The digital map market is estimated to capture a valuation of US$ 18.3 billion in 2023 and is projected to reach US$ 73.1 billion by 2033. The market is estimated to secure a CAGR of 14.8% from 2023 to 2033.

Attributes	Details
Market CAGR (2023 to 2033)	14.8%
Market Valuation (2023)	US$ 18.3 billion
Market Valuation (2033)	US$ 73.1 billion

How are the Various Regions Affecting the Growth of Digital Map in the Market?

Countries	Current Market Share 2023
United States	16.5%
Germany	9.1%
Japan	7.1%
Australia	3.5%

Countries	Current Market CAGR 2023
China	16.7%
India	18.7%
United Kingdom	15.4%

Scope of Report

Attributes	Details
Forecast Period	2023 to 2033
Historical Data Available for	2018 to 2022
Market Analysis	US$ billion for Value
Key Countries Covered	United States, United Kingdom, Japan, India, China, Australia, Germany
Key Segments Covered	Type Application Region
Key Companies Profiled	TomTom NV HERE Technologies Apple Inc. Alibaba Group Navinfo Co., Ltd INRIX Inc. Baidu, Inc. MapBox Inc. Environmental Systems Research Institute (ESRI) Alphabet Inc.
Report Coverage	Market Forecast, Company Share Analysis, Competition Intelligence, DROT Analysis, Market Dynamics and Challenges, and Strategic Growth Initiatives
Customization & Pricing	Available upon Request

Compare Analysis is a configurable app template with the ability to display and compare up to four web maps at a time. This app relates web map content side by side for visual analysis. The first web map chosen in the app controls the extent of the succeeding web maps. Use CasesCompare Analysis provides the ability to do side-by-side comparison of several maps.Use this app to present the results from a variety of different analytic methods.Show the difference between household income in multiple places, or the difference between household income and home values in a single location.Configurable OptionsCompare Analysis can be used to present content from a web maps and configured using the following options:Select up to four maps to be presented within the app.Enable a side panel that can contain custom text and a custom title. If included it can be opened or closed on load.Configure place searching and limit search to the current map extent.Select text color and side panel background color.Include a home extent button for returning to the original web map’s default extent.Supported DevicesThis application is responsively designed to support use in browsers on desktops and tablets.Data RequirementsThis application has no data requirements.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to create a web appOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

Analysis of ‘Wicklow Mountains National Park Story Map Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/1ee94bdc-c026-472c-93f4-c975e4f3f75b on 18 January 2022.

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

This dataset shows points of interest around Wicklow Mountains National Park, which have been included in an online mapping application - Wicklow Mountains Story Map Tour.

CSV file contains points of interest in Wicklow Mountains National Park, along with descriptions and coordinates (Irish Transverse Mercator, Irish Grid and WGS84). Zip folder contains the images used in the Story Map.

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

This online map tool allows users to review the various data sets of the Market Value Analysis from the city-wide view down to the block group level. This analysis incorporates data from 2016-2017. Download is available for the polygons with the cluster letter and underlying variables as an attached zipped shapefile below. Column titles are explained further in the metadata file that is available as well.

The global digital indoor map market is experiencing robust growth, driven by the increasing adoption of indoor navigation systems in various sectors. The market size is projected to reach $XXX million by 2033, expanding at a CAGR of XX% over the forecast period of 2025-2033. Key drivers include the rising demand for indoor mapping solutions in public sector agencies and retail establishments, the integration of advanced technologies such as augmented reality and artificial intelligence, and the growing emphasis on indoor safety and security measures. Segmentation of the digital indoor map market reveals distinct application areas including automotive navigation, mobile and the internet, public sector agencies and enterprises, and others. Types of digital indoor maps available in the market include retail indoor maps, airport indoor maps, and others. Geographic regions considered in the analysis include North America, South America, Europe, Middle East & Africa, and Asia Pacific. Leading companies operating in the digital indoor map market include WoNoBo, Bing Maps, GeoMapserver, MapQuest, ArcGIS Online, and Yahoo! Maps, among others.

The Global Digital Map Market is segmented by Solution (Software, Services), Deployment (On-Premise, Cloud), Industry (Automotive, Engineering & Construction, Logistics & Transportation, Energy & Utilities, Telecommunication), and Geography (North America, Europe, Asia-Pacific, Rest of the world). The market sizes and forecasts are provided in terms of value (USD million) for all the above segments.

This online map tool allows users to review the various data sets of the Market Value Analysis from the city-wide view down to the block group level. Download is available for the polygons with the cluster letter as an attached zipped shapefile below.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

The global HD Maps market size was valued at USD 2.20894 billion in 2025 and is projected to expand at a CAGR of 25.33% from 2025 to 2033, reaching a market size of USD 11.30504 billion by 2033. HD maps are detailed digital representations of the road network, including information such as lane markings, traffic signs, and road geometry. They are used by autonomous vehicles to navigate and make decisions. The growth of the HD Maps market is being driven by several factors, including the increasing adoption of autonomous vehicles, the rising demand for mapping data from other industries, and the growing popularity of ride-hailing services. Additionally, the development of new technologies, such as LiDAR and computer vision, is also contributing to the growth of the market. The market is segmented by component (hardware, software, and services), deployment (on-cloud and on-premise), end-user (automotive, defense & aerospace, internet service providers, and others), and region. The automotive segment is the largest end-user of HD maps, and this is expected to continue in the future. The Asia-Pacific region is the largest regional market for HD maps, followed by North America and Europe. Recent developments include: May 2019, AutoNavi, an Alibaba-backed online mapping cable operator, started charging its car partners for the use of its elevated or HD map, which really is essential for self-driving options. When it comes to licensing HD maps, the business charges RMB 100 (USD 15) per vehicle each year, which captures the surrounding area with centimeter-level precision as opposed to conventional online map apps, which record the nearby region with meter-level precision.. Key drivers for this market are: Increasing Investment by Start-UPS in Development of HD Maps, Growing Trend of Autonomous Cars. Potential restraints include: Limited Standardization in HD Maps.

Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use in landscape-scale analysis.Dataset SummaryThis layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in the Global Lithological Map and the Global Landcover Map. The layer was created by Esri in 2014.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Analysis of ‘Street Sweeping - 2019 - Map’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c46ba9d0-5d4f-47b8-bafa-6bafe2d5b709 on 12 February 2022.

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

Street sweeping zones by Ward and Ward Section Number. For the corresponding schedule, see https://data.cityofchicago.org/d/k737-xg34.

For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP.

The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

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

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The global 3D mapping and modeling market is expected to grow significantly in the next few years as demand increases for detailed and accurate representations of physical environments in three-dimensional space. Estimated to be valued at USD 38.62 billion in the year 2025, the market was expected to grow at a CAGR of 14.5% from 2025 to 2033 and was estimated to reach an amount of USD 90.26 billion by the end of 2033. The high growth rate is because of improvement in advanced technologies with the development of high-resolution sensors and methods of photogrammetry that make possible higher-resolution realistic and immersive 3D models.Key trends in the market are the adoption of virtual and augmented reality (VR/AR) applications, 3D mapping with smart city infrastructure, and increased architecture, engineering, and construction utilization of 3D models. Other factors are driving the growing adoption of cloud-based 3D mapping and modeling solutions. The solutions promise scalability, cost-effectiveness, and easy access to 3D data, thus appealing to business and organizations of all sizes. Recent developments include: Jun 2023: Nomoko (Switzerland), a leading provider of real-world 3D data technology, announced that it has joined the Overture Maps Foundation, a non-profit organization committed to fostering collaboration and innovation in the geospatial domain. Nomoko will collaborate with Meta, Amazon Web Services (AWS), TomTom, and Microsoft, to create interoperable, accessible 3D datasets, leveraging its real-world 3D modeling capabilities., May 2023: The Sanborn Map Company (Sanborn), an authority in 3D models, announced the development of a powerful new tool, the Digital Twin Base Map. This innovative technology sets a new standard for urban analysis, implementation of Digital Cities, navigation, and planning with a fundamental transformation from a 2D map to a 3D environment. The Digital Twin Base Map is a high-resolution 3D map providing unprecedented detail and accuracy., Feb 2023: Bluesky Geospatial launched the MetroVista, a 3D aerial mapping program in the USA. The service employs a hybrid imaging-Lidar airborne sensor to capture highly detailed 3D data, including 360-degree views of buildings and street-level features, in urban areas to create digital twins, visualizations, and simulations., Feb 2023: Esri, a leading global provider of geographic information system (GIS), location intelligence, and mapping solutions, released new ArcGIS Reality Software to capture the world in 3D. ArcGIS Reality enables site, city, and country-wide 3D mapping for digital twins. These 3D models and high-resolution maps allow organizations to analyze and interact with a digital world, accurately showing their locations and situations., Jan 2023: Strava, a subscription-based fitness platform, announced the acquisition of FATMAP, a 3D mapping platform, to integrate into its app. The acquisition adds FATMAP's mountain-focused maps to Strava's platform, combining with the data already within Strava's products, including city and suburban areas for runners and other fitness enthusiasts., Jan 2023: The 3D mapping platform FATMAP is acquired by Strava. FATMAP applies the concept of 3D visualization specifically for people who like mountain sports like skiing and hiking., Jan 2022: GeoScience Limited (the UK) announced receiving funding from Deep Digital Cornwall (DDC) to develop a new digital heat flow map. The DDC project has received grant funding from the European Regional Development Fund. This study aims to model the heat flow in the region's shallower geothermal resources to promote its utilization in low-carbon heating. GeoScience Ltd wants to create a more robust 3D model of the Cornwall subsurface temperature through additional boreholes and more sophisticated modeling techniques., Aug 2022: In order to create and explore the system's possibilities, CGTrader worked with the online retailer of dietary supplements Hello100. The system has the ability to scale up the generation of more models, and it has enhanced and improved Hello100's appearance on Amazon Marketplace.. Key drivers for this market are: The demand for 3D maps and models is growing rapidly across various industries, including architecture, engineering, and construction (AEC), manufacturing, transportation, and healthcare. Advances in hardware, software, and data acquisition techniques are making it possible to create more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations.

. Potential restraints include: The acquisition and processing of 3D data can be expensive, especially for large-scale projects. There is a lack of standardization in the 3D mapping modeling industry, which can make it difficult to share and exchange data between different software and systems. There is a shortage of skilled professionals who are able to create and use 3D maps and models effectively.. Notable trends are: 3D mapping and modeling technologies are becoming essential for a wide range of applications, including urban planning, architecture, construction, environmental management, and gaming. Advancements in hardware, software, and data acquisition techniques are enabling the creation of more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations..

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Analysis of ‘SFMTA Projects - Points’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4369ebc7-297f-446a-81fe-c9d67c126ec7 on 27 January 2022.

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

A. SUMMARY This dataset is the data behind the Interactive Project Map (IPM) Collector Tool web map. The IPM web map gathers spatial data for agency projects that have project pages on SFMTA.com and CIP projects.

For more information about this dataset please contact ProjectMap@sfmta.com

B. METHODOLOGY Data is collected from project managers and SFMTA.com project page editors through the Interactive Project Map Collector Tool.

C. UPDATE FREQUENCY The data is updated on an as needed basis. Project geographies should be updated by project managers and project pge editors whenever project extents change.

D. OTHER CRITICAL INFO Please note there is a 250ft buffer for projects to more accurately represent the supervisor districts and neighborhoods that could potentially be affected by a project.

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

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

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Digital Map Market Size 2024-2028

The digital map market size is forecast to increase by USD 19.75 billion at a CAGR of 26.06% between 2023 and 2028.

What will be the Size of the Digital Map Market During the Forecast Period?

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The market In the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries. The proliferation of connected devices, including PDAs, Cortana, Siri, Amazon Echo, and Google Now, has increased the demand for digital maps in real-time mapping applications and map analytics. Real-time tracking systems are gaining popularity in sectors such as energy & power, automobile, telecommunication, and transportation, providing valuable spatial data on terrain, roads, buildings, rivers, and other features. APIs enable seamless integration of digital maps into various applications, enhancing user experience and ROI.
The internet has made digital maps accessible from anywhere, further fueling market growth. Overall, the market is poised for significant expansion, offering numerous opportunities for businesses and innovators alike.

How is this Digital Map Industry segmented and which is the largest segment?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Application Insights

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

Digital maps play a crucial role in various industries, particularly in automotive applications for driver assistance systems. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. The increasing use of connected cars and the development of Long-Term Evolution (LTE) technologies are driving the demand for digital maps. These maps provide real-time traffic information, helping drivers navigate urban areas with high population density and traffic congestion more efficiently. Additionally, digital maps are essential for transportation route planning, public services, agriculture, and conservation efforts. In agriculture, digital maps help determine soil types, nutrient levels, and crop yields.

Waste reduction and the protection of sensitive ecosystems and habitats are also facilitated by digital maps. Overall, digital maps offer valuable insights for urban planning, emergency situations, and various industries, making them an indispensable tool for businesses and individuals alike.

Get a glance at the Digital Map Industry report of share of various segments. Request Free Sample

The navigation segment was valued at USD 4.58 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 43% 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 the Asia-Pacific (APAC) region, the market for digital maps is experiencing growth due to the increasing use of Internet of Things (IoT) devices and real-time mapping technologies. Countries such as Japan, China, and South Korea, along with a few Southeast Asian nations, are key contributors to this market expansion. IoT devices, including GPS-enabled PDAs, professional assistants, and smart home devices, are being integrated into digital maps to provide real-time data. This data can be used to develop real-time dashboards, enabling organizations and local governments to effectively manage traffic, monitor oil field equipment, and more.

The growing digital connectivity landscape in APAC is fueling the demand for digital maps and related technologies, including APIs, SDKs, and mapping solutions from providers such as Nearmap, ESRI, and INRIX.

Digital Map Market Dynamics

Our digital map market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise In the adoption of Digital Map Industry?

Adoption of intelligent PDAs is the key driver of the market.

The markets encompass a range of advanced technologies and applications that leverage Geographic Information Systems (

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.Coordinate System: Web Mercator Auxiliary Sphere 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 Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Number of Features: 3,035,617 flowlines, 473,936 waterbodies, 16,658 sinksSource: EPA and USGSPublication 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 Feature 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.

Analysis of ‘Riparian - ACE [ds2724]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d1dd829b-7507-4bb6-acb6-dd710e8fa723 on 28 January 2022.

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

For more information, see the Terrestrial Significant Habitats Factsheet at https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=150834. The California Department of Fish and Wildlife''s (CDFW) Areas of Conservation Emphasis (ACE) is a compilation and analysis of the best-available statewide spatial information in California on biodiversity, rarity and endemism, harvested species, significant habitats, connectivity and wildlife movement, climate vulnerability, climate refugia, and other relevant data (e.g., other conservation priorities such as those identified in the State Wildlife Action Plan (SWAP), stressors, land ownership). ACE addresses both terrestrial and aquatic data. The ACE model combines and analyzes terrestrial information in a 2.5 square mile hexagon grid and aquatic information at the HUC12 watershed level across the state to produce a series of maps for use in non-regulatory evaluation of conservation priorities in California. The model addresses as many of CDFWs statewide conservation and recreational mandates as feasible using high quality data sources. High value areas statewide and in each USDA Ecoregion were identified. The ACE maps and data can be viewed in the ACE online map viewer, or downloaded for use in ArcGIS. For more detailed information see https://www.wildlife.ca.gov/Data/Analysis/ACEand https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=24326.

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

Analysis of ‘Setting’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1b74851e-676e-4e0f-aa97-90874676fef9 on 27 January 2022.

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

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