London census data

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

License: UK Open Government License

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'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.

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

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

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).

This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Two files are available to download: Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole. A Tableau visualisation tool is also available. Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

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.

In November 2014, 3,674 Londoners took part in the first London Survey run by Talk London, to tell us what they thought of the city and their neighbourhood. The London Survey enables us to: • Assess Londoners’ priorities across the breadth of Mayoral responsibilities • Understand Londoners’ perceptions of their quality of life • Identify those areas that require improvement, or where we need to improve outcomes for particular groups of people. TECHNICAL DETAILS • Results are based on interviews with 3,674 London residents aged 18+. • Interviews were carried out online via the Talk London community between 3 Oct and 5 Nov. • Interviews were not randomly sampled, but self-selecting via a number of known databases. This achieved a non-representative sample of Londoners. • The data has been weighted by age, gender and ethnicity to reflect that of the London population. • A minimum number of responses were achieved for each key demographic group to maintain a robust sample. • Where results do not sum to 100% this may be due to multiple responses, computer rounding or the exclusion of don’t knows/not stated. • The qualitative analysis of the open-ended questions 36, 37 and 38 was undertaken by SPA Future Thinking. Top level themes and sub themes are reported as a percentage of the overall base number of respondents (3,421 to all three questions). The top three sub themes are presented where available. • This is the first London Survey conducted by Talk London for City Hall. INFOGRAPHICS

About this dataset

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).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the New London is shown in this column.
• Year on Year Change: This column displays the change in New London population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 New London Population by Year. You can refer the same here

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.

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 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.

Air Pollution Health Impacts in London Boroughs

Incidence and 95% Confidence intervals

By data.world's Admin [source]

About this dataset

This dataset reveals the long-term health impacts of air pollution in London's boroughs. Home to over 8 million people, London's air pollution is a growing health concern and this study provides invaluable insights into the devastating effects of exposure.

More Datasets

For more datasets, click here.

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How to use the dataset

How to Use this Dataset:

This dataset provides detailed analysis of the long-term health impacts of air pollution. It includes estimated cases and costs associated with each borough, as well as projections for each scenario used in modelling the effects. This dataset is useful for learners who want to learn about how various factors, such as population growth or new technologies, may affect future health outcomes related to air pollution in London.

The columns included are ‘Scenario’ (the scenario used), ‘Year’ (the year modelled), ‘Disease’ (the type of disease modelled), ‘AgeGroup’ (the age group of the population modelled) and ‘95% CL’ (confidence level).

To understand these columns further we recommend looking at the original source report. This will provide additional detail about each element considered when modelling.

To get started with analysing this data set we recommend exploring how estimates differ between scenarios and considering which ages benefit most from different interventions proposed by London Environment Strategy for reducing diseases caused by air pollution. Additionally you could look at different diseases separately, or consider disease costs versus number of cases across different age groups and scenarios

Research Ideas

• Analyzing the long-term impact of air pollution on London's NHS and social care system by borough.
• Comparing the health impacts of different scenarios related to air pollution in different years and age groups to inform effective policymaking.
• Modeling how changes in air pollution levels might affect different diseases or health outcomes over time in a particular area or community

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: newham-no2-xlsm-18.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

File: bromley-pm25-xlsm-35.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

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 (1 hour 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 48420 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 UK 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.

This table shows whether people aged 16 or over have ever used or never used the internet by a range of variables such as age, ethnicity, pay, occupation, qualifications, and disability. The question asked in the Labour Force Survey is "When did you last use the internet?" This question is only asked to people aged 16 and over. The first time this data was available was 2011 Q1. At borough level the data showed ever used or never used. For London and Rest of UK the data is broken down by a range of indicators, including age, ethnic group, weekly pay, occupation levels, qualification levels, and economic activity. The APS sampled around 333,000 people in the UK (around 27,000 in London). As such all figures must be treated with some caution. Data was supplied directly by ONS under request from the Greater London Authority. Numbers rounded to the nearest thousand. Other Internet Access data can be found on the ONS website. This is national data based on the Opinions and Lifestyle Survey.

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.

Context

London is the capital and most populous city of England and the United Kingdom. Standing on the River Thames in the south east of the island of Great Britain, London has been a major settlement for two millennia. Source: https://en.wikipedia.org/wiki/London

Content

This data counts the number of crimes at two different geographic levels of London (LSOA and borough) by year, according to crime type. Includes data from 2008 to present. Crime categories are included in the BigQuery table description.

Fork this kernel to get started with this dataset.

Acknowledgements

https://data.london.gov.uk/

https://bigquery.cloud.google.com/dataset/bigquery-public-data:london_crime?_ga=2.189454959.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/greater-london-authority/london-crime?filter=category:public-safety

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Luca Micheli from Unplash.

Inspiration

What is the change in the number of crime incidents from 2011 to 2016?

What were the top 3 crimes per borough in 2016?

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

The provided dataset contains financial and operational metrics spanning from January to September 2020 for a company operating in the UK. It reflects key aspects like revenue, expenses, profit, customer count, transactions, stock price, market sentiment, loan approval rate, employee count, and marketing spend.

London, as a part of the UK, likely shares these trends but could have its specific nuances due to being a distinct economic hub within the country. In this period:

1. Financial Performance: The company's revenue fluctuates throughout the months, peaking at £65,090 in June and dipping to £35,184 in July. Despite varying expenses, profits generally stay positive, showcasing resilience in managing costs against revenue. London, being a financial center, might witness higher revenue or fluctuations due to specific industries concentrated there.

2. Customer Engagement: Customer metrics show variation. Customer count ranges from 131 to 426, with transactions varying from 57 to 188. This indicates fluctuations in customer activity, potentially influenced by market trends, seasonal patterns, or even regional events.

3. Stock Performance: Stock prices show fluctuation, hitting a high of 138.53 and a low of 78.79. Market sentiment, indicating public confidence, also fluctuates, potentially influencing stock prices. London's stock market might reflect similar volatility but could be influenced by the performance of prominent companies headquartered there.

4. Business Operations: Loan approval rates stay relatively stable between 70% to 97%, indicating a consistent approach to risk management. Employee count remains somewhat constant, which could signify stable operations without significant expansion or downsizing.

5. Marketing and Growth: The company's marketing spend varies, suggesting a willingness to adapt strategies based on performance or seasonal demands. London might have higher marketing expenditures due to the competitive market and the need to stand out amidst numerous businesses.

6. Economic Impact: Economic factors affecting the UK market—Brexit discussions, global economic shifts, or even local policies—might influence these metrics. London, as a financial center, could be more sensitive to global economic changes, impacting revenue, market sentiment, and stock prices more profoundly.

7. Covid-19 Influence: Given the timeframe (2020), the dataset might reflect the initial impact of the COVID-19 pandemic. The varying metrics could illustrate the company's adaptation strategies in response to changing consumer behaviors and economic uncertainties.

In London specifically, these trends might amplify due to its prominence in finance, trade, and services. The city's diverse industries and international connections might lead to more pronounced fluctuations in financial indicators like stock prices and market sentiment. Moreover, its position as a global economic hub might expose businesses to unique challenges and opportunities, potentially reflected in the provided dataset.

Understanding London's specific dynamics within the UK would require deeper analysis, considering sector-specific influences, competitive landscape, and regional economic factors. Nevertheless, this dataset offers insights into the company's adaptability and performance within the broader context of the UK's economic landscape.