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.

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...

The number of smartphone users in Ireland was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+6.15 percent). After the seventh consecutive increasing year, the smartphone user base is estimated to reach 5.22 million users and therefore a new peak in 2029. 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).Find more information concerning Serbia and Sweden.

The number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 information concerning Denmark and Latvia.

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

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.

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.

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.

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

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

Data Series: Proportion of individuals who own a mobile telephone, by sex Indicator: I.18 - Proportion of individuals who own a mobile telephone, by sex Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Economic structures, participation in productive activities and access to resources

this graph was created in R and Canva :

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Recent years have witnessed a rapid growth in the use of mobile devices, enabling people to access the Internet in various contexts. More than 77% of Americans now own a smartphone, with an increasing trend in terms of the time people spend on their phones. More recently, with the release of intelligent assistants such as Google Assistant, Apple Siri, and Microsoft Cortana, people are experiencing mobile search through a single voice-based interface. These systems introduce several research challenges. Given that people spend most of their times in apps and, as a consequence, most of their search interactions would be with apps (rather than a browser), one limitation is that users are unable to use a intelligent assistants to search within many apps.

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.

Series Name: Proportion of individuals who own a mobile telephone by sex (percent)Series Code: IT_MOB_OWNRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sexTarget 5.b: Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of womenGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

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.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Individuals who own a mobile telephone (%)





  Dataset Description

This dataset provides country-level data for the indicator "5.b.1 Individuals who own a mobile telephone (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format, where each row… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/mobile-phone-ownership-for-african-countries.

This dataset provides detailed insights into mobile phone operating systems, covering their versions, release years, market shares, and technical capabilities. It includes 15,000 rows of data across 15 attributes, making it valuable for data analysis, machine learning, and trend forecasting. Researchers and developers can use this dataset to analyze OS adoption rates, security improvements, and technological advancements over time.

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.

Forecast: Total Individuals Who Own a Mobile Telephone in Brazil 2024 - 2028 Discover more data with ReportLinker!

The percentage of respondents who report not owning a mobile phone because people in their family or community do not approve of them using a mobile phone. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.