Abstract: In Brazil, the LDB - Law of Guidelines and Bases nº. 9394(Brazil, 1996) and the PCN - National Curricular Parameters, determines that the Geography discipline is recognized as autonomous and should not be understood as a complement to other disciplines. In this way, the improvement in Geography teaching passes through cartographic literacy. The focus is on offering the student the capacity to carry out the appropriation, analysis, reflection and criticism on geographical space. In this way, this paper presents a resource that consisted of the development of the application called MapVoice. The purpose of the software is to enable Blind or visually impaired students, from basic education, in the learning of Cartography in Geography classes. MapVoice provides the understanding and interpretation of physical environments transformed into thematic maps based on data from the 2010 Brazilian Demographic Census executed by IBGE. The software used sound and image resources developed for Windows environment. The research concludes that it is necessary to prepare the infrastructure of the schools for the reception of these students, but mainly the continuing training of teachers and teaching assistants. Mapvoice was tested at the Institute of the Blind for validation, achieving a satisfactory result and making enthusiasm for the development of new researches.

According to Cognitive Market Research, the global electronic cartography market size is USD 26.94 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 9.49% from 2024 to 2031. Market Dynamics of Electronic Cartography Market

Key Drivers for Electronic Cartography Market

Rising use of Smartphones and IoT - The prominent factor that drives the market growth include the widespread use of smartphones, tablets, and electronic devices. In addition rise in the usage of Internet of Things (IoT) devices, heightened the demand for real-time mapping solutions, consequently driving the demand for the electronic cartography market. In addition, growing dependence on location-based services (LBS), Geographic Information Systems (GIS), and GPS applications for searching nearby theatre halls, gasoline stations, restaurants, urban planning, disaster management, is another factor that drives the demand for electronic cartography during the forecast period.
The increasing need for real-time data mapping to create precise and current digital representations, combined with the capability to analyze and visualize streaming data from sensors, devices, and social media feeds, is expected to propel market growth.

Key Restraints for Electronic Cartography Market

Integrating geographic,and geo-social data from different sources, such as social media and satellite imagery, can be challenging due to differences in data formats and scales.
Lack of expertise among users regarding the adoption of electronic cartography in marine industry may hampered the market growth

Introduction of the Electronic Cartography Market

Electronic cartography is a technology that allows to simulate the surrounding area with the help of special technical means and computer programs. Electronic cartography integrated with various processes such as data processing, data acquisitions, map distribution, and map creation. As the demand for topographical information systems grows, the deployment of digital mapping has grown in the government and public sectors. The Science & Technology Directorate (S&T), in May 2024,has launched a digital indoor map navigator Mappedin. This digital indoor map navigator transform floor plans into interactive and easily maintainable digitized maps, and is currently being used by both response agencies and corporate clients. Mappedin provides high-quality 3D map creation, easy-to-use mapping tools and data, map sharing, and data maintenance, to city executives, building owner operators and first responders to make and deliver maps for a variety of safety-related situations—from advance preparation and planning to assistance during emergency incidents. Additionally the rapid rise in the number of smartphone and internet users has fueled industry expansion. Additionally, the increasing number of connected and semi-autonomous vehicles along with anticipated advancements in self-driving and navigation technologies, are expected to boost the demand for electronic cartography market.

This layer shows Computers and Internet Use. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the 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 Percentage of Households with a Broadband Internet Subscription. 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: 2018-2022ACS Table(s): DP02, S2801Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2022National 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. 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:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Cartography Software Market Outlook

According to our latest research, the global cartography software market size reached USD 2.15 billion in 2024, driven by increasing demand for advanced mapping solutions across diverse sectors. The market is expected to expand at a CAGR of 9.2% between 2025 and 2033, with the market size forecasted to reach USD 4.79 billion by 2033. This robust growth is primarily attributed to rapid urbanization, the proliferation of geospatial data, and growing integration of GIS technologies in government and commercial applications.

The primary growth factor propelling the cartography software market is the accelerating adoption of geospatial intelligence and geographic information systems (GIS) across various sectors. Governments, urban planners, and commercial enterprises are increasingly leveraging cartography software for enhanced decision-making, spatial data visualization, and resource management. The surge in smart city initiatives and infrastructure development projects worldwide is further boosting demand for sophisticated mapping tools. These tools enable stakeholders to visualize complex datasets, analyze spatial relationships, and optimize planning processes, thereby improving efficiency and reducing operational costs.

Another significant driver is the technological evolution within the cartography software landscape. The integration of artificial intelligence, machine learning, and cloud computing has transformed traditional mapping solutions into dynamic, interactive, and real-time platforms. These advancements have broadened the application scope of cartography software, making it indispensable in fields such as disaster management, environmental monitoring, and business intelligence. The ability to process large volumes of geospatial data quickly and accurately has enhanced the value proposition of cartography solutions, attracting investments from both public and private sectors.

Furthermore, the growing need for disaster risk management and environmental monitoring is catalyzing the adoption of cartography software. Governments and humanitarian organizations are increasingly utilizing these tools to map vulnerable areas, monitor climate change impacts, and plan emergency response strategies. The software’s capability to provide real-time situational awareness and predictive analytics is critical in mitigating risks and enhancing preparedness. As climate-related challenges intensify, the reliance on advanced cartographic solutions is expected to deepen, further fueling market growth.

From a regional perspective, North America currently dominates the cartography software market, supported by substantial investments in geospatial infrastructure and a high concentration of technology-driven enterprises. However, Asia Pacific is poised for the fastest growth, driven by rapid urbanization, expanding infrastructure projects, and increasing government focus on smart city development. Europe also holds a significant share, benefiting from robust regulatory frameworks and widespread adoption of GIS technologies across various sectors. The Middle East & Africa and Latin America are emerging as promising markets, with growing awareness of the benefits of digital mapping in resource management and urban planning.

Component Analysis

The cartography software market by component is bifurcated into software and services. The software segment captures the largest market share, accounting for over 65% in 2024, owing to the widespread adoption of advanced mapping solutions across government, commercial, and utility sectors. Modern cartography software platforms offer comprehensive features such as data visualization, spatial analysis, and real-time collaboration, making them indispensable tools for urban planners, environmental agencies, and businesses. The proliferation of open-source platforms and the availability of customizable mapping solutions have further accelerated the adoption of cartography software globally.
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Group of movement trajectories of couriers operating in London, UK.

This dataset is a collection of courier trajectories, captured principally around London over a continuous eight week period. This is a useful example of real movement trajectories, which could potentially be used for benchmarking or for the development of spatio-temporal analytics. In addition to the principle data file,which contains 9,917,703 discrete data points, sixteen different spatial and temporal summaries have been included in the related experiment to aid analysis.

The Audio Cartography project investigated the influence of temporal arrangement on the interpretation of information from a simple spatial data set. I designed and implemented three auditory map types (audio types), and evaluated differences in the responses to those audio types.

The three audio types represented simplified raster data (eight rows x eight columns). First, a "sequential" representation read values one at a time from each cell of the raster, following an English reading order, and encoded the data value as loudness of a single fixed-duration and fixed-frequency note. Second, an augmented-sequential ("augmented") representation used the same reading order, but encoded the data value as volume, the row as frequency, and the column as the rate of the notes play (constant total cell duration). Third, a "concurrent" representation used the same encoding as the augmented type, but allowed the notes to overlap in time.

Participants completed a training session in a computer-lab setting, where they were introduced to the audio types and practiced making a comparison between data values at two locations within the display based on what they heard. The training sessions, including associated paperwork, lasted up to one hour. In a second study session, participants listened to the auditory maps and made decisions about the data they represented while the fMRI scanner recorded digital brain images.

The task consisted of listening to an auditory representation of geospatial data ("map"), and then making a decision about the relative values of data at two specified locations. After listening to the map ("listen"), a graphic depicted two locations within a square (white background). Each location was marked with a small square (size: 2x2 grid cells); one square had a black solid outline and transparent black fill, the other had a red dashed outline and transparent red fill. The decision ("response") was made under one of two conditions. Under the active listening condition ("active") the map was played a second time while participants made their decision; in the memory condition ("memory"), a decision was made in relative quiet (general scanner noises and intermittent acquisition noise persisted). During the initial map listening, participants were aware of neither the locations of the response options within the map extent, nor the response conditions under which they would make their decision. Participants could respond any time after the graphic was displayed; once a response was entered, the playback stopped (active response condition only) and the presentation continued to the next trial.

Data was collected in accordance with a protocol approved by the Institutional Review Board at the University of Oregon.

• Additional details about the specific maps used in this are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

• Details of the design process and evaluation are provided in the associated dissertation, which is available from ProQuest and University of Oregon's ScholarsBank.

• Scripts that created the experimental stimuli and automated processing are available through University of Oregon's ScholarsBank (DOI 10.7264/3b49-tr85).

Preparation of fMRI Data

Conversion of the DICOM files produced by the scanner to NiFTi format was performed by MRIConvert (LCNI). Orientation to standard axes was performed and recorded in the NiFTi header (FMRIB, fslreorient2std). The excess slices in the anatomical images that represented tissue in the next were trimmed (FMRIB, robustfov). Participant identity was protected through automated defacing of the anatomical data (FreeSurfer, mri_deface), with additional post-processing to ensure that no brain voxels were erroneously removed from the image (FMRIB, BET; brain mask dilated with three iterations "fslmaths -dilM").

Preparation of Metadata

The dcm2niix tool (Rorden) was used to create draft JSON sidecar files with metadata extracted from the DICOM headers. The draft sidecar file were revised to augment the JSON elements with additional tags (e.g., "Orientation" and "TaskDescription") and to make a more human-friendly version of tag contents (e.g., "InstitutionAddress" and "DepartmentName"). The device serial number was constant throughout the data collection (i.e., all data collection was conducted on the same scanner), and the respective metadata values were replaced with an anonymous identifier: "Scanner1".

Preparation of Behavioral Data

The stimuli consisted of eighteen auditory maps. Spatial data were generated with the rgeos, sp, and spatstat libraries in R; auditory maps were rendered with the Pyo (Belanger) library for Python and prepared for presentation in Audacity. Stimuli were presented using PsychoPy (Peirce, 2007), which produced log files from which event details were extracted. The log files included timestamped entries for stimulus timing and trigger pulses from the scanner.

• Log files are available in "sourcedata/behavioral".
• Extracted event details accompany BOLD images in "sub-NN/func/*events.tsv".
• Three column explanatory variable files are in "derivatives/ev/sub-NN".

References

Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. The name Audacity® is a registered trademark of Dominic Mazzoni.

FMRIB (Functional Magnetic Resonance Imaging of the Brain). FMRIB Software Library (FSL; fslreorient2std, robustfov, BET). Oxford, v5.0.9, Available: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/

FreeSurfer (mri_deface). Harvard, v1.22, Available: https://surfer.nmr.mgh.harvard.edu/fswiki/AutomatedDefacingTools)

LCNI (Lewis Center for Neuroimaging). MRIConvert (mcverter), v2.1.0 build 440, Available: https://lcni.uoregon.edu/downloads/mriconvert/mriconvert-and-mcverter

Peirce, JW. PsychoPy–psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2):8 – 13, 2007. Software Available: http://www.psychopy.org/

Python software is copyright © 2001-2015 Python Software Foundation. Web site: https://www.python.org

Pyo software is copyright © 2009-2015 Olivier Belanger. Web site: http://ajaxsoundstudio.com/software/pyo/.

R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available: https://www.R-project.org/.

rgeos software is copyright © 2016 Bivand and Rundel. Web site: https://CRAN.R-project.org/package=rgeos

Rorden, C. dcm2niix, v1.0.20171215, Available: https://github.com/rordenlab/dcm2niix

spatstat software is copyright © 2016 Baddeley, Rubak, and Turner. Web site: https://CRAN.R-project.org/package=spatstat

sp software is copyright © 2016 Pebesma and Bivand. Web site: https://CRAN.R-project.org/package=sp

Vector datasets in the ESRI's Shapefile format. Location: Izu-Oshima island, Tokyo, Japan Projection: EPSG-4301-Tokyo Elevation interval: e10 (10m), e25 (25m)

The data records a single route taken through the suburb of St Lucia, Queensland, Australia. The route was traversed at five different times of the day to capture the difference in appearance between early morning and late afternoon. The route was traversed again, another five times, two weeks later for a total of ten datasets.

The data was recorded with a forward facing webcam attached to the roof of a car.

GPS data is included for each dataset.

Each dataset is labelled with the date and time it was collected in the following format DD/MM/YY_24HOUR. Each dataset has 5 files.

• (webcam_video.avi) video imagery. This file is compressed and no uncompressed version is available.
• (gps_log.txt) raw GPS data as logged
• (frame_log.txt) time stamps of each video frame compatible with GPS data
• (fGPS.txt) processed version of GPS and frame time stamps providing the GPS point for each frame in the video file
• (fGPS.mat) as above, saved for easy importation to MATLAB

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. 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.

How is this Digital Map Industry segmented?

The digital map 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.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

Dataset

This dataset was created by K Scott Mader

Contents

The Cloud GIS market is booming, projected to reach $1513.8 million by 2025 with a 17.2% CAGR. Discover key drivers, trends, and leading companies shaping this rapidly evolving sector. Explore market forecasts, regional insights, and the future of cloud-based geographic information systems.

This U.S. Geological Survey (USGS) data release presents a digital database of geospatially enabled vector layers and tabular data transcribed from the geologic map of the Lake Owen quadrangle, Albany County, Wyoming, which was originally published as U.S. Geological Survey Geologic Quadrangle Map GQ-1304 (Houston and Orback, 1976). The 7.5-minute Lake Owen quadrangle is located in southeastern Wyoming approximately 25 miles (40 kilometers) southwest of Laramie in the west-central interior of southern Albany County, and covers most of the southern extent of Sheep Mountain, the southeastern extent of Centennial Valley, and a portion of the eastern Medicine Bow Mountains. This relational geodatabase, with georeferenced data layers digitized at the publication scale of 1:24,000, organizes and describes the geologic and structural data covering the quadrangle's approximately 35,954 acres and enables the data for use in spatial analyses and computer cartography. The data types presented in this release include geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS) developed and released by the U.S. Geological Survey's National Cooperative Geologic Mapping Program (GeMS, 2020). When reconstructed from the geodatabase's vector layers and tabular data that has been symbolized according to specifications encoded in the accompanying style file, and using the supplied Federal Geographic Data Committee (FGDC) GeoAge font for labeling formations and GeoSym fonts for structural line decorations and orientation measurement symbols, this data release presents the Geologic Map as shown on the published GQ-1304 map sheet. These GIS data augment but do not supersede the information presented on GQ-1304. References: Houston, R.S., and Orback, C.J., 1976, Geologic Map of the Lake Owen Quadrangle, Albany County, Wyoming: U.S. Geological Survey Geologic Quadrangle Map GQ-1304, scale 1:24,000, https://doi.org/10.3133/gq1304. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema)- A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

A web map displaying a series of Esri Living Atlas feature services added as items that pertain to poverty and Internet and Computer access for Broward County and its Census Tracts. The web map is used to analyze computer and internet access by the Black/African race category and poverty.

According to Cognitive Market Research, the global Digital Maps market size was USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.

The global Digital Maps market will expand significantly by XX% CAGR between 2024 to 2031.


North America held the major market of more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.


Europe accounted for a share of over XX% of the global market size of USD XX million.


Asia Pacific held a market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.


Latin America's market will have more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.


Middle East and Africa held the major market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.


The Tracking and Telematics segment is set to rise GPS tracking enables fleet managers to monitor their cars around the clock, avoiding expensive problems like speeding and other careless driving behaviors like abrupt acceleration. 


The digital maps market is driven by mobile computing devices that are increasingly used for navigation, and the increased usage of geographic data.


The retail and real estate segment held the highest Digital Maps market revenue share in 2023.

Market Dynamics of Digital Maps:

Key drivers of the Digital Maps Market

Mobile Computing Devices Are Increasingly Used for Navigation leading to market expansion-

Since technology is changing rapidly, two categories of mobile computing devices—tablets and smartphones—are developing and becoming more diverse. One of the newest features accessible in this category is map software, which is now frequently preinstalled on smartphones. Meitrack Group launched the MD500S, a four-channel AI mobile DVR, for the first time in 2022. The MD500S is a 4-channel MDVR with excellent stability that supports DMS, GNSS tracking, video recording, and ADAS. Source- https://www.meitrack.com/ai-mobile-dvr/#:~:text=Mini%204CH%20AI%20Mobile%20DVR,surveillance%20solutions%20that%20uses%20H.

It's no secret that people who own smartphones routinely use built-in mapping apps to find directions and other driving assistance. Furthermore, these individuals use georeferenced data from GPS and GIS apps to find nearby establishments such as cafes, movie theatres, and other sites of interest. Mobile computing devices are now commonly used to acquire accurate 3D spatial information. A personal digital assistant (PDA) is a software agent that uses information from the user's computer, location, and various web sources to accomplish tasks or offer services. Thus, mobile computing devices are increasingly used for navigation leading to market expansion.

The usage of geographic data has increased leading to market expansion-

Since it is used in so many different industries and businesses—including risk and emergency management, infrastructure management, marketing, urban planning, resource management (oil, gas, mining, and other resources), business planning, logistics, and more—geospatial information has seen a boom in recent years. Since location is one of the essential components of context, geo-information also serves as a basis for applications in the future. For example, Atos SE provides services to companies in supply chain management, data centers, infrastructure development, urban planning, risk and emergency management, navigation, and healthcare by utilizing geographic information system (GIS) platforms with location-based services (LBS).

Furthermore, augmented reality-based technologies make use of 3D platforms and GIS data to offer virtual information about people and their environment. Businesses can offer users customized ads by using this information to better understand their needs.Thus, the usage of geographic data has increased leading to market expansion.

Restraints of the Digital Maps Market

Lack of knowledgeable and skilled technicia...

The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules. A Map Sentiment Dataset with five discrete emotions is constructedMap emotional semantics are classified by deep learning approachesVisual variables Influencing map sentiment are analyzed. A Map Sentiment Dataset with five discrete emotions is constructed Map emotional semantics are classified by deep learning approaches Visual variables Influencing map sentiment are analyzed.

A web map displaying a series of Esri Living Atlas feature services added as items that pertain to poverty and Internet and Computer access for Broward County and its Census Tracts. The web map is used to analyze computer and internet access by the Hispanic/Latino category and poverty.

Our dataset delivers unprecedented scale and diversity for geospatial AI training:

🌍 Massive scale: 165,000 unique 3D map sequences and locations, 82,500,000 images, 0.73 PB of Data, orders of magnitude larger than datasets currently used for SOTA Vision/Spatial Models.

⏱️ Constantly growing dataset: 12k new 3D Map sequences and locations monthly.

📷 Full-frame, high-res captures: OVER retains full-resolution, dynamic aspect-ratio images with complete Exif metadata (GPS, timestamp, device orientation), multiple resolutions 1920x1080 - 3840x2880, pre-computed COLMAP poses.

🧭 Global diversity: Environments span urban, suburban, rural, and natural settings across 120+ countries, capturing architectural, infrastructural, and environmental variety.

📐 Rich metadata: Per-image geolocation (±3 m accuracy), timestamps, device pose, COLMAP pose; per-map calibration data (camera intrinsics/extrinsics).

🧠 Applications: Spatial Models Training, Multi-view stereo & NeRF/3DGS training, semantic segmentation, novel view synthesis, 3D object detection, geolocation, urban planning, AR/VR, autonomous navigation.

GPS Bike Computer Mapping Market Outlook

According to our latest research, the Global GPS Bike Computer Mapping market size was valued at $1.22 billion in 2024 and is projected to reach $3.18 billion by 2033, expanding at a CAGR of 11.2% during 2024–2033. The increasing integration of smart technologies and real-time navigation features in cycling computers stands out as a major driver fueling the growth of this market globally. As cyclists, both professional and recreational, demand more sophisticated mapping and performance tracking capabilities, manufacturers are responding with innovative products that offer advanced connectivity, enhanced durability, and seamless integration with mobile applications. This rapid technological evolution, coupled with the rising popularity of cycling as a fitness and commuting option, is expected to sustain the upward trajectory of the GPS Bike Computer Mapping market over the forecast period.

Regional Outlook

North America currently commands the largest share in the GPS Bike Computer Mapping market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the region's mature cycling culture, high disposable incomes, and widespread adoption of connected fitness devices. The presence of leading manufacturers and robust distribution networks further consolidates North America's leadership. Additionally, supportive government policies promoting eco-friendly transportation and urban cycling infrastructure have spurred the uptake of advanced GPS mapping devices among both professional and recreational cyclists. The region's early adoption of cutting-edge technology and a strong focus on health and wellness trends continue to drive demand for mapping-enabled bike computers.

The Asia Pacific region is emerging as the fastest-growing market, projected to expand at a remarkable CAGR of 13.8% through 2033. Rapid urbanization, increasing health consciousness, and the rising popularity of outdoor sports are key factors boosting market growth in countries like China, Japan, South Korea, and Australia. Significant investments in smart city projects and cycling infrastructure, particularly in metropolitan hubs, are fostering the adoption of advanced cycling gadgets. Local manufacturers are also entering the market with competitively priced products, making GPS bike computers accessible to a broader consumer base. Furthermore, the proliferation of e-commerce platforms and improvements in logistics are enhancing product availability, accelerating the market's expansion in this region.

Emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady rise in the adoption of GPS Bike Computer Mapping solutions. However, these regions face unique challenges such as limited cycling infrastructure, lower consumer awareness, and price sensitivity. Governments are beginning to recognize the benefits of promoting cycling for urban mobility and public health, leading to policy initiatives and infrastructure investments. Nevertheless, the market's growth in these areas is somewhat constrained by inconsistent internet connectivity, limited distribution networks, and a lack of localized product offerings. Overcoming these barriers will be crucial for manufacturers seeking to tap into the latent demand in these emerging markets.

Report Scope

The Traveling Salesperson Problem (TSP) is a class problem of computer science that seeks to find the shortest route between a group of cities. It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/world-tsp.png" alt="World Map">

In this Kaggle competition, your goal is not to find the shortest route among cities. Rather, you must attempt to determine the route labeled on a map.

Calculating Line Distances

The data for this competition is not made up of real-world maps, but rather randomly generated maps of varying attributes of size, city count, and optimality of the routes. The following image demonstrates a relatively small map, with few cities, and an optimal route.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/1.jpg" alt="Small Map">

Not all maps are this small, or contain this optimal a route. Consider the following map, which is much larger.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/6.jpg" alt="Larger Map">

The following attributes were randomly selected to generate each image.

• Height
• Width
• City count
• Cycles of Simulated Annealing optimization of initial random path

The path distance is based on the sum of the Euclidean distance of all segments in the path. The distance units are in pixels.

Dataset Challenges

This is a regression problem, you are to estimate the total path length. Several challenges to consider.

• If you indiscriminately scale the maps, you will lose size information.
• Paths might overlap, causing the ration of total pixels to total length to become misleading.
• As paths overlap bot other path segments and cities, the resulting color becomes brighter.

The following picture shows a section from one map zoomed to the pixel-level:

https://data.heatonresearch.com/images/wustl/kaggle/tsp/tsp_zoom.jpg" alt="TSP Zoom">

CSV Files

The following CSV files are provided, in addition to the images.

• train.csv - Training data, with distance labels.
• test.csv - Test data without distance labels.
• tsp-all.csv - Training and test data combined with complete labels and additional information about each generated map.

CSV File Format

The tsp-all.csv file contains the following data.

id,filename,distance,key
0,0.jpg,83110,503x673-270-83110.jpg
1,1.jpg,1035,906x222-10-1035.jpg
2,2.jpg,20756,810x999-299-20756.jpg
3,3.jpg,13286,781x717-272-13286.jpg
4,4.jpg,13924,609x884-312-13924.jpg

The columns:

• id - A unique ID that allows linking across all three CSV files.
• filename - The name of each map's image file.
• distance - The total distance through the cities, this is the y/label.
• key - The generator filename, provides the dimensions, city count, & distance.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.