Dataset

This dataset was created by nickwu7

Contents

Dataset

This dataset was created by linze.yu

Released under CC BY-NC-SA 4.0

Contents

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

About this dataset

Context

The dataset tabulates the population of London by race. It includes the population of London across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of London across relevant racial categories.

Key observations

The percent distribution of London population by race (across all racial categories recognized by the U.S. Census Bureau): 91.78% are white, 2.40% are Black or African American, 0.12% are American Indian and Alaska Native, 1.70% are Asian, 0.14% are some other race and 3.87% are multiracial.

https://i.neilsberg.com/ch/london-oh-population-by-race.jpeg" alt="London population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the London
• Population: The population of the racial category (excluding ethnicity) in the London is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of London total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for London Population by Race & Ethnicity. You can refer the same here

London Airbnb Dataset (2009 - Nov 2019)

This dataset comprises Airbnb listings in London, UK from 2009 to 5th November 2019. The original dataset comes from InsideAirbnb but I have done some cleaning of the data to make it easier for analysis and visualization.

Cleaning Process

I have:

• Removed what I felt were irrelevant columns or columns with majority NA values
• Changed "t" and "f" values to boolean form
• Replaced "none" and "N/A" with NA values
• Changed Dollar and Percentage values from character format to numeric
• Changed Dates to R readable format
• Changed numerical variables to numerical form
• Added a numerical factor form for neighbourhood_cleansed and room_type
• Removed punctuations for amenities

Information These published reports present information from the multi-agency database Combined Homelessness and Information Network (CHAIN), about people seen rough sleeping by outreach teams in London. CHAIN, which is commissioned and funded by the Greater London Authority (GLA) and managed by Homeless Link, represents one of the UK’s most detailed and comprehensive sources of information about rough sleeping. Services that record information on CHAIN include outreach teams, assessment centres, accommodation projects, day centres and other specialist projects. The system allows users to share information about work done with people sleeping rough and about their needs, ensuring that they receive the most appropriate support and that efforts are not duplicated. In these reports, people are counted as having been seen rough sleeping if they have been encountered by a commissioned outreach worker bedded down on the street, or in other open spaces or locations not designed for habitation, such as doorways, stairwells, parks or derelict buildings. The report does not include people from “hidden homeless” groups such as those “sofa surfing” or living in squats, unless they have also been seen bedded down in one of the settings outlined above. Separate reports are produced for London as a whole and for individual boroughs, and these are published each quarter. There are also annual reports that contain aggregated information for each full year. Interactive Visualisation Tool Quarterly Data Tool Annual Data Tool A suite of online interactive charts and maps based on CHAIN data is available by clicking the above links. The data available via these tools mirrors that presented in the published PDF documents, with the addition of filters and other enhancements to allow users to interrogate the data. The Quarterly Data Tool shows data from the last eight quarters, and the Annual Data Tool shows data from the last five years. Organisations Using CHAIN A list of the organisations which have signed the CHAIN Data Protection Agreement and are able to access the live CHAIN system is also available to download. PDF Reports & Data tables As of January 2024, published CHAIN PDF reports are accompanied by an OpenDocument Spreadsheet file providing the underlying data in an accessible aggregated tabular format. The file includes data at local authority level, and for London overall, including comparative data for previous periods. There is also an accompanying explanatory notes document, which provides important contextual information about the data. Please click the links below to download a zip file containing the PDF reports and OpenDocument Spreadsheet for the corresponding timeframe. Publication Schedule Reports are published 1 month after the end of each quarter and one quarter after the end of each year. The linked document below provides details of forthcoming publications Quarterly and Annual Report Schedule 2025/26 2025/26

About this dataset

Context

The dataset tabulates the population of London by race. It includes the population of London across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of London across relevant racial categories.

Key observations

The percent distribution of London population by race (across all racial categories recognized by the U.S. Census Bureau): 89.24% are white, 1.70% are Black or African American, 0.23% are American Indian and Alaska Native, 1.86% are Asian, 0.02% are Native Hawaiian and other Pacific Islander, 0.15% are some other race and 6.79% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the London
• Population: The population of the racial category (excluding ethnicity) in the London is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of London total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for London Population by Race & Ethnicity. You can refer the same here

London's Airbnb

Airbnb listings in London

About this dataset

This dataset provides information on Airbnbs in London. Each row represents one listing, and there are a variety of columns with information on the listing, such as the name, host, price, etc.

This dataset could be used to study patterns in Airbnb pricing, to understand how Airbnbs are being used in London, or to compare different neighborhoods in London

How to use the dataset

If you're looking for information on Airbnbs in London, this dataset is a great place to start. It provides information on the listings and reviews for Airbnb in the city of London.

Airbnb is a popular vacation rental platform that allows travelers to find and book accommodations around the world. With over 3 million listings in more than 65,000 cities, Airbnb has something for everyone.

London is one of the most popular tourist destinations in the world, and Airbnb offers a unique way to experience the city. With so many different neighborhoods to choose from, there's an Airbnb listing for everyone.

This dataset includes information on the listing price, minimum nights required, number of reviews, and more. With this data, you can begin to understand how people are using Airbnb in London and what factors affect pricing. So whether you're looking for a place to stay during your next trip or just curious about how Airbnb is being used in different cities, this dataset is for you!

Research Ideas

• If there's a relationship between the price per listing and how long it is available on Airbnb, this could be used to recommend lower prices for listings that are unlikely to stay booked for very long periods of time.
• There might be a relationship between the number of reviews per month and the calculated host listings count. If there is, this information could be used to help improve customer satisfaction by either recommending that hosts with lots of listings receive more reviews or that they stagger their listing availabilities so that they can provide better service.
• The neighbourhood data could be used to cluster listings into areas with similar characteristics, which would then allow customers to easily find similar listings in different areas of the city based on their preferences

Acknowledgements

This dataset is brought to you by Kelly Garrett. If you use it in your research, please cite her Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: listings.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------| | name | The name of the listing. (String) | | host_name | The name of the host. (String) | | neighbourhood_group | The neighbourhood group the listing is in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | room_type | The type of room. (String) | | price | The price of the listing. (Integer) | | minimum_nights | The minimum number of nights required to stay at the listing. (Integer) | | number_of_reviews | The number of reviews for the listing. (Integer) | | last_review | The date of the last review. (Date) | | reviews_per_month | The number of reviews per month. (Float) | | calculated_host_listings_count | The number of listings the host has. (Integer) | | availability_365 | The number of days the listing is available in a year. (Integer) |

File: reviews.csv | Column name | Description | |:----------------|:--------------------------------------| | last_review | The date of the last review. (String) |

File: neighbourhoods.csv | Column...

The GLA Demography Team offers a bespoke population projection service to London local authorities. Boroughs can request population projections based on their own choice of assumptions about future housing delivery. These assumptions are submitted to the team via a standard template. The resulting projections are referred to as the Borough Preferred Option (or BPO) and are commonly used to help support local planning and service delivery. The GLA does not make the BPO projections and submitted housing trajectories publicly available or share them with anyone other than the commissioning borough. Boroughs wishing to publish BPO projections themselves are free to do so. This service is offered as an optional, free of charge service to London authorities, and is intended to provide users with an alternative to the standard projections that the GLA publishes on the London Datastore. Access to outputs The BPO projections are shared with users via private pages on the London Datastore. These pages include all outputs produced under the service since 2019. To access outputs, users must have a current Datastore account linked to their local government email address and contact the Demography Team to request permissions be granted for the individual pages relating to their local authority. Notes on completing the development data template What periods do the year labels in the template refer to? The year labels in the template nominally refer to periods ending in the middle of that year (i.e. 2025 refers to the 12 month period ending June 30th 2025). However, development data is often readily available only for financial years and it is common to submit data on this basis, with financial year 2024/25 aligning with 2025 in the template. Development trajectory The cells in the template represent annual net changes in the number of dwellings. The current template covers the period 2012-2041 and are pre-populated with estimated annual net dwelling changes for the period 2012-2019, based on modelling of data from the London Development Database. For the 2022-based and subsequent projections, dwelling stock estimates are anchored to the results of the 2021 Census and it is not essential to include data for dwelling stock changes that occured prior to this point (i.e. up to and including '2021'). Past development data from 2022 up to the base year of the projections, affects the projected population in all future years as dwelling stock in the base year is used in the estimation of relationships between housing and population in the model. We are not yet able to pre-populate templates with estimated dwelling changes for years after 2019. In future rounds of projections we intend to incorporate data from the Planning Data Hub. Blank cells are treated as missing rather than no change, and data based on the 2017 Strategic Housing Land Availability Assessment (SHLAA) will be substituted in its place. To indicate no net change in dwellings in a ward in a particular year, users must explicitly enter a zero in the relevant cell. Self-contained and Non-self-contained development Self-contained development should be used for standard residential development (e.g. new build/conversion). Non-self-contained development should be used for development such as student accommodation. This should be added to the template as the equivalent of self-contained units (i.e. a ratio of non-self-contained to self-contained should be applied). The London Plan ratios are: · 2.5:1 for student housing · 1:1 for housing for older people (C2) · 1.8:1 for all other non-self-contained housing Requesting projections based on multiple different housing scenarios While we are willing to try and accommodate requests for multiple sets of projections, capacity in the team is limited and there is no guarantee that we will be able to do so in a timely manner. Please do not Add rows or columns to the template Change ward names or codes Include formulas or new formatting Add notes or comments to the template Return data in a different version of the template to those included here Please return completed templates to: demography@london.gov.uk

Information These published reports present information from the multi-agency database Combined Homelessness and Information Network (CHAIN), about people seen rough sleeping by outreach teams in London. CHAIN, which is commissioned and funded by the Greater London Authority (GLA) and managed by Homeless Link, represents one of the UK’s most detailed and comprehensive sources of information about rough sleeping. Services that record information on CHAIN include outreach teams, assessment centres, accommodation projects, day centres and other specialist projects. The system allows users to share information about work done with people sleeping rough and about their needs, ensuring that they receive the most appropriate support and that efforts are not duplicated. In these reports, people are counted as having been seen rough sleeping if they have been encountered by a commissioned outreach worker bedded down on the street, or in other open spaces or locations not designed for habitation, such as doorways, stairwells, parks or derelict buildings. The report does not include people from “hidden homeless” groups such as those “sofa surfing” or living in squats, unless they have also been seen bedded down in one of the settings outlined above. Separate reports are produced for London as a whole and for individual boroughs, and these are published each quarter. There are also annual reports that contain aggregated information for each full year. Interactive Visualisation Tool Quarterly Data Tool Annual Data Tool A suite of online interactive charts and maps based on CHAIN data is available by clicking the above links. The data available via these tools mirrors that presented in the published PDF documents, with the addition of filters and other enhancements to allow users to interrogate the data. The Quarterly Data Tool shows data from the last eight quarters, and the Annual Data Tool shows data from the last five years. Organisations Using CHAIN A list of the organisations which have signed the CHAIN Data Protection Agreement and are able to access the live CHAIN system is also available to download. PDF Reports & Data tables As of January 2024, published CHAIN PDF reports are accompanied by an OpenDocument Spreadsheet file providing the underlying data in an accessible aggregated tabular format. The file includes data at local authority level, and for London overall, including comparative data for previous periods. There is also an accompanying explanatory notes document, which provides important contextual information about the data. Please click the links below to download a zip file containing the PDF reports and OpenDocument Spreadsheet for the corresponding timeframe. Publication Schedule Reports are published 1 month after the end of each quarter and one quarter after the end of each year. The linked document below provides details of forthcoming publications Quarterly and Annual Report Schedule 2024/25 2024/25 Q3 2024/25 Greater London 2024/25 Q3 Borough Reports 2024/25 Q3 Quarterly Data Tables 2024/25 Q3 Q2 2024/25 Greater London 2024/25 Q2 Borough Reports 2024/25 Q2 Quarterly Data Tables 2024/25 Q2 Q1 2024/25 Greater London 2024/25 Q1 Borough Reports 2024/25 Q1 Quarterly Data Tables 2024/25 Q1 2023/24 Greater London Bulletin Greater London full report Borough Annual Reports Annual Data Tables Quarterly Reports and Data Tables (for Q3 and Q4 only) 2022/23 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2021/22 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2020/21 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2019/20 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2018/19 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2017/18 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2016/17 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2015/16 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2014/15 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports Pre-2014/15 For earlier reports please see the end of this page. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

The Mayor of London committed to developing a London Wellbeing and Sustainability Measure in his 2021 manifesto. This was to help measure London’s success as a place to live and work for all its residents. It would counteract the fact that, for years, London’s success has been mostly measured only in terms of its material wealth. GLA City Intelligence led the development of this measure of wellbeing and sustainability. It brings together data on the multiple aspects of our lives that form the basis of our collective wellbeing. This enables us to track which aspects of our lives are getting better over time and focus on the areas that need improvement. The London Wellbeing and Sustainability Framework was developed through a review of frameworks used by other cities, liaising with likely users of the measure to understand how it could inform their work, and by conducting in-depth qualitative research with a range of Londoners to ensure it reflects the things that matter to people. From this, City Intelligence produced a draft framework, a qualitative research report based on research with Londoners between December 2021 and January 2022, and a community engagement report based on roundtables with community groups in January 2022. Following this, in August 2022, City Intelligence produced a draft proposal for a measure, which included a document setting out the work undertaken so far and how this had led to the proposals, and a document providing further supporting detail on the recommended data for the measure. In January 2023, City Intelligence held a key stakeholder consultation involving boroughs and London organisations to refine its proposal and finalise the draft framework. You can download detailed reports below on this participatory research process we underwent with Londoners. In October 2023, City Intelligence officially launched the London Wellbeing and Sustainability Measure . This followed an intensive period of data collation of a wide array of London's data on wellbeing, which we have made available via the Data Explorer tool . If you have any questions regarding the London Wellbeing and Sustainability Measure, please get in touch by sending an email to socialevidence@london.gov.uk.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

The GLA Demography Team offers a bespoke population projection service to London local authorities. Boroughs can request population projections based on their own choice of assumptions about future housing delivery. These assumptions are submitted to the team via a standard template. The resulting projections are referred to as the Borough Preferred Option (or BPO) and are commonly used to help support local planning and service delivery.

The GLA does not make the BPO projections and submitted housing trajectories publicly available or share them with anyone other than the commissioning borough. Boroughs wishing to publish BPO projections themselves are free to do so.

This service is offered as an optional, free of charge service to London authorities, and is intended to provide users with an alternative to the standard projections that the GLA publishes on the London Datastore.

Access to outputs

The BPO projections are shared with users via private pages on the London Datastore. These pages include all outputs produced under the service since 2019.

To access outputs, users must have a current Datastore account linked to their local government email address and contact the Demography Team to request permissions be granted for the individual pages relating to their local authority.

Notes on completing the development data template

What periods do the year labels in the template refer to?

The year labels in the template nominally refer to periods ending in the middle of that year (i.e. `2025` refers to the 12 month period ending June 30th 2025). However, development data is often readily available only for financial years and it is common to submit data on this basis, with financial year 2024/25 aligning with `2025` in the template.

Development trajectory

The cells in the template represent annual net changes in the number of dwellings.

The current template covers the period 2012-2041 and are pre-populated with estimated annual net dwelling changes for the period 2012-2019, based on modelling of data from the London Development Database.

For the 2022-based and subsequent projections, dwelling stock estimates are anchored to the results of the 2021 Census and it is not essential to include data for dwelling stock changes that occured prior to this point (i.e. up to and including '2021').

Past development data from 2022 up to the base year of the projections, affects the projected population in all future years as dwelling stock in the base year is used in the estimation of relationships between housing and population in the model.

We are not yet able to pre-populate templates with estimated dwelling changes for years after 2019. In future rounds of projections we intend to incorporate data from the Planning Data Hub.

Blank cells are treated as missing rather than no change, and data based on the 2017 Strategic Housing Land Availability Assessment (SHLAA) will be substituted in its place. To indicate no net change in dwellings in a ward in a particular year, users must explicitly enter a zero in the relevant cell.

Self-contained and Non-self-contained development

Self-contained development should be used for standard residential development (e.g. new build/conversion).

Non-self-contained development should be used for development such as student accommodation. This should be added to the template as the equivalent of self-contained units (i.e. a ratio of non-self-contained to self-contained should be applied). The London Plan ratios are:

· 2.5:1 for student housing

· 1:1 for housing for older people (C2)

· 1.8:1 for all other non-self-contained housing

Requesting projections based on multiple different housing scenarios

While we are willing to try and accommodate requests for multiple sets of projections, capacity in the team is limited and there is no guarantee that we will be able to do so in a timely manner.

Please do not

• Add rows or columns to the template
• Change ward names or codes
• Include formulas or new formatting
• Add notes or comments to the template
• Return data in a different version of the template to those included here

Please return completed templates to:

<a href="mailto:demography@london.gov.uk"

This dataset provides Census 2021 estimates that classify households in England and Wales by accommodation type, by type of central heating in household, and by tenure. The estimates are as at Census Day, 21 March 2021.

The ONS have made changes to housing definitions since the 2011 Census. Take care if you compare Census 2021 results for this topic with those from the 2011 Census. Read more about this quality notice.

There is evidence of people incorrectly identifying their type of landlord as ”Council or local authority” or “Housing association”. You should add these two categories together when analysing data that uses this variable. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, data is available by:

country - for example, Wales region - for example, London local authority - for example, Cornwall health area – for example, Clinical Commissioning Group statistical area - for example, MSOA or LSOA

Accommodation type

The type of building or structure used or available by an individual or household.

This could be:

• the whole house or bungalow
• a flat, maisonette or apartment
• a temporary or mobile structure, such as a caravan

More information about accommodation types

Whole house or bungalow:

This property type is not divided into flats or other living accommodation. There are three types of whole houses or bungalows.

Detached:

None of the living accommodation is attached to another property but can be attached to a garage.

Semi-detached:

The living accommodation is joined to another house or bungalow by a common wall that they share.

Terraced:

A mid-terraced house is located between two other houses and shares two common walls. An end-of-terrace house is part of a terraced development but only shares one common wall.

Flats (Apartments) and maisonettes:

An apartment is another word for a flat. A maisonette is a 2-storey flat.

Type of central heating in household

Central heating is a heating system used to heat multiple rooms in a building by circulating air or heated water through pipes to radiators or vents. Single or multiple fuel sources can fuel these systems.

Central heating systems that are unused or not working are still considered. No information is available for household spaces with no usual residents.

Tenure of household

Whether a household owns or rents the accommodation that it occupies.

Owner-occupied accommodation can be:

• owned outright, which is where the household owns all of the accommodation
• with a mortgage or loan
• part-owned on a shared ownership scheme

Rented accommodation can be:

• private rented, for example, rented through a private landlord or letting agent
• social rented through a local council or housing association

This information is not available for household spaces with no usual residents.

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.

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 7 October 2015 and replaces the previous estimates for 2013.

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

See a visualisation of this data using Tableau.

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.