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.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. 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.

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.

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.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.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 population share with mobile internet access in countries like Caribbean and Europe.

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

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.

English(North America) Scripted Monologue Smartphone and PC speech dataset, collected from monologue based on given scripts, covering common expressions. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(302 North American), geographicly speaking, enhancing model performance in real and complex tasks.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.

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviour: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

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.

Mobile Computer Workstation Market Outlook

The global mobile computer workstation market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.2 billion by 2032, growing at a robust CAGR of 8.7% during the forecast period. The key growth factors driving this market include the increasing demand for mobility within workplaces, advancements in wireless communication technologies, and the need for efficient data management systems in various industries.

One of the primary growth factors for the mobile computer workstation market is the increasing demand for mobility within enterprises. The trend towards remote and flexible working conditions necessitates the use of portable and efficient devices that can assist employees in maintaining productivity regardless of their location. Mobile workstations offer high performance and portability, enabling employees to perform computationally intensive tasks on the go. This is particularly critical in industries like healthcare, warehousing, and manufacturing where real-time data processing and access to computing power are essential.

Technological advancements, particularly in wireless communication and battery technology, have also significantly contributed to the market's growth. The development of more reliable, faster wireless communication networks and longer-lasting batteries has made mobile computer workstations more viable and appealing to businesses. Enhanced connectivity solutions ensure that mobile workstations can function as effectively as stationary ones, without compromising on performance or speed. These advancements have broadened the scope of applications for mobile workstations, making them indispensable tools across various sectors.

Another significant growth driver is the need for efficient data management systems, especially in data-intensive sectors like healthcare, logistics, and manufacturing. Mobile workstations facilitate real-time data collection, processing, and analysis, thereby enhancing operational efficiency and decision-making. In healthcare, for instance, mobile workstations enable medical professionals to access patient records, diagnostic tools, and treatment systems at the point of care. Similarly, in logistics and manufacturing, they streamline inventory management, production monitoring, and quality control processes.

In addition to mobile solutions, the demand for Workstation Desktops remains strong in various sectors. These desktops are essential for professionals who require high-performance computing power for tasks such as 3D rendering, video editing, and complex data analysis. Unlike mobile workstations, desktop workstations offer greater expandability and customization options, allowing users to tailor their systems to specific needs. Industries such as media and entertainment, engineering, and scientific research continue to rely on workstation desktops for their robust processing capabilities and reliability. As technology advances, workstation desktops are being equipped with the latest processors and graphics cards, ensuring they remain a vital tool for professionals across the globe.

From a regional perspective, North America is anticipated to hold the largest market share throughout the forecast period, driven by the high adoption rate of advanced technologies and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate due to rapid industrialization, increasing investments in IT infrastructure, and a growing emphasis on technological advancements across various sectors. Europe also presents significant growth opportunities, supported by strong government initiatives for digital transformation and the presence of a robust manufacturing sector.

Product Type Analysis

The mobile computer workstation market can be segmented based on product type into powered mobile workstations and non-powered mobile workstations. Powered mobile workstations have been increasingly adopted due to their enhanced functionality and ability to operate independently of external power sources. These workstations come equipped with high-capacity batteries, making them suitable for environments where continuous operation is critical, such as healthcare settings and remote industrial sites. The ongoing advancements in battery technology are likely to further boost the adoption of powered mobile workstations.

Non-powered mobile workstat

BB-MAS is a behavioural biometrics dataset. It consists of data collected from 117 subjects for typing (both fixed and free text), gait (walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. The dataset consists a total of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; 1.7 million data-points for swipes; and enables future research to explore previously unexplored directions in inter-device and inter-modality biometrics.

The global mobile computer (PDA) market, valued at $2405 million in 2025, is projected to experience robust growth, driven by the increasing adoption of mobile computing solutions across diverse sectors. A compound annual growth rate (CAGR) of 5.2% from 2025 to 2033 indicates a significant expansion, fueled primarily by the rising demand for efficient data capture and real-time information access in retail and logistics. The market is segmented by application (retail & wholesale, logistics & warehousing, industrial manufacturing, healthcare, others) and type (keyboard type, full-touch type). The retail and wholesale sectors are major contributors, driven by the need for streamlined inventory management, point-of-sale transactions, and improved customer service. Similarly, the logistics and warehousing sector leverages mobile computers for efficient order processing, tracking, and delivery management. The increasing integration of mobile computers with enterprise resource planning (ERP) and supply chain management (SCM) systems further accelerates market growth. Technological advancements such as enhanced processing power, improved battery life, and ruggedized designs contribute to the growing appeal of PDAs across various industries. Competition among established players like Zebra, Honeywell, and Datalogic, alongside emerging technology providers, fosters innovation and drives pricing competitiveness, ultimately benefiting end-users. The forecast period (2025-2033) anticipates continued market expansion, with North America and Europe retaining significant market shares due to established technological infrastructure and high adoption rates. However, Asia-Pacific is expected to witness substantial growth, driven by rapid industrialization and expanding e-commerce sectors in countries like China and India. The increasing demand for mobile data capture solutions in healthcare, particularly for patient data management and medication tracking, also presents significant growth opportunities. Market restraints include the high initial investment costs associated with implementing mobile computing solutions and the potential security concerns related to data handling. Nevertheless, the overall market outlook remains positive, driven by the ongoing need for enhanced operational efficiency and improved data management across diverse industry verticals.

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.

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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.

Mobile PCs and routers processed a reported *** exabyte of data per month in 2023, up from *** exabytes the previous year. This figure is expected to reach *** exabytes per month by 2029, with global data use set to explode over the coming years.

The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions.

Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth).

Demography: sex, age; federal state; highest level of school education; highest level of vocational education.

Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

Internetnutzung. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. über Telefon, ISDN, ADSL, via Kabel oder WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, TV set-top box, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen am Computer mit Internetzugang (Anti-Virus Software, smart card reader, Verschlüsselungssoftware, Firewall, elektronische Signatur); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet und Art dabei aufgetretener Probleme (verspätete Lieferung, fehlgeschlagene Lieferungen, keine Rückgabemöglichkeit, Probleme bei der Bezahlung, unzureichende Preisinformationen, schlechter Kundenservice, Missbrauch der persönlichen Daten z.B. in Form von Spam, Probleme mit der Navigation auf den Webseiten der Anbieter, unlautere/irreführende Angebote); Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, Online-Banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen. Demographie: Geschlecht; Alter; Studienstatus; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Region; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Land; Fragebogennummer; Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the television cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets. Demography: sex; age; current type of education; age at end of education; occupation; professional position; region; type of community; household composition and household size. Additionally coded was: country; questionnaire number; weighting factor. Telephone interview Bevölkerung im Alter von 15 Jahren und älter

Internetnutzung und Online-Aktivitäten. Themen: Internetzugang im Haushalt; Art des Internetzugangs (z.B. ISDN, ADSL, WLAN); Art der Hardware für den Internetzugang (z.B. PC, Laptop, digital TV, Spielkonsole, Mobiltelefon mit WAP, Palm); vorhandene Sicherheitsvorrichtungen an einem Computer mit Internetzugang (Anti-Virus Software, Firewall, elektronische Signatur usw.); Orte der Internetnutzung (zu Hause, Arbeitsplatz, Schule, public access point, Cybercafe usw.); Nutzungshäufigkeit; aufgetretene Sicherheitsprobleme bei der Internetnutzung (Virenbefall, Missbrauch der Kreditkartennummer, Spam); durchgeführte Behördenkontakte via Internet; Häufigkeit privater Einkäufe im Internet; Art der aufgetretenen Probleme bei Interneteinkäufen; Herkunftsregion der Anbieterseiten bei privaten Interneteinkäufen; weitere Nutzungsarten für das Internet: E-Mails, online banking, Nachrichten und Informationsbeschaffung, Gesundheitsinformationen, Jobangebote, Teilnahme an Foren und Chats, Weiterbildung, Reiseinformationen, Ticketbuchungen; Vorhandensein einer chronischen Krankheit oder dauerhaften Behinderung beim Befragten sowie dadurch verursachte Einschränkung des Alltagslebens. Demographie: Geschlecht; Alter; Ausbildungsgrad; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Region; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Fragebogennummer; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Gewichtungsfaktor. Internet use and online activities. Topics: internet connection at home; type of internet access in the household: standard telephone line, ISDN, ADSL, special modem using the TV cable, mobile / wireless connection; internet connection via: desktop computer, laptop, TV set-top box, video game console, mobile telephone, handheld / pocket computer; security features of the aforementioned device: antivirus software, smart card reader or other authentication device, encryption software, firewall software, electronic signature software, other security feature; internet use (at home, at work, at school); frequency of internet use; experienced security problems while using the internet: computer virus, fraudulent use of credit card number, unsolicited e-mail (spamming), other security problems, no problems; contact to public administration via the internet: to find administrative information, send e-mail, carry out procedures online, other reasons, no contact; frequency of purchasing products or services via the internet; experienced problems with regard to shopping on the internet: late delivery, product or service not delivered, no possibility to return faulty or unwanted goods, payment problems, unclear pricing, unsatisfactory communication, unauthorized use of personal data, website navigation problems, misleading offer; purchase of goods and services via websites in the following regions: EU, in Europe but outside the EU, North America, Latin America, Africa, Asia, Oceania; online activities: email, banking, read news, seek health-related information, find job ads, take part in forums, improve training or education, seek information on travels, book event tickets; chronic physical or mental health problem; limitation in daily activities due to health problems. Demography: sex; age; current type of education; age at end of education; occupation; professional position; type of community; region; household composition and household size. Additionally coded was: country; questionnaire number; date of interview; time of the beginning of the interview; duration of the interview; weighting factor.