Imagine waking up to the gentle buzz of your phone, checking the morning news, scrolling through messages, and booking your ride to work, all before even leaving your bed. This small routine speaks volumes about the place mobile phones hold in our lives today. By 2025, mobile phones aren’t just...

"Mobile phone usage is a global phenomenon, with billions of people worldwide using smartphones for communication, entertainment, and information. Average daily screen time varies across countries, with some nations spending over 5 hours per day on their devices."

In 2021, the number of mobile devices operating worldwide stood at almost 15 billion, up from just over 14 billion in the previous year. The number of mobile devices is expected to reach 18.22 billion by 2025, an increase of 4.2 billion devices compared to 2020 levels.

Moving forward with 5G

As the number of devices grows, so does our dependence on them to fulfill daily functions and activities. The use cases for mobile devices increasingly demand faster connection speeds and lower latency. The 5G network will be critical to fulfilling those demands, operating at significantly faster rates than 4G. In North America, for example, it is expected that there will be 218 million 5G connections, up from just ten million in 2020. This means around 48 percent of all mobile connections in North America. Globally, this figure should reach 20.1 percent by 2025.

6G: looking beyond 5G

While 5G has entered commercialization and is already creating new opportunities, researchers and engineers are already experimenting with 6G. Not only will the number of mobile devices continue to grow but cellular internet-of-things (IoT) devices are set to permeate more industrial sectors in the coming years, meaning a solution will eventually be required for network congestion and data transfer speeds.

6G ought to be capable of solving those problems before they arise, potentially enabling a network connection density ten times greater than that of 5G, and peak data rates up to fifty times faster than the rate of 5G. The Federal Communications Commission in the United States has opened spectrum for experimentation, and China have already launched what is described as a 6G satellite, so that actual potential of 6G should be revealed over the coming decade.

This dataset provides a comprehensive collection of information about all the latest smartphones available in the market as of the current time.

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The dataset was created by web scraping reputable online sources to gather accurate and up-to-date information about various smartphone models, their specifications, features, and pricing.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion 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 *** 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).Find more key insights for the number of smartphone users in countries like the Americas and Asia.

Business Context

With the availability of internet services on mobile devices, the way that people work, socialize, organize, and entertain themselves has radically changed. With access to entertainment channels, news, learning and research material, real-time video calling, and more, these multimedia communication devices have become an integral part of our day-to-day lives.

Objective

A reputed research and consultation firm recently conducted a study on the increasing rate of internet usage over the past decade and reported that a typical American spends 144 minutes (2.4 hours) per day, on average, accessing the internet via a mobile device. You wish to test the validity of this statement. So, you reached out to friends and family to understand the time they spend per day accessing the internet via mobile devices. You received responses from 29 people and based on that, you want to check if there is enough evidence to suggest that the mean time spent per day accessing the internet via mobile devices is different from 144 minutes. A 5% significance level has been chosen.

Data Dictionary

The results for the time spent per day accessing the Internet via a mobile device (in minutes) are stored in InternetMobileTime.csv.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users 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).

Chad: Mobile phone subscribers, per 100 people: The latest value from 2023 is 70.19 subscribers per 100 people, an increase from 65.44 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Chad from 1960 to 2023 is 12.59 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 70.19 subscribers per 100 people was recorded in 2023.

Technical Report: ResearchGate link

Motivation

The associated concerns have increased with increasing reliance, malpractices, and time-spending habits when it comes to mobile phones. It has become a veritable cause of distraction and malicious activities, even as far as creating life threatening circumstances like road accidents, security breaches, etc. To address this issue, we have manually prepared a custom image dataset of people using a mobile-phone while performing day-to-day activities.

Previously available similar datasets faced considerable shortcomings such as: - Suffered from minimal variations in foreground-background imagery and were intended for only very specialized activities - Lacked high quality images with proper annotation

Our dataset solves these caveats as follows: - It includes more generic, higher quality images of people using a phone(s) in various environments and a wide range of scenarios - For the annotation process, the labelImg tool was used to diligently create labels by using “compound-bounding boxes” for both the hand and the device. This ensures detection of actual usage and not just the presence of a phone.

It is broadly divided into two subsets. First is a set of 540 positive images with subject(s) using a mobile phone. Accordingly, the other set consists of 348 negative images with no perceivable mobile-phone usage.

Potential applications

The primary use case of the dataset is for mobile phone usage detection. Ultimately, it can be put to a variety of applications such as for detecting and ultimately preventing: - Usage at inappropriate places like in a classroom, confidential meetings, etc. - A person using a phone in an endangering situation, like while driving, standing at elevated surfaces, etc. - As unfair means like in examination halls, pirating private artistic properties, etc. - Theft and loss of mobile phones

These can help authorities and lawmakers ensure the best interests and well-being of the public.

Possible limitations and directions to the users

Although of appreciable quality, the dataset is limited in its size. It can be extended further by the collective contributions of images by users. It can also be duly combined with other similar datasets to meet the large training data requirements of deep learning models. As the images are of different file sizes and dimensions, they need to be resized prior to usage.

However, the contents of this dataset must not be reuploaded elsewhere or distributed as part of another dataset. Publications/reports by the users must acknowledge this dataset and duly cite this report. The task of mobile-usage detection is in itself a challenging task given the size of the object(s) of interest and even human examination might not yield complete accuracy. Thus, this dataset should only be used for academic purposes such as study of computer vision applications. It cannot be used for any legal/commercial purposes.

Percentage of smartphone users by selected smartphone use habits in a typical day.

The average for 2023 based on 156 countries was 120.02 subscribers per 100 people. The highest value was in Hong Kong: 319.49 subscribers per 100 people and the lowest value was in Papua New Guinea: 34.06 subscribers per 100 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Quantitative and qualitative data sets for 24 sites across Ghana, Malawi and South Africa:
a) SPSS dataset on young people’s use of mobile phones in Ghana, Malawi and South Africa.  4626 cases (young people aged 7-25 years): 1568 Ghana; 1544 Malawi; 1514 South Africa.  719 variables (+ 11 ‘navigation facilitators’) b) 1,620 Qualitative transcripts from interviews with people of diverse ages, 8y upwards: individual interviews [using either i.theme checklist or ii call register checklist]; focus group interviews [not all sites]: 50-80 transcripts for most sites.

This research project, which commenced in August 2012, explored how the rapid expansion of mobile phone usage is impacting on young lives in sub-Saharan Africa. It builds directly on our previous research on children’s mobility within which baseline quantitative data and preliminary qualitative information was collected on mobile phone usage (2006-2010) across 24 research sites, as an adjunct to our wider study of children’s physical mobility and access to services.

In this study our focus is specifically on mobile phones and we cover a much wider range of phone-related issues, including changes in gendered and age patterns of phone use over time; phone use in building social networks (for instance to support job search); impacts on education, livelihoods, health status, safety and surveillance, physical mobility and possible connections to migration, youth identity, and questions of exploitation and empowerment associated with mobile phones.

Mixed-method, participatory youth-centred studies have been conducted in the same 24 sites as in our earlier work across Ghana, Malawi and South Africa (urban, peri-urban, rural, remote rural, in two agro-ecological zones per country). We have built on the baseline data for 9-18 year-olds gathered in 2006-2010, through repeat and extended studies, but also included additional studies with 19-25 year-olds (to capture changing usage and its impacts as our initial cohort move into their 20s).

This forecast shows the number of smartphone users in France from 2018 to 2024. For 2020, the number of smartphone users in France is estimated to reach 47.18 million, with the number of smartphone users worldwide forecast to exceed 2 billion users by that time. From 2018 to 2024 the number of smartphone users in France is expected to grow by close to four million users. This equates to a growth in the share of users by 26.26 percent. The data was calculated in July 2018 and covers all individuals of any age who own one or more smartphones and use at least one of those devices every month.

The leading operating system on the the French market is Android with a 75.6 percent market share followed by Apple's iOS with a 18.8 percent share. Most individuals without a smartphone still owned a regular mobile phone and only 7 percent of the population did not own either. The most common smartphone owned in January 2017 was the Apple iPhone 7 followed by the iPhone 7 Plus. The three most common activities carried out weekly with a smartphone were the use of search engines, checking email accounts, and visiting social networks.

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This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

Video Dataset - 1,300+ files

The dataset comprises 1,300+ videos of 300+ people captured using mobile phones (including Android devices and iPhone) and webcams under varying lighting conditions. It is designed for research in face detection, object recognition, and event detection, leveraging high-quality videos from smartphone cameras and webcam streams. — Get the data

  Dataset characteristics:

Characteristic Data

Description Each person recorded 4 videos… See the full description on the dataset page: https://huggingface.co/datasets/ud-biometrics/phone-and-webcam-dataset.

In 2022, the average data used per smartphone per month worldwide amounted to ** gigabytes (GB). The source forecasts that this will increase almost four times reaching ** GB per smartphone per month globally in 2028.

South Korea: Mobile phone subscribers, per 100 people: The latest value from 2023 is 162.11 subscribers per 100 people, an increase from 148.68 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for South Korea from 1960 to 2023 is 52.88 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 162.11 subscribers per 100 people was recorded in 2023.

Mexico: Mobile phone subscribers, per 100 people: The latest value from 2023 is 111.56 subscribers per 100 people, an increase from 105.71 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Mexico from 1960 to 2023 is 33.67 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 111.56 subscribers per 100 people was recorded in 2023.

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