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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
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TwitterThis is the NDOT GeoHub data page for the Traffic Operations Division, Operations & Network Analysis Section.
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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
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This is a tutorial on how to use GIP data for the ESRI ArcGIS Network Analyst.
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TwitterDownload 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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Inspired by "Add GTFS to a Network Dataset" tool by Melinda Morang, I have generated this tool to use GTFS public transit data in ArcGIS so you can run schedule-aware analyses without using the Network Analyst.
The abundant access is the first in series of tools I am developing for ArcGIS to analyse the GTFS data. Simplicity is the main objective here, therefore all the analysis will be done in-fly.
The term "abundant access" is borrowed from Jarrett Walker's book, Human transit. You can use the abundant access to perform transit/pedestrian accessibility analyses, controlling for the number of transfers, walking between transfers, walking to transit and walking from transit. My aim is to develop a method that is useful for practitioners and decision-makers to make day-to-day decisions.
Note: No installation is necessary. This tool is only available for ArcGIS 10.4 or higher. It also works with ArcGIS Pro. This tool is still under development so please feel free to contact me if you encounter bugs or other problems or you simply have ideas or suggestions.For more information and updates, visit www.spatialanalyst.ir.
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TwitterThis table contains a list of the participants, or named organizations, of the Social Network Analysis done as part of the Pacific Northwest Coastal Conservation Blueprint which is a component of the Pacific Northwest Coast Landscape Conservation Design. A social network analysis maps out the who, what, and where of conservation collaboration, helping us to think more strategically about conservation at the landscape scale by identifying who entities collaborate with, and the conservation priorities, strategies, capacity needs, strengths, and geographic areas of interest.For more information on the larger Pacific Northwest Coast Landscape Conservation Design project that the Social Network Analysis is a part of please see the project website: http://columbiacoastblueprint.org/
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TwitterThis dataset contains a simplified network representation of bike paths across City of Melbourne. The dataset can be used to create a digital bicycle network with route modelling capabilities that integrated existing bicycle infrastructure. The network has been created to be used with ArcGIS network analyst. The resulting network was connected to the City of Melbourne property layer through centroids created for this project: The network can assist in multiple modelling tasks including catchment analysis and route analysis. The download is a zip file containing compressed .json files Please see the metadata attached for further information.
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TwitterThe 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.
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Features of different accessibility analysis methods.
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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
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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.
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TwitterThe 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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TwitterThe Freight Analysis Framework (FAF5) - Highway Network Assignments was created from 2017 base year and 30 year forecast 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). This data package includes tabular files with results from FAF5 2017, 2022, and 2050 baseline assignments to represent freight flows by three separate truck only flows type (Total Truck, Single Unit, and Combination Unit) and three freight flow markets (domestic, import and export). 2017 and 2022 model years contain 6 data tables and 2050 model year contains 11 data tables, representing the truck only flows. Each data table can be linked to the FAF5 network geography to display truck flows by link.
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In recent years, public sports services have attracted great attention owing to their increasingly important role in public health. However, effective evaluation metrics measuring the efficiency of such services from a spatial perspective (e.g., accessibility and distribution of sports parks) remain absent. Indeed, most designs of sports park distribution in urban areas did not consider practical factors such as local road networks, population distribution, and resident preference, resulting in low utilization rates of these parks. In this study, a spatial accessibility-based method is proposed for evaluation of the distributions of sports parks. As a demonstration, the distribution of sports parks in the central urban area of Changsha, China was investigated using the proposed method by the GIS network analysis. Additionally, optimization strategies for sports park distribution (in terms of spatial distribution and overall accessibility) were developed by using spatial syntax.
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Criteria and weights for time intervals of the accessibility of social services being evaluated using TOPSIS.
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Road classes and road speeds in KwaZulu-Natal province, as defined by the KwaZulu-Natal Department of Transport.
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Indicators X1 –X3 for individual regions of the Czech Republic in 2018, * in thousands.
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TwitterIn 2022, DDOT conducted an analysis of all corridors in the District based on reported injury and fatality crash data from June of 2016 – July of 2021. The resulting corridors are mapped here as the High Injury Network (HIN). Tier 1 streets segments and corridors represent the highest priority segments citywide. The Tier 2 segments supplement Tier 1, to represent the highest priority segments and corridors for each Ward. The streets highlighted in green are streets that were evaluated in 2015, continue to see high rates of injury, and are funded in the FY2023 budget to receive safety treatments in the next few years. In service of the Mayor's Vision Zero initiative, DC Government will prioritize safety interventions on the roadways with the most deaths and injuries as our High Injury Network.
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TwitterSupermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on NAVTEQ streets, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...assuming they have access to a car. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more. Populations in poverty are represented by taking the block group poverty rate (e.g. 10%) from the Census and symbolizing each block in that block group based on that percentage. Demographic data from U.S. Census 2010 and Esri Business location from infoUSAData sources: see this map package.
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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