Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

Handphone Users Survey - Use of Smartphones for Phone Calls since 2012

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

Dataset

This dataset was created by Ali-arch808

Contents

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.

This data set is composed of the table with the references used for the review.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_65567444c3df02aceb795897bbd183c9/view

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

The number of smartphone users in Australia was forecast to continuously increase between 2024 and 2029 by in total 1.6 million users (+6.39 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 26.58 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

No of Mobile Phone Subscribers: per 1000 Persons: SB: Tomsk Region data was reported at 1.792 Unit th in 2023. This records an increase from the previous number of 1.784 Unit th for 2022. No of Mobile Phone Subscribers: per 1000 Persons: SB: Tomsk Region data is updated yearly, averaging 1.416 Unit th from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 1.825 Unit th in 2019 and a record low of 0.003 Unit th in 1999. No of Mobile Phone Subscribers: per 1000 Persons: SB: Tomsk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TG009: Number of Mobile Phone Subscribers: per 1000 Persons: by Region.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

No of Mobile Phone Subscribers: per 1000 Persons: UF: Tumen Region: ow Yamalo Nenetsky Area data was reported at 2.436 Unit th in 2023. This records a decrease from the previous number of 2.492 Unit th for 2022. No of Mobile Phone Subscribers: per 1000 Persons: UF: Tumen Region: ow Yamalo Nenetsky Area data is updated yearly, averaging 2.383 Unit th from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 2.787 Unit th in 2015 and a record low of 0.000 Unit th in 2000. No of Mobile Phone Subscribers: per 1000 Persons: UF: Tumen Region: ow Yamalo Nenetsky Area data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TG009: Number of Mobile Phone Subscribers: per 1000 Persons: by Region.

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”.

Dataset Info:

MyDigitalFootprint (MDF) is a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modelling in mobile environments. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users by following the in-the-wild data collection approach: the data has been collected in the users' natural environment, without limiting their usual behaviour. Existing public datasets generally consist of a limited set of context data, aimed at optimising specific application domains (human activity recognition is the most common example). On the contrary, the dataset contains a comprehensive set of information describing the user context in the mobile environment.

The complete analysis of the data contained in MDF has been presented in the following publication:

https://www.sciencedirect.com/science/article/abs/pii/S1574119220301383?via%3Dihub

The full anonymised dataset is contained in the folder MDF. Moreover, in order to demonstrate the efficacy of MDF, there are three proof of concept context-aware applications based on different machine learning tasks:

1. A social link prediction algorithm based on physical proximity data,
2. The recognition of daily-life activities based on smartphone-embedded sensors data,
3. A pervasive context-aware recommender system.

For the sake of reproducibility, the data used to evaluate the proof-of-concept applications are contained in the folders link-prediction, context-recognition, and cars, respectively.

Brazil Number of Cell Phone User data was reported at 141,644.130 Person th in 2017. This records an increase from the previous number of 138,319.640 Person th for 2016. Brazil Number of Cell Phone User data is updated yearly, averaging 139,981.885 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 141,644.130 Person th in 2017 and a record low of 138,319.640 Person th in 2016. Brazil Number of Cell Phone User data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.TB010: Number of Cell Phone User: by Sex and Age.

Pearson Correlation Coefficient between mobile phone usage duration and mobile phone addiction.

At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

Brazil Number of Mobile Phone User: Male data was reported at 66,563.444 Person th in 2017. This records an increase from the previous number of 65,377.609 Person th for 2016. Brazil Number of Mobile Phone User: Male data is updated yearly, averaging 65,970.527 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 66,563.444 Person th in 2017 and a record low of 65,377.609 Person th in 2016. Brazil Number of Mobile Phone User: Male data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.TB010: Number of Cell Phone User: by Sex and Age.

East Baton Rouge Parish Library computer usage statistics are organized by branch, year, and month. This dataset only includes the count for library patrons who have logged in to the Library’s public computers, located at any of the 14 locations.

Phone and Webcam videos

Dataset comprises 1,300+ videos featuring over 300 individuals who each recorded 4 videos while pronouncing a set of numbers. This dataset is designed to facilitate research in biometric verification, face recognition, and action recognition. The videos include 2 recordings on mobile devices, each lasting approximately 30 seconds and 8 seconds, and 2 webcam recordings with the same durations.

By utilizing this dataset, developers and researchers can enhance their understanding of human activity and improving object detection. - Get the data

Examples of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fcb41444ba6f5bd4f48f2250f686fed22%2FFrame%20174%20(5).png?generation=1733179922956991&alt=media" alt=""> This extensive collection provides high-quality video recordings that are ideal for training and testing various vision models and learning algorithms. The dataset is a valuable resource for developing and evaluating detection algorithms and object tracking systems.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fe6eb3bdb6c82da56c879aa7d1afddb61%2FFrame%20136%20(2).png?generation=1733170796165756&alt=media" alt="">

This dataset is an invaluable asset for researchers aiming to achieve higher detection accuracy and improve the performance of face recognition systems, ultimately contributing to advancements in biometric security and liveness detection technologies.

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