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.

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.

United Kingdom UK: Population in Largest City: as % of Urban Population data was reported at 19.234 % in 2017. This records an increase from the previous number of 19.203 % for 2016. United Kingdom UK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 18.336 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 19.939 % in 1960 and a record low of 17.256 % in 1973. United Kingdom UK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

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

Between 1500 and 1800, London grew to be the largest city in Western Europe, with its population growing almost 22 times larger in this period. London would eventually overtake Constantinople as Europe's largest in the 1700s, before becoming the largest city in the world (ahead of Beijing) in the early-1800s.

The most populous cities in this period were the capitals of European empires, with Paris, Amsterdam, and Vienna growing to become the largest cities, alongside the likes of Lisbon and Madrid in Iberia, and Naples or Venice in Italy. Many of northwestern Europe's largest cities in 1500 would eventually be overtaken by others not shown here, such as the port cities of Hamburg, Marseilles or Rotterdam, or more industrial cities such as Berlin, Birmingham, and Munich.

Cambridge was the fastest growing city in the United Kingdom between 2013 and 2023, with its population increasing by 17.3 percent. Exeter, Milton Keynes, and Peterborough also grew quite fast, with their populations increasing by 15.2 percent, 14.9 percent, and 14 percent, respectively. Largest UK urban areas When looking at cities defined by their urban agglomerations, as of 2023, London had approximately 9.65 million people living there, far larger than any other city in the United Kingdom. The urban agglomeration around the city of Birmingham had a population of approximately 2.67 million, while the urban areas around Manchester and Leeds had populations of 2.79 and 1.92 million respectively. London not only dominated other UK cities in terms of its population, but in its importance to the UK economy. In 2023, the gross domestic product of Greater London was approximately 569 billion British pounds, compared with 101 billion for Greater Manchester, and 85 billion in the West Midlands Metropolitan Area centered around Birmingham. UK population growth In 2023, the overall population of the United Kingdom was estimated to have reached approximately 68.3 million, compared with around 58.9 million in 2000. Since 1970, 2023 was also the year with the highest population growth rate, growing by 0.98 percent, and was at its lowest in 1982 when it shrank by 0.12 percent. Although the UK's birth rate has declined considerably in recent years, immigration to the UK has been high enough to drive population growth in the UK, which has had a positive net migration rate since 1994.

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).

Population and household characteristics by built-up area (BUA) size classification and individual BUAs, England (excluding London) and Wales, Census 2021. Data are available at a country, BUA size classification and individual BUA level.

By 1800, London had grown to be the largest city in Western Europe with just under one million inhabitants. Paris was now the second largest city, with over half a million people, and Naples was the third largest city with 450 thousand people. The only other cities with over two hundred thousand inhabitants at this time were Vienna, Amsterdam and Dublin. Another noticeable development is the inclusion of many more northern cities from a wider variety of countries. The dominance of cities from France and Mediterranean countries was no longer the case, and the dispersal of European populations in 1800 was much closer to how it is today, more than two centuries later.

🇬🇧 영국 English How would you define the boundaries of a town or city in England and Wales in 2016? Maybe your definition would be based on its population size, geographic extent or where the industry and services are located. This was a question the ONS had to consider when creating a new statistical geography called Towns and Cities. In reality, the ability to delimit the boundaries of a city or town is difficult! Major Towns and Cities The new statistical geography, Towns and Cities has been created based on population size and the extent of the built environment. It contains 112 towns and cities in England and Wales, where the residential and/or workday population > 75,000 people at the 2011 Census. It has been constructed using the existing Built-Up Area boundary set produced by Ordnance Survey in 2011. This swipe map shows where the towns and cities and built-up areas are different. Just swipe the bar from left to right. The blue polygons are the towns and cities and the purple polygons are the built-up areas.

Maybe your definition would be based on its population size, geographic extent or where the industry and services are located. This was a question the ONS had to consider when creating a new statistical geography called Towns and Cities. In reality, the ability to delimit the boundaries of a city or town is difficult! Major Towns and Cities The new statistical geography, Towns and Cities has been created based on population size and the extent of the built environment. It contains 112 towns and cities in England and Wales, where the residential and/or workday population > 75,000 people at the 2011 Census. It has been constructed using the existing Built-Up Area boundary set produced by Ordnance Survey in 2011. This swipe map shows where the towns and cities and built-up areas are different. Just swipe the bar from left to right. The blue polygons are the towns and cities and the purple polygons are the built-up areas.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

As of 2023, the population density in London was by far the highest number of people per square km in the UK, at *****. Of the other regions and countries which constitute the United Kingdom, North West England was the next most densely populated area at *** people per square kilometer. Scotland, by contrast, is the most sparsely populated country or region in the United Kingdom, with only ** people per square kilometer. Countries, regions, and cities According to the official mid-year population estimate, the population of the United Kingdom was just almost **** million in 2022. Most of the population lived in England, where an estimated **** million people resided, followed by Scotland at **** million, Wales at **** million and finally Northern Ireland at just over *** million. Within England, the South East was the region with the highest population at almost **** million, followed by the London region at around *** million. In terms of urban areas, Greater London is the largest city in the United Kingdom, followed by Greater Manchester and Birmingham in the North West and West Midlands regions of England. London calling London's huge size in relation to other UK cities is also reflected by its economic performance. In 2021, London's GDP was approximately *** billion British pounds, almost a quarter of UK GDP overall. In terms of GDP per capita, Londoners had a GDP per head of ****** pounds, compared with an average of ****** for the country as a whole. Productivity, expressed as by output per hour worked, was also far higher in London than the rest of the country. In 2021, London was around **** percent more productive than the rest of the country, with South East England the only other region where productivity was higher than the national average.

This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

When surveyed in the fourth quarter of 2024, it was found that York was the most popular city in the United Kingdom among residents of the UK. In total, 76 percent of the UK public had a popular opinion of the city, which is famed for its historical architecture.

Urban planners and landscape architects play a pivotal role in managing the growth of urban areas. The industry thrives on strategic planning for efficient land use, creating sustainable environments and contributing to infrastructure development initiatives. Significant investments from the private and public sectors and continuous urbanisation have powered demand for urban planners and landscape architects. However, economic hardship has been detrimental to performance, with periods of severe inflation and high interest rates weakening downstream activity and investment in projects. Despite this, interest rates have been falling since 2024-25, boosting demand from downstream markets in the last two years. Revenue is expected to grow by 2% to £864.5 million over the five years through 2025-26, including a projected 0.1% rise in the current year. More people are moving to urban areas, with the UK’s urban population growing as a share of the total population. The strain on urban infrastructure requires continuous improvements and expansions to be made. This fuels demand for urban planners and landscape architects as they find ways to develop and design land use, focusing on community needs and sustainability. Heavy government investment in various housing and infrastructure projects has boosted construction activity, supporting demand. The economic downturn from the COVID-19 outbreak subdued business confidence, disrupting projects and denting construction activity to the detriment of the industry. As restrictions eased, the industry saw a glimmer of hope with a rebound in project development. Still, economic challenges since 2022-23, including soaring inflation and geopolitical uncertainty, have hampered recovery. The volatile economic climate and subdued business confidence have constrained project investment, which has constrained profit and rising costs amid inflation. Improving economic conditions combined with government initiatives will fuel industry growth. The increasing emphasis on sustainable and environmental design will shape future demand, requiring professionals to pick up new skills. Technology adoption will provide growth opportunities, with advanced software solutions boosting operations, enhancing efficiency and reducing costs. There will also be an escalated focus on local cultures and community needs, with the 100 new towns being considered for building during the current Labour parliamentary term, bringing inclusivity and communal cohesion to the fore. Revenue is forecast to swell at a compound annual rate of 1.6% to £933.7 million over the five years through 2030-31.

Among selected cities in the United Kingdom, Birmingham received the highest score in the Clean Cities ranking of space for walking and cycling in 2022 at a score of ** percent followed closely by Manchester with a score of ** percent. London and Edinburgh also made the list of selected European cities ranked by space for people and received a score of ** percent and ** percent, respectively. The Clean Cities' score of "space for people" measures the allocation of road space to pedestrians and cyclists as well as levels of congestion.

Anthropogenic noise can adversely impact urban bird populations by interfering with vocal communication. Less research has addressed if anthropogenic noise masks the adventitious sounds that birds use to aid predator detection, which may lead to increased vigilance and reduced feeding efficiency. We test this hypothesis using a controlled playback experiment along an urban-rural gradient in Sheffield (UK). We also test the related predictions that anthropogenic noise has the greatest impacts on vigilance and feeding efficiency in rural populations, and on species that are more sensitive to urbanisation. We focus on six passerines, in order from most to least urbanised (based on how urbanisation influences population densities): blue tit Cyanistes caeruleus, robin Erithacus rubeculla, great tit Parus major, chaffinch Fringilla coelebs, coal tit Periparus ater and nuthatch Sitta europaea. We used play-back of anthropogenic urban noise and a control treatment at 46 feeding stations located along the urban-rural gradient. We assess impacts on willingness to visit feeders, feeding and vigilance rates. Exposure to anthropogenic noise reduced visit rates to supplementary feeding stations, reduced feeding rates and increased vigilance. Birds at more urban sites exhibit less marked treatment induced reductions in feeding rates, suggesting that urban populations may be partially habituated or adapted to noisy environments. There was no evidence, however, that more urbanised species were less sensitive to the impacts of noise on any response variable. Our results support the adventitious sound masking hypothesis. Urban noise may thus interfere with the ability of birds to detect predators, reducing their willingness to use food rich environments and increase vigilance rates resulting in reduced feeding rates. These adverse impacts may compromise the quality of otherwise suitable foraging habitats in noisy urban areas. They are likely to be widespread as they arise in a range of species including common urban birds.

Methods The methodology used to collect these data is described in Merrall, E. S. and Evans, K. L. 2020. Anthropogenic noise reduces avian feeding efficiency and increases vig-ilance along an urban-rural gradient regardless of species’ tolerances to urbanisation. – J. Avian Biol. 2020: e02341

Site selection and urbanisation metrics

Work was conducted in and around Sheffield (53°22′N, 1°20′W), which is England’s fifth largest city, with a population of c. 575,000. We defined urban areas as 1km x 1km grid cells with more than 25% impervious surface (following Gaston et al. 2005) and selected 16 urban sites within 3 km of the city centre, 19 urban sites that were more than 3 km from the city centre and 11 rural sites that were between 1 and 3 kms away from the city’s outer limits (using the above definition of urban areas). This approach enabled us to haphazardly select sites that were spread across the urban to rural gradient (see below for quantification), with the fewest sites in rural areas due to their greater homogeneity in background noise levels. All sites within the urban area were public parks and green-spaces with some woody vegetation cover, and all rural sites were woodland (rather than the alternatives of moorland or farmland) to maximise the similarity of the focal bird community along the urban to rural gradient.

Urbanisation intensity was quantified at each site using the ‘Urbanisation Index’ software developed by Seress et al., 2014 (available at: https://keplab.mik.uni-pannon.hu/en/urbanization-index). This software uses a semi-automated method where it takes a 1km2 area from google maps around the coordinates of each location, and then uses manually inputted training points to score each image for vegetation cover, forest, buildings and paved roads using 100x100m2 cells. It then uses principle component analysis (PCA) to calculate an urbanisation intensity score for each area.

Experimental design

Field work took place between the 12th February and 3nd April 2019. Our general approach was to assess avian feeding and vigilance rates at feeding stations under three experimental conditions: play back of urban noise and two control treatments (play back of natural noise and a silent control without playback). The urban recording was created by splicing together 5-minute sound recordings from each of four locations in central urban Sheffield using a Zoom H4n sound recorder and Cubase LE AI Elements 10, with a mixture of traffic, pedestrian and construction noise. The urban noise treatment was played at approximately 80 decibels (dbc), i.e. the typical volume of anthropogenic noise in busy urban areas during the day (Maryland SHA, 2018). The natural control used a mixture of songs of summer migrants (barn swallow Hirundo rustica; whinchat Saxicola rubetra; redstart Phoenicurus phoenicurus and common whitethroat Sylvia communis). Recordings were obtained from Xeno-canto (2005-2019) and were vetted to ensure that they did not include alarm calls to ensure that this treatment did not include vocalisations to which birds using the feeders were likely to respond. The natural sound control treatment was played at approximately 40 decibels (dbc) that matched natural sound levels of avian vocalisations. The urban treatment lasted 40 minutes while each control lasted 20 minutes, with a 10-minute habituation period between equipment set-up and beginning playback, and between the urban treatment and the two control treatments (which each lasted 20 minutes). We used two green, portable SONY SRS-XB10 Bluetooth controlled speakers (IPX rated 5) at each site located approximately a metre from the feeding station in a spatial configuration that created a surround sound effect.

Each site’s feeding station was set up four to seven days before conducting the experiment to enable birds to habituate to the presence of the food source. Each station consisted of two standardised hanging feeders, each with two feeding ports, filled with sunflower hearts. These are a nutritious food source that has a negligible handling time and is thus widely used by a wide range of species. To reduce the risk of disturbance from grey squirrels Sciurus carolinensis, which can discourage birds from using feeders (Bonnington et al. 2014), poles were greased and sunflower hearts were coated in chilli powder (which squirrels avoid, whilst birds exhibit no adverse response). Feeders were placed in relatively open locations to allow easy observation, but close to vegetation cover to encourage birds to approach and use the feeders. Feeding stations were located away from footpaths to minimise effects of human disturbance.

Treatments were applied in a randomised order at each site (with the three treatments being applied sequentially). Sites were visited in a haphazard manner with regard to the site’s urbanisation score. All data collection took place at least 1.5 hours after civil dawn and before civil dusk, to avoid spikes in bird activity early and late in the day. Data were not collected when it was snowing or raining (except occasional light drizzle), or at high wind speeds as such conditions interfered with activity levels and noise transmission.

Each treatment was filmed using a Panasonic (HC-X920) HD Camcorder and observed from an approximately 10m to 15m distance. Videos recorded birds feeding on the feeder and those feeding on spilt food beneath the feeder. If disturbance events occurred during the treatment, such as a human or dog passing close to the feeder, and interrupted birds’ feeding behaviour, data collection was paused until birds resumed normal activity. Videos were analysed, and for each visit we recorded the species, visit duration (seconds), number of pecks (as a measure of feeding rate) and the amount of time (seconds) spent performing vigilance behaviour, defined following Quinn et al. (2006), as when the bird raised its head and scanned. For each site, the temperature (oC) and wind speed (kn) were also recorded using data from the nearest weather observation site (Met Office, 2019). The seven weather stations used ranged from 220 m to 6.1 km away from the study site.

Statistical analysis

All statistical analyses were conducted using R Studio (RStudio Team, 2016). Three response metrics were calculated from the videos for each species per treatment per site: i) visit rate, i.e. the rate at which the species visited the feeder per hour of treatment, ii) the peck rate per hour of treatment, and iii) the vigilance rate (in minutes) per hour of time spent on the feeder. A total of 19 species were observed using the feeders or feeding on fallen seed on the ground below, of which six occurred at ten or more sites and were included in data analysis (great tit Parus major 46 sites; blue tit Cyanistes caeruleus 44 sites; coal tit Periparus ater 33 sites; Eurasian robin Erithacus rubeculla 35 sites; nuthatch Sitta europaea 14 sites; and chaffinch Fringilla coelebs 13 sites). These species vary substantially in their ability to maintain high population densities in urban environments. This is indicated by their urbanisation scores as calculated by Evans et al. (2011), i.e. the ratio of urban to rural population densities obtained from Breeding Bird Survey data from approximately 3,000 randomly selected 1 km x 1 km squares located across the UK. These scores, from most to least urbanised species, are: blue tit 1.46; robin 0.99; great tit 0.74; chaffinch 0.25; coal tit 0.23, nuthatch 0.17. Matched paired t-tests demonstrated that each of these six species’ visit, peck and vigilance rates did not differ between the two forms of control (P ranges from 0.110 to 0.877) and these data were thus merged to form a single control treatment.

We modelled visit, peck and vigilance rates using mixed effect models (lme4 package; Bates et al, 2015). These models pool data across