This file contains names and codes for Major Towns and Cities (TCITY) in England and Wales as at December 2015. (File size - 16KB). The TCITY statistical geography provides a precise definition of the major towns and cities in England and Wales. The geography has been developed specifically for the production and analysis of statistics, and is based on the Built-Up Areas geography that was created for the release of 2011 Census data. Field Names - TCITYCD, TCITYNM, FID Field Types - Text, Text, Number Field Lengths - 9, 20 FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Major_Towns_and_Cities_Dec_2015_Names_and_Codes_in_England_and_Wales_2022/FeatureServer

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

INDEX VILLARIS: or, An Alphabetical Table of all the cities, market-towns, parishes, villages, and private seats in England and Wales was first published by John Adams in 1680. This dataset consists of a transcription of all 24,000 place-names listed in Index Villaris, together with the the symbols representing Adams's categorisation of each place and modern versions of the place-names and the counties and administrative hundred in which they lie or lay. It also comprises a transcription of the latitude and longitude recorded by Adams, and another set of coordinates generated by the application of a thin plate spline transformation calculated by matching some 2,000 place-names to the accurately-georeferenced CAMPOP Towns dataset.

The dataset is being checked, corrected, and refined to include linkage to other geospatial references such as OpenStreetMap and Wikidata, and will in due course be made available in the Linked Places Format.

This statistic shows the ten largest cities in the United Kingdom in 2021. In 2021, around 8.78 million people lived in London, making it the largest city in the United Kingdom.

London was by far the largest urban agglomeration in the United Kingdom in 2025, with an estimated population of *** million people, more than three times as large as Manchester, the UK’s second-biggest urban agglomeration. The agglomerations of Birmingham and Leeds / Bradford had the third and fourth-largest populations, respectively, while the biggest city in Scotland, Glasgow, was the fifth largest. Largest cities in Europe Two cities in Europe had larger urban areas than London, with Istanbul having a population of around **** million and the Russian capital Moscow having a population of over **** million. The city of Paris, located just over 200 miles away from London, was the second-largest city in Europe, with a population of more than **** million people. Paris was followed by London in terms of population size, and then by the Spanish cities of Madrid and Barcelona, at *** million and *** million people, respectively. The Italian capital, Rome, was the next largest city at *** million, followed by Berlin at *** million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of **** million people in 1981 to a low of **** million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from ****million at the start of the decade to **** million by 1999. London's population has continued to grow since the turn of the century, and despite declining between 2019 and 2021, it reached *** million people in 2023 and is forecast to reach almost *** million by 2047.

A comprehensive dataset of place names, roads numbers and postcodes for Great Britain.

Accurate locations Let your customer-facing staff find places quickly when talking to callers. OS Open Names provides the accurate locations of streets and postcodes in Great Britain.

Place name data Quickly look up places and roads with two names. OS Open Names contains place name data in English and their Welsh, Scots or Gaelic alternatives.

Simple licensing Save money and benefit from simple licensing terms. OS Open Names is free to view, download and use for commercial, education and personal purposes.

Statistics on towns and cities in England and Wales with a focus on housing and deprivation.

Great Britain's (England, Scotland, Wales) cities (e.g. London, Birmingham, Edinburgh) named and represented as point features with an indicative bounding box. This data is often used for geocoding, service delivery and statistical analysis. OS Cities Data is available in a number of Ordnance Survey (OS) products: OS Open Names (bounding box and point geometry), OS Names API, MasterMap Topography Layer (point geometry), Vector Map Local (point geometry) and Vector Map District (point geometry). Small-scale cartographic representations are also available in OS cartographic products. All data is collected by Ordnance Survey as part of their role as the National Mapping Agency of Great Britain.

This is a collection of Opportunity Maps for mine water heat, produced for the Department of Energy Security and Net Zero, and their contractor AECOM, covering the following 10 cities: Birmingham, Bristol, Coventry, Leeds, Manchester, Newcastle, Nottingham, Sheffield, Stoke-on-Trent, Sunderland. Also included is a report outlining the methodology criteria for the opportunity map assessment. The dataset has been developed using Coal Authority data, consisting of Underground Workings data, and Environmental Data, and a bespoke assessment methodology. It consists of 15m x 15m square grid cells, containing attribution of Good, Possible, Challenging on the basis of the opportunity method criteria and expert input. In November 2024, the Coal Authority changed its name to the Mining Remediation Authority to better reflect its mission and continued commitment to environmental sustainability, safety, and community support.

About this dataset

Context

This list ranks the 365 cities in the Florida by English population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by English Population: This column displays the rank of city in the Florida by their English population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• English Population: The English population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as English. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Florida English Population: This tells us how much of the entire Florida English population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

The right to hold a market on England was historically granted by royal charter. Such a privilege provides an alternative to population size as a proxy for importance. This dataset contains the latitude and longitude I have determined for the market place in 698 historic English market towns which are recorded in a list published in "Owen's New Book of Fairs" (London: 1813), https://www.google.co.nz/books/edition/Owen_s_New_Book_of_Fairs_A_new_edition_e/lrdVAAAAcAAJ?hl=en&gbpv=1&dq=adwalton&pg=PR3&printsec=frontcover

Inclusion of a town in this list does not imply that the market was still being held as of 1813.

A full description of the method for determining the location, as well as the data in both GeoJSON and CSV formats can be found in the associated Github repository: https://github.com/philipallfrey/english-market-towns-1813

Historically the right to hold a market in England was regulated, most often by royal charter. Other markets were then prohibited within a radius of 6 2/3 miles around that town. This privilege provides an alternative to population size as a proxy for importance. This dataset contains the latitude and longitude I have determined for the market place in 698 historic English market towns which are recorded in a list published in "Owen's New Book of Fairs" (London: 1813), https://www.google.co.nz/books/edition/Owen_s_New_Book_of_Fairs_A_new_edition_e/lrdVAAAAcAAJ

Inclusion of a town in this list does not imply that the market was still being held as of 1813, nor that towns in the list were the only markets active as of 1813. A full description of the method for determining the location, and of the data format, can be found in the README file, or in the associated data paper (https://doi.org/10.17175/2025_005).

About this dataset

Context

This list ranks the 184 cities in the Utah by English population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by English Population: This column displays the rank of city in the Utah by their English population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• English Population: The English population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as English. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Utah English Population: This tells us how much of the entire Utah English population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

Context

This dataset is the result of my study on web-scraping of English Wikipedia in R and my tests on regression and classification modelization in R.

Content

The content is create by reading the appropriate articles in English Wikipedia about Italian cities: I did'nt run NPL analisys but only the table with the data and I ranked every city from 0 to N in every aspect. About the values, 0 means "*the city is not ranked in this aspect*" and N means "*the city is at first place, in descending order of importance, in this aspect* ". If there's no ranking in a particular aspect (for example, the only existence of the airports/harbours with no additional data about the traffic or the size), then 0 means "*no existence*" and N means "*there are N airports/harbours*". The only not-numeric column is the column with the name of the cities in English form, except some exceptions (for example, "*Bra (CN)* " because of simplicity.

Acknowledgements

I acknowledge the Wikimedia Foundation for his work, his mission and to make available the cover image of this dataset, (please read the article "The Ideal city (painting)") . I acknowledge too StackOverflow and Cross-Validated to be the most important focus of technical knowledge in the world, all the people in Kaggle for the suggestions.

Inspiration

As a beginner in data analisys and modelization (Ok, I passed the exam of statistics in Politecnico di Milano (Italy), but there are more than 10 years that I don't work in this topic and my memory is getting old ^_^) I worked more on data clean, dataset building and building the simplest modelization.

You can use this datase to realize which city is good to live or to expand this to add some other data from Wikipedia (not only reading the tables but too to read the text adn extrapolate the data from the meaningless text.)

About this dataset

Context

This list ranks the 80 cities in the Montana by English population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by English Population: This column displays the rank of city in the Montana by their English population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• English Population: The English population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as English. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Montana English Population: This tells us how much of the entire Montana English population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

Coastal towns list, population and employment data.

About this dataset

Context

This list ranks the 34 cities in the Essex County, MA by English population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by English Population: This column displays the rank of city in the Essex County, MA by their English population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• English Population: The English population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as English. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Essex County English Population: This tells us how much of the entire Essex County, MA English population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

About this dataset

Context

This list ranks the 4 cities in the Essex County, VT by English population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by English Population: This column displays the rank of city in the Essex County, VT by their English population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• English Population: The English population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as English. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Essex County English Population: This tells us how much of the entire Essex County, VT English population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

The British English SpeechDat-Car database contains the recordings of 300 British English speakers from 6 different regions (170 males, 130 females), recorded over the GSM telephone network, in a car. This database is partitioned into 115 CDs (DVDs are also available).The speech data files are in two formats. Four of the 5 microphones were recorded on the computer in the boot of the car. The speech data are stored as sequences of 16 kHz, 16 bit and uncompressed. The fifth microphone was connected to the cell phone, and was recorded on a remote machine. The data are stored as sequences of 8 kHz 8 bit A-law. Each signal file is accompanied by an ASCII SAM label file which contains the relevant descriptive information.This speech database was validated by SPEX (the Netherlands) to assess its compliance with the SpeechDat-Car format and content specifications.Each speaker uttered the following items: * 2 voice activation keywords * 1 sequence of 10 isolated digits * 7 connected digits (1 sheet number -5 digits, 1 spontaneous telephone number, 3 read telephone numbers, 1 credit card number –14/16 digits, 1 PIN code -6 digits) * 3 dates (1 spontaneous date e.g. birthday, 1 prompted date, 1 relative or general date expression) * 2 word spotting phrases using an embedded application word * 4 isolated digits * 7 spelled words (1 spontaneous e.g. own forename or surname, 1 directory city name, 4 real word/name, 1 artificial name for coverage) * 1 money amount * 1 natural number * 7 directory assistance names (1 spontaneous e.g. own forename or surname, 1 city of birth/growing up, 2 most frequent cities, 2 most frequent company/agency, 1 "forename surname") * 9 phonetically rich sentences * 2 time phrases (1 spontaneous time of day, 1word style time phrase) * 4 phonetically rich words * 67 application words (13 mobile phone application words, 22 IVR function keywords, 32 car products keywords) * 2 additional language dependent keywords * Prompts for spontaneous speechThe following age distribution has been obtained: 119 speakers are between 16 and 30, 109 speakers are between 31 and 45, 57 speakers are between 46 and 60, and 15 speakers are over 60.A pronunciation lexicon with a phonemic transcription in SAMPA is also included.

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}
}