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).

Towns in England and Wales: towns list, cities list, classification and population data.

Population of Urban Agglomerations with 300,000 Inhabitants or more in 2014, by city, 1950-2030 (thousands). Data for 1,692 cities contained in the Excel file. Note: Each country has its own definition of what is 'urban' and therefore use exercise caution when comparing cities in different countries. Data available from the United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, CD-ROM Edition. Further detail of population estimates, land area, and population density for world urban areas with over 500,000 people (924 areas) is available with Demographia's World Urban Areas report (2014). Much of this data is based on the UN urban agglomerations, though a range of other sources are also used.

A story map on how and why the boundaries were made, and a guide to their use for statistics

Annual estimates of balanced UK regional gross domestic product (GDP). Current price estimates and chained volume measures for combined authorities and city regions.

About this dataset

Context

The dataset tabulates the England median household income by race. The dataset can be utilized to understand the racial distribution of England income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• England, AR median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in England, AR (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of England median household income by race. You can refer the same here

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for the wards of Leicester and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsGeneral HealthThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by the state of their general health. The estimates are as at Census Day, 21 March 2021.Definition: A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.This dataset contains details for Leicester city and England overall.

About this dataset

Context

The dataset tabulates the England household income by gender. The dataset can be utilized to understand the gender-based income distribution of England income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• England, AR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• England, AR annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of England income distribution by gender. You can refer the same here

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for all MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsProficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021.Definition: How well people whose main language is not English (English or Welsh in Wales) speak English.This dataset provides details for the MSOAs of Leicester city.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsOccupationThis dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by occupation. The estimates are as at Census Day, 21 March 2021.Definition: Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.This dataset includes details for Leicester city and England overall.

This is a comprehensive list of all datasets currently published by Leeds City Council. The register states the URLs for each dataset as listed on the Leeds Data Mill and on council's data and information website. It also provides information on how often the dataset is updated and when the next update is due.

Publishing/signposting sites codes

• LDM = Leeds Data Mill
• DGU = Data.gov.uk
• LCCOD = LCC open data site

Weather Data for Locations Across the UK

This dataset contains historical weather data from various locations across the UK, spanning from 2009 to 2024. Each entry records the weather conditions for a specific day, providing insights into temperature, rain, humidity, cloud cover, wind speed, and wind direction. The data is useful for analyzing weather patterns and trends over time.

Columns:

• location: The name of the location (e.g., Holywood, Ardkeen).
• date: The date of the weather record (format: YYYY-MM-DD).
• min_temp (°C): The minimum temperature recorded on that day (in degrees Celsius).
• max_temp (°C): The maximum temperature recorded on that day (in degrees Celsius).
• rain (mm): The amount of rainfall recorded (in millimeters).
• humidity (%): The percentage of humidity.
• cloud_cover (%): The percentage of cloud cover.
• wind_speed (km/h): The wind speed recorded (in kilometers per hour).
• wind_direction: The direction of the wind (e.g., N, SSE, WSW).
• wind_direction_numerical: The numerical representation of the wind direction (e.g., 90.0 for east).

Example Data:

Use Cases:

• Weather pattern analysis for specific regions: By analyzing temperature, humidity, and wind patterns across different locations, users can study how weather behaves seasonally and regionally, identifying patterns or anomalies.
• Long-term climate studies: This dataset spans over 15 years, making it useful for examining long-term climate trends such as temperature fluctuations, increased rainfall, or shifts in wind direction.
• Building predictive models for weather forecasting: The data can be used to build machine learning models that predict future weather conditions based on historical patterns. This is helpful for industries such as agriculture, transportation, and event planning.
• Climate change research: Researchers can use this dataset to study the effects of climate change on temperature, precipitation, and wind patterns over time.
• Energy sector applications: The wind speed and temperature data can be used to optimize energy production, especially for renewable energy sources like wind and solar power.
• Tourism and event planning: By analyzing weather trends, businesses in the tourism and event industries can better plan for weather conditions, helping with decisions on the best times to host outdoor events or market destinations.
• Agriculture and crop planning: Farmers and agronomists can use this data to analyze seasonal weather conditions, helping them plan for crop planting, growth, and harvesting by understanding local climate conditions.
• Urban planning and infrastructure design: City planners can use this data to assess how weather conditions impact infrastructure, from drainage and flood risk management to energy usage patterns.

Data Range:

• Start Date: 2009-01-01
• End Date: 2024-11-12

A joint map of resources targeted towards the remedy and recovery during and after the COVID 19 crisis. Information about resources and support services provided by a number of organisations across the city.If you are a provider of services and resources, your information can be added and made public via the form available from here.If you have any questions about this dataset please email smart@leicester.gov.uk or complete the form available from here.

This dataset includes synthetically produced data from 10 different cities (Istanbul, Nablus, Chattogram, Cox’s Bazaar, Nairobi, Nakuru, Quito, Kokhana, Rapti and Darussalam) for a future urban context. The data includes physical elements in a city such as buildings, roads, and power networks, as well as social elements such as households and individuals. The dataset contains a maximum of 9 different data types, described below. For some cities power and road network data were not considered due to context specific priorities. landuse: The land use plan data depicting how the land will be zoned and used in the next fifty years within the area or interest. The attributes include the land use type, areal coverage in hectares, maximum population density and existing population. building: Data representing the building footprints that will emerge as a result of the future exposure generation procedure. It includes the attributes of the building such as its identifier number, construction type, number of floors, footprint area, occupation type and construction code level. road nodes: Data representing the points where road segments (edges) are connected to each other, including the identifier number for each node. road edges: Data representing the road segments, including the ID numbers of the starting and ending point (node). power nodes: Data representing the points where power lines (edges) are connected to each other, including the identifier number for each node. power edges: Data representing the power segments, including the including the ID numbers of the starting and ending point (node). household: Data that contains social attributes of a household living in a building. The attributes include number of individuals, income level and commonly used facility ID (such as hospital). individual: Data that contains the attributes of the individuals that are a part of a household. The attributes are age, gender, school ID (if relevant), workplace ID (if relevant) and last attained education level. Distribution table: The future projections for each city that identifies the socio-demographic changes and expected physical development in the next 50 years. The data can be used in geospatial platforms. The nomenclature for the data is as follows: “CitynameFutureExposureDataset/Cityname_CommunityCode_DataType”. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1).

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?

The dataset has been created by http://www.gigl.org.uk" target="_blank">Greenspace Information for Greater London CIC (GiGL). GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to enable informed discussion and decision-making in policy and practice.

GiGL maps under licence from the https://www.london.gov.uk/" target="_blank">Greater London Authority. Research for this dataset has been assisted by https://www.theguardian.com/cities" target="_blank">The Guardian Cities team.

Data sources

Boundaries and attributes are based on GiGL’s http://www.gigl.org.uk/our-data-holdings/open-spaces/" target="_blank">Open Space dataset, which is a collated dataset of spatial and attribute information from various sources, including: habitat and open space survey information provided to GiGL by the GLA and London boroughs, borough open space survey information where provided to GiGL or available under open licence, other attribute information inferred from field visits or research. Available open space information has been analysed by GiGL to identify POPS included in this dataset. Future updates to the GiGL Open Space dataset will inform future, improved releases of the POPS dataset.

Definition

For the purposes of creating the dataset, POPS have been carefully defined as below. The definition is based on review of similar definitions internationally and appropriateness for application to available London data.

Privately Owned Public Spaces (POPS): publicly accessible spaces which are provided and maintained by private developers, offices or residential building owners. They include city squares, atriums and small parks. The spaces provide several functional amenities for the public. They are free to enter and may be open 24 hours or have restricted access arrangements. Whilst the spaces look public, there are often constraints to use.

For the Greater London dataset no consideration is taken as to a site’s formal status in planning considerations, and only unenclosed POPS are included.

POPS may be destination spaces, which attract visitors from outside of the space’s immediate area and are designed for use by a broad audience, or neighbourhood spaces, which draw residents and employees from the immediate locale and are usually strongly linked with the adjacent street or host building. These spaces are of high quality and include a range of amenities. The POPS may also be a hiatus space, accommodating the passing user for a brief stop only – for example it may include seating but few other amenities, a circulation space, designed to improve a pedestrian’s journey from A to B, or a marginal space, which whilst a public space is not very accommodating and experiences low levels of usage. (Ref: Privately Owned Public Space: The New York City Experience, by Jerold S. Kayden, The New York City Department of City Planning, and the Municipal Art Society of New York, published by John Wiley & Sons, 2000).

NOTE: The boundaries are based on Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'.

Contains OS data © Crown copyright and database rights 2017.

The 20,000+ registered companies with a registered address in Glasgow. The information is extracted from Companies House. It includes the company name, number, category (private limited, partnership), registered address, postcode, industry (SIC code), status (ex: active or liquidation), incorporation date... It is likely that some companies may just lie off Glasgow City Council's boundary. If you find a problem in the data, you can check the source either in the full UK list or by looking up a company or let us know. The data dictionary supplied by Companies House can be viewed here. There is also a data dictionary with field names and meanings contained in the resources. This dataset does not imply: - a partnership with Companies House - an endorsement by Companies House - a product approval by Companies House Licence: None glasgow-post-codes-py.txt - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cc57ac4b-12d5-43b1-ad25-434638eec18c/Dataset/3093e34f-6dcb-4980-840b-965421c1b091/File/c2634107-bd43-4537-adb8-9046aeed844e/Version/c8fde78e-5396-4293-ac35-6f6c96a5d642

EngRAD Dataset

The EngRAD dataset contains measurements of 5 different weather variables collected at 487 grid points in England from 2018 to 2020.

Dataset Overview

Data has been provided by Open-Meteo and licensed under Attribution 4.0 International (CC BY 4.0). The numerical weather prediction model used to generate the data is ECMWF IFS, which has a spatial resolution of 9 km. The grid points are located in correspondence with cities. Each point is associated with location information such as geographic coordinates, elevation, closest city, and the county to which it belongs. The physical variables collected are:

1. Air temperature at 2 meters above ground (°C)
2. Relative humidity at 2 meters above ground (%)
3. Summation of total precipitation (rain, showers, snow) during the preceding hour (mm)
4. Total cloud cover (%)
5. Global horizontal irradiance (W/m²)

These variables are typically of interest in applications related to solar radiation, such as solar power production.

Channels:

The data.h5 file contains two tables, accessible by the following keys:

• data: Contains the measurements for each point across different weather variables.

  • temperature_2m: Air temperature at 2 meters above ground (°C). Instantaneous measurement.
  • relative_humidity_2m: Relative humidity at 2 meters above ground (%). Instantaneous measurement.
  • precipitation: Total precipitation (rain, showers, snow) sum of the preceding hour (mm). Preceding hour sum.
  • cloud_cover: Total cloud cover as an area fraction (%). Instantaneous measurement.
  • shortwave_radiation: Global horizontal irradiation (GHI) (W/m²). Preceding hour mean.

• metadata: Contains the following detailed information for each point:

  • city: The name of the city where the measurement point is located.
  • county: The county in which the city is situated.
  • admin_name: The administrative name associated with the city or region.
  • lat: The latitude coordinate of the measurement point.
  • lon: The longitude coordinate of the measurement point.
  • elevation: The elevation (in meters) above sea level at the measurement point.
  • population: The population of the city where the measurement point is located.

Dataset size

• Time steps: 26304
• Points: 487
• Channels: 5
• Sampling rate: 1 hour
• Missing values: 0.00%

Credits

This dataset has been introduced in the paper:

Ivan Marisca, Cesare Alippi, and Filippo Maria Bianchi. "Graph-based forecasting with missing data through spatiotemporal downsampling." Proceedings of the 41st International Conference on Machine Learning, PMLR 235:34846-34865, 2024.

Please consider citing the paper if you use the dataset for your research.

@inproceedings{marisca2024graph,
 title   = {Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling},
 author  = {Marisca, Ivan and Alippi, Cesare and Bianchi, Filippo Maria},
 booktitle = {Proceedings of the 41st International Conference on Machine Learning},
 pages   = {34846--34865},
 year   = {2024},
 volume  = {235},
 series  = {Proceedings of Machine Learning Research},
 publisher = {PMLR}
}

Data containing the Output Areas and Output Areas to higher geographies within Glasgow City Council based on the 2011 Census aggregates. It shows a output area codes for Glasgow and a lookup of postcode to output area, data zone, intermediate geography, electoral ward code and other higher geographies with their corresponding Easting and Northing. Output Areas are the main building bricks for census areas. They are derived from aggregating a small number of neighbouring postcodes although in few cases, some postcodes are large enough to become single output areas. Output Areas will aggregate exactly to council areas but not necessarily to other higher geographies. Glasgow City Council Area Code used in the 2011 census is S12000046. For a more detailed report on the classification of Output areas used in the 2011 census, please click here. Data extracted 2013-11-13T11:12:42 Contains NRS data (c) Crown copyright 2013. Contains Ordnance Survey data (c) Crown copyright 2013. Licence: None output-area.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cb95330b-d7c0-472b-b3a7-a1b520bffd18/Dataset/39016946-3b63-448e-931e-04ead35a138f/File/3cc12f2d-513d-4176-a19c-0e4121507b47/Version/d169dac5-8b3e-498f-8b6b-9aa849dfdd6f output-areas-to-higher-areas.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cb95330b-d7c0-472b-b3a7-a1b520bffd18/Dataset/39016946-3b63-448e-931e-04ead35a138f/File/cb26c01d-c177-44dc-89cc-7446fad75817/Version/e5637660-4c0a-4850-8372-0e9229a13bbe output-areas-to-higher-areas.geojson - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cb95330b-d7c0-472b-b3a7-a1b520bffd18/Dataset/39016946-3b63-448e-931e-04ead35a138f/File/f86e025e-750f-4e0e-a735-b21a0a8312ae/Version/7ed87d76-1907-4924-b664-6716a11a9621 glasgow-2011-output-area.geojson - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cb95330b-d7c0-472b-b3a7-a1b520bffd18/Dataset/39016946-3b63-448e-931e-04ead35a138f/File/759cf6b9-95d8-4739-a2bc-f77d8cbc6a59/Version/8d51f7e6-347f-4a42-aef9-dce91b748e63