This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS).

The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided.

Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed.

Two files are available to download:

• Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level.
• Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole.

A Tableau visualisation tool is also available.

Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth

Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

Face-to-face interview survey data from a baseline sample of 1360 people aged 65+ and 85+ living at home in East London and Essex from 1987, about their physical and mental health, well-being, social networks, support and needs, using established measurement scales. They were also followed up and re-interviewed face-to-face 2.5-3 years post baseline interview; and all responders' deaths were flagged (the non responders and untraced (usually ghost patients as the sample was taken from family practitioner authority lists) are assigned missing in the dataset). It was funded by health authorities and the Joseph Rowntree Trust.

In 2024, Croydon had the largest population among London boroughs at just over 409,340, followed by Barnet at 405,050.

The English Longitudinal Study of Ageing (ELSA) is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the "https://www.elsa-project.ac.uk/"> ELSA project website, the or Natcen Social Research: ELSA web pages.

Wave 11 data has been deposited - May 2025

For the 45th edition (May 2025) ELSA Wave 11 core and pension grid data and documentation were deposited. Users should note this dataset version does not contain the survey weights. A version with the survey weights along with IFS and financial derived datasets will be deposited in due course. In the meantime, more information about the data collection or the data collected during this wave of ELSA can be found in the Wave 11 Technical Report or the User Guide.

Wave 10 Accelerometry data has been deposited - August 2025

For the 46th edition (August 2025) ELSA Wave 10 Accelerometry data and documentation, along with a new version of the Wave 10 Technical Report, have been deposited. Between June 2021 and October 2022, approximately 75% of ELSA households (including core members and partners) were randomly selected and invited to wear an Axivity AX3 tri-axial accelerometer for eight days and nights. Accelerometer has been used to objectively measure movement behaviours for the first time in ELSA. Four datasets including data collected by accelerometers were deposited. Datasets include: output from the Biobank accelerometer analysis (bbaa), and 24 hour movement behaviours; the step count data; overnight sleep and sleep stage data.

Wave 10 HCAP2 End of Life data has been deposited - September 2025:

For the 47th edition (September 2025), the HCAP2 (Wave 10) End of Life interview data and questionnaire documentation were deposited. The End of Life interview completes the information collected at previous waves of ELSA by interviewing a close friend or relative of the deceased ELSA sample member after their death. Previous End of Life interviews were carried out alongside Waves 2, 3, 4, and 6 of ELSA. The fieldwork for HCAP2 (Wave 10) End of Life took place between 2022-2024. For more information please refer to the questionnaire documentation. The End of Life User Guide will be updated at a later date.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please read the ELSA User Guide or if you still have questions contact elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

For information on obtaining data from ELSA that are not held at the UKDS, see the ELSA Genetic data access and Accessing ELSA data webpages.

Harmonized dataset:

Users of the Harmonized dataset who prefer to use the Stata version will need access to Stata MP software, as the version G3 file contains 11,779 variables (the limit for the standard Stata 'Intercooled' version is 2,047).

ELSA COVID-19 study:
A separate ad-hoc study conducted with ELSA respondents, measuring the socio-economic effects/psychological impact of the lockdown on the aged 50+ population of England, is also available under SN 8688, English Longitudinal Study of Ageing COVID-19 Study.

This GLA Intelligence Update takes a brief look at evidence around the wealth gap in London and examines how this has changed in recent years. Key Findings There is a significant gap between the rich and poor in London, both in terms of their wealth and their income. A higher proportion of the wealthiest households are in the South East of England than in London. Pension wealth accounts for more than half the wealth of the richest ten per cent of the population. In London, the tenth of the population with the highest income have weekly income after housing costs of over £1,000 while people in the lowest tenth have under £94 per week. The gap between rich and poor is growing, with the difference between the average income for the second highest tenth and second lowest tenth growing around 14 per cent more than inflation since 2003.

FOCUSONLONDON2012:SKILLS:DEGREESOFQUALIFICATION London’s diverse economy, and status as one of the most important cities in the world calls for a highly skilled workforce. Qualifications are considered an important predictor of success in a labour market. This edition of Focus on London, authored by Slawek Kozdras, explores the skills and qualification levels of young people before moving on to an analysis of adults’ qualifications and the skills that different occupations in London require. PRESENTATION: This interactive presentation focuses on achievements of 15 year olds, and compares trends of GCSE results in London, and England, with results in the OECD PISA tests, and shows that while GCSE results are still rising sharply, results in the PISA tests have a slight downward trend in London and the UK. View Degrees of Qualification on Prezi.com FACTS: Some interesting facts from the data… ● Boroughs with the highest increase in the proportion of pupils achieving 5 GCSEs at least A*-C level, including in English and mathematics (maintained schools), between 2005/06 and 2010/11: Tower Hamlets (+26%) Southwark (+23%) Haringey (+23%) -32. Ealing (+7%) ● Regions with the highest proportion of people aged 25-44 with degree-level qualifications: London (51%) Scotland (44%) South East (41%) -13. Merseyside (30%) ● Industries with the highest percentage of people with degree-level qualifications: Banking and finance (64%) Public administration, education and health (63%) Other services (53%) -9. Agriculture, forestry and fishing (23%)

T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples) is a NIDDK-funded international research consortium which seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. T2D-GENES Project 1 is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from 14 cohorts that are listed in Table 1. The strategy was to perform deep exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. Sequencing was performed at the Broad Institute using the Agilent v2 capture reagent on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study does not have a sub study, as it is not consented to be deposited in dbGAP. Table 1. T2D-GENES Whole Exome Sequencing Studies Ancestry Study Countries of Origin # Cases # Controls African American Jackson Heart Study US 502 527 African American Wake Forest School of Medicine Study US 518 532 East Asian Korea Association Research Project Korea 526 561 East Asian Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 486 592 European Ashkenazi US, Israel 506 352 European Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European Malmö-Botnia Study Finland, Sweden 478 443 Hispanic San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 219 Hispanic Starr County, Texas US 749 704 South Asian London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 530 538 South Asian Singapore Indian Eye Study Singapore (Indian Asian) 563 585 The London Life Sciences Population Study (LOLIPOP) contributed 530 cases and 538 controls to T2D-GENES Project 1.

Data supporting “Weekend and weekday differences in associations between the residential built environment and physical activity: findings from the ENABLE-London Study”The Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) study is a natural experiment. The primary aims were to examine whether objectively measured physical activity levels and body fatness showed change after two years, amongst individuals who moved to East Village compared with a control population who did not move to East Village. East Village is the former London 2012 Olympic Athletes’ Village repurposed to provide social, affordable and market-rent accommodation with high levels of walkability and close proximity to improved public transport. Other data collected include mental health and wellbeing, mode of travel and information on the participants’ built environment where they were living. A description of the baseline cohort, results from the main follow-up analysis and other secondary follow-up analyses of the data have already been published and are referenced below. These data relate to baseline analyses of the built environment data.A cohort of 1278 adults aged 16+ who were looking to move into three different housing tenures in East Village was recruited between January 2013 and January 2016. Ethical approval was granted by City Road and Hampstead Review Board (REC reference number 12LO1031). The three housing tenures were social, intermediate (affordable market-rent/shared ownership/shared equity) and market-rent and were used as a marker of socio-economic status. Follow-up of participants was carried out two years after their baseline examination when approximately half the cohort had moved to East Village. Information was collected on participants at baseline and follow-up using computer-assisted self-complete questionnaires and physical examination (height, weight, body fat). Participants were asked to wear an accelerometer for seven consecutive days to objectively measure their physical activity. For those participants living in Greater London at baseline (n=1064), their residential locations were used to derive a range of built environment factors including walkability, distance to parks and accessibility to public transport.The aims of this study were to assess whether at baseline, the residential built environment was associated with physical activity on week days and weekend days separately. Also, to explore two possible pathways in which the built environment may contribute to household-level socio-economic differences in physical activity levels.The dataset available includes the variables listed below. There are restrictions on the availability of these data due to the signed consent agreements around data security, which only allow access to external researchers for research monitoring purposes. Requestors wishing to access the data for the purposes of replicating or checking our analyses should contact the SGUL RDM service at researchdata@sgul.ac.uk.Variables availableDemographics: sex, age group, ethnic group, housing groupResidential built environment variables at baseline: walkability, distance to closest local park, distance to closest district park, distance to closest metropolitan park, accessibility to public transport (Transport for London PTAL score)Physical activity variables at baseline: average adjusted daily steps on week days and weekend days, average adjusted minutes of moderate-to-vigorous-physical-activity (MVPA) on week days and weekend days.

IRR, incidence rate ratio (XLSX)

This data is experimental, see the ‘Access Constraints or User Limitations’ section for more details. This dataset has been generalised to 10 metre resolution where it is still but the space needed for downloads will be improved.A set of UK wide estimated travel area geometries (isochrones), from Output Area (across England, Scotland, and Wales) and Small Area (across Northern Ireland) population-weighted centroids. The modes used in the isochrone calculations are limited to public transport and walking. Generated using Open Trip Planner routing software in combination with Open Street Maps and open public transport schedule data (UK and Ireland).The geometries provide an estimate of reachable areas by public transport and on foot between 7:15am and 9:15am for a range of maximum travel durations (15, 30, 45 and 60 minutes). For England, Scotland and Wales, these estimates were generated using public transport schedule data for Tuesday 15th November 2022. For Northern Ireland, the date used is Tuesday 6th December 2022.The data is made available as a set of ESRI shape files, in .zip format. This corresponds to a total of 18 files; one for Northern Ireland, one for Wales, twelve for England (one per English region, where London, South East and North West have been split into two files each) and four for Scotland (one per NUTS2 region, where the ‘North-East’ and ‘Highlands and Islands’ have been combined into one shape file, and South West Scotland has been split into two files).The shape files contain the following attributes. For further details, see the ‘Access Constraints or User Limitations’ section:AttributeDescriptionOA21CD or SA2011 or OA11CDEngland and Wales: The 2021 Output Area code.Northern Ireland: The 2011 Small Area code.Scotland: The 2011 Output Area code.centre_latThe population-weighted centroid latitude.centre_lonThe population-weighted centroid longitude.node_latThe latitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_lonThe longitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_distThe distance, in meters, between the population-weighted centroid and the nearest Open Street Map “highway” node.stop_latThe latitude of the nearest public transport stop to the population-weighted centroid.stop_lonThe longitude of the nearest public transport stop to the population-weighted centroid.stop_distThe distance, in metres, between the population-weighted centroid and the nearest public transport stop.centre_inBinary value (0 or 1), where 1 signifies the population-weighted centroid lies within the Output Area/Small Area boundary. 0 indicates the population-weighted centroid lies outside the boundary.node_inBinary value (0 or 1), where 1 signifies the nearest Open Street Map “highway” node lies within the Output Area/Small Area boundary. 0 indicates the nearest Open Street Map node lies outside the boundary.stop_inBinary value (0 or 1), where 1 signifies the nearest public transport stop lies within the Output Area/Small Area boundary. 0 indicates the nearest transport stop lies outside the boundary.iso_cutoffThe maximum travel time, in seconds, to construct the reachable area/isochrone. Values are either 900, 1800, 2700, or 3600 which correspond to 15, 30, 45, and 60 minute limits respectively.iso_dateThe date for which the isochrones were estimated, in YYYY-MM-DD format.iso_typeThe start point from which the estimated isochrone was calculated. Valid values are:from_centroid: calculated using population weighted centroid.from_node: calculated using the nearest Open Street Map “highway” node.from_stop: calculated using the nearest public transport stop.no_trip_found: no isochrone was calculated.geometryThe isochrone geometry.iso_hectarThe area of the isochrone, in hectares.Access constraints or user limitations.These data are experimental and will potentially have a wider degree of uncertainty. They remain subject to testing of quality, volatility, and ability to meet user needs. The methodologies used to generate them are still subject to modification and further evaluation.These experimental data have been published with specific caveats outlined in this section. The data are shared with the analytical community with the purpose of benefitting from the community's scrutiny and in improving the quality and demand of potential future releases. There may be potential modification following user feedback on both its quality and suitability.For England and Wales, where possible, the latest census 2021 Output Area population weighted centroids were used as the starting point from which isochrones were calculated.For Northern Ireland, 2011 Small Area population weighted centroids were used as the starting point from which isochrones were calculated. Small Areas and Output Areas contain a similar number of households within their boundaries. 2011 data was used because this was the most up-to-date data available at the time of generating this dataset. Population weighted centroids for Northern Ireland were calculated internally but may be subject to change - in the future we aim to update these data to be consistent with Census 2021 across the UK.For Scotland, 2011 Output Area population-weighted centroids were used as the starting point from which isochrones were calculated. 2011 data was used because this was the most up-to-date data available at the time of work.The data for England, Scotland and Wales are released with the projection EPSG:27700 (British National Grid).The data for Northern Ireland are released with the projection EPSG:29902 (Irish Grid).The modes used in the isochrone calculations are limited to public transport and walking. Other modes were not considered when generating this data.A maximum value of 1.5 kilometres walking distance was used when generating isochrones. This approximately represents typical walking distances during a commute (based on Department for Transport/Labour Force Survey data and Travel Survey for Northern Ireland technical reports).When generating Northern Ireland data, public transport schedule data for both Northern Ireland and Republic of Ireland were used.Isochrone geometries and calculated areas are subject to public transport schedule data accuracy, Open Trip Planner routing methods and Open Street Map accuracy. The location of the population-weighted centroid can also influence the validity of the isochrones, when this falls on land which is not possible or is difficult to traverse (e.g., private land and very remote locations).The Northern Ireland public transport data were collated from several files, and as such required additional pre-processing. Location data are missing for two bus stops. Some services run by local public transport providers may also be missing. However, the missing data should have limited impact on the isochrone output. Due to the availability of Northern Ireland public transport data, the isochrones for Northern Ireland were calculated on a comparable but slight later date of 6th December 2022. Any potential future releases are likely to contained aligned dates between all four regions of the UK.In cases where isochrones are not calculable from the population-weighted centroid, or when the calculated isochrones are unrealistically small, the nearest Open Street Map ‘highway’ node is used as an alternative starting point. If this then fails to yield a result, the nearest public transport stop is used as the isochrone origin. If this also fails to yield a result, the geometry will be ‘None’ and the ‘iso_hectar’ will be set to zero. The following information shows a further breakdown of the isochrone types for the UK as a whole:from_centroid: 99.8844%from_node: 0.0332%from_stop: 0.0734%no_trip_found: 0.0090%The term ‘unrealistically small’ in the point above refers to outlier isochrones with a significantly smaller area when compared with both their neighbouring Output/Small Areas and the entire regional distribution. These reflect a very small fraction of circumstances whereby the isochrone extent was impacted by the centroid location and/or how Open Trip Planner handled them (e.g. remote location, private roads and/or no means of traversing the land). Analysis showed these outliers were consistently below 100 hectares for 60-minute isochrones. Therefore, In these cases, the isochrone point of origin was adjusted to the nearest node or stop, as outlined above.During the quality assurance checks, the extent of the isochrones was observed to be in good agreement with other routing software and within the limitations stated within this section. Additionally, the use of nearest node, nearest stop, and correction of ‘unrealistically small areas’ was implemented in a small fraction of cases only. This culminates in no data being available for 8 out of 239,768 Output/Small Areas.Data is only available in ESRI shape file format (.zip) at this release.https://www.openstreetmap.org/copyright