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.

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.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

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.

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

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/

Georeferenced map of 'Plan of the City of Edinburgh, including all the latest and intended improvements' By John Wood (1831) as part of the Visualising Urban Geographies project- view other versions of the map at http://geo.nls.uk/urbhist/resources_maps.html. Scanned map. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-05-30 and migrated to Edinburgh DataShare on 2017-02-21.

Population in largest city in United Kingdom was reported at 9748033 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Kingdom - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population in the largest city (% of urban population) in United Kingdom was reported at 16.59 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Kingdom - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Urban population (% of total population) in United Kingdom was reported at 84.88 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Kingdom - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

1:1,000,000 raster map of Northern Ireland with place names. A raster map is a static image displayed on screen which is suitable as background mapping. 1:1 000,000 Raster is smallest scale OSNI raster product giving an excellent overview of Northern Ireland. Published here for OpenData. By download or use of this dataset you agree to abide by the Open Government Data Licence.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

Georeferenced map of 'he City of Edinburgh and its environs' By Robert Kirkwood (1804) as part of the Visualising Urban Geographies project- view other versions of the map at http://geo.nls.uk/urbhist/resources_maps.html. Scanned map. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-05-31 and migrated to Edinburgh DataShare on 2017-02-21.

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

Georeferenced map of 'A plan of the city and suburbs of Edinburgh' By Alexander Kincaid (1784) as part of the Visualising Urban Geographies project- view other versions of the map at http://geo.nls.uk/urbhist/resources_maps.html. Scanned map. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-05-31 and migrated to Edinburgh DataShare on 2017-02-21.

Georeferenced map of 'City and Castle of Edinburgh' by William Edgar (1765), as part of the Visualising Urban Geographies project - view other versions of map at: http://geo.nls.uk/urbhist/resources_maps.html. Scanned map. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-05-30 and migrated to Edinburgh DataShare on 2017-02-21.

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.

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.

Georeferenced map of 'Plan City and Castle of Edinburgh ' By William Edgar (1765) as part of the Visualising Urban Geographies project- view other versions of the map at http://geo.nls.uk/urbhist/resources_maps.html. Scanned from original paper map and georeferenced by The National Library Of Scotland. Scanned map. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-09-01 and migrated to Edinburgh DataShare on 2017-02-21.

This file contains the digital vector boundaries for Urban Audit Greater Cities, in the United Kingdom, as at December 2016. The boundaries are full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Urban_Audit_GC_(Dec_2016)_FEB_in_the_UK/MapServerREST URL of ArcGIS for INSPIRE Feature DownloadService – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Urban_Audit_GC_Dec_2016_Full_Extent_Boundaries_in_the_UK/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Urban_Audit_GC_Dec_2016_FEB_in_the_UK_2022/FeatureServer

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.