🇬🇧 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.

Important Note: This item is in mature support as of July 2021. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This map is designed to be used as a general reference map for informational and educational purposes as well as a basemap by GIS professionals and other users for creating web maps and web mapping applications.The map was developed by National Geographic and Esri and reflects the distinctive National Geographic cartographic style in a multi-scale reference map of the world. The map was authored using data from a variety of leading data providers, including Garmin, HERE, UNEP-WCMC, NASA, ESA, USGS, and others.This reference map includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings and landmarks, overlaid on shaded relief and land cover imagery for added context. The map includes global coverage down to ~1:144k scale and more detailed coverage for North America down to ~1:9k scale.Map Note: Although small-scale boundaries, place names and map notes were provided and edited by National Geographic, boundaries and names shown do not necessarily reflect the map policy of the National Geographic Society, particularly at larger scales where content has not been thoroughly reviewed or edited by National Geographic.Data Notes: The credits below include a list of data providers used to develop the map. Below are a few additional notes:Reference Data: National Geographic, Esri, Garmin, HERE, iPC, NRCAN, METILand Cover Imagery: NASA Blue Marble, ESA GlobCover 2009 (Copyright notice: © ESA 2010 and UCLouvain)Protected Areas: IUCN and UNEP-WCMC (2011), The World Database on Protected Areas (WDPA) Annual Release. Cambridge, UK: UNEP-WCMC. Available at:www.protectedplanet.net.Ocean Data: GEBCO, NOAA

London was by far the largest urban agglomeration in the United Kingdom in 2023, with an estimated population of 9.65 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 the Russian capital Moscow having a population of almost 12.7 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 11.2 million people. Paris was followed by London in terms of population-size, and then by the Spanish cities of Madrid and Barcelona, at 6.75 million and 5.68 million people respectively. Russia's second-biggest city; St. Petersburg had a population of 5.56 million, followed by Rome at 4.3 million, and Berlin at 3.5 million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of 6.81 million people in 1981 to a low of 6.73 million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from 6.8 million at the start of the decade to 7.15 million by 1999. London's population has continued to grow since the turn of the century, reaching a peak of 8.96 million people in 2019, and is forecast to reach 9.8 million by 2043.

A PDF map that shows the counties and unitary authorities in the United Kingdom as at 1 April 2023. (File Size - 583 KB)

This is the Index of Place Names (IPN) in Great Britain as at December 2021 (published December 2022). The IPN was first produced after the 1831 Census; this new version has been greatly expanded in content and extent. Featuring over 100,000 entries, it lists the names of localities and geography areas throughout England, Scotland and Wales. The IPN is published annually and with an updated and informative user guide giving a full rundown and explanation of the contents (File Size - 7 MB).

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

This spatial dataset is an output of the Natural England County & City Natural Capital Atlas project (July 2020). It shows variation in ecosystem service flow for habitats across England, based on indicators identified by NE in the 2018 Natural Capital Indicators project. The dataset comprises a hexagonal grid which summarises indicator values across the country (each unit = 5km²).

Natural Capital is an important aspect of current environmental policy and management. This dataset, in combination with the other project outputs, will support understanding of Natural Capital in England and serve as a valuable engagement tool to communicate concepts of the Natural Capital approach to a wide variety of stakeholders.

For full methodology and user guide see documents ‘NCAtlas_Devon’ and ‘NC-Mapping-User-Guidance’ at http://publications.naturalengland.org.uk/publication/6672365834731520.

For full metadata documentation see the data package download below.

Copyright statement: LCM2015 © NERC (CEH) 2011. Contains Ordnance Survey data © Crown Copyright 2007. © Defra. Contains Defra information © Defra - Project MB0102. © Environment Agency. © Forestry Commission. © Historic England [year]. © Joint Nature Conservation Committee. © Natural England copyright. Contains Ordnance Survey data © Crown copyright and database right [year]. Contains data supplied by © NERC - Centre for Ecology & Hydrology. © Natural England copyright. Natural England Licence No. 2011/052 British Geological Survey © NERC, all rights reserved, © NSRI Cranfield University. Contains National Statistics data © Crown copyright and database right [year]. Contains Ordnance Survey data © Crown copyright and database right [year]. Contains Rural Payments Agency. © Barnsley Metropolitan Borough Council. © Bath & North East Somerset Council. © Bedford Borough Council. © London Borough of Bexley. © Birmingham City Council. © Blackburn with Darwen Borough Council. © Blackpool Council. © Bolton Council. © BCP Council. © Bracknell Forest Council. © City of Bradford Metropolitan District Council. © Brighton & Hove City Council. © Bristol City Council. © London Borough of Bromley. © Buckinghamshire County Council. © Bury Council. © Calderdale Council. © Cambridgeshire County Council. © Central Bedfordshire Council. © Cheshire East Council. © Cheshire West and Chester Council. © Cornwall Council. © Cumbria County Council. © Derbyshire County Council. © Devon County Council. © Doncaster Council. © Dorset Council. © Dudley Metropolitan Borough Council. © Durham County Council. © East Riding of Yorkshire Council. © East Sussex County Council. © Essex County Council. © Gateshead Council. © Gloucestershire County Council. © Hampshire County Council. © Herefordshire Council. © Hertfordshire County Council. © Hull City Council. © Isle of Anglesey County Council. © Isle of Wight Council. © Kent County Council. © Kirklees Council. © Knowsley Metropolitan Borough Council. © Lake District National Park. © Lancashire County Council. © Leicester City Council. © Leicestershire County Council. © Lincolnshire County Council. © Manchester City Council. © Medway Council. © Norfolk County Council. © North Lincolnshire Council. © North Somerset Council. © North Yorkshire County Council. © Northamptonshire County Council. © Northumberland County Council. © Nottingham City Council. © Nottinghamshire County Council. © Oldham Council. © Oxfordshire County Council. © Peterborough City Council. © Plymouth City Council. © Bournemouth, Christchurch and Poole Council. © Portsmouth City Council. © Reading Borough Council. © Redcar and Cleveland Borough Council. © Rochdale Borough Council. © Rotherham Metropolitan Borough Council. © Rutland County Council. © Salford City Council. © Sefton Council. © Sheffield City Council. © Shropshire Council. © Slough Borough Council. © Somerset County Council. © South Gloucestershire Council. © Southampton City Council. © St Helens Council. © Staffordshire County Council. © Stockport Metropolitan Borough Council. © Stockton Council. © Suffolk County Council. © Surrey County Council. © Tameside Metropolitan Borough Council. © Thurrock Council. © Torbay Council. © Trafford Council. © Wakefield Council. © Walsall Council. © Warrington Borough Council. © Warwickshire County Council. © West Berkshire Council. © West Sussex County Council. © Wigan Council. © Wiltshire Council. © Royal Borough of Windsor and Maidenhead Council. © Wirral Council. © Wokingham Borough Council. © Worcestershire County Council. © City of York Council.

This is a collection of simple maps in PDF format that are designed to be printed off and used in the classroom. The include maps of Great Britain that show the location of major rivers, cities and mountains as well as maps of continents and the World. There is very little information on the maps to allow teachers to download them and add their own content to fit with their lesson plans. Customise one print out then photocopy them for your lesson. data not available yet, holding data set (7th August). Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-08-07 and migrated to Edinburgh DataShare on 2017-02-22.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

World Heritage Sites are part of the World Heritage Convention, established in 1972 by UNESCO (United Nations Educational, Scientific and Cultural Organisation). They are landscapes, cities, monuments or buildings of exceptional natural or cultural value. The World Heritage List includes the Great Wall of China, the Pyramids, the Great Barrier Reef and the City of Venice. Sites in England include Stonehenge and Avebury, Canterbury Cathedral, the Tower of London, Hadrian’s Wall and the whole of the City of Bath. Please note: this dataset represents Historic England’s interpretation of the UNESCO World Heritage Site boundaries for sites wholly in or crossing into England.

The table Limited English Proficiency Towns is part of the dataset Connecticut EJ Communities Maps, available at https://redivis.com/datasets/ck4g-d60ynh7dt. It contains 171 rows across 3 variables.

A PDF map that shows the local authority districts, counties and unitary authorities in the United Kingdom as at April 2023. The map has been created to show the United Kingdom from country level down to local authority district level. (File Size - 1,909 KB)

This is a city map of London, England, shown at a 1:63,360 scale. This city map was created by the Director General of the Ordnance Survey.

The population of the United Kingdom in 2023 was estimated to be approximately 68.3 million in 2023, with almost 9.48 million people living in South East England. London had the next highest population, at over 8.9 million people, followed by the North West England at 7.6 million. With the UK's population generally concentrated in England, most English regions have larger populations than the constituent countries of Scotland, Wales, and Northern Ireland, which had populations of 5.5 million, 3.16 million, and 1.92 million respectively. English counties and cities The United Kingdom is a patchwork of various regional units, within England the largest of these are the regions shown here, which show how London, along with the rest of South East England had around 18 million people living there in this year. The next significant regional units in England are the 47 metropolitan and ceremonial counties. After London, the metropolitan counties of the West Midlands, Greater Manchester, and West Yorkshire were the biggest of these counties, due to covering the large urban areas of Birmingham, Manchester, and Leeds respectively. Regional divisions in Scotland, Wales and Northern Ireland The smaller countries that comprise the United Kingdom each have different local subdivisions. Within Scotland these are called council areas whereas in Wales the main regional units are called unitary authorities. Scotland's largest Council Area by population is that of Glasgow City at over 622,000, while in Wales, it was the Cardiff Unitary Authority at around 372,000. Northern Ireland, on the other hand, has eleven local government districts, the largest of which is Belfast with a population of around 348,000.

In 2016, it was estimated that Birmingham had the largest Muslim population of any local authority in England and Wales at approximately 280 thousand people. Newham and Tower Hamlets, both boroughs of London, had the second and third-largest Muslim populations at 135 and 128 thousand respectively.

Table from the American Community Survey (ACS) 5-year series on languages spoken and English ability related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B16004 Age by Language Spoken at Home by Ability to Speak English, C16002 Household Language by Household Limited English-Speaking Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B16004, C16002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The data set of this article is related to the paper "Dynamical structure of social map in ancient China" (2022, Physica A, https://doi.org/10.1016/j.physa.2022.128209) . This article demonstrates the data of social relations between cities in ancient China, ranging from 618 AD to 1644 AD. The raw data of social associations between elites used to build social maps are extracted from the China Biographical Database. The raw data contain 14610 elites and 29673 social associations, which cover 366 cities in China. The dataset of this article is relevant both for social and natural scientists interested in the social and economic history of ancient China. The data can be used for further insights/analyses on the evolutionary pattern of geo-social architecture, and the geo-history from the viewpoint of social network.

The dataset contains $3$ files: "Networks.xlsx", "Coordinates.xlsx", and "SocialMap.html". The "Networks.xlsx" has 3 columns, representing the source node (city), target node (city), and weight of a link between two nodes, respectively. The "Networks.xlsx" contains $9$ sheets, which are the data for different dynasties named by Early Tang, Late Tang, Early Northern-Song, Late Northern-Song, Early Southern-Song, Late Southern-Song, Yuan, Early Ming, and Late Ming. Noticeably, the "Networks.xlsx" can be visualized by the network software of Gephi directly. The "Coordinates.xlsx" has 4 columns storing longitude and latitude for all cities that appeared in 9 networks. The first and second columns are English names and Chinese names of cities; the third and fourth columns are longitudes and latitudes of cities. The "SocialMap.html" provides a visualization platform, in which users could select and illustrate the evolution of social maps intuitively.

This GIS shapefile provides boundary and attribute data for the parishes and places enumerated in the 1851 census for England and Wales. These data derive from the 173 digital maps of the boundaries of English and Welsh parishes and their subdivisions produced to a very high standard by Roger Kain and Richard Oliver in 2001, which was expertly converted into a single GIS of some 28000 polygons by Burton et al in 2004. However, what they produced was not yet ready for the mapping of census data due to a modest number (

Road graph data by street section of the city of Barcelona.

In 2023, London had a gross domestic product of over 569 billion British pounds, by far the most of any region of the United Kingdom. The region of South East England which surrounds London had the second-highest GDP in this year, at over 360 billion pounds. North West England, which includes the major cities of Manchester and Liverpool, had the third-largest GDP among UK regions, at almost 250 billion pounds. Levelling Up the UK London’s economic dominance of the UK can clearly be seen when compared to the other regions of the country. In terms of GDP per capita, the gap between London and the rest of the country is striking, standing at over 63,600 pounds per person in the UK capital, compared with just over 37,100 pounds in the rest of the country. To address the economic imbalance, successive UK governments have tried to implement "levelling-up policies", which aim to boost investment and productivity in neglected areas of the country. The success of these programs going forward may depend on their scale, as it will likely take high levels of investment to reverse economic neglect regions have faced in the recent past. Overall UK GDP The gross domestic product for the whole of the United Kingdom amounted to 2.56 trillion British pounds in 2024. During this year, GDP grew by 0.9 percent, following a growth rate of 0.4 percent in 2023. Due to the overall population of the UK growing faster than the economy, however, GDP per capita in the UK fell in both 2023 and 2024. Nevertheless, the UK remains one of the world’s biggest economies, with just five countries (the United States, China, Japan, Germany, and India) having larger economies. It is it likely that several other countries will overtake the UK economy in the coming years, with Indonesia, Brazil, Russia, and Mexico all expected to have larger economies than Britain by 2050.