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.

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

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.

Context

This data was made available on April 14th by Apple as an effort to expand the available data for the COVID response. The data is then augmented with some geography and population data. If there is other enriching information anyone thinks would be valuable please leave a note in the discussion!

Content

The data is geographically divided into countries/regions, but does have some greater specificity in some larger/capitol cities. The data is broken down into two main categories: walking and driving. This data set measures the change in routing requests since January 13, 2020 across those two categories on a daily abases and per geographical division. A full data description can be found on the Apple web site. under > About This Data

Acknowledgements

This data is sourced daily from the Apple website and is then enriched with other publicly available information.

Terms of Use

You may use Mobility Trends Reports provided on the Site, including any updates thereto (collectively, the “Apple Data”), only for so long as reasonably necessary to coordinate a response to COVID-19 public health concerns (including the creation of public policy) while COVID-19 is defined as a pandemic by the World Health Organization. You will not use the Apple Data to attempt to derive the identity or movements of any specific end user or device. Except as expressly set forth herein, Apple will retain all of its rights, title and interest in the Apple Data and no other licenses or rights are granted or to be implied.

This case study document provides information on how Apple Maps is using our open datasets and articulates citizen benefits.

The COVID-19 outbreak is changing the traffic flow in all countries. Using Apple Maps data (provided by apple), can we analyze the traffic flow and transportation means used by most people these days. Is there a different trend across countries?

This dataset includes hundreds of sub-regions and cities across countries so that we can get a good idea about the transportation means preferred across countries. The data is also given for a duration of time, so we can see if as the virus progresses, does traffic also change.

This data was provided by Apple, after removing all user-related information.

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)

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.

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and qui..., We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative..., , # Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

https://doi.org/10.5061/dryad.ncjsxkt35

Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code.

Description of the data and file structure

Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create bound...

This raster dataset represents the agricultural census data quality for cashew apple crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Suitability is based on consideration of multiple suitability criteria, and expressed as a score from 0 (totally unsuitable) to 1 (perfectly suited with no limitations with respect to any criteria). Potential yield was estimated as a theoretical maximum (based on the published literature) weighted by suitability scores for suitability criteria directly related to productivity, and is an estimate of production when climate and land limitations are not mitigated. Date: May 2023 Owner: MPI Contact: Kumar Vetharaniam, Plant and Food Research

This dataset was produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about this layer and links to download the data, can be found at the Whitiwhiti Ora Data Supermarket.

N.B. The information provided here is not sufficiently accurate for detailed farm-scale use.

Mobility Trends Reports

Learn about COVID⁠-⁠19 mobility trends in countries/regions and cities. Reports are published daily and reflect requests for directions in Apple Maps. Privacy is one of our core values, so Maps doesn’t associate your data with your Apple ID, and Apple doesn’t keep a history of where you’ve been.

Sourced directly from Apple

The global Car GPS Navigation System market, valued at $15.91 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 10.03% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of smartphones with advanced navigation capabilities and the integration of GPS systems into infotainment systems are significant drivers. Furthermore, the rising demand for enhanced safety features, including real-time traffic updates and advanced driver-assistance systems (ADAS), is boosting market growth. Consumer preference for seamless navigation experiences and the growing popularity of connected cars are also contributing to market expansion. The market is segmented into hardware and software/services components, with software and services witnessing faster growth due to the increasing demand for subscription-based services, map updates, and advanced features like voice control and augmented reality navigation. While the market faces challenges like the increasing prevalence of built-in navigation systems in vehicles and the rise of smartphone-based navigation apps, the continuous innovation in GPS technology, including the integration of high-definition maps and artificial intelligence (AI)-powered features, will continue to drive market growth across key regions including North America (particularly the US), Europe (Germany and France being major contributors), and APAC (with China and Japan leading the way). The competitive landscape is characterized by a mix of established automotive component suppliers, technology companies, and specialized map providers. Companies like Robert Bosch GmbH, TomTom NV, and Garmin Ltd. hold significant market share, leveraging their expertise in hardware and software development. Apple Inc. and Google (although not explicitly listed) exert indirect influence through their integrated navigation systems on smartphones and their mapping technologies. The market witnesses intense competition driven by product differentiation through features, pricing strategies, and partnerships with automotive manufacturers. Risks include potential disruptions from technological advancements, economic fluctuations impacting consumer spending, and the increasing regulatory landscape concerning data privacy and security. The ongoing evolution of autonomous driving technology may present both opportunities and challenges, potentially reshaping the future of the car GPS navigation system market in the long term.

FROM PUNCH CARDS TO MOBILE APPS: A GEOLOGIST'S 40 YEAR ADVENTURE IN COMPUTING

ALLMENDINGER, Richard W., Department of Earth and Atmospheric Sciences, Cornell University, Snee Hall, Ithaca, NY 14853-1504

Few things have changed more than computing over the last 40 years: from slide rulers and expensive calculators (early 70s), punch cards (late 70s and early 80s), desktop computers with graphical user interfaces (mid-1980s to 1990s) laptop computers of the (1990s to mid-2000s), to the current explosion of mobile devices/apps along with the Internet/Cloud. I started developing apps in the mid-1980s and today, my desktop and mobile apps touch about 50,000 people per year. I will highlight two of my 12 major apps: Stereonet and GMDE (Geologic Map Data Extractor). Stereonet was first written and distributed in the 1980s for the Mac. Today it is available for the Mac, Windows, and Linux and, although it remains single-user focused, it has been expanded to include visualization of observations in a Google satellite view, export 3D symbols for plotting in Google Earth, and upload of data directly to the StraboSpot website/database, tagged with StraboSpot-specific nomenclature. Stereonet also made the jump to iOS where the user can, not only see and plot their data on their iPhone or iPad, but can also use device orientation to make basic measurements in the field. GMDE is also available for all three desktop platforms but not (yet) for mobile devices. In short, GMDE facilitates the task of extracting quantitative data from geologic maps and satellite imagery. A georeferenced basemap with realtime access to elevation at any point from internet elevation services makes it easy to leverage all of the information hidden in a century of high quality geologic mapping. GMDE specializes in structural calculations: 3-point and piercing point problems, rapid digitization of existing orientation symbols, topographic sections, and down-plunge projections as well as an integrated Google satellite view. The digitized data from a static, raster map can be analyzed quantitatively and shared over the Internet to enable new scientific studies. In the future, the algorithms in GMDE can be adapted to enable better geologic mapping itself by allowing the geologist to make realtime calculations in the field that can be interrogated immediately for their significance. After all, technology should not just make our lives easier but enable genuinely new science to be done. http://www.geo.cornell.edu/geology/faculty/RWA/programs/.

The global road safety app market, 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 telematics and connected car technologies. A compound annual growth rate (CAGR) of 8.4% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the increasing demand for features like speed monitoring, driver behavior analysis, and emergency assistance functionalities. The integration of these apps with connected car systems further enhances their appeal and utility, leading to wider adoption across various demographics. The market is segmented by application (enterprise and personal use) and operating system (iOS and Android), reflecting the diverse needs and preferences of users. While the enterprise segment may see growth from fleet management solutions, the personal segment is expected to dominate due to increasing individual awareness of road safety and the desire for enhanced protection during commutes and travels. Geographic distribution reveals a strong presence in North America and Europe, largely attributable to higher technological adoption and established safety regulations. However, emerging markets in Asia-Pacific and Middle East & Africa are expected to show significant growth as infrastructure improves and smartphone usage increases. Competitive rivalry is intense, with established players like Google Maps and Waze competing against specialized road safety apps offering unique functionalities. This competitive landscape is pushing innovation and improvement in app features and user experience. The market's restraints primarily involve data privacy concerns and the need for robust regulatory frameworks to ensure accurate and reliable data collection and use. Furthermore, challenges related to app integration with different car models and operating systems continue to hinder widespread adoption. To overcome these challenges, app developers are focusing on improved user interfaces, enhanced data security features, and stronger partnerships with automotive manufacturers and insurance providers. The future trajectory suggests a considerable expansion of the road safety app market, with increasing focus on preventative measures, advanced driver-assistance systems integration, and sophisticated analytics to understand and mitigate road accident risks. This is fueled by a growing awareness of road safety as a crucial public health concern. The market will likely see further segmentation based on user-specific needs and preferences, such as tailored safety apps for specific demographics (e.g., elderly drivers, teenagers).

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

Public Trash Cans Map was created to have a inventory of the trash cans in the City of Harrisburg. The Public Trash Cans Hosted Feature layer is the primary dataset in this map. Data was collected using ArcGIS Collector Field Application on an Iphone. Photos were taken of each public trash can and are visible as a link in the item pop-up. Questions regarding this dataset can be forwarded to the GIS Administrator (Evan Rubin), John Rarig or Chris Nafe. Data does not include:Temporary trash barrels primarily located in the City Parks.Trash containers at bus stops or parking garagesTrash containers in the Capitol Park Complex Map Created by Evan Rubin - Aug 26th 2021Uses: Public Trash Cans Hosted Feature Layer