By 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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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): 94.59% are white, 1.10% are Black or African American, 1.29% are Asian, 0.77% are some other race and 2.26% 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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the New London population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New London across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of New London was 631, a 2.10% increase year-by-year from 2022. Previously, in 2022, New London population was 618, an increase of 0.82% compared to a population of 613 in 2021. Over the last 20 plus years, between 2000 and 2023, population of New London decreased by 11. In this period, the peak population was 743 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
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 New London Population by Year. You can refer the same here
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Dataset Overview This dataset provides a snapshot of real estate transactions in London for 2024. It includes key property details such as the number of bedrooms, bathrooms, living space size, lot size, and transaction price. Additionally, it incorporates information about property features like waterfront views, renovation history, and construction quality. Designed for educational and research purposes, the dataset offers insights into London's real estate market trends and serves as a valuable resource for data analysis and machine learning applications.
Data Science Applications This dataset is ideal for students, researchers, and professionals seeking to apply data science techniques to real-world real estate data. Potential applications include:
Exploratory Data Analysis (EDA): Investigate price trends, property characteristics, and geographical distribution of transactions. Price Prediction Models: Develop machine learning models to predict property prices based on features like size, location, and condition. Trend Analysis: Analyze historical and geographical trends in property prices and features. Geospatial Analysis: Map properties based on latitude and longitude to identify hotspots or underserved areas.
Column Descriptions
Column Name | Description |
---|---|
id | Unique identifier for the property listing. |
date | Transaction date in YYYYMMDDT000000 format. |
price | Sale price of the property in GBP (£). |
bedrooms | Number of bedrooms in the property. |
bathrooms | Number of bathrooms in the property. |
sqft_living | Living area size in square feet. |
sqft_lot | Lot size in square feet. |
floors | Number of floors in the property. |
waterfront | Indicates if the property has a waterfront view (1: Yes, 0: No). |
view | Property view rating (scale of 0–4). |
condition | Property condition rating (scale of 1–5, 5 being best). |
grade | Property construction and design rating (scale of 1–13, higher is better). |
sqft_above | Square footage of the property above ground level. |
sqft_basement | Square footage of the basement area. |
yr_built | Year the property was built. |
yr_renovated | Year the property was last renovated (0 if never renovated). |
zipcode | Zip code of the property's location. |
lat | Latitude coordinate of the property. |
long | Longitude coordinate of the property. |
sqft_living15 | Average living area square footage of 15 nearby properties. |
sqft_lot15 | Average lot size square footage of 15 nearby properties. |
Ethically Mined Data This dataset was ethically sourced from publicly available property listings. It does not include any Personally Identifiable Information (PII) or data that could infringe on individual privacy. All information represents public details about properties for sale in London.
Acknowledgements
Data Source: Real estate data provided from publicly accessible resources. Image Credit: Unsplash for real estate-themed visuals. Use this dataset responsibly for educational and analytical purposes!
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).
http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence
ONS Mid-year estimates (MYE) of resident populations for London boroughs are available in the following files:
Read the GLA Intelligence Updates about the MYE data for 2011 and 2012.
Mid-year population by single year of age (SYA) and sex, for each year 1999 to 2014.
ONS mid-year estimates data back to 1961 total population for each year since 1961.
These files take into account the revised estimates released in 2010.
Ward level Population Estimates
London wards single year of age data covering each year since 2002.
Custom Age Range Tool
An Excel tool is available that uses Single year of age data that enables users to select any age range required.
ONS policy is to publish population estimates rounded to at least the nearest hundred persons. Estimates by single year of age, and the detailed components of change are provided in units to facilitate further calculations. They cannot be guaranteed to be as exact as the level of detail implied by unit figures.
Estimates are calculated by single year of age but these figures are less reliable and ONS advise that they should be aggregated to at least five-year age groupings for use in further calculations, onwards circulation, or for presentation purposes. (Splitting into 0 year olds and 1-4 year olds is an acceptable exception).
ONS mid-year population estimates data by 5 year age groups going all the way back to 1981, are available on the NOMIS website.
Data are Crown Copyright and users should include a source accreditation to ONS - Source: Office for National Statistics. Under the terms of the Open Government License (OGL) and UK Government Licensing Framework, anyone wishing to use or re-use ONS material, whether commercially or privately, may do so freely without a specific application. For further information, go to http://www.nationalarchives.gov.uk/doc/open-government-licence/ or phone 020 8876 3444.
For a detailed explanation of the methodology used in population estimates, see papers available on the Population Estimates section of the ONS website. Additional information can also be obtained from Population Estimates Customer Services at pop.info@ons.gsi.gov.uk (Tel: 01329 444661).
This dataset shows different breakdowns of London's resident population by their country of birth. 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. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. 2011 Census Country of Birth data can be found here: https://data.london.gov.uk/census/themes/diversity/ Nationality data can be found here: https://data.london.gov.uk/dataset/nationality 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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about books. It has 1 row and is filtered where the book is The City of London : who, what, why? : a collection of articles explaining the City of London; its civic traditions, historic offices, people and current purpose. It features 7 columns including author, publication date, language, and book publisher.
https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer
People data provides complete people information and gives the ability to link individual information to organizations and roles.
This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.
The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (90 Mins in this case) from that record's location.
The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.
The dataset provides 76174 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.
A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km
Higher resolutions or alternate geographies are available on request.
More information on the h3 system is available here: https://eng.uber.com/h3/
WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.
More information on the WorldPop data is available here: https://www.worldpop.org/
One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the German population within 4 hours by Truck and show only the areas which do exhibit this characteristic.
Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.
Number of people belonging to a visible minority group as defined by the Employment Equity Act and, if so, the visible minority group to which the person belongs. The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.' The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Latin American, Arab, Southeast Asian, West Asian, Korean and Japanese.
https://discover-now.co.uk/make-an-enquiry/https://discover-now.co.uk/make-an-enquiry/
Providers of publicly-funded community services are legally mandated to collect and submit community health data, as set out by the Health and Social Care Act 2012.
The Community Services Data Set (CSDS) expands the scope of the Children and Young People's Health Services Data Set (CYPHS) data set, by removing the 0-18 age restriction. The CSDS supersedes the CYPHS data set, to allow adult community data to be submitted.
The structure and content of the CSDS remains the same as the CYPHS data set. The Community Information Data Set (CIDS) has been retired, to remove the need for a separate local collection and reduce burden on providers.
Reports from the CSDS are available to download from the Community Services Data Set reports webpage.
Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. You are welcome to contact us directly by emailing transport.statistics@dft.gov.uk with any comments about how we meet these standards.
These statistics on transport use are published monthly.
For each day, the Department for Transport (DfT) produces statistics on domestic transport:
The associated methodology notes set out information on the data sources and methodology used to generate these headline measures.
From September 2023, these statistics include a second rail usage time series which excludes Elizabeth Line service (and other relevant services that have been replaced by the Elizabeth line) from both the travel week and its equivalent baseline week in 2019. This allows for a more meaningful like-for-like comparison of rail demand across the period because the effects of the Elizabeth Line on rail demand are removed. More information can be found in the methodology document.
The table below provides the reference of regular statistics collections published by DfT on these topics, with their last and upcoming publication dates.
Mode | Publication and link | Latest period covered and next publication |
---|---|---|
Road traffic | Road traffic statistics | Full annual data up to December 2023 was published in May 2024. Quarterly data up to September 2024 was published December 2024. |
Rail usage | The Office of Rail and Road (ORR) publishes a range of statistics including passenger and freight rail performance and usage. Statistics are available at the https://www.orr.gov.uk/published-statistics" class="govuk-link">ORR website. Statistics for rail passenger numbers and crowding on weekdays in major cities in England and Wales are published by DfT. |
ORR’s latest quarterly rail usage statistics, covering July to September 2024, was published in December 2024. DfT’s most recent annual passenger numbers and crowding statistics for 2023 were published in September 2024. |
Bus usage | Bus statistics | The most recent annual publication covered the year ending March 2024. The most recent quarterly publication covered October to December 2024. |
TfL tube and bus usage | Data on buses is covered by the section above. https://tfl.gov.uk/status-updates/busiest-times-to-travel" class="govuk-link">Station level business data is available. | |
Cycling usage | Walking and cycling statistics, England | 2023 calendar year published in August 2024. |
Cross Modal and journey by purpose | National Travel Survey | 2023 calendar year data published in August 2024. |
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about books. It has 3 rows and is filtered where the book is An unconsidered people : the Irish in London. It features 7 columns including author, publication date, language, and book publisher.
http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence
This table shows resident population broken down into country of birth, showing data for London's largest communities (over 10,000 people) in 2004, and 2008 to 2014 from the Annual Population Survey (APS). The 2011 Census data is also provided in the spreadsheet to provide a comparison to the APS data.
The table also shows the percentage of the UK community that live in London.
The Annual Population Survey (APS) sampled around 325,000 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.
All populations of fewer than 10,000 have been suppressed.
Numbers are rounded to the nearest thousand.
The APS is the only inter-censal data source that can provide estimates of the population stock by nationality. The data have a range of limitations, particularly in relation to their poor coverage of short-term migrants or recent arrivals. They also struggle to provide estimates for small migrant populations due to small sample sizes.
Information about Londoners by Country of Birth using APS data, can be found in DMAG Briefing 2008-05 http://legacy.london.gov.uk/gla/publications/factsandfigures/dmag-briefing-2008-05.pdf
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Non-Hispanic population of London by race. It includes the distribution of the Non-Hispanic population of London across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of London across relevant racial categories.
Key observations
Of the Non-Hispanic population in London, the largest racial group is White alone with a population of 7,120 (95.80% of the total Non-Hispanic population).
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
This dataset shows the most spoken languages by borough and MSOAs in London. It provides numbers of the population aged 3+ who speak specified languages as their main language.
Main language is from 2011 Census (detailed) - Census table QS204EW.
This data is presented alongside Annual Population Survey (APS) data showing the top nationalities of residents in January - December 2019 by borough. The top 3 non-British nationalities are at the far right of the table. This is to highlight areas which may now have other common non-British languages spoken compared to 2011 (the year in which the Census information was gathered). The top non-British nationalities in 2019, which did not feature in 2011 as one of the most spoken non-British languages, are highlighted in column AD.
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. Estimates for non-British nationalities at borough level that are below 10,000 are considered too small to be reliable and should be treated with additional caution.
MSOA codes have now been linked to House of Commons MSOA names
On 1 April 2025 responsibility for fire and rescue transferred from the Home Office to the Ministry of Housing, Communities and Local Government.
This information covers fires, false alarms and other incidents attended by fire crews, and the statistics include the numbers of incidents, fires, fatalities and casualties as well as information on response times to fires. The Ministry of Housing, Communities and Local Government (MHCLG) also collect information on the workforce, fire prevention work, health and safety and firefighter pensions. All data tables on fire statistics are below.
MHCLG has responsibility for fire services in England. The vast majority of data tables produced by the Ministry of Housing, Communities and Local Government are for England but some (0101, 0103, 0201, 0501, 1401) tables are for Great Britain split by nation. In the past the Department for Communities and Local Government (who previously had responsibility for fire services in England) produced data tables for Great Britain and at times the UK. Similar information for devolved administrations are available at https://www.firescotland.gov.uk/about/statistics/" class="govuk-link">Scotland: Fire and Rescue Statistics, https://statswales.gov.wales/Catalogue/Community-Safety-and-Social-Inclusion/Community-Safety" class="govuk-link">Wales: Community safety and https://www.nifrs.org/home/about-us/publications/" class="govuk-link">Northern Ireland: Fire and Rescue Statistics.
If you use assistive technology (for example, a screen reader) and need a version of any of these documents in a more accessible format, please email alternativeformats@homeoffice.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use.
Fire statistics guidance
Fire statistics incident level datasets
https://assets.publishing.service.gov.uk/media/67fe79e3393a986ec5cf8dbe/FIRE0101.xlsx">FIRE0101: Incidents attended by fire and rescue services by nation and population (MS Excel Spreadsheet, 126 KB) Previous FIRE0101 tables
https://assets.publishing.service.gov.uk/media/67fe79fbed87b81608546745/FIRE0102.xlsx">FIRE0102: Incidents attended by fire and rescue services in England, by incident type and fire and rescue authority (MS Excel Spreadsheet, 1.56 MB) Previous FIRE0102 tables
https://assets.publishing.service.gov.uk/media/67fe7a20694d57c6b1cf8db0/FIRE0103.xlsx">FIRE0103: Fires attended by fire and rescue services by nation and population (MS Excel Spreadsheet, 156 KB) Previous FIRE0103 tables
https://assets.publishing.service.gov.uk/media/67fe7a40ed87b81608546746/FIRE0104.xlsx">FIRE0104: Fire false alarms by reason for false alarm, England (MS Excel Spreadsheet, 331 KB) Previous FIRE0104 tables
https://assets.publishing.service.gov.uk/media/67fe7a5f393a986ec5cf8dc0/FIRE0201.xlsx">FIRE0201: Dwelling fires attended by fire and rescue services by motive, population and nation (MS Excel Spreadsheet, <span class="gem-c-attachm
By 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.