This report was released in September 2010. However, recent demographic data is available on the datastore - you may find other datasets on the Datastore useful such as: GLA Population Projections, National Insurance Number Registrations of Overseas Nationals, Births by Birthplace of Mother, Births and Fertility Rates, Office for National Statistics (ONS) Population Estimates

FOCUSONLONDON2010:POPULATIONANDMIGRATION

London is the United Kingdom’s only city region. Its population of 7.75 million is 12.5 per cent of the UK population living on just 0.6 per cent of the land area. London’s average population density is over 4,900 persons per square kilometre, this is ten times that of the second most densely populated region.

Between 2001 and 2009 London’s population grew by over 430 thousand, more than any other region, accounting for over 16 per cent of the UK increase.

This report discusses in detail the population of London including Population Age Structure, Fertility and Mortality, Internal Migration, International Migration, Population Turnover and Churn, and Demographic Projections.

Population and Migration report is the first release of the Focus on London 2010-12 series. Reports on themes such as Income, Poverty, Labour Market, Skills, Health, and Housing are also available.

REPORT:

Read the full report in PDF format.

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PRESENTATION:

To access an interactive presentation about population changes in London click the link to see it on Prezi.com

DATA:

To access a spreadsheet with all the data from the Population and Migration report click on the image below.

Report data

MAP:

To enter an interactive map showing a number of indicators discussed in the Population and Migration report click on the image below.

Interactive Maps

FACTS:

● Top five boroughs for babies born per 10,000 population in 2008-09:

1. Newham – 244.4
2. Barking and Dagenham – 209.3
3. Hackney – 205.7
4. Waltham Forest – 202.7
5. Greenwich – 196.2

-32. Havering – 116.8

-33. City of London – 47.0

● In 2009, Barnet overtook Croydon as the most populous London borough. Prior to this Croydon had been the largest since 1966

● Population per hectare of land used for Domestic building and gardens is highest in Tower Hamlets

● In 2008-09, natural change (births minus deaths) led to 78,000 more Londoners compared with only 8,000 due to migration. read more about this or click play on the chart below to reveal how regional components of populations change have altered over time.

Abstract copyright UK Data Service and data collection copyright owner.To conduct a course exercise that collects questionnaire-based information each year from a sample of students at the London School of Economics. The studies focus on background characteristics relevant to a student population, on attitudes to selected political and social issues, and on participation in various activities at LSE. Questions vary somewhat from year to year. Quota sample based on sex, undergraduate/graduate status, domestic/overseas status, and department Face-to-face interview 1991 1992 AGE AIDS DISEASE ALCOHOL USE ATTITUDES BIRTH ORDER BRITISH POLITICAL P... CHILD CARE COMPUTERS CRIME AND SECURITY DEGREES DEMONSTRATIONS PROT... DEVELOPING COUNTRIES DRUG ABUSE DRUG USE ECONOMIC SYSTEMS EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL INSTITU... EDUCATIONAL POLICY ENVIRONMENTAL CONSE... ENVIRONMENTAL MOVEM... EQUAL OPPORTUNITY ETHNIC GROUPS EUTHANASIA EXPECTATION FATHER S OCCUPATION FOOD ADDITIVES FOREIGN STUDENTS GENDER GENETIC SCREENING GOVERNMENT POLICY HAPPINESS HEALTH HIGHER AND FURTHER ... HIV INFECTIONS HOMOSEXUALITY Higher and further ... ILLEGITIMATE BIRTHS IMMIGRATION INCOME DISTRIBUTION INDUSTRIES INTERPERSONAL RELAT... KNOWLEDGE AWARENESS LEADERSHIP LECTURES LEGISLATURE LEISURE TIME ACTIVI... MARRIAGE MEAT MEN MENTAL DISORDERS MONETARY POLICY MOTHER S OCCUPATION NATIONALITY OCCUPATIONS PARENTS PARTNERSHIPS PERSONAL POLICE COMMUNITY RE... POLICE SERVICES POLICING POLITICAL ALLEGIANCE POLITICAL INTEREST POLITICAL UNIFICATION POLLUTION CONTROL POOR PERSONS POSTGRADUATE COURSES PRIVATE EDUCATION PRIVATE SCHOOLS PROPORTIONAL REPRES... RACIAL PREJUDICE RACISM RELIGION RELIGIOUS AFFILIATION RESOURCES CONSERVATION SATISFACTION SEMINARS SEXUAL BEHAVIOUR SIBLINGS SOCIAL CLASS SOCIAL INEQUALITY STUDENTS STUDY METHODS TRANSNATIONAL ENTER... UNEMPLOYMENT UPPER HOUSE VEGETARIANISM

UK residents by broad country of birth and citizenship groups, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

Seasonally and non-seasonally adjusted estimates of completed international visits. Based on International Passenger Survey data.

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project. Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK. The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older. Further information about the survey, including publications, can be downloaded from the Young Lives website. Constructed Files: The Rounds 1-3 Constructed Files, 2002-2009 are combined sub-sets of selected variables from Round 1, 2 and 3 of the Young Lives survey. One main constructed data file is available for each of the four countries. These are presented in a panel format and contain approximately 200 original and constructed variables, with the majority comparable across all three rounds.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Heating Degree Day (HDD) is a day in which the average temperature is below 15.5°C. It is the number of degrees above this threshold that counts as a Heating Degree Day. For example if the average temperature for a specific day is 15°C, this would contribute 0.5 Heating Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Heating Degree Days. Given the data shows the annual sum of Heating Degree Days, this value can be above 365 in some parts of the UK.Annual Heating Degree Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of HDD to previous values.What are the possible societal impacts?Heating Degree Days indicate the energy demand for heating due to cold days. A higher number of HDD means an increase in power consumption for heating, therefore this index is useful for predicting future changes in energy demand for heating.What is a global warming level?Annual Heating Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Heating Degree Days, an average is taken across the 21 year period. Therefore, the Annual Heating Degree Days show the number of heating degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each warming level and two baselines. They are named ‘HDD’ (Heating Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'HDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'HDD 2.5 median' is 'HDD_25_median'. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Heating Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Annual estimates of foreign-owned businesses by industry group, section, employment and turnover group, and country breakdown.

Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.

The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.

In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.

The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.

Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).

There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.

Further information about Next Steps may be found on the CLS website.

Secure Access datasets:

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

Users are only allowed one of the three Geographical Identifiers Census Boundaries studies: SN 8189 (2001 Census Boundaries), SN 8190 (2011 Census Boundaries), or SN 9337 (2021 Census Boundaries).

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

Internal migration by local authorities in England and Wales

Definitions: Internal migration is defined as residential moves between different local authorities (LAs) in the UK, including those that cross the boundaries between the four UK nations: England, Wales, Scotland and Northern Ireland. However, only moves affecting LAs in England and Wales are included; moves that occur solely within Scotland and/or Northern Ireland are excluded. The statistics also exclude any moves within a single LA and any international moves either into or out of the UK. The local authority and age/sex tables use this definition.

The regional tables follow the same principles, except that they only include moves that cross the boundaries of the English regions or the boundaries between the four UK nations. Any moves occurring within a single English region, or within Wales, are excluded.

Data sources: The quality of administrative data used in the production of internal migration estimates may change over time. These changes are outside the control of ONS.

Age: Defined as age as at 30 June 2012, so in many cases will be one year older than age at actual move.

Rounding: Values in tables are rounded to the nearest unit. For the age and sex table the rounding is to the nearest 10. This may mean that totals do not sum exactly.

Summary statistics data generated in Ferrari et al, 2014, Lancet Neurol (PMID: 24943344)The International FTD-Genetics Consortium (IFGC) shares the summary results data to allow other researchers to explore variants and/or loci for hypothesis driven work. The data provides information on ~ 6M markers and includes information about: marker – trait – allele 1 and 2 – OR or Beta – standard error – p-value, chromosome and bp position.To prevent identification of individuals, allele frequency data are not released.The data consists of the summary statistics generated during discovery phase (phase I) of the study including: bvFTD (n=1377 vs 2754 ctrls) AND/OR SD (n=308 vs 616 ctrls) AND/OR PNFA (n=269 vs 538 ctrls) AND/OR FTD-MND (n=200 vs 400 ctrls) AND/OR subtypes meta-analysis.Note:1. The IFGC requests to be included among co-authors in publications that might result from the use of this data as “The International FTD-Genetics Consortium (IFGC)” following Pubmed guidelines where Consortia or working group authors shall be listed on PubMed as collaborators rather than authors, where collaborator names are searchable on PubMed in the same way as authors. The acknowledgments associated with the IFGC as well as the IFGC members are provided as separate pdf document, together with the summary statistics;2. Publications (including but not limited to manuscripts, presentation, patent, grant) based on this IFGC’s dataset shall include the citation of the original work (Ferrari et al, 2014, Lancet Neurol, PMID: 24943344) and add the following to the acknowledgement section: “We thank the International FTD-Genetics Consortium (IFGC) for summary data”.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.2.]What does the data show? The Annual Count of Hot Summer Days is the number of days per year where the maximum daily temperature is above 30°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘hot summer days’ is used to refer to the threshold and temperatures above 30°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Hot Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of hot summer days to previous values.What are the possible societal impacts?The Annual Count of Hot Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 30°C. Impacts include:Increased heat related illnesses, hospital admissions or death.Transport disruption due to overheating of railway infrastructure. Overhead power lines also become less efficient. Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Extreme Summer Days (days above 35°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Hot Summer Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Hot Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Hot Summer Days show the number of hot summer days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘HSD’ (where HSD means Hot Summer Days), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Hot Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Hot Summer Days 2.5 median’ is ‘HotSummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HSD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Hot Summer Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

GLA Economics is providing on an occasional basis medium-term forecasts of tourism visitor nights in London for domestic and international tourists. The forecasts are on a quarterly basis to 2026 and an annual basis to 2032. The estimates tri-angulate data from a number of sources. The methodology note provides more information on how this has been done.

Abstract copyright UK Data Service and data collection copyright owner.

The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration.

The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration.

For the International Passenger Survey 2023, additional variables have been included in the “QReg” dataset, as detailed in the linked document. The “Custom” dataset is the updated version of “QcontCust,” with changes also outlined in the linked document. The “QReg” dataset has been expanded to provide more variables for analysis. The “Custom” dataset has been updated with extra variables for enhanced analysis and updated variable names where necessary.

Main Topics:

Each of the four subject areas of this study covers different topics, as it follows:

'Airmiles': quarter; flow; serial; UK port or route; direct leg overseas port; final overseas port; distance from UK port to first port; from first to second port; from UK port to second port

'Alcohol': year; quarter; month; flow; serial; money spent on spirits; wine; beer; champagne; cigarettes; hand-rolled and other tobacco

'Qreg': year; quarter; month; flow; serial; towns stayed in overnight; details of type of accommodation; number of nights spent in towns; expenditure in towns; regional stay weight; regional visit weight; regional expenditure weight; various validation checks

'Custom': year; quarter; month; flow; serial; nationality; country of visit/residence; UK counties; date visit began; purpose of visit; intended length of stay; number of people; package tour and cost; expenditure pre-, post- and during visit; flight prefix and suffix; first carrier air or shipping line; direct leg overseas port; final overseas port; long- or short-haul; type of vehicle; number travelling in vehicle; fare type and cost; class of travel; business trip; type of flight; flight origin or destination; gender; age group; UK port or route; quality of response; date of interview; money transfer, net and total expenditure; type of transport; arrivals (number of adults); departures (type of travelling group, number of adults and children); weighting variables; various validation checks

Daytime population - The estimated number of people in a borough in the daytime during an average day, broken down by component sub-groups.

The figures given are an average day during school term-time. No account has been made for seasonal variations, or for people who are usually in London (resident, at school or working), but are away visiting another place.

Sources include the Business Register and Employment Survey (BRES) (available under license), Annual Population Survey (APS), 2011 Census, Department for Education (DfE), International Passenger Survey (IPS), GB Tourism Survey (GBTS), Great Britain Day Visit Survey (GBDVS), GLA Population Projections, and GLA Economics estimates (GLAE).

The figures published in these sources have been used exactly as they appear - no further adjustments have been made to account for possible sampling errors or questionnaire design flaws.

Day trip visitors are defined as those on day trips away from home for three hours or more and not undertaking activities that would regularly constitute part of their work or would be a regular leisure activity.

International visitors – people from a country other than the UK visiting the location;
Domestic overnight tourists – people from other parts of the UK staying in the location for at least one night.
All visitor data is modelled and unrounded.

This edition was released on 14 January 2015 and replaces the previous estimates for 2012.

GLA resident population, 2011 Census resident population, and 2011 Census workday populations (by sex) included for comparison.

For more workday population data by age use the Custom Age-Range Tool for Census 2011 Workday population , or download data for a range of geographical levels from NOMIS.

Annual estimates of UK regional gross disposable household income (GDHI) at current prices for ITL1, ITL2 and ITL3 regions.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).

Release of Sweeps 1 to 4 to Long Format (Summer 2020)
To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

Latest edition information:
For the second edition (February 2017), the data file has been updated to correct discrepancies found in the first edition.

This report contains an introduction to PM2.5, summarises our current understanding of PM2.5 concentrations and exposure, discusses the findings of research undertaken by the GLA and TfL into the extent of PM2.5 pollution in London, and assesses the potential for meeting World Health Organisation guidelines by 2030. Our analysis found that at present all Londoners are exposed to concentrations higher than WHO air quality guidelines, but, if PM2.5 reduction measures within the Mayor’s Transport Strategy and London Environment Strategy are accompanied by co-operation on a national and international level, the guideline limit is achievable by 2030.

The accompanying map is the annual mean PM2.5 concentration in Greater London for 2013 by Output Area, also provided is the data behind this map, which includes the annual average PM2.5 concentration of each Output Area (OA) in Greater London. You may need to use the OA data mapping available from the London Datastore to identify specific output areas.

This dataset is supplementary data to "Parameter Estimation for Water Distribution Networks with Multiple Head Loss Formulae" in ASCE Journal of Water Resources and Planning Management (under review). The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence. Any use of this dataset must credit the authors. BWFLnet is an operational network in Bristol, UK, operated by Bristol Water. The data provided is a the product of a long term research partnership between Bristol Water and Infrasense Labs at Imperial College London on dynamically adaptive networks. We acknowledge the financial support of EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future) for the acquisition of this data set.

All data provided is recorded hydraulic data with locations and names anonymised. The authors hope that the publication of this dataset will facilitate the reproducibility of research in hydraulic model calibration as well as broader research in the water distribution sector.