Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

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

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 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/

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.

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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.

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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 datasets are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/ The transmission line geojson and zipped shapefiles contain existing, planned, under construction lines. The source link: http://data-energy-gis.opendata.arcgis.com/datasets/6db06d51b0a34c9b989fc54c0d25c092_0 The substation geojson and zipped shapefiles contain existing, planned, under construction substations. The source link: http://data-energy-gis.opendata.arcgis.com/datasets/a7ef2af5ca9249babc5b20602edaba59_0 The transmission and substation datasets were last updated on March 9 2017.

The on-street bike and pedestrian facilities represented in this dataset can be used in a network (with the off-street bike and pedestrian data) to create bike or pedestrian routes through the City.

Intermodal Freight facility locations are transfer points to move freight from ship to rail or truck.

Features of different accessibility analysis methods.

These shapefiles were created while sampling lakes in 2019. Files include: 1) amphibians and invertebrates- these files includes points collected when invertebrate sampling was done, or when an amphibian was detected, 2) waterbody boundaries are polylines that were created by walking lake perimeters and noting habitat associated with lines.

The National Highway Planning Network (NHPN) dataset was compiled on May 01, 2014 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). This dataset is a comprehensive network database of the nation's major highway system. It consists of the nation's highways comprised of Rural Arterials, Urban Principal Arterials and all National Highway System routes. The data set covers the 48 contiguous States plus the District of Columbia, Alaska, Hawaii, and Puerto Rico. The nominal scale of the data set is 1:100,000 with a maximal positional error of 80 meters. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529044

Freight related data grouped together and made up of major truck streets, freight network, over legal routes, and heavy haul network. Data is maintained by Seattle Department of Transportation.Feature Class:MajTrkStrtsFMP_FreightNetworkFreight_OverLegalRoutesFreight_HeavyHaulNetworkRefresh Cycle: Nightly Refresh

Utility Network GIS Migration Market Outlook

According to our latest research, the global Utility Network GIS Migration market size reached USD 2.04 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected for the period from 2025 to 2033. By 2033, the market is anticipated to attain a value of USD 5.67 billion. The primary growth factor driving this surge is the increasing need for utilities to modernize legacy Geographic Information Systems (GIS) and integrate advanced digital mapping, asset management, and real-time data analytics to enhance operational efficiency and regulatory compliance.

One of the key growth drivers for the Utility Network GIS Migration market is the accelerating pace of digital transformation across utility sectors such as electricity, water, gas, and telecommunications. Utilities are under immense pressure to improve service reliability, reduce operational costs, and comply with evolving regulatory frameworks. The migration from traditional GIS platforms to next-generation utility network GIS solutions enables organizations to leverage spatial analytics, automate workflows, and support the integration of smart grid technologies. The proliferation of distributed energy resources, IoT devices, and the need for advanced outage management systems have further intensified the demand for robust and scalable GIS migration strategies. Utilities are increasingly prioritizing the modernization of their spatial data infrastructure to ensure seamless data flow, improve asset tracking, and enhance customer engagement, thereby fueling market expansion.

Another significant growth factor is the rising adoption of cloud-based GIS solutions, which offer utilities unparalleled flexibility, scalability, and cost-effectiveness. Cloud deployment models enable utilities to efficiently manage and analyze vast volumes of spatial and non-spatial data without the burden of maintaining on-premises infrastructure. This shift not only reduces capital expenditure but also accelerates the deployment of new functionalities and ensures rapid disaster recovery. Moreover, cloud-based GIS platforms facilitate real-time collaboration among field and office teams, enabling faster decision-making and improving response times during emergencies. The growing emphasis on sustainability, grid modernization, and the integration of renewable energy sources is prompting utilities to invest in cloud-enabled GIS migration projects to future-proof their operations and achieve long-term operational excellence.

The increasing regulatory focus on data accuracy, cybersecurity, and interoperability is also propelling the Utility Network GIS Migration market. Regulatory bodies worldwide are mandating utilities to maintain precise and up-to-date spatial data for effective asset management, outage response, and infrastructure planning. As a result, utilities are compelled to migrate from outdated GIS systems to advanced platforms that offer robust data governance, security, and integration capabilities. The need to comply with standards such as the Common Information Model (CIM) and industry-specific regulations is driving utilities to adopt sophisticated GIS migration strategies. Furthermore, the emergence of advanced technologies such as artificial intelligence, machine learning, and big data analytics is enabling utilities to extract deeper insights from spatial data, optimize maintenance schedules, and proactively address infrastructure vulnerabilities, thereby fostering market growth.

From a regional perspective, North America continues to dominate the Utility Network GIS Migration market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The rapid modernization of utility infrastructure, extensive deployment of smart grids, and the presence of leading GIS solution providers have positioned North America at the forefront of market growth. In Europe, stringent regulatory mandates and the push for sustainable energy transition are driving significant investments in GIS migration projects. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by large-scale infrastructure development, urbanization, and increasing government initiatives to improve utility services. The Middle East & Africa and Latin America are also emerging as promising markets, supported by ongoing digitalization efforts and investments in utility infrastructure upgrades.

Component Analysis

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

OUTDATED. See the current data at https://data.cityofchicago.org/d/pnta-kuqa -- District-run elementary schools in CPS are organized into 13 Geographic Networks, which provide administrative support, strategic direction, and leadership development to the schools within each Network. To view or use these shapefiles, compression software, such as 7-Zip, and special GIS software, such as Google Earth or ArcGIS, are required

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.

Freight related data grouped together and made up of major truck streets, freight network, over legal routes, and heavy haul network. Data is maintained by Seattle Department of Transportation.

This road network dataset was created from data extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to represent motor-vehicle traversable public roads within Australia. Note, however, as the original dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. This road network has been topologically corrected for the purposes of network analysis for motor vehicles. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 05 August 2020. AURIN has filtered the original data and omitted features to present the topologically correct, motor-vehicle traversable road network.