Verified dataset of 2025 device usage: share of global web traffic, mobile commerce share of transactions, US daily time spent, app vs web breakdown, and tablet decline.

Mobile and desktop both shape how we access the web, but mobile clearly pulls ahead in most metrics. Two real-world examples show this shift: e-commerce brands ramp up mobile-first checkout experiences, and media platforms optimize video feeds for mobile screens. Let’s explore the stats driving these trends. Editor’s Choice 59–64%...

In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Mobile PCs and routers processed a reported ****exabyte of data per month in 2024, up from *** exabyte the previous year. This figure is expected to reach * exabytes per month by 2030, with global data use set to explode over the coming years.

Context

It is no secret that mobile devices are increasingly taking over the market at the expense of stationary equipment and many forgotten tablets. Trends change over time and the data collected helps us understand them. So let's look at the share of these three sections in the most populous country in the world, which is India.

Content

The database saved in .csv form contains 4 columns. The first column contains the date (YYYY-MM) from the measurement period. Each subsequent column contains the percentage of market share in mobile, desktop and tablet markets, given as a percentage, rounded to 2 decimal places (if the share is less than 0.5%, the value 0 remains, even though it may constitute a very small percentage of the share). We have a total of 180 rows, i.e. full 15 years of data for each month.

Source

The database comes from the statcounter website and is available under the CC BY-SA 3.0 license, which allows you to copy, use and distribute the data also for commercial purposes after citing the source.

Photo by Andrew Neel on Unsplash

This web map visualizes the prevalence of households in a given geography that do not own a computer, smartphone, or tablet. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

Facebook is the leading social network worldwide, and its accessibility through multiple mobile apps as well as its mobile website. In January 2021, over 98 percent of active user accounts worldwide accessed the social network via any kind of mobile phone.

Facebook in mobile-first markets India is thecountry with the largest Facebook audience by far, with 340 million users on the platform, followed the United States, Indonesia, and Brazil all of which have more than 100 million Facebook users each. With the exception of the United States, all of these are digital markets with mobile-first audiences. In many emerging markets, mobile is often the first online experience, providing online users with their first internet experience through inexpensive smartphones and mobile data contracts. In India and Indonesia, mobile by far surpasses desktop in terms of audiences and time spent.

Mobile Facebook access Due to the social network’s wide reach on mobile, it is unsurprising that Facebook consistently ranks as one of the most-downloaded app publishers worldwide. Some of the apps published by Facebook include the eponymous social networking app (and its low-bandwidth version, Facebook Lite), Facebook Messenger (also available as Messenger Lite), Facebook Pages Manager and Facebook Local. In the Google Play Store, Facebook Messenger, Messenger Lite and Facebook frequently rank among the top downloaded apps every month.

This dataset simulates sales transactions for mobile phones and laptops, including product specifications, customer details, and sales information. It contains 50,000 rows of randomly generated data to help analyze product sales trends, customer purchasing behavior, and regional distribution of sales.

Dataset Overview

• Dataset Type: Structured tabular data
• Number of Rows: 50,000
• Number of Columns: 16

Purpose of the Dataset
This dataset can be used for:
✅ Sales Analysis – Understanding product demand and pricing trends.
✅ Customer Behavior Analysis– Identifying buying patterns across locations.
✅ Inventory Management – Monitoring inward and dispatched product movements.
✅ Machine Learning & AI – Predicting sales trends, customer preferences, and stock management.

Key Features in the Dataset

1. Product Information

   • Product: Type of product (Mobile Phone / Laptop).
   • Brand: Various brands like Apple, Samsung, Dell, Lenovo, OnePlus, etc.
   • Product Code: Unique identifier for each product.
   • Product Specification: Brief description of the product features.

2. Sales & Pricing Details

   • Price: Cost of the product (randomly generated).
   • Inward Date: Date when the product was received in stock.
   • Dispatch Date: Date when the product was sold/dispatched.
   • Quantity Sold: Number of units sold per transaction.

3. Customer & Location Details

   • Customer Name: Randomly generated customer names.
   • Customer Location: City of the customer.
   • Region: Sales region (North, South, East, West, Central).

4. Technical Specifications -Core Specification (For Laptops): Includes processor models like i3, i5, i7, i9, Ryzen 3-9.
   -Processor Specification (For Mobiles): Includes processors like Snapdragon, Exynos, Apple A-Series, and MediaTek Dimensity.
   -RAM: Randomly assigned memory sizes (4GB to 32GB).
   -ROM: Storage capacity (64GB to 1TB).
   -SSD (For Laptops): Additional storage (256GB to 2TB), "N/A" for mobile phones.

Potential Use Cases: Business Intelligence Dashboards Market Trend Analysis Supply Chain Optimization
Customer Segmentation
Machine Learning Model Training (Sales Prediction, Price Optimization, etc.)

The smartphone helps workers balance the demands of their professional and personal lives but can also be a distraction, affecting productivity, wellbeing, and work-life balance. Drawing from insights on the impact of physical environments on object engagement, this study examines how the distance between the smartphone and the user influences interactions in work contexts. Participants (N = 22) engaged in two 5h knowledge work sessions on the computer, with the smartphone placed outside their immediate reach during one session. Results show that limited smartphone accessibility led to reduced smartphone use, but participants shifted non-work activities to the computer and the time they spent on work and leisure activities overall remained unchanged. These findings suggest that discussions on smartphone disruptiveness in work contexts should consider the specific activities performed, challenging narratives of ‘smartphone addiction’ and ‘smartphone overuse’ as the cause of increased disruptions and lowered work productivity.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES COMPUTERS AND INTERNET USE - DP02 Universe - Total households Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The 2008 Broadband Improvement Act mandated the collection of data about computer and internet use. As a result, three questions were added to the 2013 American Community Survey (ACS) to measure these topics. The computer use question asked if anyone in the household owned or used a computer and included four response categories for a desktop or laptop, a smartphone, a tablet or other portable wireless computer, and some other type of computer. Respondents selected a checkbox for “Yes” or “No” for each response category. Respondents could select all categories that applied. Question asked if any member of the household has access to the internet. “Access” refers to whether or not someone in the household uses or can connect to the internet, regardless of whether or not they pay for the service. If a respondent answers “Yes, by paying a cell phone company or Internet service provider”, they are asked to select the type of internet service.

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.

Statistics of how many adults access the internet and use different types of technology covering:

home internet access

how people connect to the web

how often people use the web/computers

whether people use mobile devices

whether people buy goods over the web

whether people carried out specified activities over the internet

For more information see the ONS website and the UKDS website.

This dataset captures random yet realistic smartphone usage behavior of 50 users, including their daily screen time, app opens, primary app category, notifications received, and battery usage. It can be used for mobile analytics, user behavior research, productivity improvement studies, and predictive modeling.

Obtained from the Digital Nation Data Explorer. Data Explorer enables tracking of metrics about computer and Internet use over time. It allows metrics to be broken down by demographics and by state and viewed as either percentages of the population or estimated numbers of people or households.

In 2022, according to the survey, ** percent of respondents in Poland rarely share their data about family via computer or apps installed on their tablet or phone.

Dataset

This dataset was created by Bilal Ahmad

Contents

As of the second quarter of 2025, approximately **** percent of internet users worldwide accessed the web via smartphones, making them the most commonly used device for going online. Laptops and desktop computers ranked second, with nearly ** percent of users. Online video consumption In 2023, texting and watching online videos were among the most popular activities for smartphone users worldwide. By the first quarter of 2024, ** percent of internet users globally were watching online videos monthly. TikTok is a prime example of this trend, as it became the platform where U.S. adults spent more daily time than on any other social media app as of June 2023. Gaming and live-streaming Video game streaming has become a leading trend in watched video content, accounting for ** percent of online reach by the fourth quarter of 2024. This growth is driven mostly by the shift from single player to multiplayer gaming. For example, the multiplayer game Grand Theft Auto V was the most-watched game, with over *** million monthly watch hours across live-streaming platforms in June 2024. On Twitch alone, gamers watched over *** billion hours of live-streamed content in the first quarter of 2024.

Rapid technological innovations over the past few years have led to dramatic changes in today's mobile phone technology. While such changes can improve the quality of life of its users, problematic mobile phone use can result in its users experiencing a range of negative outcomes such as anxiety or, in some cases, engagement in unsafe behaviors with serious health and safety implications such as mobile phone distracted driving. The aims of the present study are two-fold. First, this study investigated the current problem mobile phone use in Australia and its potential implications for road safety. Second, based on the changing nature and pervasiveness of mobile phones in Australian society, this study compared data from 2005 with data collected in 2018 to identify trends in problem mobile phone use in Australia. As predicted, the results demonstrated that problem mobile phone use in Australia increased from the first data collected in 2005. In addition, meaningful differences were found between gender and age groups in this study, with females and users in the 18–25 year-old age group showing higher mean Mobile Phone Problem Use Scale (MPPUS) scores. Additionally, problematic mobile phone use was linked with mobile phone use while driving. Specifically, participants who reported high levels of problem mobile phone use, also reported handheld and hands-free mobile phone use while driving.

A dataset of search logs collected from a commercial search engine in the period of 11/22/2020 ∼ 11/28/2020.

Abstract

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestinian Territory can be summarized in the following:-

· The possession of PCs, TV sets, telecommunication means and satellite dish · Access to the Internet. · Prevalence of computers and access to the Internet. · Possession and Use of Recreation Devices. · The permeation of Persons Practice in Recreational and Leisure Activities. · The permeation of newspapers and magazines · Tendency towards TV and radio stations

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates

Analysis unit

Household, individual

Universe

The survey covered all the Palestinian households who are a usual residence in Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size is 7,557 households, of which 4,992 households in the West Bank and 2,565 households in Gaza Strip. About 6,779 households have been interviewed 4,508 in the West Bank and 2,271 households in Gaza Strip.

Sampling deviation

Sample Design: The sample strata have been designed on two levels: 1) First level: the governorate (16 governorates). 2) Second level: type of locality (urban, rural and camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire for the Computer, Internet and Mobile Phone Survey, 2004, consists of three parts:

The First Part: It is composed of the following:- First Section: It is composed of identification data, quality control criteria, households members data that include data on demographic, social and economic characteristics such as: age, sex, refugee status, education and main profession.

Second Section: Data on characteristics of housing.

The Second Part: household Questionnaire: It is composed of questions about having computer, access to the Internet, having TV sets, telecommunication means and satellite dish and use of recreation devices

The Third Part: Questionnaire of Persons aged 10 years and over: Use of Computer, access to the Internet, having Mobil Phone, reading newspapers and magazines, The permeation of Persons Practice in Recreational and Leisure Activities.

Cleaning operations

The project's management developed a clear mechanism for editing the data and trained the team of editors accordingly. The mechanism was as follows: · Receiving completed questionnaires on daily basis; · Checking each questionnaire to make sure that they were completed and that the data covered all eligible. Checkes also focuse on the accuracy of the answers to the questions. · Returning the uncompleted questionnaires as well as those with errors to the field for completion. · Re-interviewing 10% of the sample households using a special questionnaire for the supervisors to ensure the accuracy of the data when compared to the interviewers' completed questionnaires.

Response rate

The survey sample consists of about 7,557 households of which 6,779 households completed the interview; whereas 4,508 households from the West Bank and 2,271 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 90.3% while in the Gaza Strip it reached 88.5%. The response rate in the Palestinian Territory reached 89.7%.

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report.