The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 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 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 key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

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

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

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.

Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

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.

Organization of data

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

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. 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

1. 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.
2. 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

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

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This synthetic yet realistic dataset offers insights into smartphone features, customer reviews, and sales data. It includes over 90 customer reviews for six popular smartphone models from leading brands such as Apple, Samsung, and Google. The dataset is designed to help understand how various product specifications influence purchasing decisions and overall customer satisfaction. It combines detailed product specifications, customer star ratings, review texts, and verified purchase status with estimated sales figures per model.

Columns

• model_id (Integer): A unique identifier for each distinct phone model.
• brand (String): The manufacturer of the phone (e.g., "Apple", "Samsung", "Google").
• model_name (String): The specific name of the phone model (e.g., "iPhone 15").
• price (Integer): The retail price of the phone in USD.
• screen_size (Float): The diagonal screen size of the phone in inches.
• battery (Integer): The battery capacity of the phone in mAh.
• camera_main (String): The resolution of the phone's main camera (e.g., "48MP").
• ram (Integer): The amount of RAM (Random Access Memory) in GB.
• storage (Integer): The internal storage capacity in GB.
• has_5g (Boolean): Indicates whether the phone model supports 5G connectivity (TRUE/FALSE).
• water_resistant (String): The water resistance rating, if any (e.g., "IP68" or "None").
• units_sold (Integer): An estimated number of units sold for market analysis purposes.
• review_id (Integer): A unique identifier for each customer review.
• user_name (String): A randomly generated name for the reviewer.
• star_rating (Integer): The customer's rating, ranging from 1 (worst) to 5 (best).
• verified_purchase (Boolean): Indicates whether the reviewer's purchase was verified (TRUE/FALSE).
• review_date (Date): The date when the review was submitted, in YYYY-MM-DD format (e.g., "2023-05-10").
• review_text (String): Simulated text of the customer's review, based on features and rating (e.g., "The 48MP camera is amazing!").

Distribution

The dataset is typically provided in a CSV file format. It comprises over 90 customer review records, along with corresponding smartphone product specifications and sales data for 6 distinct phone models. The exact total number of rows or the specific file size in MB/GB is not specified.

Usage

This dataset is ideal for various analytical applications, including: * Feature importance analysis: Determining which smartphone specifications (e.g., battery life, camera quality) most significantly influence customer ratings and purchasing decisions. * Sentiment analysis: Applying Natural Language Processing (NLP) techniques to extract insights and sentiment from customer review texts. * Pricing strategy optimisation: Analysing the correlation between price and customer satisfaction or sales volume. * Market research: Comparing performance and customer perception across different brands (e.g., Apple vs. Samsung vs. Google) and models. * Sales vs. features correlation: Investigating how product features and pricing impact estimated units sold.

Coverage

This dataset has a Global region coverage. It includes data pertaining to six smartphone models from three major brands: Apple (iPhone 14, iPhone 15), Samsung (Galaxy S22, Galaxy S23), and Google (Pixel 7, Pixel 8). The review dates are indicative of data from around 2023. While it includes customer reviews, specific demographic details of the reviewers are not available beyond randomly generated usernames. As a synthetic dataset, it is designed to be realistic for general market analysis.

License

CC0

Who Can Use It

This dataset is suitable for: * Data Analysts and Scientists: For performing regression analysis, sentiment analysis, and predictive modelling. * Marketing Professionals: To understand consumer preferences, optimise product features, and refine marketing strategies. * Product Managers: To inform product development, feature prioritisation, and competitive analysis. * Market Researchers: To study market trends, brand comparisons, and consumer behaviour in the smartphone industry. * Academics and Students: For educational purposes and research projects related to consumer electronics, e-commerce, and data analysis.

Dataset Name Suggestions

• Smartphone Customer Satisfaction Data
• Mobile Phone Market & Reviews Dataset
• Consumer Electronics Feature Analysis
• Smartphone Product Performance
• Mobile Device Sales and Reviews

Attributes

Original Data Source: Smartphone Feature Optimization (Marketing Mix)

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. 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).

Handphone Users Survey: Hand Phone - Targeted Price Range for Smartphone since 2012

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated through the collection of data from features while using the Android operating system. This is a database generated from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 9,752,529 records related to dynamic, background, list of applications, and static data. Device records were collected every day from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

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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.

Analysis of ‘Proportion of people who use mobile phones for private reasons (from 16 to 74 years old) by autonomous communities and sex (API identifier: 45691)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-175-45691 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Proportion of people who use mobile phones for private reasons (from 16 to 74 years old) by autonomous communities and sex. Annual. Autonomous Communities and Cities. Survey on Equipment and Use of Information and Communication Technologies in Households

--- Original source retains full ownership of the source dataset ---

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 Description: Trending eBay Phone Charger Prices Dataset🔋⚡

Overview:

This dataset encapsulates a dynamic snapshot of over 3500 phone charger listings from eBay, reflecting the latest market trends, pricing variations, and consumer choices. Each entry is carefully curated to provide a comprehensive understanding of the current online marketplace for phone chargers.

Columns:

• Title: The product name as listed, reflecting brand, model, and key features.
• Price: Listed price, captured as a snapshot of the market's current state.

Ethical Consideration:

The data was ethically obtained, adhering to eBay's terms of service and respecting user privacy. It's a product of meticulous aggregation aimed at providing insights into pricing trends and market behavior for educational and analytical purposes.

User Advisory:

We encourage users to utilize this dataset responsibly, considering the dynamic nature of online marketplaces. It's ideal for trend analysis, market research, or academic study. Ensure your use of this data complies with legal standards and respects intellectual property rights. As market conditions fluctuate, we advise cross-referencing with current data for time-sensitive projects.

In General:

The "Trending eBay Phone Charger Prices Dataset" serves as a powerful tool for understanding e-commerce trends, pricing strategies, and consumer preferences. Dive into this electrifying compilation and energize your research and analysis with the most current and comprehensive data available.

the dataset contains phone data. scraped the data from flipkart. useful for regression model and EDA columns: model price rating ram display camera battery processor warranty

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Infant Laugh Smartphone speech dataset, Our dataset was collected Laugh sound of 20 infants and young children aged 0~3 years old. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.