Features of different accessibility analysis methods.

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

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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

Statistical indicators of the networks.

This repository is the dataset of the related paper "Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques". The data presented here are collected and gathered together from several separate locations. All the probable original sources of the dataset are open-source or free to distribute licensed. The dataset has the following items: 1. Road network of Dhaka city. 2. Bus Route network of Dhaka city. 3. Future metro Route network of Dhaka city. 4. All the bus stands in Bangladesh. 5. All planned metro station in Dhaka city. 6. The output of some sample random two points shortest or cheapest path from the related paper.

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

Key cities based on information diffusing.

The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.†Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety eleme..., The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk., , # Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities

https://doi.org/10.5061/dryad.w9ghx3g0j

Description of the data and file structure

For this project’s analysis, the team obtained data from FEMA's National Risk Index, including the Social Vulnerability Index (SOVI).

To estimate SOVI, the team used data from the American Community Survey (ACS) to calculate SOVI at the census block level.

Using the graphs obtained from OpenStreetMap (OSM), the authors estimated the Hansen Accessibility Index (Ai) and the normalized betweenness centrality (BC) for each node in the graph.

The authors estimated the Road Network Performance (RNP) risk at the node level by combining NRI, Ai, and BC. They then grouped the RNP to determine the RNP risk at the regional level and generated the radial histogram. Finally, the authors calculated each ana...

To calculate the California’s Proposed Network Standards on the Potential MediCAL Population DHCS used the Network Analysis Tool from ESRI OD Cost Matrix.

The Access to Parks indicator measures the number of residential parcels in the Champaign-Urbana-Savoy urbanized area that are within one half mile and within one quarter mile of a public park or other public open space. The half mile and quarter mile are used here as representations of reasonable walking distance to an amenity, with a half mile generally representing a 10 minute walk, and a quarter mile representing a 5 minute walk.

Public parks, public golf courses, forest preserves, and public/private recreational facilities (i.e. privately owned recreational land available for public use) were counted toward this indicator. Private golf courses and country clubs were not counted toward this indicator.

The access analysis was performed using linear distance, rather than distance along a street network, so access in some areas may be limited by characteristics of the street network (e.g., form, lack or condition of sidewalks) or major barriers (e.g., highways and other wide roads that are difficult or dangerous to cross).

Taking into account the limitations of our methodology, as of the analysis performed in June 2022, Champaign-Urbana-Savoy residents as a whole have very good access to parks and open space: over 74 percent of the Champaign-Urbana-Savoy residential area is within one quarter mile of a park or open space, and almost 97 percent of the Champaign-Urbana-Savoy residential area is within one half mile of a park or open space.

Parks and open space are valuable amenities that have recreational, environmental, and public health benefits. The ability of residents to visit parks and access these benefits without a car is a measure of both equity and quality of life.

The percentage analysis was performed in GIS using map layers from the Champaign County Regional Planning Commission (CCRPC) and Champaign County GIS Consortium (CCGISC). The analysis is done on an annual basis, to account for any changes in both parks and residential areas.

Top 10 circulation paths (according to the number of circulation paths).

This dataset contains an inventory of existing bicycle facilities within the City of Boston. It is intended for information purposes only. It is not intended to aid in trip routing. Actual conditions may vary from what is indicated in the dataset due to construction or other reasons. Updated by BTD in June 2022.

Analysis of ‘GIS area of Natura 2000 network’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/dat-110-en on 14 January 2022.

--- Dataset description provided by original source is as follows ---

GIS area of Natura 2000 network calculated on the data set versions published end 2009 onwards

--- Original source retains full ownership of the source dataset ---

This data contains general information about Pedestrian Network in Hong Kong. Pedestrian Network is a set of 3D line features derived from road features and road furniture from Lands Department and Transport Department. A number of attributes are associated with the pedestrian network such as spatially related street names. Besides, the pedestrian network includes information like wheelchair accessibility and obstacles to facilitate the digital inclusion for the needy. Please refer to this video to learn how to use 3D Pedestrian Network Dataset in ArcGIS Pro to facilitate your transportation analysis.The data was provided in the formats of JSON, GML and GDB by Lands Department and downloaded via GEODATA.GOV.HK website.

The original data files were processed and converted into an Esri file geodatabase. Wheelchair accessibility, escalator/lift, staircase walking speed and street gradient were used to create and build a network dataset in order to demonstrate basic functions for pedestrian network and routing analysis in ArcMap and ArcGIS Pro. There are other tables and feature classes in the file geodatabase but they are not included in the network dataset, users have to consider the use of information based on their requirements and make necessary configurations. The 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 Lands Department at https://geodata.gov.hk/gs/download-datadict/201eaaee-47d6-42d0-ac81-19a430f63952.

For details about the data, source format and terms of conditions of usage, please refer to the website of GEODATA STORE at https://geodata.gov.hk.Dataset last updated on: 2022 Oct

ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

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

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

Last Update: 02/04/2025The statewide roads dataset is a multi-purpose statewide roads dataset for cartography and range based-address location. This dataset is also used as the base geometry for deriving the GIS-representation of UDOT's highway linear referencing system (LRS). A network analysis dataset for route-finding can also be derived from this dataset. This dataset utilizes a data model based on Next-Generation 911 standards and the Federal Highway Administration's All Roads Network Of Linear-referenced Data (ARNOLD) reporting requirements for state DOTs. UGRC adopted this data model on September 13th, 2017.The statewide roads dataset is maintained by UGRC in partnership with local governments, the Utah 911 Committee, and UDOT. This dataset is updated monthly with Davis, Salt Lake, Utah, Washington and Weber represented every month, along with additional counties based on an annual update schedule. UGRC obtains the data from the authoritative data source (typically county agencies), projects the data and attributes into the current data model, spatially assigns polygon-based fields based on the appropriate SGID boundary, and then standardizes the attribute values to ensure statewide consistency. UGRC also generates a UNIQUE_ID field based on the segment's location in the US National Grid, with the street name then tacked on. The UNIQUE_ID field is static and is UGRC's current, ad hoc solution to a persistent global id. More information about the data model can be found here: https://docs.google.com/spreadsheets/d/1jQ_JuRIEtzxj60F0FAGmdu5JrFpfYBbSt3YzzCjxpfI/edit#gid=811360546 More information about the data model transition can be found here: https://gis.utah.gov/major-updates-coming-to-roads-data-model/We are currently working with US Forest Service to improve the Forest Service roads in this dataset, however, for the most up-to-date and complete set of USFS roads, please visit their data portal where you can download the "National Forest System Roads" dataset.More information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/transportation/roads-system/

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

GIS in Transportation Market Outlook

The global GIS in Transportation market size was valued at USD 9.5 billion in 2023 and is expected to reach USD 21.8 billion by 2032, growing at a CAGR of 9.5%. This rapid growth is driven by advancements in spatial data analytics and the increasing need for efficient transportation management systems across various sectors. The surge in urbanization, coupled with the rising adoption of smart city initiatives, has propelled the demand for geographic information systems (GIS) in transportation, making it an indispensable tool for urban planners and transportation authorities.

One of the primary growth factors in the GIS in Transportation market is the rising need for traffic management solutions. With increasing vehicle ownership and congested road networks, the implementation of GIS-based traffic management systems has become crucial. These systems help in real-time traffic monitoring, congestion management, and route optimization, thereby enhancing overall transportation efficiency. Additionally, the integration of GIS with Internet of Things (IoT) devices and sensors provides valuable data to city planners and traffic authorities, enabling better decision-making and improved traffic flow.

Another significant driver for the market is the growing emphasis on asset management in the transportation sector. GIS technology plays a pivotal role in tracking and managing transportation infrastructure assets such as roads, bridges, and tunnels. By leveraging GIS, transportation agencies can efficiently monitor the condition of these assets, schedule maintenance activities, and allocate resources effectively. This not only extends the lifespan of infrastructure assets but also ensures safety and reduces operational costs, thus driving the adoption of GIS in the transportation sector.

Moreover, the increasing focus on sustainable and eco-friendly transportation solutions is fostering the growth of the GIS in Transportation market. Governments and transportation authorities worldwide are promoting the use of public transit and non-motorized transportation modes to reduce carbon emissions and combat climate change. GIS technology aids in public transit planning and route optimization, ensuring efficient and sustainable transportation systems. Additionally, GIS-based solutions enable the assessment of environmental impacts and support the implementation of green transportation initiatives, further bolstering market growth.

Regionally, North America holds a significant share in the GIS in Transportation market, attributed to the early adoption of advanced technologies and substantial investments in transportation infrastructure. The presence of key market players and the implementation of smart city projects in the United States and Canada further drive the market's growth in this region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, propelled by rapid urbanization, increasing government initiatives for smart transportation, and the expansion of transportation networks in countries like China and India.

Component Analysis

The GIS in Transportation market is segmented by component into software, hardware, and services. The software segment dominates the market, driven by the rising demand for advanced GIS applications that provide comprehensive spatial analysis, mapping, and visualization capabilities. GIS software solutions, such as geographic information systems for traffic management and route planning, are extensively utilized by transportation authorities and urban planners to improve operational efficiency and decision-making processes. The continuous evolution of GIS software, incorporating advanced features like real-time data integration and predictive analytics, further propels market growth.

Hardware components, although smaller in market share compared to software, play a crucial role in the GIS in Transportation market. Hardware components include GPS devices, sensors, and data collection tools, which are essential for gathering accurate spatial data. The increasing deployment of IoT devices and sensors in transportation infrastructure enhances data collection capabilities, thus supporting the effective implementation of GIS solutions. The integration of GIS hardware with software solutions provides a holistic approach to transportation management, driving the adoption of GIS technology in this sector.

The services segment encompasses a wide range of professional services, including consulting, implementation, and maint