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...

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.

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.

People Using Cellphone

## Overview

People Using Cellphone is a dataset for object detection tasks - it contains Cellphone annotations for 1,266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Lebanon Number Data is a list of phone numbers that you can filter in many ways. You can filter by gender, age, or even relationship status. To contact young people, filter the list to show only numbers from that age group. This helps you connect with the right individual quickly. The list also follows GDPR rules, which means it protects people’s privacy. Furthermore, we regularly update the Lebanon Number Data for clarity. It removes invalid numbers, saving time by avoiding outdated contact details. This feature keeps the list fresh and up-to-date and makes your work more efficient. With Lebanon contact data, you can trust accurate, up-to-date details and filter them to meet your needs. Lebanon phone data is a collection of phone numbers that is 100% correct and valid. The companies that provide this data check every number carefully to make sure it works. So, when you use this cellphone data, you don’t have to worry about the wrong numbers. If, for some reason, a number doesn’t work, you get a replacement guarantee. This means, if a number is invalid, they will give you a new dialing number at no extra cost. Moreover, Lebanon phone data comes with all phone number subscribers’ permission. This means the people who own the numbers have agreed to share their information. It’s very important to have this permission because it keeps you out of legal trouble. Using this database without the customer’s permission can be problematic, but this data is safe and secure. Lebanon phone number list is a collection of phone numbers of people living in Lebanon. This list is very helpful for businesses that need to contact people in Lebanon. The information comes from reliable sources, such as government records, websites, and phone service providers. You can even check the URLs where the data came from. This ensures that the phone numbers are accurate. Also, if you need help, 24/7 support is available. Also, the Lebanon phone number list follows the opt-in rule. Number owners know that others use their info, making it safe to use the data. You won’t face any trouble, and it respects people’s privacy. Using the Lebanon contact number list from our List to Data website, you can confidently connect with the right people.

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13571604%2Fb608498b1cf7f70b9a22952566197db6%2FScreenshot%202023-08-02%20003740.png?generation=1690961033930490&alt=media" alt="">

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.

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.

Egypt number dataset can be a great element for direct marketing nationwide right now. Also, this Egypt number dataset has thousands of active mobile numbers that help to increase sales in the company. Most importantly, you can develop your business by bringing many trustworthy B2C customers. Likewise, clients can send you a fast response whether they need it or not. Furthermore, this Egypt number dataset is a very essential tool for telemarketing. In other words, you get all these 95% valid leads at a very cheap price from us. Most importantly, our List To Data website still follows the full GDPR rules strictly. In addition, the return on investment (ROI) will give you satisfaction from the business. Egypt phone data is a very powerful contact database that you can get in your budget. Moreover, the Egypt phone data is very beneficial for fast business growth through direct marketing. In fact, our List To Data assures you that we give verified numbers at an affordable cost. As such, you can say that it brings you more profit than your expense. Additionally, the Egypt phone data has all the details like name, age, gender, location, and business. Anyway, people can connect with the largest group of consumers quickly through this. However, people can use these cell phone numbers without any worry. Thus, buy it from us as our experts are ready to present the most satisfactory service. Egypt phone number list is very helpful for any business and marketing. People can use this Egypt phone number list to develop their telemarketing. They can easily reach consumers through direct calls or SMS. In other words, we gather all the database and recheck it, so you should buy our packages right now. Furthermore, you can believe this correct directory to maximize your company’s growth rapidly. Also, we deliver the Egypt phone number list in an Excel and CSV file. Actually, the country’s mobile number library will help you in getting more profit than investment. Similarly, the List To Data expert team is ready to help you 24 hours with any necessary details that can help your business. Hence, buy this telemarketing lead at a very reasonable price to expand sales through B2C customers.

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.

Why Choose Success.ai’s Phone Number Data?

Direct Communication: Reach out directly to professionals with verified phone numbers and work emails, ensuring your message gets to the right person without delay. Global Coverage: Our data spans across continents, providing phone numbers for professionals in North America, Europe, APAC, and emerging markets. Continuously Updated: We regularly refresh our dataset to maintain accuracy and relevance, reflecting changes like promotions, company moves, or industry shifts. Comprehensive Data Points:

Verified Phone Numbers: Direct lines and mobile numbers of professionals across various industries. Work Emails: Reliable email addresses to complement phone communications. Professional Profiles: Decision-makers’ profiles including job titles, company details, and industry information. Flexible Delivery and Integration: Success.ai offers this dataset in various formats suitable for seamless integration into your CRM or sales platform. Whether you prefer API access for real-time data retrieval or static files for periodic updates, we tailor the delivery to meet your operational needs.

Competitive Pricing with Best Price Guarantee: We provide this essential data at the most competitive prices in the industry, ensuring you receive the best value for your investment. Our best price guarantee means you can trust that you are getting the highest quality data at the lowest possible cost.

Targeted Applications for Phone Number Data:

Sales and Telemarketing: Enhance your telemarketing campaigns by reaching out directly to potential customers, bypassing gatekeepers. Market Research: Conduct surveys and research directly with industry professionals to gather insights that can shape your business strategy. Event Promotion: Invite prospects to webinars, conferences, and seminars directly through personal calls or SMS. Customer Support: Improve customer service by integrating accurate contact information into your support systems. Quality Assurance and Compliance:

Data Accuracy: Our data is verified for accuracy to ensure over 99% deliverability rates. Compliance: Fully compliant with GDPR and other international data protection regulations, allowing you to use the data with confidence globally. Customization and Support:

Tailored Data Solutions: Customize the data according to geographic, industry-specific, or job role filters to match your unique business needs. Dedicated Support: Our team is on hand to assist with data integration, usage, and any questions you may have. Start with Success.ai Today: Engage with Success.ai to leverage our Phone Number Data and connect with global professionals effectively. Schedule a consultation or request a sample through our dedicated client portal and begin transforming your outreach and communication strategies today.

Remember, with Success.ai, you don’t just buy data; you invest in a partnership that grows with your business needs, backed by our commitment to quality and affordability.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

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.

Abstract

As of June 7, 2015, Sierra Leone had reported more than 12,900 cases of Ebola Virus Disease (EVD), and over 3,900 deaths since the outbreak began. The Government of Sierra Leone, with support from the World Bank Group, has been conducting mobile phone surveys with the aim of capturing the key socio-economic effects of the virus. Three rounds of data collection have been conducted, in November 2014, January-February 2015, and May 2015. The survey was given to household heads for whom cell phone numbers were recorded during the nationally representative Labor Force Survey conducted in July and August 2014. Overall, 66 percent of the 4,199 households sampled in that survey had cell phones, although this coverage was uneven across the country, with higher levels in urban areas (82 percent) than rural areas (43 percent). Of those with cell phones, 51 percent were surveyed in all three rounds, and 79 percent were reached in at least one round.

The main focus of the data collection was to capture impacts of EVD on labor market indicators, agricultural production, food security, migration, and utilization of non-Ebola essential health services.

Geographic coverage

Due to differing characteristics between responding and non-responding households, the results should be considered “descriptive” rather than representative of the Sierra Leonean population. Overall the response rate was higher than expected given the nature of the survey and the difficult conditions under which it was conducted. In Sierra Leone, of the 4,199 households interviewed in the LFS, 65.8 percent (2,764 households) recorded a cell phone number for the household head, and, of those, 80.0 percent responded to at least one round of the cell phone survey. The unweighted sample was 59.1 percent urban (2,483 households) and 40.9 percent rural (1,716 households). Of urban households, 81.4 percent (2,021 households) listed a cell phone number for the household head, and, of those, 88.1 percent (1,780 households) responded in at least one of the three rounds of the cell phone survey. Of rural households, 43.1 percent (740 households) listed a cell phone number for the household head, and, of those, 58.1 percent (430 households) responded in at least one of the three rounds.

Analysis unit

• Individual
• Household Head

Universe

All households from the 2014 Sierra Leone Labor Force Survey which provided cell phone numbers.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the cell phone survey was the Sierra Leone Labor Force Survey (LFS) 2014. The LFS is a nationally representative stratified cluster sample survey conducted in July and August 2014, and includes the oversampling of urban areas. As part of the LFS, a total of 4199 households in 280 enumeration areas were interviewed. Interviewers collected the phone number, if available, for the head of household, and 2,764 households interviewed in the LFS included phone numbers. All available numbers from the LFS were included in the cell phone survey. The phone numbers were reported for 43 percent of rural households and 82 percent of urban households. Those households reporting numbers are unevenly distributed across the sample though there is at least partial coverage in all districts, ranging from 93 percent in Freetown (Western urban) to 30 percent in Kailahun district.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

As the survey was administered by telephone, the length of the questionnaire was targeted as 20 to 25 minutes. In Round 1, the questionnaire focused on employment and labor market conditions, non-agricultural business operations, agricultural activity, food security, health responses (covering only fever and pregnancy), remittances, travel, trust and knowledge about Ebola. In Round 2, questions were added on social assistance and education on the radio, and there were small changes to the existing questions based on the results from Round 1.

Questions on earnings were revised to match the Labor Force Survey questions more closely, in particular to account for earnings that were expressed in time unit other than months, and questions on the incidence and treatment of child diarrhea were adding using identical wording to the Demographic and Health Survey (DHS). The most substantial changes were to the migration section as the Round 1 analysis found inconsistencies in the migration reporting. Details of these changes can be found in the Round 2 report. In Round 3, the agriculture, social assistance, and education sections were expanded while the trust section was dropped due to limited variation between Rounds 1 and 2.

The only questions on Ebola Virus Disease (EVD) specifically were in Round 1 and focused on whether the respondent had heard of Ebola and what were their main sources of information were. This section was placed at the end of the questionnaire in order to elicit unbiased responses in other sections, since people may be distrustful of the government especially regarding Ebola, at a time of such emergency.

Questions related directly to incidence of EVD within the household were excluded for two reasons. First EVD is a relatively rare event and the sample was unlikely to yield sufficient observations for meaningful analysis, and secondly, the respondents will be called repeatedly as part of the high frequency survey therefore it was necessary to avoid sensitive questions that may increase attrition in later rounds. The included questions were worded in such a way as to facilitate differences-in-differences comparisons. The vast majority of questions were identical in their wording to those asked during the LFS or other nationally representative surveys for which detailed data were available including the DHS, the National Public Services Survey (NPS) and the Agricultural Households Tracking Survey (AHTS).

In a few cases, the time period over which the questions were asked was shortened to make it relevant to the last few months during which the outbreak has been growing. For example, the NPS asked about remittances in the last year whereas in November 2014, respondents were asked about remittances received in the last month.

Cleaning operations

The datasets were cleaned and compiled by teams from Innovations for Poverty Action and the World Bank's Poverty Global Practice and Social Protection and Labor Global Practice.

Response rate

Overall the response rate was higher than expected given the nature of the survey and the difficult conditions under which it was conducted. In Sierra Leone, of the 4,199 households interviewed in the LFS, 65.8 percent (2,764 households) recorded a cell phone number for the household head, and, of those, 80.0 percent responded to at least one round of the cell phone survey.

The unweighted sample was 59.1 percent urban (2,483 households) and 40.9 percent rural (1,716 households). Of urban households, 81.4 percent (2,021 households) listed a cell phone number for the household head, and, of those, 88.1 percent (1,780 households) responded in at least one of the three rounds of the cell phone survey. Of rural households, 43.1 percent (740 households) listed a cell phone number for the household head, and, of those, 58.1 percent (430 households) responded in at least one of the three rounds.

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.

simple dataset is for beginners who have started their journey in data analytics.

dataset from a real shop repair phone technician. this dataset contains some phones problems that have been fixed and some other that not fixed during two months.

• Name : brand name + model , for example : SAMSUNG A50 , Brand : SAMSUNG, Model : A50.
• Type : this device is smartphone , pad or cell 'not smartphone'.
• Problem : 1. LCD = change broken display 2.FRP = Factory reset protection . 'this issue comes from people who factory their phone and forget their google account' 3.lock = passcode 4.off = this device turn off without indicate anything 5.flash = firmware issue 6.touch = phone touch screen 7.charge = charge issue 8.battery = change battery 9.mic = microphone 10. sound = speaker issue
• fixed YES/NO = Boolean indicate if fixed or not

-- feel free to ask any question -

vote if this dataset helpful for you, to improve it

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).

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.

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