As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

The Navigation and Mapping Solutions market is experiencing robust growth, driven by the increasing adoption of location-based services (LBS) across various sectors. The market's expansion is fueled by several key factors, including the proliferation of smartphones equipped with advanced GPS technology, the rising demand for real-time traffic updates and navigation assistance, and the increasing integration of mapping solutions into automotive systems. Furthermore, the development of sophisticated mapping technologies, such as 3D mapping and augmented reality (AR) overlays, is enhancing user experience and driving market penetration. The expanding use of these solutions in logistics and transportation, coupled with the growth of e-commerce and the demand for efficient delivery services, contributes significantly to the market's upward trajectory. We estimate the market size in 2025 to be around $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 12% through 2033. Despite the promising outlook, market growth faces certain challenges. High initial investment costs associated with developing and maintaining advanced mapping systems may limit entry for smaller players. Data privacy concerns and regulatory restrictions regarding data collection and usage pose significant hurdles. The accuracy and reliability of mapping data remain critical factors affecting market adoption, particularly in remote or rapidly changing areas. Competition among established players like Google, TomTom, and Garmin is intense, demanding continuous innovation and strategic partnerships to maintain a competitive edge. Despite these restraints, the long-term prospects for the navigation and mapping solutions market remain positive, driven by ongoing technological advancements and expanding applications across diverse industries.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)

Context

Realising which routes a taxi takes while going from one location to another gives us deep insights into why some trips take longer than others. Also, most taxis rely on navigation from Google Maps, which reinforces the use case of this dataset. On a deeper look, we can begin to analyse patches of slow traffic and number of steps during the trip (explained below).

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Content

The data, as we see it contains the following columns :

• trip_id, pickup_latitude, pickup_longitude (and equivalents with dropoff) are picked up from the original dataset.
• distance : Estimates the distance between the start and the end latitude, in miles.
• start_address and end_address are directly picked up from the Google Maps API
• params : Details set of parameters, flattened out into a single line. (Explained below)

Parameters

The parameters field is a long string of a flattened out JSON object. At its very basic, the field has space separated steps. The syntax is as follows :

Step1:{ ... }, Step2:{ ...

Each step denotes the presence of an intermediate point.

Inside the curly braces of each of the steps we have the distance for that step measured in ft, and the start and end location. The start and end location are surrounded by round braces and are in the following format :

Step1:{distance=X ft/mi start_location=(latitude, longitude) end_location ...}, ...

One can split the internal params over space to get all the required values.

Acknowledgements

All the credit for the data goes to the Google Maps API, though limited to 2000 queries per day. I believe that even that limited amount would help us gain great insights.

Future prospects

• More data : Since the number of rows processed are just 2000, with a good response we might be able to get more. If you feel like contributing, please have a look at the script here and try and run in for the next 2000 rows.

• Driver instructions : I did not include the driver instruction column in the data from the google API as it seemed to complex to use in any kind of models. If that is not the general opinion, I can add it here.

Offline Maps for Travel Market Outlook

According to our latest research, the offline maps for travel market size reached USD 4.21 billion globally in 2024, registering a robust growth trajectory. The market is expected to expand at a CAGR of 11.8% from 2025 to 2033, with the forecasted market value projected to reach USD 11.74 billion by 2033. This significant growth is primarily driven by rising global travel activity, increasing smartphone penetration, and growing concerns over connectivity and data privacy during travel. As per the latest research, the demand for reliable, data-independent navigation solutions is shaping the future of the offline maps for travel market.

One of the primary growth factors propelling the offline maps for travel market is the surge in international and domestic travel, particularly among millennials and Gen Z travelers. With the proliferation of budget airlines, improved visa policies, and the emergence of experiential travel trends, more individuals are exploring remote and off-the-grid destinations where internet connectivity is often unreliable or unavailable. In such scenarios, offline maps become indispensable tools, providing travelers with uninterrupted access to navigation, points of interest, and route planning. Furthermore, the increasing popularity of adventure tourism, including hiking, biking, and camping, is fueling the adoption of offline maps, as travelers seek to navigate challenging terrains with confidence, regardless of network availability.

Another key driver is the advancement in smartphone technology and the integration of sophisticated offline mapping functionalities within mobile applications. Modern navigation apps now offer features such as turn-by-turn directions, offline search, and real-time location tracking without the need for an active data connection. These innovations have significantly enhanced the user experience, making offline maps not only a backup solution but a primary navigation tool for many travelers. Additionally, heightened concerns over data privacy, particularly when using public or unsecured Wi-Fi networks abroad, have led to a preference for offline solutions that minimize data exposure and potential cyber threats. This shift is further supported by the growing awareness among travelers regarding the risks associated with sharing location data with third-party services.

The offline maps for travel market is also benefiting from strategic partnerships and collaborations between map developers, tourism boards, and local governments. Many destinations are now promoting the use of offline maps to enhance visitor experiences, reduce congestion at popular sites, and support sustainable tourism initiatives. For instance, tourism authorities are increasingly offering downloadable maps that highlight eco-friendly routes, cultural landmarks, and local businesses, thereby fostering economic growth within communities. These initiatives not only boost the adoption of offline maps but also align with broader trends in responsible and tech-enabled travel.

From a regional perspective, the Asia Pacific region is emerging as a major growth engine for the offline maps for travel market, driven by the rapid expansion of the travel and tourism sector in countries such as China, India, Japan, and Southeast Asia. The region's vast and diverse geography, coupled with varying levels of internet infrastructure, underscores the need for reliable offline navigation solutions. North America and Europe also continue to hold significant market shares, supported by high smartphone adoption rates, advanced digital ecosystems, and a strong culture of independent travel. Meanwhile, Latin America, the Middle East, and Africa are witnessing increasing uptake of offline maps, spurred by growing mobile internet penetration and the rising popularity of adventure and eco-tourism.

Product Type Analysis

The product type segment of the offline maps for travel market is categorized into navigation apps, downloadable map software, and dedicated GPS devices. Navigation apps have emerged as the leading product type, owing to their widespread availability, user-friendly interfaces, and seamless integration with smartphones. These apps, such as Google Maps, Maps.me, and Sygic, allow users to download maps for offline use, providing essential navigation features even in areas with limited or no connectivity. The convenience of having a comprehensive navigation tool on

India Location-based Services Market size was valued at USD 460 Million in 2024 and is projected to reach USD 1563 Million by 2032, growing at a CAGR of 16.7% from 2026 to 2032.India Location-based Services Market: Definition/ OverviewLocation-based services (LBS) are applications or services that use a user's geographic location to provide personalized content, services, or information. These services typically rely on technologies such as GPS, Wi-Fi, or cellular data to determine the user's position and tailor experiences based on that location. LBS can be offered through mobile apps, websites, or IoT devices, providing users with relevant information or guidance wherever they are.The application of location-based services spans across various industries, from navigation and travel to retail and marketing. For instance, apps like Google Maps or Uber use LBS to offer real-time route guidance, ride-hailing services, and traffic updates. Retailers use LBS for targeted advertising, sending promotional offers to customers when they are near a store. Additionally, LBS are used in healthcare for monitoring patient movement, in logistics for fleet management, and even in social networking apps where users can share their locations with friends.

Discover the explosive growth of the HD Live Map market, projected to reach $1279 million by 2025 with a 24.8% CAGR. This in-depth analysis explores key drivers, trends, and regional market shares, highlighting leading companies and future opportunities in autonomous driving, ADAS, and smart city initiatives.

The Digital Salzburg app is an application that can be used to display coordinate data packets of the city and the state of Salzburg. There are 4 different ways to display information about the data points that are in the data packets: * GPS * NFC * QR code * About the Google map GPS: If there is a data point in the radius defined in the settings and you align your Android device in the direction of the data point, you will be shown the information about this data point. NFC: If you hold your Android device to an NFC tag, the Digital Salzburg app recognizes this and shows you the information about this data point. QR code: If you scan a QR code, which includes a supported format, with the built-in QR code scanner, you will be shown the data on the QR code. About the Google map: When you select a data point marker, a window with the name appears. If you want more information about this data point, press once on this window. Now you will be shown the information about this data point. The Digital Salzburg app can be used with or without data glasses. By default, however, the use of data glasses in the app is disabled.

The Location-Based Services (LBS) market, currently valued at approximately $87.65 billion in 2025, is projected for robust growth over the forecast period (2025-2033). While the exact CAGR is unspecified, considering the rapid technological advancements in mobile devices, AI, and increased data availability, a conservative estimate places the annual growth rate in the range of 12-15%. Key drivers fueling this expansion include the proliferation of smartphones and increased mobile internet penetration, particularly in emerging economies. The rising adoption of IoT devices further contributes to LBS market growth by generating location data from various sources. Furthermore, the increasing demand for personalized experiences and targeted advertising, leveraging location data, is another significant factor driving market expansion. The integration of LBS with other technologies like augmented reality (AR) and virtual reality (VR) is opening up new avenues for innovation and application development, further accelerating market growth. However, challenges remain. Data privacy concerns and regulatory hurdles surrounding the collection and use of location data pose significant restraints. Ensuring data security and user consent are crucial for sustainable growth in this sector. Competitive pressures from established tech giants like Google, Apple, and Facebook, as well as the emergence of innovative start-ups, create a dynamic and competitive landscape. Nevertheless, the long-term outlook for the LBS market remains positive, driven by ongoing technological advancements and the increasing reliance on location intelligence across diverse sectors, including transportation, retail, and healthcare. The market segmentation is likely diverse, encompassing various applications like navigation, location-based advertising, and tracking solutions, each contributing to the overall market value.

The Location-Based Services (LBS) market is experiencing robust growth, driven by the increasing penetration of smartphones, the proliferation of mobile data, and the rising adoption of location-aware applications. The market's expansion is fueled by several key factors, including the growing demand for personalized services, improved navigation systems, enhanced location-based advertising, and the integration of LBS into various industries such as logistics, retail, and healthcare. The increasing availability of high-quality location data and advancements in mapping and positioning technologies also contribute significantly to market growth. Competition among key players like Google, TomTom, and Foursquare is intensifying, leading to continuous innovation and improvements in LBS accuracy, functionality, and user experience. Market segmentation, based on type (e.g., navigation, tracking, advertising) and application (e.g., transportation, retail, emergency services), reveals significant growth opportunities across diverse sectors, with the navigation segment likely dominating due to the widespread use of GPS technology. Geographic expansion is also a prominent feature, with North America and Europe currently holding substantial market shares, followed by the Asia-Pacific region experiencing rapid growth. While the market exhibits considerable potential, certain challenges remain. Data privacy concerns and the need for robust security measures are crucial factors that influence adoption. The accuracy and reliability of location data can also be impacted by factors such as GPS signal limitations in urban areas or indoor environments. Furthermore, regulatory hurdles and the need for interoperability between different LBS providers pose ongoing challenges for the market's seamless expansion. However, ongoing technological advancements in areas like augmented reality (AR) and 5G connectivity are poised to overcome some of these limitations and propel the market towards continued growth and diversification. We estimate a sustained CAGR of around 15% for the forecast period, resulting in significant market expansion over the next decade.

To use the interactive map navigation features on a phone, use the Google app or any web browser other than Safari. Click the box with the two arrows on the map. Then click the circular symbol at the bottom right of the enlarged map. You may be prompted to agree to allow the app to use your location.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Crowdsourced Speed Limit Data Market Outlook

According to our latest research, the global Crowdsourced Speed Limit Data market size stands at USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.51 billion. This impressive growth is primarily driven by the increasing adoption of connected vehicles, advancements in real-time navigation systems, and the rising demand for accurate road and traffic data across various sectors.

One of the primary growth factors fueling the expansion of the Crowdsourced Speed Limit Data market is the proliferation of mobile devices and navigation applications. The widespread usage of smartphones equipped with GPS and location-based services has made it easier than ever to collect and share speed limit data in real time. This democratization of data collection not only enhances the accuracy of mapping platforms but also supports a dynamic ecosystem where users contribute to and benefit from up-to-date road information. Furthermore, the integration of crowdsourced data into popular navigation apps such as Google Maps and Waze has set new standards for user expectations, pushing other industry players to adopt similar approaches and fueling further market growth.

Another significant driver is the rapid development of autonomous and connected vehicles. For autonomous vehicles to operate safely and efficiently, they require access to highly accurate and current speed limit information. Crowdsourced speed limit data, constantly updated by millions of users and vehicles, offers a scalable solution that traditional mapping methods cannot match. Automotive OEMs are increasingly integrating this data into their advanced driver-assistance systems (ADAS) and infotainment platforms, enhancing both safety and user experience. The synergy between automotive innovation and crowdsourced data is expected to remain a key catalyst for market expansion through the forecast period.

In addition, the growing emphasis on traffic management and road safety initiatives by government agencies worldwide is propelling the Crowdsourced Speed Limit Data market. Authorities are leveraging crowdsourced data to enhance their traffic monitoring capabilities, optimize traffic flow, and reduce accident rates. The ability to gather granular, real-time speed limit information from a diverse pool of contributors enables more responsive and data-driven policy decisions. As governments increasingly collaborate with technology providers and automotive OEMs, the adoption of crowdsourced speed limit data is anticipated to accelerate, further strengthening the market’s growth trajectory.

From a regional perspective, North America currently leads the market, closely followed by Europe and the Asia Pacific. The presence of major technology companies, high smartphone penetration, and advanced transportation infrastructure have positioned North America at the forefront of this market. Meanwhile, Europe’s strict regulatory environment and focus on road safety have driven significant adoption across the continent. The Asia Pacific region is emerging as a high-growth market due to rapid urbanization, increasing vehicle ownership, and government investments in smart transportation systems. As these regions continue to innovate and expand their digital ecosystems, their contributions to the global crowdsourced speed limit data market will become even more pronounced.

Data Source Analysis

The Data Source segment is a cornerstone of the Crowdsourced Speed Limit Data market, encompassing mobile applications, navigation devices, automotive OEMs, government platforms, and other sources. Mobile applications represent the largest and fastest-growing sub-segment, thanks to the ubiquity of smartphones and the widespread adoption of GPS-enabled apps. These applications allow users to report and validate speed limits, feeding real-time information into

The road safety app market, currently valued at $239 million in 2025, is projected to experience robust growth, driven by increasing smartphone penetration, rising concerns about road accidents, and the growing adoption of connected car technologies. The 8.4% CAGR indicates a significant expansion over the forecast period (2025-2033). Key drivers include the increasing demand for features like real-time accident reporting, driver behavior monitoring (through features such as speed detection and distraction alerts), and integration with emergency services. Furthermore, the market is witnessing a surge in the adoption of advanced features such as AI-powered driver assistance, proactive safety warnings based on location data, and community-based reporting of hazardous road conditions. The competitive landscape is characterized by a mix of established players like Google Maps and emerging startups focusing on niche functionalities. This competitive environment is fostering innovation and driving the development of more sophisticated and user-friendly apps. Market restraints include data privacy concerns, the potential for app fatigue due to the proliferation of road safety apps, and the need for continuous improvements in user interface and functionality to maintain high adoption rates. The market is segmented by various features (e.g., GPS navigation, driver monitoring, emergency assistance), operating systems, and geographic regions. Future growth will likely be influenced by technological advancements, regulatory changes related to road safety, and public awareness campaigns promoting the usage of such applications. The integration of these apps with autonomous vehicle technology is also a significant emerging trend that will likely significantly impact market growth in the later years of the forecast period. The rising demand for enhanced safety features in developing economies will also contribute to market expansion.

Here are a few use cases for this project:

1. Road Safety Improvement: Government road maintenance departments or highway authorities can use this model to proactively identify and fix road anomalies, thus dramatically improving road safety and comfort for all road users.

2. Autonomous Vehicles: This model could be integrated into the systems of self-driving cars. It would allow these vehicles to accurately detect road anomalies in real-time and navigate around them appropriately, ensuring a safer and smoother journey.

3. Ride-Share Companies: Companies like Uber or Lyft could use this model to gather data on the condition of roads used by their drivers, and then prioritize routes with fewer road anomalies for the comfort and safety of their passengers.

4. Dynamic Navigation and Mapping Apps: Real-time road anomalies detection could be used to update navigation apps like Google Maps or Waze. This would provide real-time alerts about road conditions to users and suggest alternative routes to avoid problematic areas.

5. Infrastructure Maintenance: Urban planners and city maintenance departments could use this model as a tool to monitor urban infrastructure. It would assist in identifying areas requiring maintenance promptly, thus efficiently planning their repair and maintenance schedules.

The current range of services of the POIRadar app includes the following main functionalities * Automatic provision of POIs published via the Open Data Government platform data.gv.at, without the need to constantly update the app for this purpose. * Cross-location display of POIs (no restriction to individual locations; e.g. you will find all published pharmacy locations with the POIRadar app, whether you are in Graz, Linz or in the municipality of Engerwitzdorf...) * Powerful geodata integration based on Google Maps (map display, route calculation, navigation and an animated "map camera" function) * Radar function: Automatic monitoring of currently up to three freely selectable POI categories (e.g. automatic display of all sights within 500m of the current location with ongoing update) * Connection to a powerful cloud service, which keeps both the POI directory structure and the POIs up-to-date in the background. This cloud service is also able to import POIs from different data sources. * Centrally maintained and categorized POI directory, which can be searched in the context of the current location using the POIRadar app. * Possibility to save POIs directly on the mobile device or to share POIs (email, social networks, SMS, etc.) * Native app to be able to use the full power of the respective mobile platform without restrictions and intermediate layers.

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Coal Mines in Illinois Viewer (ILMINES)If you are experiencing issues with interacting with this map, please make sure you have the most up-to-date web browser or try a different web browser. At this time the map may not load properly in the Mobile Google Chrome web browser for android, please try a different web browsing app.Instructions: The Coal Mines in Illinois Viewer illustrates a general depiction of underground mining in the state and will help determine the proximity of coal mines and underground industrial mines to your home or business. Please follow the instructions below for using this viewer and linking to additional map products that contain more information. Read the disclaimer below and click “Okay” when finished. This will bring up the map and search box. In the box that says “Find address or place” enter the address you are looking for and click the magnifying glass to the right or click “enter” on your keyboard. The map will recenter to the location entered. You can also use the navigation tools on the map to navigate to the location you are interested in.Consult the legend on the left for the types of mines displayed. Click on a mine you are interested in. In the box that pops up you will find links to the corresponding Quadrangle and/or County studies for the mine you are looking at. What is the yellow area on the map?Data ExplanationThese data were compiled by the ISGS for known underground and surface coal mines as well as underground industrial mineral mines. For more information including links to coal mine maps and informational directories, coal resource maps, and coal logs please see the County Coal Map Series.The underground coal mine points consist of mine entrances and may also contain uncertain underground mine locations. The underground mine proximity region incorporates coal mines as well as industrial mineral mines, and it was calculated and constructed using the methodology outlined in ISGS Circular 575. These generalized areas are not meant to replace site-specific studies; they conservatively illustrate areas overlying and adjacent to underground coal and industrial mineral mines that may potentially be exposed to subsidence based on 1) angle of draw from the edge of the underground workings up to the land surface, and 2) potential inaccuracy or uncertainty in mine boundary locations. Please see ISGS Circular 575. for a full explanation. Areas outside the proximity region also could be undermined. Old, undocumented mine openings have been discovered in many parts of the state. However, most undocumented mines were prospect pits or short-term operations that undermined only a few acres.The maps and digital files used for this study were compiled from data obtained from a variety of public and private sources and have varying degrees of completeness and accuracy. They present reasonable interpretations of the geology of the area and are based on available data. Locations of some features may be offset by 500 feet or more due to errors in the original source maps, the compilation process, digitizing, or a combination of these factors. These data are not intended for use in site-specific screening or decision-making.If you believe that you have mine subsidence contact your insurance companyand download: Mine Subsidence in Illinois: Facts for Homeowners - Circular 569, 2013, 9 MB PDF fileData DisclaimerThe Illinois State Geological Survey and the University of Illinois make no guarantee, expressed or implied, regarding the correctness of the interpretations presented in this data set and accept no liability for the consequences of decisions made by others on the basis of the information presented here.ISGS Terms of Usehttps://isgs.illinois.edu/terms-useUniversity of Illinois web privacyhttps://www.vpaa.uillinois.edu/resources/web_privacyQuestions about ILMINES/Contact usEmail

Crowdsourced Traffic Data Market Outlook

According to our latest research, the global crowdsourced traffic data market size reached USD 3.12 billion in 2024, driven by the rapid digitalization of transportation systems and rising demand for real-time traffic intelligence. The market is expected to grow at a robust CAGR of 15.7% from 2025 to 2033, projecting a market value of USD 10.61 billion by 2033. This impressive growth is primarily attributed to increasing urbanization, the proliferation of connected devices, and the growing emphasis on smart city initiatives worldwide. As per the latest research, the need for efficient traffic management and improved commuter experiences is fueling widespread adoption of crowdsourced traffic data solutions across various sectors.

The expansion of the crowdsourced traffic data market is significantly propelled by the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics into traffic data platforms. These technologies enable the efficient aggregation and analysis of vast volumes of real-time data sourced from millions of connected devices, including smartphones, GPS units, and vehicle sensors. The continuous evolution of mobile applications and the widespread usage of navigation apps like Google Maps and Waze have contributed to an exponential increase in data points, enhancing the accuracy and reliability of traffic predictions and congestion alerts. As urban populations grow and road networks become more complex, the demand for dynamic, real-time traffic information continues to surge, making crowdsourced data an indispensable asset for both public and private sector stakeholders.

Another critical growth factor is the increasing collaboration between public agencies and private technology providers. Governments and transportation authorities are recognizing the value of crowdsourced data in optimizing traffic flow, reducing congestion, and improving road safety. By leveraging data from diverse sources, these entities can make more informed decisions regarding infrastructure investments, emergency response, and urban planning. The shift towards data-driven governance is further supported by policy frameworks that encourage open data sharing and public-private partnerships. This collaborative ecosystem not only accelerates innovation but also ensures that traffic management solutions are scalable, adaptable, and responsive to evolving urban mobility needs.

The proliferation of Internet of Things (IoT) devices and the deployment of 5G networks are also playing a pivotal role in expanding the market. High-speed connectivity and ubiquitous sensor networks enable the seamless transmission and integration of traffic data from various sources, including vehicles, roadside sensors, and wearable devices. This interconnected infrastructure supports the development of intelligent transportation systems (ITS) capable of real-time monitoring, predictive analytics, and automated incident detection. As cities worldwide invest in smart infrastructure and digital transformation, the adoption of crowdsourced traffic data solutions is expected to become even more widespread, driving sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the global crowdsourced traffic data market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high penetration of smartphones, advanced transportation networks, and supportive regulatory environments in these regions have contributed to the early adoption and rapid expansion of crowdsourced traffic data solutions. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing accelerated growth due to urbanization, increased investments in smart city projects, and rising demand for efficient traffic management systems. The Middle East and Africa are also showing promising potential as governments prioritize digital transformation and infrastructure modernization initiatives.

Data Source Analysis

The data source segment is a cornerstone of the crowdsourced traffic data market, encompassing mobile applications, GPS devices, social media, sensors, and other emerging technologies. Mobile applications represent the largest and most dynamic data source, primarily due to the ubiquity of smartphones and the popularity of navigation and ride-sharing apps. These applications continuously gather