The SFR includes statistics on the attainment of young people aged 19, showing the proportion of young people qualified to at least levels 2 and 3 by age and by cohort. Source agency: Education Designation: National Statistics Language: English Alternative title: Level 2 and 3 attainment by young people in England

Reports describing the characteristics of the older population in England and Wales Source agency: Office for National Statistics Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Characteristics of Older People

Mid-year population estimates relate to the usually resident population. They account for long-term international migrants (people who change their country of usual residence for a period of 12 months or more) but do not account for short-term migrants (people who come to or leave the country for a period of less than 12 months). This approach is consistent with the standard UN definition for population estimates which is based upon the concept of usual residence and includes people who reside, or intend to reside, in the country for at least twelve months, whatever their nationality.

Quality information on the mid-year population estimates at local authority and region level for England and Wales, by age and sex.

Compendium of housing statistics covering all aspects of housing in England. Source agency: Communities and Local Government Designation: National Statistics Language: English Alternative title: Housing Statistics, England

This dataset integrates historical financial market data, macroeconomic indicators, corporate financial statements, and sentiment analysis of financial news to study risk factors and trading behaviors in the UK financial markets. The dataset is designed for econometric and machine learning applications in quantitative finance. Key features include:

Historical Market Data: Daily stock prices, trading volumes, and market indices sourced from simulated UK financial market data. Financial Statements: Revenue, net profit, and total assets for 20 simulated companies across multiple years. Macroeconomic Indicators: Monthly GDP growth, inflation rates, and unemployment rates. Sentiment Data: Daily sentiment scores based on the analysis of news headlines. This dataset can be used to explore the relationship between market behaviors, macroeconomic trends, corporate performance, and sentiment-driven investor decisions. It is suitable for applications in econometric analysis, predictive modeling, and machine learning research.

Columns in the Dataset:

Historical Market Data: Date, Stock_Price, Trading_Volume, Market_Index Financial Statements: Company, Year, Revenue, Net_Profit, Total_Assets Macroeconomic Indicators: Date, GDP_Growth, Inflation_Rate, Unemployment_Rate Sentiment Data: Date, Article, Sentiment_Score

Usage:

This dataset is ideal for researchers and practitioners in quantitative finance, trading behavior analysis, and risk management, providing a realistic foundation for model development and validation.

11th January 2020 Change to vaccination data made available by UK gov - now just cumulative number of vaccines delivered are available for both first and second doses. For the devolved nations the cumulative totals are available for the dates from when given, however for the UK as a whole the total doses given is just on the last date of the index, regardless of when those vaccines were given.

4th January 2020 VACCINATION DATA ADDED - New and Cumulative First Dose Vaccination Data added to UK_National_Total_COVID_Dataset.csv and UK_Devolved_Nations_COVID_Dataset.csv

2nd December 2020:

NEW population, land area and population density data added in file NEW_Official_Population_Data_ONS_mid-2019.csv. This data is scraped from the Office for National Statistics and covers the UK, devolved UK nations, regions and local authorities (boroughs).

20th November 2020:

With European governments struggling with a 'second-wave' of rising cases, hospitalisations and deaths resulting from the SARS-CoV-2 virus (COVID-19), I wanted to make a comparative analysis between the data coming out of major European nations since the start of the pandemic.

I started by creating a Sweden COVID-19 dataset and now I'm looking at my own country, the United Kingdom.

The data comes from https://coronavirus.data.gov.uk/ and I used the Developer's Guide to scrape the data, so it was a fairly simple process. The notebook that scapes the data is public and can be found here. Further information about data collection methodologies and definitions can be found here.

The data includes the overall numbers for the UK as a whole, the numbers for each of the devolved UK nations (Eng, Sco, Wal & NI), English Regions and Upper Tier Local Authorities (UTLA) for all of the UK (what we call Boroughs). I have also included a small table with the populations of the 4 devolved UK nations, used to calculate the death rates per 100,000 population.

As I've said for before - I am not an Epidemiologist, Sociologist or even a Data Scientist. I am actually a Mechanical Engineer! The objective here is to improve my data science skills and maybe provide some useful data to the wider community.

Any questions, comments or suggestions are most welcome! I am open to requests and collaborations! Stay Safe!

Indicators from the Office for National Statistics (ONS) Opinions and Lifestyle Survey to understand the impacts of the coronavirus (COVID-19) pandemic on disabled people in Great Britain.

Dataset

This dataset was created by Jitendra Khuntia

Contents

This data measures the size of the population of England and Wales, and the number and percentage of people from each ethnic group. Data is taken from the 2021 Census and published on 'Ethnicity facts and figures'.

300+ groceries information from tesco UK found in CSV file. This is clean and ready to use dataset and extracted all the data points present in the source page.

Total fields: 22 name, url, sku, gtin13, price, currency, availability, description, brand, breadcrumbs, images, avg_rating, reviews_count, pack_size, ingredients, storage_details, product_origin, percentage_alcohol, serving_size, nutrition, uniq_id, scraped_at

Get the dataset weekly with latest prices and other information. Link

Acknowledgement: Crawl feeds team extracted data using in-house tools. You can download multiple groceries datasets from the crawl feeds store. Link

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Electoral registrations for parliamentary and local government elections as recorded in electoral registers for England, Wales, Scotland and Northern Ireland.

Purpose

This datasets overcome the performance issues of the The United Kingdom Real Estate dataset - the large size of the data can slow analysis, use up all the memory, and long computations. Some analysis may take hours to runs. Instead, a simplified version of the data is provided. Statistical summary prices - non-parametric and parametric is provided for (1) each location, (2) type of tenure, (3) year, (4) month, and (5) type of property. Some geolocations is also provided to support the creation of maps.

The data was transformed using descriptive statistics (parametric and non-parametric), and ratios for some categorical data items. The transformation is documented and reproducible by using this notebook.

Datasets details

Summary of property prices

Monthly aggregations are structured using a series of dimensions

Code : Geography code that can be merged with location with groupings and other datasets.

Month: Numerical value representing the month between 1 and 12. Temporal dimension.

Year :. Numerical value representing the year. Temporal dimension. **Property type : ** Categorical values describing the type of values - Detached (D) - Semi-Detached (S) - Terraced (T) - Flats/Maisonettes (F), - Other (O).

The following measures aPre provided:

** Percentage of New and Old properties:** indicates the proportion that were new or older sold during a month period.

Percentage of Freehold or Leasehold:. indicates the proportion of freehold and leasehold properties during this month period.

Proportion of monthly sales: indicates the proportion of a type of property were sold during a month period. The total proportion of monthly sales should add up to 1.

** Mean, standard deviation, median, IQR, Q1, and Q3** describes statistically the data distribution of a mongh period. ** No of observations:** size of the monthly

• Month and Year are temporal dimensions.
• Property types includes the categories: Detached (D), Semi-Detached (S), Terraced (T), Flats/Maisonettes (F), and Other (O).
• Old or new is a Boolean flag indicates indicates the age of the property as old or new. That is a newly built property is set to yes (Y), and an established residential building to no (N).
• Tenure is is freehold (F) or leasehold (L).
• Price: Count is a descriptive statistical measure indicating the number of observations for a grouping.
• Price:Mean and Price:STD are parametric statistical measure of centrality and spread for a grouping.
• Price:Min, Price:Max, Price:Q1, Price:Q2, Price:Q3, Price:IQR, and Price:Range are non-parametric statistical summary. It describes the proportions with in a probabilistic distribution. If the number of observations is low - less 31 - it should be used carefully as it is not significant.

Geolocation

More than 95% of locations with their latitudes and longitudes of locations listed in summary of prices are available.

Location with clustering

Areas and population have been clustered to improve variation and inaccurate population per square metre and model fitting.

Source of the data

Without the OGL licence of HM Land Registry, this dataset would have not been created.

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.

The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.

The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

Paints a picture of the people aged 50 and over in the UK today. It includes information on their characteristics, lifestyles and experiences, placing particular emphasis on changes with age. Source agency: Office for National Statistics Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Ageing in the UK Datasets

Published by: UK Race Disparity Unit Last updated: 16 August 2021 Topic: Crime and justice Licence: Open Government Licence

Summary The data shows:

• The number of stop and searches for different ethnic groups in England and Wales
• How many stop and searches there were for every 1,000 people in each ethnic group (the ‘rate’)
• Data is also shown for the most police force areas.

Data comes from the Home Office and is published on 'Ethnicity facts and figures'.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey is a school-based survey (i.e., historically always completed at school as part of lessons. Due to the closure of school sites during the coronavirus pandemic, the survey was adapted to allow at-home completion. This approach was retained into academic year 2021-22 to help maximise response numbers and as the year was still impacted by the pandemic as a result of the requirements for self-isolation. The at-home completion approach was actively offered for secondary school pupils and allowed but not encouraged for primary pupils.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and to enable completion at home), and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic years 2020-21 and 2021-22 saw a more even split of responses by term across the year, compared to 2019-20, which had a reduced proportion of summer term responses due to the disruption caused by Covid-19.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and well-being measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset – this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels; they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset – this file include responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

3) Teacher dataset – this file includes responses from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.