Mobility data is collected through location-aware mobile apps using an SDK-based implementation. Users explicitly consent to share their location data via a clear opt-in process and are provided with clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure the highest quality data is available for analysis.

• Record Count: 90 Billion
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Mobility Data Reach

Our data reach encompasses the total counts available across various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology

We collect data dynamically, offering the most updated data and insights at the best-suited intervals (daily, weekly, monthly, or quarterly).

Business Needs

Our data supports various business needs, including consumer insight, market intelligence, advertising, and retail analytics.

A. SUMMARY The dataset contains data by SFMTA for use according to the Mobility Data Specification (MDS) Geography API. The Geography API allows regulatory agencies to define geographies that may be used by providers of mobility services and may be referenced in other portions of a MDS implementation. More information about MDS can be found here. The SFMTA uses MDS data to enforce regulations and conduct planning analyses related to its shared micromobility permit programs. The Geography API currently contains the key neighborhoods used by the SFMTA’s Powered Scooter Share Permit Program to help ensure that permitted operators offer an equitable and convenient distribution of services across San Francisco. Additional geographies may be added for analytical and/or other regulatory needs. B. HOW THE DATASET IS CREATED Staff create the geographies according to a regulatory and/or analytical need according to the MDS Geography API specification. C. UPDATE PROCESS Updates are made as needed. All previous geographies are included, even if they are no longer in effect. D. HOW TO USE THIS DATASET The data by itself may be of limited value, as the MDS Geography API is intended to be used in conjunction with other MDS APIs and regulatory functions. E. RELATED DATASETS SFMTA also makes the geographies data available via an API at services.sfmta.com/mobility/2_0/geographies. Dashboards containing aggregated MDS data as well as other datasets related to SFMTA's shared micromobility programs can be found here.

This dataset is used for the analysis in the publication entitled "Using data derived from cellular device locations to estimate visitation to natural areas: an application to the U.S. National Park system". It includes cell data purchased from Airsage Inc. at the monthly resolution for years 2018 and 2019 for 38 park units in the U.S. National Park system, corresponding monthly visitation obtained from the NPS Stats (https://irma.nps.gov/STATS/), and park attributes that are considered to affect the relationships between Cell and NPS data in the analysis.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Context

COVID‑19 mobility trends in countries/regions and cities.

Visualization

Example Animated Simulation/Visualization for Mobility data trend can be found at: https://covid19-mobility.netlify.app/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F7533e73654a0de3af312be402aa2383a%2Fmobility_2.gif?generation=1588577316142432&alt=media" alt="">

Content

Added geo, ISO 3166, and wikidata-id Information to make it easier to analyze and cross-reference the mobility data rows.

New rows for Apple Mobility Data:

wikidata: Wikidata ids, that can be used to get to page such as https://www.wikidata.org/wiki/Q123709
lat: Latitude of Geo coordinate
lng: Longitude of Geo coordinate
short-code: abbrev code for the region
iso_3166_alpha_2: Alpha 2 form of ISO 3166 code
iso_3166_alpha_3: Alpha 3 form of ISO 3166 code
iso_3166_numeric: Numeric form of ISO 3166 code

New rows for Google Mobility Data:

wikidata: Wikidata ids, that can be used to get to page such as https://www.wikidata.org/wiki/Q123709
lat: Latitude of Geo coordinate
lng: Longitude of Geo coordinate
short-code: abbrev code for the region

Acknowledgements

Sources: https://www.apple.com/covid19/mobility https://www.google.com/covid19/mobility/

Thanks Mapbox for Geocoding API

Inspiration

How did global mobility change over time in response to Covid-19

Mobility Data Analytics Services Market Outlook

According to our latest research, the global Mobility Data Analytics Services market size in 2024 stands at USD 6.9 billion. The market is experiencing robust expansion, supported by a CAGR of 21.7% from 2025 to 2033. By the end of 2033, the market is expected to reach a value of USD 48.1 billion, as per our in-depth analysis. This remarkable growth is driven primarily by the surging adoption of smart mobility solutions, increasing urbanization, and the need for real-time data-driven decision-making in the transportation and logistics sectors.

One of the most significant growth factors for the Mobility Data Analytics Services market is the escalating demand for intelligent transportation systems (ITS) globally. As urban populations swell and cities become more congested, governments and private enterprises are increasingly investing in data analytics to optimize mobility infrastructure and enhance commuter experiences. The integration of advanced analytics with IoT devices and sensors enables real-time monitoring of traffic flows, predictive maintenance, and improved safety measures. These capabilities are indispensable for modern cities seeking to reduce congestion, minimize environmental impact, and provide efficient public transportation services. The proliferation of connected vehicles and the rollout of 5G networks further amplify the volume and velocity of mobility data, creating new opportunities for analytics service providers.

Another pivotal driver propelling the market is the rapid digital transformation within the transportation and logistics sectors. Companies are leveraging mobility data analytics services to streamline operations, reduce fuel consumption, and improve fleet utilization. The adoption of cloud-based analytics platforms allows organizations to process vast datasets, extract actionable insights, and make informed decisions in real time. This not only enhances operational efficiency but also supports the development of innovative business models such as ride-sharing, car-pooling, and mobility-as-a-service (MaaS). Additionally, the integration of artificial intelligence (AI) and machine learning algorithms in analytics solutions is enabling predictive analytics, route optimization, and dynamic pricing strategies, further boosting market growth.

The increasing focus on sustainability and environmental conservation is also shaping the trajectory of the Mobility Data Analytics Services market. Governments worldwide are implementing stringent regulations to curb emissions and promote the adoption of electric vehicles (EVs) and green mobility solutions. Mobility data analytics services play a crucial role in supporting these initiatives by enabling real-time tracking of emissions, monitoring EV charging infrastructure, and facilitating the transition to sustainable transportation networks. The ability to analyze and visualize mobility patterns helps urban planners and policymakers design eco-friendly cities, optimize public transport routes, and incentivize the use of low-emission vehicles. This alignment with global sustainability goals is expected to drive substantial investments in mobility analytics over the coming years.

Regionally, North America holds the largest share of the market, driven by advanced digital infrastructure, high adoption of smart mobility solutions, and significant investments in urban mobility projects. However, the Asia Pacific region is poised for the fastest growth, owing to rapid urbanization, government-led smart city initiatives, and the burgeoning demand for efficient transportation systems in emerging economies such as China and India. Europe follows closely, with a strong emphasis on sustainable mobility and integrated transport networks. The Middle East & Africa and Latin America are also witnessing increased adoption of mobility analytics, albeit at a slower pace, as infrastructure development and digital transformation gain momentum in these regions.

Service Type Analysis<

Our high fidelity data feed is meticulously aggregated from multiple sources and delivered daily to your preferred location. This anonymized data, collected with explicit opt-in consent, adheres to strict usage terms and provides invaluable insights for various business needs.

High Fidelity Mobility Data

Reach Our reach data encompasses a vast array of attributes, including user demographics, anonymous IDs, device details, location, affluence, interests, and travel history. This comprehensive dataset ensures a detailed understanding of user behavior and patterns.

• Record Count: 200 Billion+
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Data Export Methodology

Our dynamic data collection process ensures the most up-to-date data and insights are delivered at intervals best suited to your needs, whether daily, weekly, or monthly.

Use Cases

High fidelity mobility data supports consumer insights, advertising strategies, and retail analytics, providing a robust foundation for informed decision-making and targeted marketing efforts.

This portal, from the Natal Urban Mobility Secretariat (STTU), aims to provide the public with access to the city's urban mobility data, whether through open data files or public software services. According to the Open Knowledge Foundation (OKF), "data is open when anyone can freely use, reuse, and redistribute it, subject only, at most, to the requirement to attribute authorship and share under the same license." In the context of the Brazilian government, Article 8 of Law 12.527/2011 (Access to Information Law – LAI) establishes that information of collective or general interest must be compulsorily disclosed by public bodies and entities on their official websites, which must meet, among other things, the following requirements: In line with the Access to Information Law and "Open Data" initiatives, the STTU launches its Natal Urban Mobility Transparency Portal, providing information and data channels through the areas of Static Data, Dynamic Data - Developer Area, and Public Software Services. Translated from Portuguese Original Text: Esse portal, da Secretaria da Mobilidade Urbana de Natal (STTU), tem por objetivo fornecer à população acesso aos dados da mobilidade urbana da cidade, seja através de arquivos de dados abertos, seja através de serviços públicos de software. Segundo a Fundação do Conhecimento Aberto (Open Knowledge Foundation – OKF), “dados são abertos quando qualquer pessoa pode livremente usá-los, reutilizá-los e redistribuí-los, estando sujeito a, no máximo, a exigência de creditar a sua autoria e compartilhar pela mesma licença”. No contexto do governo brasileiro, o art. 8º da Lei 12.527/2011 (Lei de Acesso à Informação – LAI) estabelece que as informações de interesse coletivo ou geral devem ser obrigatoriamente divulgadas pelos órgãos e entidades públicos em seus sítios oficiais, os quais devem atender, entre outros, aos seguintes requisitos: Em sintonia com a Lei de Acesso a Informação e com as iniciativas de "Open Data", a STTU lança seu Portal da Transparência da Mobilidade Urbana de Natal, disponibilizando canais de informação e de dados, através das áreas de Dados Estáticos, Dados Dinâmicos - Área do Desenvolvedor e Serviços Públicos de Software.

Raw data files corresponding to longitudinal country-level observations for two types of variable: (i) publication, citation, and international collaboration data from SCImago Journal & Country Rank (www.scimagojr.com/) and (ii) high-skilled mobility data, corresponding to headcounts by source and destination country, from the “Professionals moving abroad (Establishment)” data hosted by the European Union's "The EU Single Market Regulated professionals database". Methods Dataset (i) was scraped from the SCImago website; Dataset (ii) was obtained through personal correspondence following a help-desk inquiry at the European Union Open Data Portal.

The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.

These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

We investigate the regional distribution of the COVID-19 outbreak in Germany. We use a novel digital mobility dataset, that traces the undertaken trips on Easter Sunday 2020 and instrument them with regional accessibility as measured by the regional road infrastructure of Germany's 401 NUTS III regions. We identify a robust negative association between the number of infected cases per capita and average travel time on roads to the next major urban center. What has been a hinderance for economic performance in good economic times, appears to be a benevolent factor in the COVID-19 pandemic: bad road infrastructure. Using road infrastructure as an instrument for mobility reductions we assess the causal effect of mobility reductions on infections. The study shows that keeping mobility of people low is a main factor to reduce infections. Aggregating over all regions, our results suggest that there would have been about 55,600 infections less on May 5th, 2020, if mobility at the onset of the disease were 10 percent lower.

Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.

GapMaps Foot Traffic Data by Azira provides actionable insights on consumer travel patterns at a global scale empowering Marketing and Operational Leaders to confidently reach, understand, and market to highly targeted audiences and optimize their business results.

This dataset is a collection of higher-order mobility datasets, primarily aimed at trajectory data mining applications. These datasets have been created using the Point2Hex tool, allowing us to transform traditional GPS-based geolocations and check-in data into sequences of higher-order geometric elements, particularly hexagons. This transformation has various advantages, including reduced sparsity, analysis at different levels of granularity, improved compatibility with common machine learning architectures, enhanced generalization and overfitting reduction, and efficient visualization.

Seven popular mobility datasets, typically utilized in various trajectory-related tasks and technical problems, were subjected to this transformation process. These include applications like trajectory prediction, classification, clustering, imputation, and anomaly detection, among others.

To foster the culture of reusability and reproducibility, we are providing not only the transformed higher-order mobility flow datasets but also the source code for the Point2Hex tool and comprehensive documentation. This offering aims to streamline the generation process, ensuring that users have clear guidance on how to reproduce curated or customized versions of these datasets. The material is stored in publicly accessible repositories, ensuring its widespread accessibility.

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here. Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets. The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

MobileScapes is the most accurate, comprehensive and up-to-date mobile movement database available for marketing and business applications. It is developed from permission-based data collected by our trusted suppliers, using location-enabled apps. The data are de-identified by our suppliers before they are sent to Environics Analytics and then modelled using the best spatial data processing and analysis practices. Data is available at the ZIP+4 level and can be linked to EA’s 20,000 data points including demographic, lifestyle, social values and spending information from our PRIZM Premier segmentation system, which makes it possible to develop detailed profiles of consumers in any trade area or location whether you have customer data or not.

Use MobileScapes to learn about locations visited or competitor locations, how often, and where they live and work to inform decisions around products, program development, marketing, messaging, recovery planning and staffing levels.

https://environicsanalytics.com/data/mobile-analytics

This India mobility dataset is used by our customers for many purposes, such as to understand mobility patterns in specific areas in India, to build their own mobility data models, understand visitation into their own or competitors premises, or test hypotheses around changes in visitation patterns over time.

The Intuizi Visitation Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create an de-identified dataset of Encrypted ID visitation/mobility data.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market.

Veraset Movement (Mobile Location Data) offers unparalleled insights into footfall traffic patterns across dozens of European countries.

Covering 45+ European countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data helps shape strategy and make impactful data-driven decisions.

Veraset’s European Movement Panel includes the following countries: - United Kingdom-GB - Germany-DE - France-FR - Spain-ES - Italy-IT - The Netherlands-NL - Switzerland-CH - Belgium-BE - Sweden-SE - Austria-AT - Denmark-DK - Finland-FI - Cyprus-CY - Poland-PL - Ireland-IE - Portugal-PT - Romania-RO - Hungary-HU - Czech Republic-CZ - Greece-GR - Bulgaria-BG - Lithuania-LT - Croatia-HR - Norway-NO - Latvia-LV - Luxembourg-LU - Slovakia-SK - Estonia-EE - Cayman Islands-KY - Slovenia-SI - Vatican city-VA - Turks and Caicos Islands-TC - Bermuda-BM - Malta-MT - Iceland-IS - Liechtenstein-LI - Monaco-MC - British Virgin Islands-VG - Anguilla-AI - Andorra-AD - Greenland-GL - San Marino-SM - Federated States of Micronesia-FM - Montserrat-MS - Pitcairn islands-PN

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting