and FIRM Databases. FIRM Databases that become effective are incorporated into the NFHL. Updates to the NFHL are issued through Letters of Map Revision (LOMRs) and Letters of Map Amendment (LOMAs). Continuously updated, the NFHL serves as a Digital Flood Insurance Rate Map representing the current effective flood data for those communities where maps have been digitized. NFHL data can be viewed with widely available GIS software, including freely available programs that work with GIS shapefiles. For more information on the NFHL, see the online resources referenced herein. Using base maps: The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. Letter of Map Amendment (LOMA) point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. LOMA points can be viewed in the NFHL Interactive Map on the FEMA GeoPlatform.

This data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset is meant to depict the surface owner or manager of the land parcels. In the vast majority of land parcels, they will be one and the same. However, there are instances where the owner and manager of the land surface are not the same. When this occurs, the manager of the land is usually indicated. BLM's Master Title Plats are the official land records of the federal government and serve as the primary data source for depiction of all federal lands. Information from State of New Mexico is the primary source for the depiction of all state lands. Auxilliary source are referenced, as well, for the depiction of all lands. Collection of this dataset began in the 1980's using the BLM's ADS software to digitize information at the 1:24,000 scale. In the mid to late 1990's the data was converted from ADS to ArcInfo software and merged into tiles of one degree of longitude by one half degree of latitude. These tiles were regularly updated. The tiles were merged into a statewide coverage. The source geodatabase for this shapefile was created by loading the merged ArcInfo coverage into a personal geodatabase. The geodatabase data were snapped to a more accurate GCDB derived land network, where available. In areas where GCDB was not available the data were snapped to digitized PLSS. In 2006, the personal geodatabase was loaded into an enterprise geodatabase (SDE). This shapefile has been created by exporting the feature class from SDE.

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is valued to increase USD 2.35 billion, at a CAGR of 15.7% from 2024 to 2029. Increased use of GIS for capacity planning will drive the GIS in telecom sector market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 28% growth during the forecast period.
By Product - Software segment was valued at USD 470.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 256.91 million
Market Future Opportunities: USD 2350.30 million
CAGR from 2024 to 2029: 15.7%

Market Summary

The market is experiencing significant growth as communication companies increasingly adopt Geographic Information Systems (GIS) for network planning and optimization. Core technologies, such as satellite imagery and location-based services, are driving this trend, enabling telecom providers to improve network performance and customer experience. One major application of GIS in the telecom sector is capacity planning, which allows companies to optimize their network infrastructure based on real-time data.
However, the integration of GIS with big data and other advanced technologies presents a communication gap between developers and end-users, requiring a focus on user-friendly interfaces and training programs. Additionally, regulatory compliance and data security remain significant challenges for the market. Despite these hurdles, the opportunities for innovation and improved operational efficiency make the market an exciting and evolving space.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

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How is the GIS In Telecom Sector Market Segmented ?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The global telecom sector's reliance on Geographic Information Systems (GIS) continues to expand, with the market for GIS in telecoms projected to grow significantly. According to recent industry reports, the market for GIS data visualization and spatial data infrastructure in telecoms has experienced a notable increase of 18.7% in the past year. Furthermore, the demand for advanced spatial analysis tools, such as building penetration analysis, geospatial asset management, and work order management systems, has risen by 21.3%. Telecom companies utilize GIS for network performance monitoring, data integration platforms, and network planning. For instance, GIS enables network design, radio frequency interference analysis, route optimization software, mobile network optimization, signal propagation modeling, and service area mapping.

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The Software segment was valued at USD 470.60 billion in 2019 and showed a gradual increase during the forecast period.

Additionally, it plays a crucial role in infrastructure management, location-based services, emergency response planning, maintenance scheduling, and telecom network design. Moreover, the adoption of 3D GIS modeling, LIDAR data processing, and customer location mapping has gained traction, contributing to the market's expansion. The future outlook is promising, with industry experts anticipating a 25.6% increase in the use of GIS for telecom network capacity planning and telecom outage prediction. These trends underscore the continuous evolution of the market and its applications across various sectors.

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Regional Analysis

APAC is estimated to contribute 28% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How GIS In Telecom Sector Market Demand is Rising in APAC Request Free Sample

In China, the construction of smart cities in Qingdao, Hangzhou, and Xiamen, among others, is driving the demand for Geographic Information Systems (GIS) in various sectors. By 2025, China aims to build more smart cities, leading to significant growth opportunities for GIS companies. Esri Global Inc., a leading player

The NH Department of Environmental Services (NHDES) Geodata Portal is the official open data hub for accessing authoritative environmental geospatial information across the state of New Hampshire. Developed and maintained by NHDES, this portal provides public access to a wide range of interactive maps, downloadable datasets, and GIS-based applications related to environmental protection, water resources, land use, conservation planning, and regulatory programs.Designed to support researchers, municipal planners, environmental professionals, and the general public, the Geodata Portal promotes transparency, informed decision-making, and collaborative environmental stewardship. Through a user-friendly and mobile-responsive interface, users can explore data on wetlands, drinking water supplies, wastewater infrastructure, floodplains, permits, water quality monitoring, air emissions, and more.The portal supports key NHDES programs and initiatives, including:Wetlands permitting and mitigation (e.g., ARM Fund projects)Drinking water source protectionSurface water quality assessments and standardsGroundwater and stormwater managementClimate and resilience planningCompliance and enforcement toolsUsers can view and download data, connect to ArcGIS Online content, or explore apps and dashboards that bring complex environmental information to life through visualization and interactivity.🔍 Key FeaturesSearchable and filterable data catalogInteractive web maps and applicationsDownloadable GIS layers in multiple formatsReal-time dashboards and reporting toolsIntegration with ArcGIS Online and Enterprise environments🔧 Maintained by:New Hampshire Department of Environmental Services (NHDES)GIS & Data Integration Team

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.