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 fifteenth 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).

Smartphone usage in the United Kingdom has increased across all age ranges since 2012, most noticeably among those aged 55-64 years of age. Whereas just nine percent of mobile phone users aged 55 to 64 years used a smartphone in 2012, this number rose to over 90 percent by 2023 and reached 93 percent in 2024. Smartphones are becoming more accessibleAs well as becoming more ubiquitous, smartphones are also becoming more accessible. In terms of price, the global average selling price of smartphones has fallen from 336.8 U.S. dollars in 2010, to 276.20 U.S. dollars in 2015. However, estimates available from 2019 predicted that the average selling price of smartphones worldwide will increase again and reach 317 U.S. dollars by 2021. The average selling price for smartphones in Europe was at around 373 euros in 2019. Smartphone usage in the UK    Smartphones are the Swiss army knife of digital devices, with their capabilities limited by the creativity of developers as much as it is the technology contained in the phone. In 2017, communications were the most popular ways to use a phone, however, 87 percent of users report using camera apps frequently, 85 percent report frequent use of browser apps, and 68 percent report frequent use of navigation apps.

English(the United Kingdom) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering generic domain, human-machine interaction, smart home command and in-car command, numbers and other domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(1,651 British people in total), 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.

Hedonic quality adjustment was first introduced in the Consumer Prices Index (CPI) in 2003 for PCs. Since then the use of hedonics has expanded in UK consumer price statistics to include a further five technology products; digital cameras, laptops, mobile phones, pay as you go phones, smartphones and tablet PCs. This article reviews the use of hedonic quality adjustment in consumer price indices in the UK and internationally. It also details the reasons for changing the method of quality adjustment for pay-as-you-go phones and digital cameras, from hedonic adjustment to class mean imputation, from March 2014 onwards.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Review of hedonic quality adjustment

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.

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English(the United Kingdom) Scripted Monologue Smartphone speech dataset_Guiding, collected from monologue based on given prompts, covering smart car, smart home, voice assistant domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(349 speakers), 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.

Key information about United Kingdom Number of Subscriber Mobile

• United Kingdom Number of Subscriber Mobile was reported at 84,300,000.000 Person in Dec 2023
• This records an increase from the previous number of 81,700,000.000 Person for Dec 2022
• UK Number of Subscriber Mobile data is updated yearly, averaging 11,859,500.000 Person from Dec 1960 to 2023, with 52 observations
• The data reached an all-time high of 84,300,000.000 Person in 2023 and a record low of 0.000 Person in 1984
• UK Number of Subscriber Mobile data remains active status in CEIC and is reported by World Bank
• The data is categorized under World Trend Plus’s Association: Telecommunication Sector – Table UK.World Bank.WDI: Telecommunication

Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.;International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database;Sum;Please cite the International Telecommunication Union for third-party use of these data.

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English(the united kingdom) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics. Transcribed with text content, timestamp, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers(around 500 native speakers), 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.

United Kingdom OFCOM: Telephone Usage: By Call Type: Call to Mobile data was reported at 1,394,000,000.000 min in Jun 2018. This records a decrease from the previous number of 1,461,000,000.000 min for Mar 2018. United Kingdom OFCOM: Telephone Usage: By Call Type: Call to Mobile data is updated quarterly, averaging 2,524,500,000.000 min from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 3,967,000,000.000 min in Sep 2006 and a record low of 1,394,000,000.000 min in Jun 2018. United Kingdom OFCOM: Telephone Usage: By Call Type: Call to Mobile data remains active status in CEIC and is reported by Office of Communications. The data is categorized under Global Database’s United Kingdom – Table UK.TB005: Usage Volume: Telephone Line: By Call Type.

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.

United Kingdom UK: Internet Users: Individuals: % of Population data was reported at 94.776 % in 2016. This records an increase from the previous number of 92.000 % for 2015. United Kingdom UK: Internet Users: Individuals: % of Population data is updated yearly, averaging 64.820 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 94.776 % in 2016 and a record low of 0.087 % in 1990. United Kingdom UK: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.; ; International Telecommunication Union, World Telecommunication/ICT Development Report and database.; Weighted average; Please cite the International Telecommunication Union for third-party use of these data.

United Kingdom OFCOM: Telephone Usage: Business: Call to Mobile data was reported at 835,000,000.000 min in Jun 2018. This records a decrease from the previous number of 881,000,000.000 min for Mar 2018. United Kingdom OFCOM: Telephone Usage: Business: Call to Mobile data is updated quarterly, averaging 1,462,000,000.000 min from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 1,888,000,000.000 min in Dec 2006 and a record low of 835,000,000.000 min in Jun 2018. United Kingdom OFCOM: Telephone Usage: Business: Call to Mobile data remains active status in CEIC and is reported by Office of Communications. The data is categorized under Global Database’s United Kingdom – Table UK.TB005: Usage Volume: Telephone Line: By Call Type.

We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

Introduction

This dataset shows the maximum observed utilisations of operational data centres identified in UK Power Networks' region.

The utilisations have been determined using actual demand data from connected sites within UK Power Networks licence areas, from the past two years.

Maximum utilisations are expressed proportionally, by comparing the maximum half-hourly observed import power seen across the site's meter point(s), against the meter's maximum import capacity. Units for both measures are apparent power, in kilovolt amperes (kVA).

To protect the identity of the sites, data points have been anonymised and only the site's voltage level information has been provided.

Methodological Approach

Over 100 operational data centre sites (and at least 10 per voltage level) were identified through internal desktop exercises and corroboration with external sources.

After identifying these sites, their addresses and their MPAN(s) (Meter Point Administration Number) were identified using internal systems.

Data for each of these connected demand sites were retrieved through half-hourly smart meter data. This includes the MPAN's maximum observed import apparent power, maximum import capacity and voltage (the latter through the MPAN's Line Loss Factor Class Description).

In cases where there are numerous meter points for a given data centre site, the observed import powers across all relevant meter points are summed, and compared against the sum total of maximum import capacity for the meters.

The maximum utilisation for each site was determined via the following equation (where S = Apparent Power in kilovolt amperes (kVA)):

% Maximum Observed Utilisation =

 SUM( SMPAN Maximum Observed Demand)

 SUM( SMPAN Maximum Import Capacity)

The dataset was then cleansed to only include sites where the % maximum utilisation was between 0 and 1 (i.e., 0% and 100%).

Each data centre site was then anonymised by removing any identifiers other than voltage level.

Quality Control Statement

The dataset is primarily built upon customer smart meter data for connected customer sites within the UK Power Networks' licence areas.

The smart meter data that is used is sourced from external providers. While UK Power Networks does not control the quality of this data directly, these data have been incorporated into our models with careful validation and alignment.

Any missing or bad data has been addressed though robust data cleaning methods, such as omission.

Assurance Statement

The dataset is generated through a manual process, conducted by the Distribution System Operator's Regional Development Team.

The dataset will be reviewed quarterly - both in terms of the operational data centre sites identified, their maximum observed demands and their maximum import capacities - to assess any changes and determine if updates of demand specific profiles are necessary.

This process ensures that the dataset remains relevant and reflective of real-world data centre usage over time.

There are sufficient data centre sites per voltage level to assure anonymity of data centre sites.

Other Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/Download dataset information: Metadata (JSON)

This dataset is generated from 20 households in the south east of England, as part of a trial that used digital sensors for observational purposes in social research. While sensor-generated data is omitted from this dataset, the trial produced interviews, questionnaires, time-use diaries and ethnographic notes covering various aspects of the trial, including household configurations and practices, study participation (intrusion, burden, meaningfulness) and records of living with sensors. What actually happens within households? We know that men are increasingly sharing in domestic duties and parenting; but does this mean that these activities are being done with their partners or are they taking turns? Do families eat together and talk to each other, or do they have separate meals in different rooms while talking on social media to their friends? It is hard to observe households, and research on these issues is done through self-reporting, with people answering questions and filling in diaries, or with highly invasive methods such as video recording. There is another way. Digital devices are becoming more sophisticated. A modern mobile phone can measure position and movement, as well as what the phone is being used for. Many people wear sensors for heart rate, sleeping patterns, and physical activity. And fixed sensors in houses can be simply plugged in to measure sound and energy use. Using such sensors effectively would reduce the need for questionnaires and interviews, reducing the amount of work for respondents and providing potentially more accurate reporting. However, there are technical problems to be solved. What can be measured by these devices? How can the data be converted into meaningful descriptions of activities? How reliable are these descriptions? There are also ethical concerns. How can the datasets be securely stored and for how long? How does consent work if people forget the devices are there? When should consent be obtained from people who are monitored but not intentionally included in the research, such as visitors? This project will examine these technical and ethical issues. We will develop guidelines for social researchers who want to use digital sensing devices in their research. These will be based on expert advice and discussion with members of the general public, as well as the experience of household members and researchers in a trial study. The data collected in the trial study will be used to compare, contrast and integrate the use of sensor devices with existing research methods. The trial data and comparison of methods will be the foundation to develop analysis tools that help researchers to interpret and understand the rich data that can be collected with these methods, to answer questions about what happens within households.

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Energy consumption readings for a sample of 5,567 London Households that took part in the UK Power Networks led Low Carbon London project between November 2011 and February 2014. Readings were taken at half hourly intervals. The customers in the trial were recruited as a balanced sample representative of the Greater London population. The dataset contains energy consumption, in kWh (per half hour), unique household identifier, date and time. The CSV file is around 10GB when unzipped and contains around 167million rows. Within the data set are two groups of customers. The first is a sub-group, of approximately 1100 customers, who were subjected to Dynamic Time of Use (dToU) energy prices throughout the 2013 calendar year period. The tariff prices were given a day ahead via the Smart Meter IHD (In Home Display) or text message to mobile phone. Customers were issued High (67.20p/kWh), Low (3.99p/kWh) or normal (11.76p/kWh) price signals and the times of day these applied. The dates/times and the price signal schedule is availaible as part of this dataset. All non-Time of Use customers were on a flat rate tariff of 14.228pence/kWh. The signals given were designed to be representative of the types of signal that may be used in the future to manage both high renewable generation (supply following) operation and also test the potential to use high price signals to reduce stress on local distribution grids during periods of stress. The remaining sample of approximately 4500 customers energy consumption readings were not subject to the dToU tariff. More information can be found on the Low Carbon London webpage Some analysis of this data can be seen here.