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

Polygons showing road miles from roadway intersected points to the URL in 5, 10, 15, 20, and 25 mile increments for San Luis Obispo County.

This data was created using the "Calculate Service Area" function in the Network Analyst Extension and available ESRI Road Network. The Service Area function requires points so points were generated by intersecting the URL boundary with the road network.

The Coordinates for this dataset are State Plane Coordinate System, Zone 5, NAD 1983 Feet.

This is a tutorial on how to use GIP data for the ESRI ArcGIS Network Analyst.

Criteria and weights for time intervals of the accessibility of social services being evaluated using TOPSIS.

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.

Supermarkets 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.

Day services centres: Accessibility scale by the regions.

This layer shows the half mile walkshed area around PRT busway, incline, and light rail stations as of February 2025. Walksheds were calculated using the high-precision service area settings in ArcGIS Network Analyst, using lat/long and schedule data from the 2502 Clever GTFS feed. Multiple stops at one station were dissolved into a single walkshed. This layer does not include the temporary light rail stop at Warrington and Allen for detour service due to the Mount Washington Tunnel closure.

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GIS In Telecom Sector Market Size 2025-2029

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

Major Market Trends & Insights

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

Market Size & Forecast

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

Market Summary

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

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

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How is the GIS In Telecom Sector Market Segmented ?

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

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

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

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

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

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Regional Analysis

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

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

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

This 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.

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.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Indicators X1 –X3 for individual regions of the Czech Republic in 2018, * in thousands.

Day care centres: Accessibility scale by the regions.

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

Please see the Data Summary sheet on MDOT's website: https://www.mdot.maryland.gov/OPCP/MDOT_Walksheds_Summary_Sheet.pdfThese walkshed areas were built in ESRI Network Analyst using jurisdictional sidewalk and trail data (hand-digitized as needed) and state roadway centerline data. Roadways were restricted to only allow travel along those suitable for pedestrians. (This is a hosted feature view).The network modeling uses actual station entrance/exit locations and covers a one-half mile walking distance.

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

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. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528011

This feature layer was developed by the Central Statistics Office and represents the percentage of Census 2016 population living in settlements of 20,000 persons or more by sex, age and disability who live within 500 metres of a public transport stop.The methodology for this indicator is as follows: The coordinates of all public transport stops (Irish Rail, Luas, Dublin Bus and Bus Eireann) were downloaded from the Transport for Ireland website, link to data. Using the road network from the Tailte Éireann National Map and the ArcGIS Network Analyst tool the shortest distance path was calculated for each dwelling enumerated in the 2016 census to the nearest public transport stop. The resulting output was merged with the main Census of Population 2016 dataset to identify all persons who resided within 500 metres proximity of their nearest public transport stop and to get the relevant breakdowns of the population.Only population within large settlements (e.g. 20,000 or more) were in scope as the metadata for 11.2.1 makes reference to persons having access to public transport facilities with frequent services. The data published for this indicator is based upon the assumption that large settlements would have a greater likelihood of pubic transport services operating on a regular basis during peak times each day.

The potential for automated vehicles (AVs) to reduce parking to allow for the conversion of on-and off-street parking to new uses, such as new space for walk, bike, and shared -micro-mobility services, and housing), has sparked significant interest among urban planners. AVs could drop-off and pick-up passengers in areas where parking costs are high or limited. Personal AVs could return home or park in less expensive locations and shared AVs could serve other passengers. However, reduced demand for parking would be accompanied by increased demand for curbside drop-off/pick-up space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flow in downtown urban areas during peak hours when high volumes of drop-offs and pick-ups events are likely to occur. Only limited research examines the travel and greenhouse gas effects (GHG) of a shift from parking to drop-off/pick-up travel and the effects of changes in parking supply. Our study uses a microscopic road traffic model with local travel activity data to simulate vehicle travel in San Francisco’s downtown central business district to explore traffic flow, VMT, and GHG effects of AV scenarios in which we vary (1) the demand for drop-off and pick-up travel versus parking, (2) the supply of on-street and off-street parking, and (3) the change in demand for parking and drop-off/pick-up travel due to a significant change in price of using curbside space.

Methods Demand Modeling

We selected the microscopic road traffic model (Simulation of Urban MObility or SUMO) to simulate the traffic flow effects of the AV scenarios. SUMO is an open-source, highly portable, multimodal, microscopic road traffic simulation package designed to handle large road networks (Behrisch et al., 2011). The SUMO simulation used local travel activity data from the San Francisco Bay Area MATsim (SFBA-MATsim) model (Horni et al., 2016; Jaller et al., 2019; Rodier et al., 2018). This model was developed and calibrated with the official San Francisco Bay Area Metropolitan Transportation Commission’s Activity-Based Travel Demand Model (MTC-ABM).

The geographic focus of this study is the central business district (CBD) in the City of San Francisco. We selected individual daily activity tours with at least one vehicle stop in st CBD during the 24 hours (an average weekday) from the SFBA-MATsim model. Arrival and departure times for vehicle tour stops are in increments of seconds in the SFBA-MATsim model. We also converted transit vehicle stops to AVs stops for purposes of the scenario simulation. From the SFBA-MATsim model, we obtained about 900,000 travelers making 1.8 million trips with about 1% of the trips representing internal trips and the remainder had at least one stop in the study area. Total simulated vehicle trip volumes in the network were adjusted to match roadway supply (see discussion below), transit supply, and model year congestion levels.

Network and Traffic Analysis Zones

The SUMO simulations use transportation analysis zones (TAZ) that are consistent with both MTC-ABM and SFBA-MATsim models’ zone system. The TAZs in the study area are among the smallest in the region and include small numbers of census blocks. For this specific network, there are 45 TAZs in total.

We used the SUMO network editor to import the OpenStreetMap for the San Francisco CBD roadway network. We edited the OpenStreetMap roadway network to exclude minor roads. Major roads in the CBD were included in the final network to increase the efficiency of SUMO simulations.

Parking Supply Data

The San Francisco (SF) Parking Census is the source of the parking supply data used in this study1. The San Francisco Municipal Transportation Agency (SFMTA) collected the parking supply data: 97% through field surveys and 3% through remote resources. The on-street parking supply in the dataset includes metered on-street spaces, non-metered demarcated spaces (parking stalls), and non-metered un-demarcated spaces (unmarked curb length). For non-metered spots, we apply a standard 17 feet per parking space, which is the length needed by an average sedan to park between two vehicles. If there was a short length of curb space that could only support one vehicle, we used 12 feet as the length of the parking spot. For any unmarked perpendicular parking, we used a standard of eight feet and six inches of curb space. We did not include controlled parking and restricted parking spaces in the data set.

We used ArcGIS to model the parking data and transferred the data to SUMO using spatial analysis tools. The data set included 1,351 on-street parking locations with a total capacity of 20,019 parking spots within the study area. For off-street parking, there are 356 locations with a total capacity of 65,404 parking spots.

1 http://sfpark.org/resources-overview/