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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
- 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
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.
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 |
If you use this dataset in your research, please credit Eva Murray.
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TwitterThe ethnic group projections are produced for London borough and provide detailed projection by 17 ethnic groups of London’s future population. Two variants are produced: one consistent with the 2016-based central trend projection, and one consistent with the 2016-based housing-led projection. The 2016-based projections remain the most recent set of GLA ethnic group projections.
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Excel age range creator for GLA Projections data
This Excel based tool enables users to query the raw single year of age data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Each year the GLA demography team produce sets of population projections. On this page each of these datasets since 2009 can be accessed, though please remember that the older versions have been superceded. From 2012, data includes population estimates and projections between 2001 and 2041 for each borough plus Central London (Camden, City of London, Kensington & Chelsea, and Westminster), Rest of Inner Boroughs, Inner London, Outer London and Greater London.
The full raw data by single year of age (SYA) and gender are available as Datastore packages at the links below.
How to use the tool: Simply select the lower and upper age range for both males and females (starting in cell C3) and the spreadsheet will return the total population for the range.
Tip: You can copy and paste the boroughs you are interested in to another worksheet by clicking: Edit then Go To (or Control + G), then Special, and Visible cells only. Then simply copy and 'paste values' of the cells to a new location.
Warning: The ethnic group and ward files are large (around 35MB), and may take some time to download depending on your bandwidth.
Find out more about GLA population projections on the GLA Demographic Projections page
BOROUGH PROJECTIONS
GLA 2009 Round London Plan Population Projections (January 2010) (SUPERSEDED)
GLA 2009 Round (revised) London Plan Population Projections (August 2010) (SUPERCEDED)
GLA 2009 Round (revised) SHLAA Population Projections (August 2010) (SUPERCEDED)
GLA 2010 Round SHLAA Population Projections (February 2011) (SUPERCEDED)
GLA 2011 Round SHLAA Population Projections, High Fertility (December 2011) (SUPERCEDED)
GLA 2011 Round SHLAA Population Projections, Standard Fertility (January 2012) (SUPERCEDED)
GLA 2012 Round SHLAA Population Projections, (December 2012)(SUPERCEDED)
GLA 2012 Round Trend Based Population Projections, (December 2012) (SUPERCEDED)
GLA 2013 Round Trend Based Population Projections - High (December 2013) (SUPERCEDED)
GLA 2013 Round Trend Based Population Projections - Central (December 2013) (SUPERCEDED)
GLA 2013 Round Trend Based Population Projections - Low (December 2013) (SUPERCEDED)
GLA 2013 Round SHLAA Based Population Projections (February 2014) (SUPERCEDED) Spreadsheet now includes national comparator data from ONS.
GLA 2013 Round SHLAA Based Capped Population Projections (March 2014) (SUPERCEDED) Spreadsheet includes national comparator data from ONS.
GLA 2014 Round Trend-based, Short-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.
GLA 2014 Round Trend-based, Long-Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.
GLA 2014 Round SHLAA DCLG Based Long Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS.
GLA 2014 Round SHLAA Capped Household Size Model Short Term Migration Scenario Population Projections (April 2015) Spreadsheet includes national comparator data from ONS. This is the recommended file to use.
WARD PROJECTIONS
GLA 2008 round (High) Ward Projections (March 2009) (SUPERSEDED)
GLA 2009 round (revised) London Plan Ward Projections (August 2010) (SUPERCEDED)
GLA 2010 round SHLAA Ward Projections (February 2011) (SUPERCEDED)
GLA 2011 round SHLAA Standard Ward Projections (January 2012) (SUPERCEDED)
GLA 2011 round SHLAA High Ward Projections (January 2012) (SUPERCEDED)
GLA 2012 round SHLAA based Ward Projections (March 2013) (XLS) (SUPERCEDED)
GLA 2012 round SHLAA Ward Projections (March 2013) (XLS) (SUPERCEDED)
GLA 2013 round SHLAA Ward Projections (March 2014) (SUPERCEDED)
GLA 2013 round SHLAA Capped Ward Projections (March 2014) (SUPERCEDED)
GLA 2014 round SHLAA Capped Household Size Model Short Term Migration Scenario Ward Projections (April 2015) This is the recommended file to use.
ETHNIC GROUP PROJECTIONS FOR LOCAL AUTHORITIES
GLA 2012 Round SHLAA Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)
GLA 2012 Round Trend Based Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)
GLA 2012 Round SHLAA Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)
GLA 2012 Round Trend Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)
GLA 2013 Round SHLAA Capped Ethnic Group Borough Projections (August 2014)
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License information was derived automatically
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.
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:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for London Population by Race & Ethnicity. You can refer the same here
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Facebook
TwitterBy Eva Murray [source]
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
- 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
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.
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 |
If you use this dataset in your research, please credit Eva Murray.