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.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

This data set forms the basis of the paper 'The Hibernating Mobile Phone: Dead Storage as a Barrier to Efficient Electronic Waste Recovery'.

These results are from an online, self-completion questionnaire with mobile phone owners; distributed to a non-probability, purposive sample (i.e. aged between 18-25 years old, living and studying at a UK University, and owning a mobile phones. The survey was conducted during July 2015.

For a full description of the data collection techniques and our analysis of the data, please refer to the above paper.

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.

This dataset is maintained by Steven Firth (s.k.firth@lboro.ac.uk), Building Energy Research Group (BERG), School of Civil and Building Engineering, Loughborough University. The REFIT project (www.refitsmarthomes.org) carried out a study from 2013 to 2015 in which 20 UK homes were upgraded to Smart Homes through the installation of devices including Smart Meters, programmable thermostats, programmable radiator valves, motion sensors, door sensors and window sensors.Data was collected using building surveys, sensor placements and household interviews.The REFIT Smart Home dataset is one of the datasets made publically available by the project. This dataset includes: - Building survey data for the 20 homes. - Sensor measurements made before the Smart Home equipment was installed. - Sensor measurements made after the Smart Home equipment was installed. - Climate data recorded at a nearby weather station.--- This work has been carried out as part of the REFIT project (‘Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’, Grant Reference EP/K002457/1). REFIT is a consortium of three universities - Loughborough, Strathclyde and East Anglia - and ten industry stakeholders funded by the Engineering and Physical Sciences Research Council (EPSRC) under the Transforming Energy Demand in Buildings through Digital Innovation (BuildTEDDI) funding programme. For more information see: www.epsrc.ac.uk and www.refitsmarthomes.org---The references below provide links to the REFIT project website, the TEDDINET website, a journal article which uses the dataset, and three additional datasets collected as part of the REFIT project by the University of Strathclyde and the University of East Anglia.

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.

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.

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.

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.

Forecast: Average per Capita Monthly Mobile Data Use in the UK 2024 - 2028 Discover more data with ReportLinker!

United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data was reported at 90.223 % in 2017. This records a decrease from the previous number of 100.000 % for 2014. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ data is updated yearly, averaging 90.223 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 100.000 % in 2014 and a record low of 89.893 % in 2011. United Kingdom UK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Primary Education Or Less: % of Population Aged 15+ 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: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Forecast: Smartphone Users in Australia 2022 - 2026 Discover more data with ReportLinker!

Abstract copyright UK Data Service and data collection copyright owner.The Crime Survey for England and Wales (CSEW) asks a sole adult in a random sample of households about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked, covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS). These variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range was also selected from the household and asked about their experience of crime and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.The Telephone-operated Crime Survey for England and Wales (TCSEW) became operational on 20 May 2020. It was a replacement for the face-to-face CSEW, which was suspended on 17 March 2020 because of the coronavirus (COVID-19) pandemic. It was set up with the intention of measuring the level of crime during the pandemic. As the pandemic continued throughout the 2020/21 survey year, questions have been raised as to whether the year ending March 2021 TCSEW is comparable with estimates produced in earlier years by the face-to-face CSEW. The ONS Comparability between the Telephone-operated Crime Survey for England and Wales and the face-to-face Crime Survey for England and Wales report explores those factors that may have a bearing on the comparability of estimates between the TCSEW and the former CSEW. These include survey design, sample design, questionnaire changes and modal changes.More general information about the CSEW may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page.History - the British Crime SurveyThe CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this. Secure Access CSEW dataIn addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).New methodology for capping the number of incidents from 2017-18The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. Variable 'PFA' (Police Force Area): From 2008-2009 onwards, the BCS variable 'PFA' (Police Force Area) is now only available within the associated dataset SN 6935, British Crime Survey, 2009-2010: Special Licence Access, Low-Level Geographic Data, which is subject to restrictive access conditions; see 'Access' section below. 2009-2010 self-completion modules: From October 2016, the self-completion questionnaire modules covering drug use, drinking behaviour, and domestic violence, sexual victimisation and stalking are subject to Controlled data access conditions - see SN 7280. CSEW Historic back series – dataset update (March 2022)From January 2019, all releases of crime statistics using CSEW data adopted a new methodology for measuring repeat victimisation (moving from a cap of 5 in the number of repeat incidents to tracking the 98th percentile value for major crime types). To maintain a consistent approach across historic data, all datasets back to 2001 have been revised to the new methodology. The change affects all incident data and related fields. A “bolt-on” version of the data has been created for the 2001/02 to 2011/12 datasets. This “bolt-on” dataset contains only variables previously supplied impacted by the change in methodology. These datasets can be merged onto the existing BCS NVF and VF datasets. A template ‘merge’ SPSS syntax file is provided, which will need to be adapted for other software formats.For the third edition (March 2022), “bolt-on” datasets for the NVF and VF files, example merge syntax and additional documentation have been added to the study to accommodate the latest CSEW repeat victimisation measurement methodology. See the documentation for further details. Main Topics: Adult data The adult data includes information from two sections of the survey, the non-victim form (NVF) and the victim form (VF). The NVF gathers respondent-level data: topics covered include perceptions of crime; victimisation screener questions; performance of the CJS; mobile phone, second home and bicycle crime; experiences of the police; attitudes to the CJS; crime prevention and security; ad hoc crime topics, including concern about crime and social cohesion; plastic card fraud; identity fraud; antisocial behaviour; road safety and traffic; and demographics and media. The VF contains offence-level data. Up to six different incidents are asked about for each respondent. Each of these constitutes a separate victim form and can be matched back to the respondent-level data through the variable ROWLABEL. Topics covered include the nature and circumstances of the incident, details of offenders, security measures, costs, emotional reactions, contact with the CJS and outcomes where known. Children's data (aged 10-15 years) The child NVF questionnaire included: schooling and perceptions of crime; crime screener questions (personal incidents only); perceptions of and attitudes towards the police; anti-social behaviour; and crime prevention and security. The child self-completion questionnaire covered: use of the internet; personal safety; school truancy; bullying; street gangs; drinking behaviour; cannabis use; and verification questions. The child VF covered the nature and circumstances of the incident, series of incidents, details of offenders, weapons, injuries and medical treatment, contact with the police. Multi-stage stratified random sample Face-to-face interview Self-completion 2009 2010 ADMINISTRATION OF J... ADOLESCENTS ADVICE AGE AGGRESSIVENESS AIRPORTS ALCOHOL USE ALCOHOLISM ANGER ASSAULT ATTITUDES BICYCLES BINGE DRINKING BURGLARY CAMERAS CANNABIS CAR PARKING AREAS CHILDREN CHRONIC ILLNESS CLUBS COLOUR TELEVISION R... COMBATIVE SPORTS COMMUNITIES COMMUNITY ACTION COMMUNITY BEHAVIOUR COMMUNITY COHESION COMMUNITY SAFETY COMMUNITY SERVICE P... COMPUTER SECURITY COMPUTERS COSTS COUNSELLING COURT CASES CREDIT CARD USE CRIME AND SECURITY CRIME PREVENTION CRIME VICTIMS CRIMINAL COURTS CRIMINAL DAMAGE CRIMINAL INVESTIGATION CRIMINAL JUSTICE SY... CRIMINALS CULTURAL GOODS CULTURAL IDENTITY Crime and law enfor... DAMAGE DEBILITATIVE ILLNESS DISCIPLINE DOGS DOMESTIC RESPONSIBI... DOMESTIC VIOLENCE DOORS DRINKING BEHAVIOUR DRIVING DRUG ABUSE ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL ATTENDANCE ELECTRONIC MAIL EMERGENCY AND PROTE... EMOTIONAL DISTURBANCES EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY ETHNIC CONFLICT ETHNIC GROUPS EVERYDAY LIFE EXPOSURE TO NOISE England and Wales FAMILIES FAMILY MEMBERS FEAR FEAR OF CRIME FINANCIAL COMPENSATION FINANCIAL RESOURCES FIRE DAMAGE FIRE SAFETY MEASURES FRIENDS GENDER HARASSMENT HEADS OF HOUSEHOLD HEALTH HEALTH PROFESSIONALS HOME CONTENTS INSUR... HOME OWNERSHIP HOSPITALIZATION HOURS OF WORK HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING AGE HOUSING TENURE INDUSTRIES INFORMATION MATERIALS INFORMATION SOURCES INJURIES INSURANCE CLAIMS INTERNET ACCESS INTERNET USE INTERPERSONAL COMMU... INTERPERSONAL CONFLICT INTERPERSONAL RELAT... INTRUDER ALARM SYSTEMS JUDGES JUDGMENTS LAW JURIES JUVENILE DELINQUENCY LANDLORDS LAW ENFORCEMENT LEARNING DISABILITIES LEAVE LEGAL PROCEDURE LIGHTING LOCAL GOVERNMENT SE... LOCKS MAGISTRATES MARITAL STATUS MEDIATION MEDICAL CARE MOBILE PHONES MOTOR VEHICLES NEIGHBOURHOODS NEIGHBOURS NEWSPAPER READERSHIP NEWSPAPERS OFFENCES OFFENSIVE TELEPHONE... ONLINE SHOPPING PAYMENTS PERSONAL CONTACT PERSONAL FASHION GOODS PERSONAL IDENTIFICA... PERSONAL SAFETY POLICE COMMUNITY SU... POLICE OFFICERS POLICE SERVICES POLICING POLITICAL PARTICIPA... PORNOGRAPHY PRISON SENTENCES PROBATION PROSECUTION SERVICE PUBLIC HOUSES PUBLIC OPINION PUBLIC TRANSPORT PUNISHMENT PURCHASING QUALIFICATIONS QUALITY OF LIFE RADIO RECEIVERS RECIDIVISM REFUSE RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPONSIBILITY RISK ROAD SAFETY ROAD TRAFFIC ROBBERY SCHOOL PUNISHMENTS SECOND HOMES SECURITY SYSTEMS SELF EMPLOYED SEXUAL ASSAULT SEXUAL HARASSMENT SEXUALITY SHARED HOME OWNERSHIP SICK LEAVE SLEEP DISORDERS SMALL

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

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.

Dataset pertaining to an experiment concerning positions of ad-hoc loudspeakers and mobile phones in domestic living rooms. This forms part of the PhD research of Craig Cieciura. This was experiment-based research to determine how to render object-based audio in the domestic environment using ad-hoc, audio-capable devices. References AES148 (2020): Cieciura, C., Mason, R., Coleman, P. and Francombe, J. 2020. Understanding users’ choices and constraints when positioning loudspeakers in living rooms, Audio Engineering Society Preprint, 148th Convention, Engineering Brief (number tbc).

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.

Overview

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.

Key Definitions

Aggregation

The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.

AMR Meter

Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically.

Dataset

Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.

Data Zone

Data zones are the key geography for the dissemination of small area statistics in Scotland

Dumb Meter

A dumb meter or analogue meter is read manually. It does not have any external connectivity.

Granularity

Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours

ID

Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.

LSOA

Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.

Open Data Triage

The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.

Schema

Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.

Smart Meter

A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier.

Units

Standard measurements used to quantify and compare different physical quantities.

Water Meter

Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation

Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry:

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema

The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks

The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness

Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.

Publish Frequency

Annually

Data Triage Review Frequency

An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications

For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:

·
Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.

· Where it is necessary to estimate consumption, this is calculated based on actual meter readings.

· Meters of all types (smart, dumb, AMR) are included in this dataset.

·
The dataset is updated and published annually.

·
Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.

Context

Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary Information

1. Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.

2. Ofwat guidance on water meters

3. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data from respondents in Great Britain. Information is gathered on a range of subjects, commissioned both internally by the Office for National Statistics (ONS) and by external clients (other government departments, charities, non-profit organisations and academia).One individual respondent, aged 16 or over, is selected from each sampled private household to answer questions. Data are gathered on the respondent, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. Each regular OPN survey consists of two elements. Core questions, covering demographic information, are asked together with non-core questions that vary depending on the module(s) fielded.The OPN collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living. The OPN has expanded to include questions on other topics of national importance, such as health and the cost of living.For more information about the survey and its methodology, see the gov.uk OPN Quality and Methodology Information (QMI) webpage.Changes over timeUp to March 2018, the OPN was conducted as a face-to-face survey. From April 2018 to November 2019, the OPN changed to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for module customers.In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held under Secure Access conditions in SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. (See below for information on other Secure Access OPN modules.)From August 2021, as coronavirus (COVID-19) restrictions were lifted across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remained sustainable. Secure Access OPN modulesBesides SN 8635 (the COVID-19 Module), other Secure Access OPN data includes sensitive modules run at various points from 1997-2019, including Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See the individual studies for further details and information on how to apply to use them. Main Topics: The non-core questions for this month were: Tobacco consumption (Module 210): this module was asked on behalf of HM Revenue and Customs to help estimate the amount of tobacco consumed as cigarettes. Due to the potentially sensitive nature of the data within this module, cases for respondents aged under 18 have been removed. Charitable giving (Module 338): this module was asked on behalf of the National Council for Voluntary Organisations and looks at ways people can give to charity. Disability monitoring (Module 363): this module was asked on behalf of the Department for Work and Pensions (DWP) which is interested in information on disability. The final two questions of the module ask about awareness of the Disability Discrimination Act. The module aims to identify the scale of problems those with long-term illnesses or disabilities have accessing goods, facilities and services. Due to the potentially sensitive nature of the data within this module, certain variables have been removed. A Special Licence version of Module 363, including the anonymised variables, can be found under SN 6793. Later life (Module MCE): this module was asked on behalf of DWP on behalf of a number of other government departments who are interested in what people think of the support available to help older people to continue to live independently in later life. Mobile tourist information (Module MCH): this module was asked on behalf of the Northern Ireland Tourist Board and aims to find out the extent to which mobile phones are used to find location information whilst on holiday in the UK. Multi-stage stratified random sample Face-to-face interview

People data is our proprietary mobile user dataset that links anonymous IDs to multiple attributes related to demographics, device ownership, audience segments, key locations, and more. This enables our partner brands to get a holistic view of a consumer based on their persona and be able to instantly gain actionable insights.

People Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as user demographics,anonymous id, device details, location, affluence, interests, traveled countries, and so on.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly/quarterly).

Here's the usecases being served: Consumer Insights: Gain a complete 360-degree view of the customer to detect behavioral changes, assess patterns, and forecast business effects. Data Enrichment; Leverage O2O consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment. Sales Forecasting: Analyze consumer behavior to predict sales and monitor performance of investments Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas.

Data Attributes: anonymous id id_type Age Gender Carrier Make Model OS os_version home_country Home_geohash Work_geohash Device_price Device_age Affluence Brands_visited Place_categories Geo_behaviour Interests travelled_countries