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.

Thank you for choosing to purchase NationalMap Enhanced Roading (previously Routing add-on). This add-on was originally designed to be used in conjunction with the Routeware Routefinder software, but has been fully reviewed and enhanced to be able to be used with other routing and path finding software, like ESRI’s ArcGIS Network Analyst.

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This add-on will provide you with a road network for use with Routefinder which includes road name, surface, hierarchy, average speed and speed limit attributes.

To understand the issues, challenges and opportunities on the network analysis of the characteristics of the route has been undertaken to define the routes function. This function takes into consideration what it delivers to the residents of the West Midlands for both people and place, looking at connectivity, capacity and demand on the network. Understanding this current function enables us to understand what needs to be accommodated in the future to achieve the objectives of the Key Route Network.This app contains the outcome classification of functional segments of the Key Route Network.

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.

This layer shows the e-road routes for the sEEnergies e-road potential analysis. The routes are split by EU member state, showing the total e-road length (m) per member state. This layer shows the routes for Scenario 1 (Connecting urban areas above 500,000 inhabitants).The routes are a result of a network analysis that connects the points from the "D5_3_eroads_points_scenario_1" layer. The network analysis is based on a network dataset (streets from OpenStreetMap). Only the OSM classes motorway, primary, secondary and tertiary roads were used in the analysis.

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

Composition and speed of the road network in Lushunkou District.

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 element of the evacuation routes from different jurisdictions as well as their use in various types of evacuation scenarios such as wildfire, flooding, or landslides. 2) Perform network analyses and modeling based on the team’s experience with road network performance, access restoration, and critical infrastructure modeling, for a set of case studies, as well as, assessing their performance considering the latest evacuation research. 3) Analyze how well current bus and rail routes align with evacuation routes; and for a series of case studies, using data from previous evacuations, evaluate how well aligned the safety elements of the emerging plans are, relative to previous evacuation routes. And 4) analyze different metrics about the performance of the evacuation routes for different segments of the population (e.g., elderly, mobility constrained, non-vehicle households, and disadvantaged communities). The database and assessments will help inform infrastructure investment decisions and to develop recommendations on how best to maintain State transportation assets and secure safe evacuation routes, as they will identify the road segments with the largest impact on the evacuation route/network performance. The project will deliver a GIS of the compiled plans, a report summarizing the creation of the database and the analyses and will make a final presentation of the study results. Methods 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.

Attractions and tour schedule.

Tourism resources monomer level division standard.

BC Transit routes for Victoria, Whistler, Pemberton Local and Commuter, Squamish Commuter and Kelowna. The routes were created from the Google transit feed (GTFS) and ArcGIS Network Analyst. As no route shape information was available from the feed, the shape of the route was extrapolated from the road network and layout of transit stops. The transit routes were not verified as no maps are available. Although routes were calculated as carefully as possible, this data set carries no guarantee of accuracy beyond the information included in the Google transit feed.

Disaster Routes description courtesy of LA County DPW Disaster Routes website (http://dpw.lacounty.gov/dsg/disasterroutes/):Disaster Routes play a primary role in disaster response and recovery. During a disaster and immediately following, Disaster Routes are used to transport emergency personnel, equipment, and supplies into an affected area in order to save lives, protect property, and minimize impact to the environment. During a disaster, Disaster Routes have priority for clearing, repairing and restoration over other roads.It should be noted that Disaster Routes are not evacuation routes. Although an emergency may warrant that a road be used as both a Disaster Route and an evacuation route, they are considered to be different. An evacuation route is used to move an affected population out of an impacted area.According to the CAMS_ROADS metadata (http://egis3.lacounty.gov/dataportal/2014/06/16/2011-la-county-street-centerline-street-address-file/), this file should NOT be used for 1) routing and network analysis, and 2) jurisdiction and pavement management.Some on and off ramps are included for road continuity purposes. In addition, freeway transitions tend to be labeled as ramps in CAMS_ROADS.

This data is created and maintained by MARTA, not the City of Atlanta. The most up-to-date GTFS data can always be found at https://www.itsmarta.com/app-developer-resources.aspx This layer contains information on:route_idroute_long_name (Blue line, Green, etc)route_type_text (Subway, Bus, etc)This data can be used to display transit routes or can be used in conjunction with a Network Dataset for more advanced analysis.

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

Debtor and insolvency advisory offices in Germany: In particular, the reported data of the competent Supreme Land authorities are used. These are requested annually by the Federal Statistical Office. Since only data on recognised and/or subsidised counselling centres are known in the state authorities, the reported information is extended by own internet searches. The reporting date is as of October 2019.

Due to the type of registration of debtor and insolvency advisory offices in the Federal Statistical Office, no guarantee can be guaranteed on the completeness and correctness of the consultancies presented, since the address information is generated exclusively from external sources.

The basis for the calculation of accessibility zones of debtor counselling centres is the road data base of the open community project OpenStreetMap (OSM). With the help of the software Esri ArcGIS Desktop and the program extensions Network Analyst and ArcGIS Editor for OpenStreetMap, the relevant road data (geometric information and associated attributes) are extracted from the OSM data and converted into a routing-enabled network data model. This allows the calculation of accessibility zones, the so-called isochrons.

The isochronic calculation is performed for discrete time zones of five to 90 minutes. Within a zone, there is no further distinction between driving times. The accessibility is indicated in the form of grid cells with a spatial resolution of 100 meters * 100 meters.

Please note that the indicated travel times are derived model sizes, which may differ significantly from actual driving times. In particular, the current traffic situation or possible restrictions on road traffic, such as traffic jams, construction sites or road closures are NOT taken into account.

Furthermore, no guarantee can be assumed for the correctness of the underlying road geometry and the driving speeds derived from it. The OSM database may contain inaccuracies or errors in both geometric and descriptive information. For example, missing road sections or incorrect access restrictions can prevent the connection of individual traffic areas to the rest of the road network. This can result in incorrect calculation results.

Please note that the algorithm for calculating distance zones in the periphery of the road network may produce highly generalising results. This allows isolated island areas close to the coast to be colored, although they do not have a connection to a road network.

In principle, the accuracy of the information and calculation results presented cannot be guaranteed.

GIS in Transportation Market Analysis The global GIS in transportation market is anticipated to reach a valuation of $XX million by 2033, expanding at a CAGR of XX% from 2025. The market's growth is primarily driven by the increasing demand for efficient and sustainable transportation systems, the growing adoption of GIS technology for infrastructure planning and management, and the need for real-time data for traffic management and optimization. Additionally, the emergence of smart cities and autonomous vehicles is further fueling market demand. The market is segmented by type (software, services, data) and application (road, rail, others). The software segment holds a significant share due to the high demand for GIS software for planning, design, and analysis. The road application segment dominates the market due to the extensive use of GIS for road network management, traffic analysis, and route optimization. Key players in the market include Autodesk, Bentley Systems, ESRI, Hexagon, and MDA. The North American region is expected to maintain its market dominance, followed by Europe and Asia Pacific. The market is expected to witness continued growth over the forecast period, driven by ongoing technological advancements and the rising need for efficient and data-driven transportation solutions.

This dataset was created for Boise Fire specifically to support network analysis, routing, and other spatial analysis needs for response time analysis and master planning for evaluating station location. This data set is catered to the Boise Fire Department needs and should not be used for general mapping purposes. For general mapping of fire stations use the regional fire facility data set. The regional fire facility data includes all fire facilities (i.e. training, administration, stations, storage) in Ada and Canyon county.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS 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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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS 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


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    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 Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho

Note - this data is intended to be used with the "Maryland Road-Separated Bicycle Routes" hosted feature (view) layer hosted by MDOT and not in isolation.Maryland Road-Separated Connectors data comprises linear geometric features which represent the connections for bicycle routes that are separated from roadways carrying motorized vehicle traffic throughout the State of Maryland to road centerlines. This data is primarily used for the purposes of network analysis and in many instances, the 'connectors' are GIS vector creations and not true, paved, bicycle connections. This data is complimentary and to be used in conjunction with the "Maryland Road-Separated Bicycle Routes" hosted feature view layer (also hosted by MDOT). That data - and these connections from roadway to that data - are used to map Bicycle routes that are Shared-Use Paths, typically 10-feet wide, which can be used for transportation or recreational-related purposes.ATTRIBUTES:Route Name (if Applicable): The name of the route is provided if the route is namedCounties within Route: The counties in Maryland through which the route passes are listedRoute's Length: The route distance is calculated and listed in miles. Note that this is the length of the entire named route - and not just the segment selected. Distance calculated using the NAD 1983 StatePlane Maryland FIPS 1900 (US Feet) Projection.LTS Score: Level of Traffic Stress. For this map (road-separated routes) the scores range from 0 (road-separated) to 2 (generally low traffic). The areas that are not 0 in this map/data represent portions of the road-separated routes that cross streets or have portions that are briefly on-road as connections.

Formerly published as TRANS_NOC_GTLF_PUB_UNK_LINE

This standard houses linear features on BLM lands as well as transportation features that provide access to BLM transportation routes. In order to meet the local or state field office needs, each state may extend its GTLF data standard to collect data to fulfill local data requirements as long as the state or field office data can be cross-walked into the National GTLF data standard format. This dataset is an ongoing process, updated periodically when better data is available.

The National BLM GTLF data provides standardized transportation information for use in BLM programs and analysis. At the core, this standard stores transportation information gathered, inventoried, analyzed and recorded in a BLM Transportation Management Plan or TMP. The TMP analyzes transportation options and incorporates public participation to assist in determining travel routes, modes of travel, and season of use on selected and surrounding BLM managed lands. The Idaho BLM maintains only GTLF routes that have been recorded in a TMP. This route data is identified by COORD_SRC2 = ‘TMP – Plan Name’ and PLAN_ROUTE_DSGNTN_AUTH = ‘BLM’. All other data is considered Non-BLM authoritative, administered and maintained transportation data and is provided only as spatial and tabular reference material. As new or existing transportation data is analyzed and recorded through the TMP process, the resulting data will be incorporated into the Idaho BLM GTLF authoritative data maintenance program. The Idaho BLM GTLF data contains transportation data from many different sources across the state including the TMP process, and as such may contain a variety of spatial and tabular inaccuracies and may include errors of omission or commission. The purpose for collecting and maintaining an Idaho state-wide compilation is to provide a broad overview of the transportation network for properly focused analysis or inventory purposes and to provide connectivity between BLM managed routes and the broader transportation network. As with all spatial data, this Idaho BLM GTLF data only represents an approximate or generalized spatial and tabular description of the actual transportation route. All BLM disclaimers apply to this data and that the use of this data is at the risk of the user. Additional Notes. The purpose of this dataset is to create an Idaho BLM feature class with existing routes. Due to the lack of finalized Travel Management Plans (TMP) throughout the state the best available data was used. Where TMP data was not available we used a larger collection of 1:24,000-scale "Resource Base Data" GIS road features gathered by Idaho BLM and a variety of other data collected from the state. May 4 2018: fields added for use by ID BLM for cartographic purposes. Jan 2020 update to GTLF Schema v3.0.

This data set was created and is maintained by the Bureau of Land Management staff in Idaho for only those roads identified by the metadata. All other roads are included only as background source material and are not maintained in GIS or on the ground by BLM.

For more information contact us at blm_id_stateoffice@blm.gov.