London census data

Downloaded from: https://data.london.gov.uk/dataset/981e9136-a06a-44ec-a067-10f3d786cd3f

License: UK Open Government License

There were approximately 4.9 million people employed in London as of the second quarter of 2025, compared with 3.4 million in the first quarter of 2000.

Images are of 102 adult faces 1350x1350 pixels in full colour. Template files mark out 189 coordinates delineating face shape, for use with Psychomorph or WebMorph.org.Self-reported age, gender and ethnicity are included in the file london_faces_info.csv. Attractiveness ratings (on a 1-7 scale from "much less attractiveness than average" to "much more attractive than average") for the neutral front faces from 2513 people (ages 17-90) are included in the file london_faces_ratings.csv.All individuals gave signed consent for their images to be "used in lab-based and web-based studies in their original or altered forms and to illustrate research (e.g., in scientific journals, news media or presentations)." Images were taken in London, UK, in April 2012.

This dataset is about books. It has 1 row and is filtered where the book is Police and people in London : the PSI report. It features 7 columns including author, publication date, language, and book publisher.

The report looks into the various drivers of social exclusion amongst older people (although many of these indicators are equally relevant amongst all age groups) and attempts to identify areas in London where susceptibility is particularly high. Six key drivers have been included with various indicators used in an attempt to measure these. The majority of these indicators are at Lower Super Output Area (LSOA) level in an effort to identify areas at as small a geography as possible. Key Driver Indicator Description Economic Situation Income deprivation Income Deprivation Affecting Older People Score from the 2015 Indices of Deprivation Transport Accessibility Public Transport Average Public Transport Accessibility Score Car access Percentage aged 65 and over with no cars or vans in household Household Ties One person households Percentage aged 65+ living alone Providing unpaid care Percentage aged 65+ providing 50 or more hours of unpaid care a week Neighbourhood Ties Proficiency in English Percent aged 65+ who cannot speak English well Churn Rate Churn Rate: (inflow+outflow) per 100 population Health Mental health Estimated prevalence of dementia amongst population aged 65 and over (%) General health Percentage aged 65+ with a limiting long-term health problem or disability Safety Fear of crime Percentage in borough worried about anti-social behaviour in area Percentage in borough who feel unsafe walking alone after dark Crime rates Total offences per 100 population

Dataset

This dataset was created by Muhammad

Contents

In 2024/25, approximately 5,462 of the 13,231 people seen to be sleeping rough in London were from the UK, the most-common nationality that year. The second-most common nationality was Romanian, at 828 people.

Dataset

This dataset was created by haohao

Contents

Opinions of people aged 16 and over about their feelings of community strength and togetherness The survey tracks the latest trends and developments across areas key to encouraging social action and empowering communities. The objectives of the survey are to provide robust, nationally representative data on behaviours and attitudes within communities to inform and direct policy and action in these areas. The Community Life Survey incorporates a small number of priority measures from the Citizenship Survey, which ran from 2001-2011. Questions included in this analysis: How strongly do you belong to Britain How strongly do you belong to your neighbourhood Whether agree or disagree that people in this neighbourhood pull together to improve the neighbourhood How often do you chat to any of your neighbours, more than to just say hello Trust in people living in neighbourhood Agree that local area is place where people from different backgrounds get on well together How comfortable would you be asking a neighbour to mind your child(ren) for half an hour Formal or informal or employer volunteering in the last 12 months How often email or write to family members or friends How often exchange text messages or instant messages with family members or friends How often meet up in person with family members or friends

Estimate of People of All Ages in Poverty in New London County, CT was 23102.00000 Persons in January of 2021, according to the United States Federal Reserve. Historically, Estimate of People of All Ages in Poverty in New London County, CT reached a record high of 28868.00000 in January of 2014 and a record low of 15958.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate of People of All Ages in Poverty in New London County, CT - last updated from the United States Federal Reserve on November of 2025.

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.

Indicator Description Economic Situation Income deprivation Income Deprivation Affecting Older People Score from the 2015 Indices of Deprivation

Estimate of People Age 0-17 in Poverty in New London County, CT was 6212.00000 Persons in January of 2021, according to the United States Federal Reserve. Historically, Estimate of People Age 0-17 in Poverty in New London County, CT reached a record high of 9333.00000 in January of 2015 and a record low of 4857.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate of People Age 0-17 in Poverty in New London County, CT - last updated from the United States Federal Reserve on November of 2025.

Content

The London boroughs profiles Data about demography, diversity, labour market, economy, community safety, housing, environment, transport, children, health and governance

Acknowledgements

Thanks for taking your time to look at this data and thanks for any suggestions.

Inspiration

I am new to data analysis and I would like some suggestions on how to analyse this dataset and possibly create a visualasation, or predictive analysis.

This page contains analyses of serious youth violence conducted by the GLA’s Strategic Crime Analysis team and MOPAC Evidence and Insight Team to inform work to prevent and reduce serious violence affecting young people in London. The page contains 4 separate analyses. The first two analyses in 2018 were produced in support of an over-arching report ‘Progressing a Public Health Approach to Violence Prevention and Reduction’. The third contains an update of the data pack produced in July 2019. And the most recent analysis published in 2021, provides more comprehensive analysis and statistical modelling which makes use of improvements to public health data related to violence.

In 2024/25, 13,231 people who were seen to be sleeping rough in London compared with 11,993 in the previous reporting year, and the most reported during this time period. The number of people reported to be sleeping rough has steadily increased throughout this time period, with the dip in 2020/21, and 2022/23, likely related to the COVID-19 pandemic. Demographics of London's homeless As of the most recent reporting year, over 2,000 of London's rough sleepers were in the borough of Westminster, the most of any London borough. In terms of gender, the majority of rough sleepers are male, with more than 10,000 men seen to be sleeping rough, compared with 2,149 women, and 18 non-binary people. The most common age group was among those aged between 36 and 45 years old, at more than 3,900, compared with 1,411 25 and under, 3,580 aged between 26 and 34, 2,860 aged 45 and 55, and around 1,578 over 55s. Homelessness in the U.S. Homelessness is also an important social issue in several other countries. In the United States, for example, there were estimated to be approximately 653,104 people experiencing homelessness in 2023. This was a noticeable increase on the previous year, and the highest number between 2007 and 2023. When looking at U.S. states, New York had the highest homelessness rate, at 52 individuals per 10,000 population, followed by Vermont at 51.

Estimated Percent of People of All Ages in Poverty for New London County, CT was 8.90% in January of 2021, according to the United States Federal Reserve. Historically, Estimated Percent of People of All Ages in Poverty for New London County, CT reached a record high of 11.10 in January of 2014 and a record low of 6.30 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimated Percent of People of All Ages in Poverty for New London County, CT - last updated from the United States Federal Reserve on November of 2025.

This dataset brings together all 698 known references to ‘Black’ or possibly Black African heritage people or groups in records of London criminal justice (1720-1841). Each entry includes references to primary sources mentioning the person(s), including in the Old Bailey Proceedings, Ordinary’s Accounts, and Middlesex Criminal Registers. Individuals are trial witnesses, victims, defendants, and people mentioned in passing during testimony. For each entry, a confidence level is offered by the authors, as a person’s ethnicity cannot always be determined with certainty. Evidence for making that judgment is provided. This dataset is useful for anyone interested in Black history in Britain, Black people and justice, or Black London during the age of enslavement.

Significant background material is available on the Old Bailey Online website, which provides additional context for these records. The authors also recommend the following works:

* Kathleen Chater. Untold Histories: Black People in England and Wales during the Period of the British Slave Trade, c. 1660-1807 (Manchester, 2011).
* Norma Myers, Reconstructing the Black Past (Frank Cass, 1996).
* Marika Sherwood. ‘Blacks in the Gordon Riots’, History Today, vol. 47 (1997), 24-28.

Each record includes details on the name of the Black individual(s), as well as information on up to three sources in which he/she/they have been identified, and an indication by the authors on the likelihood the person is actually Black.

Each entry has 17 columns of data, all of which are described in full in the ReadMe.txt file.

This work looks at in-work poverty in London between 1996 and 2023. It provides an overview of the links between working-age poverty and employment participation at the individual and household levels in the capital. Some key findings include:

• Poverty has changed. There are now more people in poverty in London who are in a working family than in a workless family. The reverse was true 30 years ago.
• Insecure forms of work such as part-time work and self-employment are linked to poverty.
• Ethnic minority workers and those with no educational qualifications are most likely to be working and in poverty.
• Working families with three or more children have seen their poverty rate increase sharply since the introduction of the two-child benefit cap on Universal Credit.
• Single parents have a high likelihood of in-work poverty, though the number of people in work and in poverty has also risen sharply among Londoners living as a couple.

London's Population Over Time

For trend prediction London's population

By Eva Murray [source]

About this dataset

This file contains data on the projected population of London from 2011 to 2050. The data comes from the London Datastore and offers a glimpse into the future of one of the world's most populous cities

How to use the dataset

Research Ideas

• Predicting crime rates based on population growth
• Determining which areas of London will need more infrastructure to accommodate the growing population
• Planning for different marketing and advertising strategies based on demographics

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: central_trend_2017_base.csv | Column name | Description | |:--------------|:------------------------------------| | gss_code | The GSS code for the area. (String) | | district | The name of the district. (String) | | component | The population component. (String) | | sex | The sex of the population. (String) | | age | The age of the population. (String) | | 2011 | The population in 2011. (Integer) | | 2012 | The population in 2012. (Integer) | | 2013 | The population in 2013. (Integer) | | 2014 | The population in 2014. (Integer) | | 2015 | The population in 2015. (Integer) | | 2016 | The population in 2016. (Integer) | | 2017 | The population in 2017. (Integer) | | 2018 | The population in 2018. (Integer) | | 2019 | The population in 2019. (Integer) | | 2020 | The population in 2020. (Integer) | | 2021 | The population in 2021. (Integer) | | 2022 | The population in 2022. (Integer) | | 2023 | The population in 2023. (Integer) | | 2024 | The population in 2024. (Integer) | | 2025 | The population in 2025. (Integer) | | 2026 | The population in 2026. (Integer) | | 2027 | The population in 2027. (Integer) | | 2028 | The population in 2028. (Integer) | | 2029 | The population in 2029. (Integer) | | 2030 | The population in 2030. (Integer) | | 2031 | The population in 2031. (Integer) | | 2032 | The population in 2032. (Integer) | | 2033 | The population in 2033. (Integer) | | 2034 | The population in 2034. (Integer) | | 2035 | The population in 2035. (Integer) | | 2036 | The population in 2036. (Integer) | | 2037 | The population in 2037. (Integer) | | 2038 | The population in 2038. (Integer) | | 2039 | The population in 20 |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.