INFP, CRMD and UCL have developed a framework capable of analyzing the implications of natural hazards on transportation networks, also in a time-dependent manner. This is currently embedded into an ArcGIS toolbox entitled Network-risk, which has been successfully tested for Bucharest, contributing to an insightful evaluation of emergency intervention times for ambulances and firefighters, in the case of an earthquake. The files and the user manual allow a replication of our recent analysis in Toma-Danila et al. (2022) and a download of results (such as affected roads and unaccesible areas in Bucharest), in various formats. Some of the results are also presented in an ArcGIS Online app, called "Riscul seismic al Bucurestiului" (The seismic risk of Bucharest), available at https://tinyurl.com/yt32aeyx. In the files you can find: - the Bucharest road network used in the article; - facilities for Bucharest and Ilfov, such as hospitals, firestations, buildings with seismic risk or tramway lines accesible by emergency vehicles - results of the analysis: unaccesible roads and areas, service areas around facilities, closest facilities for representative points - Excel calculator for Z elevation from OpenStreetMap data - the user manual and a ArcGIS toolbox.

Main citation: - Toma-Danila D., Tiganescu A., D'Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science, https://doi.org/10.3389/feart.2022.834052

Previous references: - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5): 1421-1439, https://doi.org/10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111, https://link.springer.com/article/10.1007/s11069-017-3069-y

The North American Rail Network (NARN) Rail Lines: BNSF View dataset is from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset is a subset of the NARN Rail Lines dataset that show the ownership and trackage rights for the Class I railroad “Burlington Northern and Santa Fe (BNSF).†It is derived from the North American Rail Network (NARN) Lines dataset, and for more information please consult, https://doi.org/10.21949/1519415. The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528950

Data file name: Beijing.rar Data deion: 1) after finishing public issued of Beijing city traffic figure, and Beijing map, and Beijing Tourism figure, by geometry corrected, and image distribution associate, work Hou, on the year road center line for vector quantitative, on vector quantitative of network data for edit, until network full, get has Beijing city five ring within, each 10 years around time interval of network GIS data, established has Beijing history network data set. 2) data file contains years of Beijing's road network data and route data is shapefile files and named for years (1969, 1978, 1990, 2000 and 2008). 3) shapefile file's property sheet for each year, the field "year_" section belongs to the year, the field "From_" indicates that this stretch of road network from previous vintages in the sections corresponding to the FID.

If you have any questions, please contact lianggao@bjtu.edu.CN.

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is forecast to increase by USD 2.35 billion at a CAGR of 15.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Geographic Information Systems (GIS) for capacity planning in the telecommunications industry. GIS technology enables telecom companies to optimize network infrastructure, manage resources efficiently, and improve service delivery. Telecommunication assets and network management systems require GIS integration for efficient asset management and network slicing. However, challenges persist in this market. A communication gap between developers and end-users poses a significant obstacle.
Companies seeking to capitalize on opportunities in the market must focus on addressing these challenges, while also staying abreast of technological advancements and market trends. Effective collaboration between developers and end-users, coupled with strategic investments, will be essential for success in this dynamic market. Telecom companies must bridge this divide to ensure the development of user-friendly and effective GIS solutions. Network densification and virtualization platforms are key trends, allowing for efficient spectrum management and data monetization. Additionally, the implementation of GIS in the telecom sector requires substantial investment in technology and infrastructure, which may deter smaller players from entering the market.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic telecom sector, GIS technology plays a pivotal role in customer analysis, network planning, and infrastructure development. Customer experiences are enhanced through location-based services and real-time data analysis, enabling telecom companies to tailor offerings and improve service quality. Network simulation and capacity planning are crucial for network evolution, with machine learning and AI integration facilitating network optimization and compliance with industry standards.
IOT connectivity and network analytics platforms offer valuable insights for smart city infrastructure development, with 3D data analysis and network outage analysis ensuring network resilience. Telecom industry partnerships foster innovation and collaboration, driving the continuous evolution of the sector. Consulting firms offer expertise in network compliance and network management, ensuring regulatory adherence and optimal network performance.

How is this GIS In Telecom Sector Industry 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. In the telecom sector, the deployment of 5G networks is driving the need for advanced Geographic Information Systems (GIS) to optimize network performance and efficiency. GIS technology enables spatial analysis, network automation, capacity analysis, and bandwidth management, all crucial elements in the rollout of 5G networks. Large enterprises and telecom consulting firms are integrating GIS data into their operations for network planning, optimization, and troubleshooting. Machine learning and artificial intelligence are transforming GIS applications, offering predictive analytics and real-time network performance monitoring. Network virtualization and software-defined networking are also gaining traction, enhancing network capacity and improving network reliability and maintenance.

GIS software companies provide solutions for desktops, mobiles, cloud, and servers, catering to various industry needs. Smart city initiatives and location-based services are expanding the use cases for GIS in telecom, offering new opportunities for growth. Infrastructure deployment and population density analysis are critical factors in network rollout and capacity enhancement. Network security and performance monitoring are essential components of GIS applications, ensuring network resilience and customer experience management. Edge computing and network latency reduction are also signi

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

The National Highway Freight Network (NHFN) dataset was compiled on January 27, 2023 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Congress established a new National Highway Freight Program (NHFP) in 23 U.S.C. 167 to improve the efficient movement of freight on the National Highway Freight Network (NHFN) and support several goals. The law required the FHWA Administrator to strategically direct Federal resources and policies toward improved performance of the network. The NHFP provides formula funding apportioned annually to States, for use on the NHFN. The definition of the NHFN is established under 23 U.S.C. 167(c) and consists of four separate highway network components: the PHFS; Critical Rural Freight Corridors (CRFCs); Critical Urban Freight Corridors (CUFCs); and those portions of the Interstate System that are not part of the PHFS. Primary Highway Freight System (PHFS): This is a network of highways identified as the most critical highway portions of the U.S. freight transportation system determined by measurable and objective national data. The network consists of 41,800 centerlines miles, including 38,014 centerline miles of Interstate and 3,785 centerline miles of non-Interstate roads. Other Interstate portions not on the PHFS: These highways consist of the remaining portion of Interstate roads not included in the PHFS. These routes provide important continuity and access to freight transportation facilities. These portions amount to an estimated 10,265 centerline miles of Interstate, nationwide, and will fluctuate with additions and deletions to the Interstate Highway System. Critical Rural Freight Corridors (CRFCs): These are public roads not in an urbanized area which provide access and connection to the PHFS and the Interstate with other important ports, public transportation facilities, or other intermodal freight facilities. Nationwide, there are 5,389 centerline miles designated as CRFCs as of January 27, 2023. CRFCs are not included in GIS data base. Critical Urban Freight Corridors (CUFCs): These are public roads in urbanized areas which provide access and connection to the PHFS and the Interstate with other ports, public transportation facilities, or other intermodal transportation facilities. Nationwide, there are 2,656 centerline miles designated as CUFC as of January 27, 2023. CUFCs are not included in GIS data base.

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

GIS in Telecom Sector Market Outlook

The global GIS in telecom sector market size was valued at approximately USD 1.7 billion in 2023 and is projected to reach USD 4.5 billion by 2032, growing at a CAGR of 11.5% during the forecast period. This substantial growth is driven by the increasing demand for advanced mapping and analysis tools in the telecom industry, which plays a crucial role in enhancing network performance, managing assets, and optimizing location-based services. The rapid technological advancements in geospatial data processing and the increasing integration of GIS with IoT, 5G, and AI technologies further contribute to the market’s expansion.

The growth factors for the GIS in telecom sector market are multifaceted and robust. The primary driver is the rising demand for enhanced customer experience and network efficiency, which GIS technology offers through precise mapping and real-time data analytics. Telecom operators are increasingly adopting GIS to optimize their network management processes, reduce operational costs, and improve service delivery. Additionally, the burgeoning demand for location-based services and the growing utilization of GIS in planning and deploying 5G networks are significant contributors to market growth. These applications are essential for telecom companies seeking to expand their networks and enhance connectivity, especially in rural and underserved areas.

The integration of GIS with emerging technologies such as IoT and AI is also a critical growth driver in this market. As telecom companies strive to offer more personalized and efficient services, the role of GIS in analyzing large volumes of geospatial data becomes vital. This integration facilitates better decision-making processes, enabling telecom operators to tailor their services according to specific geographic and demographic needs. Furthermore, GIS technology provides significant cost benefits by optimizing asset management and ensuring more efficient use of resources, which is increasingly appealing in a competitive market landscape.

Another growth factor is the increasing regulatory mandates and policies aimed at improving telecom infrastructure. Governments across the globe are investing heavily in modernizing telecom networks, and GIS plays a crucial role in these initiatives. By providing comprehensive spatial data and analytics, GIS technology assists in the strategic planning and deployment of telecom infrastructure, ensuring compliance with regulatory standards. Moreover, the rise in smart city projects, which rely heavily on advanced telecom networks, further propels the demand for GIS solutions in the telecom sector.

Regionally, North America dominates the GIS in telecom sector market due to its early adoption of advanced technologies and significant investments in telecom infrastructure. The presence of major telecom companies and technology providers also contributes to the region's leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid expansion of telecom networks and the increasing focus on digital transformation initiatives. Emerging economies in this region are investing heavily in 5G deployment and smart city projects, which boost the demand for GIS solutions. Europe and Latin America also present significant opportunities for market growth, with ongoing investments in network modernization and digital infrastructure development.

Component Analysis

The GIS in telecom sector market is segmented into software, hardware, and services, each playing a pivotal role in the industry’s development. The software segment, which includes GIS mapping and analytics tools, is expected to hold the largest market share. This is attributed to the increasing demand for advanced software solutions that enable telecom operators to analyze geospatial data for network optimization and strategic planning. The continuous evolution of software capabilities, such as real-time analytics and cloud-based services, further propels the demand for GIS software in the telecom sector.

Hardware components, which include GPS devices, GNSS receivers, and other geospatial data collection tools, are crucial for data acquisition in GIS applications. Although this segment may not be as large as the software segment, its importance cannot be overstated. Advances in hardware technology have significantly improved data accuracy and processing speeds, enabling telecom companies to efficiently collect and analyze large volumes of geospatial data. The increasing integration of these hardwar

The Freight Analysis Framework (FAF5) - Network Nodes dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FAF (Version 5) Network Nodes contains 348,498 node features. All node features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Node and the FAF Link datasets can be used together to create a network. The link features in the FAF Network dataset include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii.

Detailed, building -specific assessment of indoor mobile signal strength and propagation across all licensed mobile operators in a given country. Signal values are provided for each H3-12 hexagon inside the building (resolution approx. 20 x 20 meters). The data is presented in GIS-compatible formats such as gpkg and geojson. The data is obtained using crowdsourced data and advanced geo-spatial algorithms and includes data on the presence of indoor coverage systems. This data can be purchased on a building-by-building basis

Typical data use cases are in the following sectors: - B2B telecommunications: assess indoor coverage quality to optimise deployment of mobile-dependent network services (e.g. SD-WAN, mobile backup, etc..). - Mobile telecoms: Mobile operators and indoor coverage solution providers (e.g. DAS providers) can use this data to identify buildings and building owners for the deployment of indoor coverage systems - Commercial real estate and property: ascertain the quality of indoor mobile coverage to ensure that tenants can actually conduct business in your premises

The Navigable Waterway Network Lines dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Waterway Network (Lines) is a comprehensive network database of the Nation's navigable waterways. The dataset covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. It consists of a line feature class of the National Waterway Network (NWN), which is based on a route feature class for the NWN update regions (“1” through “7”, as well as the open ocean region “0”) and route event table with linear referencing system measures for NWN links. This dataset is a feature class with associated measures (in miles) that are used for finding distances, locating features, and displaying route event layers. It was exported from this route event layer. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters. These data could be used for analytical studies of waterway performance, for compiling commodity flow statistics, and for mapping purposes. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529053

This dataset holds a vector layer for the road network in Lamto research station in 1963. To produce the dataset we digitized the roads from the unpublished map “Carte physionomique des faciès savanians de Lamto" drawn by de la Souchère; P. and Badarello, I. in 1969 and the mosaic of aerial photographs acquired by IGN in 1963. Most of the footpaths no longer exist in 2021. The attributes of the shapefile follow the OpenStreetMap (OMS) data schema.

Local Road Network for 31 local authorities. Extracted from MapRoad Asset Management System. The Road Management Office and Local Authorities provide this information with the understanding that it is not guaranteed to be accurate, correct or complete. The Road Management Office and Local Authorities accept no liability for any loss or damage suffered by those using this data for any purpose.The road infrastructure is the largest asset managed by local authorities in Ireland. It’s efficient management (both day to day and in the long term) is essential to economic activity as the majority of commuting and haulage occurs using it. The 31 local authorities operate, maintain and improve the network of regional and local roads.

The Virginia Geographic Information Network (VGIN) has coordinated and manages the development of a consistent, seamless, statewide digital road centerline file with address, road name, and state route number attribution, as part of the Virginia Base Mapping Program (VBMP). The Road Centerline Program (RCL) leverages the Commonwealth"s investment in the VBMP digital orthophotography and is focused on creating a single statewide, consistent digital road file.The RCL data layer is a dynamic dataset supported and maintained by Virginia"s Local Governments, VDOT, and VGIN. VBMP RCL is extracted and provided back to local governments and state agencies in many geographic data sets every quarter.GDB Version: ArcGIS Pro 3.3Additional Resources:Routable RCL With Network Dataset GDB(ArcGIS Pro 3.2)Shapefile DownloadREST EndpointRoad Centerline Data StandardArcGIS LYR FileHistorical RCL & Ancillary Centerlines -Contact VGIN

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry 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. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

This dataset holds a vector layer for the road network in Lamto research station in 1988. To produce the dataset we digitized the roads from the map “Carte du recouvrement ligneux de la réserve de Lamto" published by Gautier in 1990. Some of the roads no longer exist in 2021. The attributes of the shapefile follow the OpenStreetMap (OMS) data schema. Roads classified as "piste principale" on the original map have the OMS attributes "unclassified" or "residential". Roads classified as "piste secondaire" on the original map have the OMS attribute "track". The type "sentiers" is classified as "footway".

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

InputData DirectoryThis network dataset is an example of a network to which paths from other networks (i.e. Networks A and B) can be compared.Contains two directories:a) NetworkCb) NetworkPaths'NetworkC' Directory- This network is based upon a subset of the Missouri Department of Transportation (MoDOT) July 2016 road dataset listed in the references.- NetworkC contains an ESRI .gdb (NetworkCdata.gdb) in which the arcs and nodes for Network C can be found as well as an ArcGIS ND Network Analyst configuration file. - Featuredataset: NetworkCsub - Network arcs: NetworkCsub - Network file: NetworkCsub_ND - Network junctions: NetworkCsub_ND_Junctions'NetworkPaths' contains ESRI .gdbs representing:a) A collection of routes between OD pairs in each network (InputPaths.gdb) - The densified routes used in the application (densified at 10m): (Net_A_routelines; Net_B_routelines; Net_C_routelines) - The original routes with original set of vertices (non densified): (Net_A_routes; Net_B_routes; Net_C_routes)b) The origin and destination points for the paths (ODNodes.gdb) - These were used to generate the shortest paths for each network, serving as the paths to be compared - origins: originLocations - destinations: destinationLocations_'OutputData' DirectoryContains the comparisons of paths to networks:NetAToB: comparison of paths from network A to network BNetAToC: comparison of paths from network A to network CNetBToA: comparison of paths from network B to network ANetBToC: comparison of paths from network B to network CNetCToA: comparison of paths from network C to network ANetCToB: comparison of paths from network C to network BInside each directory is a collection of ESRI .gdb which contains the individual paths used in the analysis as inputa) NetworkAPaths.gdbb) NetworkBPaths.gdbc) NetworkCPaths.gdbInside each directory is a collection of ESRI .gdb which contains the vertices of the individual paths used in the analysis as inputa) NetworkAPathPoints.gdbb) NetworkBPathPoints.gdbc) NetworkCPathPoints.gdbAlso included is a collection of ESRI .gdb that represent the original path nodes that could be assigned to the comparison network. In this case, only nodes that were within 20m of the comparison network could be assigned. Each path node is attributed with the distance to its counterpart node in the comparison. a) Nodes in Network A paths assigned to Network B (PathANodesAssignedtoNetB.gdb)b) Nodes in Network A paths assigned to Network C (PathANodesAssignedtoNetC.gdb)c) Nodes in Network B paths assigned to Network A (PathBNodesAssignedtoNetA.gdb)d) Nodes in Network B paths assigned to Network C (PathBNodesAssignedtoNetC.gdb)e) Nodes in Network C paths assigned to Network A (PathCNodesAssignedtoNetA.gdb)f) Nodes in Network C paths assigned to Network B (PathCNodesAssignedtoNetB.gdb)Inside each directory is a collection of ESRI .gdb which contain solutions to the SCPPOD with the following naming convention:a) comparing paths in Network A to Network B SCCPODarcsPathAtoNetB.gdb for arc elements and SCCPODnodesPathAtoNetB.gdb for node elements) - The naming convention for the node solutions for path id X is ('SN_routeX_X') - The naming convention for the arc solutions for path id X is ('routX_Rt' for single polyline counterpart path; and 'routeX_Rtsplit' for a polyline representation of the counterpart path based upon the SCPPOD node output).b) comparing paths in Network A to Network C SCCPODarcsPathAtoNetC.gdb for arc elements and SCCPODnodesPathAtoNetC.gdb for node elements)c) comparing paths in Network B to Network A SCCPODarcsPathBtoNetA.gdb for arc elements and SCCPODnodesPathBtoNetA.gdb for node elements)d) comparing paths in Network B to Network C SCCPODarcsPathBtoNetC.gdb for arc elements and SCCPODnodesPathBtoNetC.gdb for node elements)e) comparing paths in Network C to Network A SCCPODarcsPathCtoNetA.gdb for arc elements and SCCPODnodesPathCtoNetA.gdb for node elements)f) comparing paths in Network C to Network B SCCPODarcsPathCtoNetB.gdb for arc elements and SCCPODnodesPathCtoNetB.gdb for node elements)The counterpart paths that were identified were then linked to the full network C to summarize the frequency with with arcs were associated with paths - Can be found in: 1. PathARepresentationinNetC.gdb 2. PathARepresentationinNetC.gdb - important attributes: a) vcntarc: number of paths utilizing arc b) ptCnt: number of path vertices associated with each arc c) AvgDist: average distance of path vertices from network arcs d) MinDist: minimum distance of path vertices from network arcs e) MaxDist: minimum distance of path vertices from network arcs